Academic literature on the topic 'Uncertain structural processes'
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Journal articles on the topic "Uncertain structural processes"
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Bozejko, Wojciech, Zdzislaw Hejducki, and Mieczyslaw Wodecki. "Flowshop scheduling of construction processes with uncertain parameters." Archives of Civil and Mechanical Engineering 19, no. 1 (March 2019): 194–204. http://dx.doi.org/10.1016/j.acme.2018.09.010.
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Pozzi, Matteo, Milad Memarzadeh, and Kelly Klima. "Hidden-Model Processes for Adaptive Management under Uncertain Climate Change." Journal of Infrastructure Systems 23, no. 4 (December 2017): 04017022. http://dx.doi.org/10.1061/(asce)is.1943-555x.0000376.
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Zhang, Yanshan, and Zhengmao Yang. "Reliability sensitivity numerical analysis of mechanical structure based on gamma processes." Advances in Mechanical Engineering 8, no. 12 (December 2016): 168781401667962. http://dx.doi.org/10.1177/1687814016679624.
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Using random variables to describe uncertain parameters in structural systems, its initial strength and the evolution process of the strength degradation is regarded as the Gamma process. In this article, we propose a new method on reliability sensitivity numerical analysis of mechanical structure based on Gamma processes. Then, we use the fourth moment method based on frequency curve of Pearson to solve the problem of reliability calculation with random parameters of arbitrary distributions. Formulas for calculating the reliability sensitivity with respect to the mean and the variance of the random variables are derived. The reliability analysis of the welded box girders of crane is taken as an example to verify the proposed method. The results show that the method can effectively solve the problem of the reliability sensitivity of structural systems with strength degradation.
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Alcaraz-González, V., J. Harmand, A. Rapaport, J. P. Steyer, V. González-Álvarez, and C. Pelayo-Ortiz. "Robust interval-based regulation for anaerobic digestion processes." Water Science and Technology 52, no. 1-2 (July 1, 2005): 449–56. http://dx.doi.org/10.2166/wst.2005.0552.
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A robust regulation law is applied to the stabilization of a class of biochemical reactors exhibiting partially known highly nonlinear dynamic behavior. An uncertain environment with the presence of unknown inputs is considered. Based on some structural and operational conditions, this regulation law is shown to exponentially stabilize the aforementioned bioreactors around a desired set-point. This approach is experimentally applied and validated on a pilot-scale (1 m3) anaerobic digestion process for the treatment of raw industrial wine distillery wastewater where the objective is the regulation of the chemical oxygen demand (COD) by using the dilution rate as the manipulated variable. Despite large disturbances on the input COD and state and parametric uncertainties, this regulation law gave excellent performances leading the output COD towards its set-point and keeping it inside a pre-specified interval.
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Stamm, Fabian Antonio, Miguel de la Varga, and Florian Wellmann. "Actors, actions, and uncertainties: optimizing decision-making based on 3-D structural geological models." Solid Earth 10, no. 6 (November 18, 2019): 2015–43. http://dx.doi.org/10.5194/se-10-2015-2019.
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Abstract. Uncertainties are common in geological models and have a considerable impact on model interpretations and subsequent decision-making. This is of particular significance for high-risk, high-reward sectors. Recent advances allows us to view geological modeling as a statistical problem that we can address with probabilistic methods. Using stochastic simulations and Bayesian inference, uncertainties can be quantified and reduced by incorporating additional geological information. In this work, we propose custom loss functions as a decision-making tool that builds upon such probabilistic approaches. As an example, we devise a case in which the decision problem is one of estimating the uncertain economic value of a potential fluid reservoir. For subsequent true value estimation, we design a case-specific loss function to reflect not only the decision-making environment, but also the preferences of differently risk-inclined decision makers. Based on this function, optimizing for expected loss returns an actor's best estimate to base decision-making on, given a probability distribution for the uncertain parameter of interest. We apply the customized loss function in the context of a case study featuring a synthetic 3-D structural geological model. A set of probability distributions for the maximum trap volume as the parameter of interest is generated via stochastic simulations. These represent different information scenarios to test the loss function approach for decision-making. Our results show that the optimizing estimators shift according to the characteristics of the underlying distribution. While overall variation leads to separation, risk-averse and risk-friendly decisions converge in the decision space and decrease in expected loss given narrower distributions. We thus consider the degree of decision convergence to be a measure for the state of knowledge and its inherent uncertainty at the moment of decision-making. This decisive uncertainty does not change in alignment with model uncertainty but depends on alterations of critical parameters and respective interdependencies, in particular relating to seal reliability. Additionally, actors are affected differently by adding new information to the model, depending on their risk affinity. It is therefore important to identify the model parameters that are most influential for the final decision in order to optimize the decision-making process.
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Moges, Edom, Yonas Demissie, Laurel Larsen, and Fuad Yassin. "Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis." Water 13, no. 1 (December 25, 2020): 28. http://dx.doi.org/10.3390/w13010028.
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Despite progresses in representing different processes, hydrological models remain uncertain. Their uncertainty stems from input and calibration data, model structure, and parameters. In characterizing these sources, their causes, interactions and different uncertainty analysis (UA) methods are reviewed. The commonly used UA methods are categorized into six broad classes: (i) Monte Carlo analysis, (ii) Bayesian statistics, (iii) multi-objective analysis, (iv) least-squares-based inverse modeling, (v) response-surface-based techniques, and (vi) multi-modeling analysis. For each source of uncertainty, the status-quo and applications of these methods are critiqued in gauged catchments where UA is common and in ungauged catchments where both UA and its review are lacking. Compared to parameter uncertainty, UA application for structural uncertainty is limited while input and calibration data uncertainties are mostly unaccounted. Further research is needed to improve the computational efficiency of UA, disentangle and propagate the different sources of uncertainty, improve UA applications to environmental changes and coupled human–natural-hydrologic systems, and ease UA’s applications for practitioners.
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Luo, Zhi Kun, Ping He, Wei Tan, and Guo Dong Jin. "Dynamic Analysis of a Truck Frame with Fuzzy Uncertain Parameters." Advanced Materials Research 466-467 (February 2012): 1279–84. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.1279.
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Frame acts as the structural backbone of a truck, which supports the components and payload placed upon it. When the truck travels along the road, the frame is subjected to vibration induced by road roughness and excitation by vibrating components such as power-train, transmission shaft and more that mounted on it. Though many researchers have made great progress in the static and dynamic analysis of truck frame, most research was based on the assumption that all the design parameters of truck frame were deterministic. However, design variables for truck frame are always uncertain in the actual realistic engineering cases due to tolerances in manufacturing and assembly processes. In this paper, fuzzy algorithm is introduced to analysis the response of the frame with uncertain parameters. By using fuzzy set theory, uncertain input parameters such as the elastic modulus, Poisson ratio are described mathematically as fuzzy variables or fuzzy random variables and integrated into mode analysis. The simulations are carried out to analysis the system performance under fuzzy uncertain parameters. Results are presented showing the effectiveness of the method for modeling systems with uncertain parameters.
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Sikorska, A. E., A. Scheidegger, K. Banasik, and J. Rieckermann. "Bayesian uncertainty assessment of flood predictions in ungauged urban basins for conceptual rainfall-runoff models." Hydrology and Earth System Sciences Discussions 8, no. 6 (December 13, 2011): 11075–113. http://dx.doi.org/10.5194/hessd-8-11075-2011.
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Abstract. Urbanization and the resulting land-use change strongly affect the water cycle and runoff-processes in watersheds. Unfortunately, small urban watersheds, which are most affected by urban sprawl, are mostly ungauged. This makes it intrinsically difficult to assess the consequences of urbanization. Most of all, it is unclear how to reliably assess the predictive uncertainty given the structural deficits of the applied models. In this study, we therefore investigate the uncertainty of flood predictions in ungauged urban basins from structurally uncertain rainfall-runoff models. To this end, we suggest a procedure to explicitly account for input uncertainty and model structure deficits using Bayesian statistics with a continuous-time autoregressive error model. In addition, we propose a concise procedure to derive prior parameter distributions from base data and successfully apply the methodology to an urban catchment in Warsaw, Poland. Based on our results, we are able to demonstrate that the autoregressive error model greatly helps to meet the statistical assumptions and to compute reliable prediction intervals. In our study, we found that predicted peak flows were up to 7 times higher than observations. This was reduced by 150% with Bayesian updating, using only a few discharge measurements. In addition, our analysis suggests that imprecise rainfall information and model structure deficits contribute mostly to the total prediction uncertainty. In the future, flood predictions in ungauged basins will become more important due to ongoing urbanization as well as anthropogenic and climatic changes. Thus, providing reliable measures of uncertainty is crucial to support decision making.
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McNeall, Doug, Jonny Williams, Ben Booth, Richard Betts, Peter Challenor, Andy Wiltshire, and David Sexton. "The impact of structural error on parameter constraint in a climate model." Earth System Dynamics 7, no. 4 (November 24, 2016): 917–35. http://dx.doi.org/10.5194/esd-7-917-2016.
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Abstract. Uncertainty in the simulation of the carbon cycle contributes significantly to uncertainty in the projections of future climate change. We use observations of forest fraction to constrain carbon cycle and land surface input parameters of the global climate model FAMOUS, in the presence of an uncertain structural error. Using an ensemble of climate model runs to build a computationally cheap statistical proxy (emulator) of the climate model, we use history matching to rule out input parameter settings where the corresponding climate model output is judged sufficiently different from observations, even allowing for uncertainty. Regions of parameter space where FAMOUS best simulates the Amazon forest fraction are incompatible with the regions where FAMOUS best simulates other forests, indicating a structural error in the model. We use the emulator to simulate the forest fraction at the best set of parameters implied by matching the model to the Amazon, Central African, South East Asian, and North American forests in turn. We can find parameters that lead to a realistic forest fraction in the Amazon, but that using the Amazon alone to tune the simulator would result in a significant overestimate of forest fraction in the other forests. Conversely, using the other forests to tune the simulator leads to a larger underestimate of the Amazon forest fraction. We use sensitivity analysis to find the parameters which have the most impact on simulator output and perform a history-matching exercise using credible estimates for simulator discrepancy and observational uncertainty terms. We are unable to constrain the parameters individually, but we rule out just under half of joint parameter space as being incompatible with forest observations. We discuss the possible sources of the discrepancy in the simulated Amazon, including missing processes in the land surface component and a bias in the climatology of the Amazon.
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Guo, Hao-Bo, Yue Ma, Gerald Tuskan, Hong Qin, Xiaohan Yang, and Hong Guo. "A Suggestion of Converting Protein Intrinsic Disorder to Structural Entropy Using Shannon’s Information Theory." Entropy 21, no. 6 (June 14, 2019): 591. http://dx.doi.org/10.3390/e21060591.
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We propose a framework to convert the protein intrinsic disorder content to structural entropy (H) using Shannon’s information theory (IT). The structural capacity (C), which is the sum of H and structural information (I), is equal to the amino acid sequence length of the protein. The structural entropy of the residues expands a continuous spectrum, ranging from 0 (fully ordered) to 1 (fully disordered), consistent with Shannon’s IT, which scores the fully-determined state 0 and the fully-uncertain state 1. The intrinsically disordered proteins (IDPs) in a living cell may participate in maintaining the high-energy-low-entropy state. In addition, under this framework, the biological functions performed by proteins and associated with the order or disorder of their 3D structures could be explained in terms of information-gains or entropy-losses, or the reverse processes.
Dissertations / Theses on the topic "Uncertain structural processes"
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Freitag, Steffen, Wolfgang Graf, and Michael Kaliske. "Prognose des Langzeitverhaltens von Textilbeton-Tragwerken mit rekurrenten neuronalen Netzen." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1244048026002-79164.
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Zur Prognose des Langzeitverhaltens textilbetonverstärkter Tragwerke wird ein modellfreies Vorgehen auf Basis rekurrenter neuronaler Netze vorgestellt. Das Vorgehen ermöglicht die Prognose zeitveränderlicher Strukturantworten unter Berücksichtigung der gesamten Belastungsgeschichte. Mit unscharfen Größen aus Messungen an Versuchstragwerken werden rekurrente neuronale Netze trainiert. Anschließend ist die unscharfe Prognose des Tragverhaltens möglich.
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Zager, Laura (Laura A. ). "Infection processes on networks with structural uncertainty." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45616.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 167-175).
Over the last ten years, the interest in network phenomena and the potential for a global pandemic have produced a tremendous volume of research exploring the consequences of human interaction patterns for disease propagation. The research often focuses on a single question: will an emerging infection become an epidemic? This thesis clarifies the relationships among different epidemic threshold criteria in deterministic disease models, and discusses the role and meaning of the basic reproductive ratio, R0. We quantify the incorporation of population structure into this general framework, and identify conditions under which interaction topology and infection characteristics can be decoupled in the computation of threshold functions, which generalizes many existing results in the literature. This decoupling allows us to focus on the impact of network topology via the spectral radius of the adjacency matrix of the network. It is rare, however, that one has complete information about every potential disease-transmitting interaction; this uncertainty in the network structure is often ignored in deterministic models. Neglecting this uncertainty can lead to an underestimate of R0, an unacceptable outcome for public health planning. Is it possible to make guarantees and approximations regarding disease spread when only partial information about the routes of transmission is known? We present methods for making predictions about disease spread over uncertain networks, including approximation techniques and bounding results obtained via spectral graph theory, and illustrate these results on several data sets. We also approach this problem by using simulation and analytical work to characterize the spectral radii that arise from members of the exponential random graph family, commonly used to model empirical networks in quantitative sociology. Finally, we explore several issues in the spatiotemporal patterns of epidemic propagation through a network, focusing on the behavior of the contact process and the influence model.
by Laura A. Zager.
Ph.D.
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Reimer, Jody. "Effective design of marine reserves : incorporating alongshore currents, size structure, and uncertainty." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:8a5e72cb-6bc9-4ef3-a991-2cc934b228fb.
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Marine populations worldwide are in decline due to anthropogenic effects. Spatial management via marine reserves may be an effective conservation method for many species, but the requisite theory is still underdeveloped. Integrodifference equation (IDE) models can be used to determine the critical domain size required for persistence and provide a modelling framework suitable for many marine populations. Here, we develop a novel spatially implicit approximation for the proportion of individuals lost outside the reserve areas which consistently outperforms the most common approximation. We examine how results using this approximation compare to the existing IDE results on the critical domain size for populations in a single reserve, in a network of reserves, in the presence of alongshore currents, and in structured populations. We find that the approximation consistently provides results which are in close agreement with those of an IDE model with the advantage of being simpler to convey to a biological audience while providing insights into the significance of certain model components. We also design a stochastic individual based model (IBM) to explore the probability of extinction for a population within a reserve area. We use our spatially implicit approximation to estimate the proportion of individuals which disperse outside the reserve area. We then use this approximation to obtain results on extinction using two different approaches, which we can compare to the baseline IBM; the first approach is based on the Central Limit Theorem and provides efficient simulation results, and the second modifies a simple Galton-Watson branching process to include loss outside the reserve area. We find that this spatially implicit approximation is also effective in obtaining results similar to those produced by the IBM in the presence of both demographic and environmental variability. Overall, this provides a set of complimentary methods for predicting the reserve area required to sustain a population in the presence of strong fishing pressure in the surrounding waters.
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Herman, Joseph L. "Multiple sequence analysis in the presence of alignment uncertainty." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:88a56d9f-a96e-48e3-b8dc-a73f3efc8472.
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Sequence alignment is one of the most intensely studied problems in bioinformatics, and is an important step in a wide range of analyses. An issue that has gained much attention in recent years is the fact that downstream analyses are often highly sensitive to the specific choice of alignment. One way to address this is to jointly sample alignments along with other parameters of interest. In order to extend the range of applicability of this approach, the first chapter of this thesis introduces a probabilistic evolutionary model for protein structures on a phylogenetic tree; since protein structures typically diverge much more slowly than sequences, this allows for more reliable detection of remote homologies, improving the accuracy of the resulting alignments and trees, and reducing sensitivity of the results to the choice of dataset. In order to carry out inference under such a model, a number of new Markov chain Monte Carlo approaches are developed, allowing for more efficient convergence and mixing on the high-dimensional parameter space. The second part of the thesis presents a directed acyclic graph (DAG)-based approach for representing a collection of sampled alignments. This DAG representation allows the initial collection of samples to be used to generate a larger set of alignments under the same approximate distribution, enabling posterior alignment probabilities to be estimated reliably from a reasonable number of samples. If desired, summary alignments can then be generated as maximum-weight paths through the DAG, under various types of loss or scoring functions. The acyclic nature of the graph also permits various other types of algorithms to be easily adapted to operate on the entire set of alignments in the DAG. In the final part of this work, methodology is introduced for alignment-DAG-based sequence annotation using hidden Markov models, and RNA secondary structure prediction using stochastic context-free grammars. Results on test datasets indicate that the additional information contained within the DAG allows for improved predictions, resulting in substantial gains over simply analysing a set of alignments one by one.
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Ezvan, Olivier. "Multilevel model reduction for uncertainty quantification in computational structural dynamics." Thesis, Paris Est, 2016. http://www.theses.fr/2016PESC1109/document.
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Ce travail de recherche présente une extension de la construction classique des modèles réduits (ROMs) obtenus par analyse modale, en dynamique numérique des structures linéaires. Cette extension est basée sur une stratégie de projection multi-niveau, pour l'analyse dynamique des structures complexes en présence d'incertitudes. De nos jours, il est admis qu'en dynamique des structures, la prévision sur une large bande de fréquence obtenue à l'aide d'un modèle éléments finis doit être améliorée en tenant compte des incertitudes de modèle induites par les erreurs de modélisation, dont le rôle croît avec la fréquence. Dans un tel contexte, l'approche probabiliste non-paramétrique des incertitudes est utilisée, laquelle requiert l'introduction d'un ROM. Par conséquent, ces deux aspects, évolution fréquentielle des niveaux d'incertitudes et réduction de modèle, nous conduisent à considérer le développement d'un ROM multi-niveau, pour lequel les niveaux d'incertitudes dans chaque partie de la bande de fréquence peuvent être adaptés. Dans cette thèse, on s'intéresse à l'analyse dynamique de structures complexes caractérisées par la présence de plusieurs niveaux structuraux, par exemple avec un squelette rigide qui supporte diverses sous-parties flexibles. Pour de telles structures, il est possible d'avoir, en plus des modes élastiques habituels dont les déplacements associés au squelette sont globaux, l'apparition de nombreux modes élastiques locaux, qui correspondent à des vibrations prédominantes des sous-parties flexibles. Pour ces structures complexes, la densité modale est susceptible d'augmenter fortement dès les basses fréquences (BF), conduisant, via la méthode d'analyse modale, à des ROMs de grande dimension (avec potentiellement des milliers de modes élastiques en BF). De plus, de tels ROMs peuvent manquer de robustesse vis-à-vis des incertitudes, en raison des nombreux déplacements locaux qui sont très sensibles aux incertitudes. Il convient de noter qu'au contraire des déplacements globaux de grande longueur d'onde caractérisant la bande BF, les déplacements locaux associés aux sous-parties flexibles de la structure, qui peuvent alors apparaître dès la bande BF, sont caractérisés par de courtes longueurs d'onde, similairement au comportement dans la bande hautes fréquences (HF). Par conséquent, pour les structures complexes considérées, les trois régimes vibratoires BF, MF et HF se recouvrent, et de nombreux modes élastiques locaux sont entremêlés avec les modes élastiques globaux habituels. Cela implique deux difficultés majeures, concernant la quantification des incertitudes d'une part et le coût numérique d'autre part. L'objectif de cette thèse est alors double. Premièrement, fournir un ROM stochastique multi-niveau qui est capable de rendre compte de la variabilité hétérogène introduite par le recouvrement des trois régimes vibratoires. Deuxièmement, fournir un ROM prédictif de dimension réduite par rapport à celui de l'analyse modale. Une méthode générale est présentée pour la construction d'un ROM multi-niveau, basée sur trois bases réduites (ROBs) dont les déplacements correspondent à l'un ou l'autre des régimes vibratoires BF, MF ou HF (associés à des déplacements de type BF, de type MF ou bien de type HF). Ces ROBs sont obtenues via une méthode de filtrage utilisant des fonctions de forme globales pour l'énergie cinétique (par opposition aux fonctions de forme locales des éléments finis). L'implémentation de l'approche probabiliste non-paramétrique dans le ROM multi-niveau permet d'obtenir un ROM stochastique multi-niveau avec lequel il est possible d'attribuer un niveau d'incertitude spécifique à chaque ROB. L'application présentée est relative à une automobile, pour laquelle le ROM stochastique multi-niveau est identifié par rapport à des mesures expérimentales. Le ROM proposé permet d'obtenir une dimension réduite ainsi qu'une prévision améliorée, en comparaison avec un ROM stochastique classiqueThis work deals with an extension of the classical construction of reduced-order models (ROMs) that are obtained through modal analysis in computational linear structural dynamics. It is based on a multilevel projection strategy and devoted to complex structures with uncertainties. Nowadays, it is well recognized that the predictions in structural dynamics over a broad frequency band by using a finite element model must be improved in taking into account the model uncertainties induced by the modeling errors, for which the role increases with the frequency. In such a framework, the nonparametric probabilistic approach of uncertainties is used, which requires the introduction of a ROM. Consequently, these two aspects, frequency-evolution of the uncertainties and reduced-order modeling, lead us to consider the development of a multilevel ROM in computational structural dynamics, which has the capability to adapt the level of uncertainties to each part of the frequency band. In this thesis, we are interested in the dynamical analysis of complex structures in a broad frequency band. By complex structure is intended a structure with complex geometry, constituted of heterogeneous materials and more specifically, characterized by the presence of several structural levels, for instance, a structure that is made up of a stiff main part embedding various flexible sub-parts. For such structures, it is possible having, in addition to the usual global-displacements elastic modes associated with the stiff skeleton, the apparition of numerous local elastic modes, which correspond to predominant vibrations of the flexible sub-parts. For such complex structures, the modal density may substantially increase as soon as low frequencies, leading to high-dimension ROMs with the modal analysis method (with potentially thousands of elastic modes in low frequencies). In addition, such ROMs may suffer from a lack of robustness with respect to uncertainty, because of the presence of the numerous local displacements, which are known to be very sensitive to uncertainties. It should be noted that in contrast to the usual long-wavelength global displacements of the low-frequency (LF) band, the local displacements associated with the structural sub-levels, which can then also appear in the LF band, are characterized by short wavelengths, similarly to high-frequency (HF) displacements. As a result, for the complex structures considered, there is an overlap of the three vibration regimes, LF, MF, and HF, and numerous local elastic modes are intertwined with the usual global elastic modes. This implies two major difficulties, pertaining to uncertainty quantification and to computational efficiency. The objective of this thesis is thus double. First, to provide a multilevel stochastic ROM that is able to take into account the heterogeneous variability introduced by the overlap of the three vibration regimes. Second, to provide a predictive ROM whose dimension is decreased with respect to the classical ROM of the modal analysis method. A general method is presented for the construction of a multilevel ROM, based on three orthogonal reduced-order bases (ROBs) whose displacements are either LF-, MF-, or HF-type displacements (associated with the overlapping LF, MF, and HF vibration regimes). The construction of these ROBs relies on a filtering strategy that is based on the introduction of global shape functions for the kinetic energy (in contrast to the local shape functions of the finite elements). Implementing the nonparametric probabilistic approach in the multilevel ROM allows each type of displacements to be affected by a particular level of uncertainties. The method is applied to a car, for which the multilevel stochastic ROM is identified with respect to experiments, solving a statistical inverse problem. The proposed ROM allows for obtaining a decreased dimension as well as an improved prediction with respect to a classical stochastic ROM
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Tavares, Ivo Alberto Valente. "Uncertainty quantification with a Gaussian Process Prior : an example from macroeconomics." Doctoral thesis, Instituto Superior de Economia e Gestão, 2021. http://hdl.handle.net/10400.5/21444.
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Doutoramento em Matemática Aplicada à Economia e GestãoThis thesis may be broadly divided into 4 parts. In the first part, we do a literature review of the state of the art in misspecification in Macroeconomics, and what so far has been the contribution of a relatively new area of research called Uncertainty Quantification to the Macroeconomics subject. These reviews are essential to contextualize the contribution of this thesis in the furthering of research dedicated to correcting non-linear misspecifications, and to account for several other sources of uncertainty, when modelling from an economic perspective. In the next three parts, we give an example, using the same simple DSGE model from macroeconomic theory, of how researchers may quantify uncertainty in a State-Space Model using a discrepancy term with a Gaussian Process prior. The second part of the thesis, we used a full Gaussian Process (GP) prior on the discrepancy term. Our experiments showed that despite the heavy computational constraints of our full GP method, we still managed to obtain a very interesting forecasting performance with such a restricted sample size, when compared with similar uncorrected DSGE models, or corrected DSGE models using state of the art methods for time series, such as imposing a VAR on the observation error of the state-space model. In the third part of our work, we improved on the computational performance of our previous method, using what has been referred in the literature as Hilbert Reduced Rank GP. This method has close links to Functional Analysis, and the Spectral Theorem for Normal Operators, and Partial Differential Equations. It indeed improved the computational processing time, albeit just slightly, and was accompanied with a similarly slight decrease in the forecasting performance. The fourth part of our work delved into how our method would account for model uncertainty just prior, and during, the great financial crisis of 2007-2009. Our technique allowed us to capture the crisis, albeit at a reduced applicability possibly due to computational constraints. This latter part also was used to deepen the understanding of our model uncertainty quantification technique with a GP. Identifiability issues were also studied. One of our overall conclusions was that more research is needed until this uncertainty quantification technique may be used in as part of the toolbox of central bankers and researchers for forecasting economic fluctuations, specially regarding the computational performance of either method.
info:eu-repo/semantics/publishedVersion
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Ola, Abdel Malik. "L’identification des opportunités d’investissement en incertitude : le jugement intuitif des Business Angels dans le financement des firmes entrepreneuriales." Thesis, Angers, 2016. http://www.theses.fr/2016ANGE0032/document.
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Nous analysons l’identification des opportunités d’investissement dans le cas spécifique du financement de l’amorçage des firmes porteuses d’innovation. L’absence d’informations pertinentes et objectives au démarrage remet en cause la capacité postulée des investisseurs à évaluer objectivement la rentabilité des firmes entrepreneuriales. Ainsi, nous étudions la vraie stratégie psycho-cognitive sous-jacente à la création du sens autour du potentiel des projets en se focalisant sur un acteur spécifique, le Business Angel (BA). Nous postulons que cet investissement suit un processus de jugement intuitif. L’analyse qualitative des notes d’observation et des entretiens permet de construire un modèle décrivant la manière dont le BA produit in situ de nouveaux construits utiles dans sa perception. Nous mettons aussi en évidence des comportements réflexifs réduisant l’erreur dans sa décision. Ainsi, l’intuition du BA doit être vue comme une réelle approche de transformation situationnelle d’indicateurs à travers des manipulations langagières. Nous offrons une nouvelle perspective dans la compréhension du comportement des capital-risqueurs qui sont susceptibles d’accompagner financièrement les firmes innovantes dès leur phase de démarrage. Nos résultats sont aussi généralisables à des contexte où l’aptitude intuitive devant une source d’efficience décisionnelle. Nous faisons des propositions théoriques qui orienteront les études futuresWe analyze the investment opportunities’s identification in the specific case of the innovative firm financing. The absence of relevant and objective informations at the early stage weaken the investor’s postulated ability inestimating objectively the profitability of the entrepreneurial firms. Then, we study the real cognitivestrategy underlying the sensemaking process around the potential of the projects by focusing on a specific actor, the Business angel (BA). We argue that this investment follows a process of intuitive judgment.The research design is a qualitative inductive approach with data collected by observation and interviews. We build a model of how the BA cognitively interpret the innovative firm’s potential in order to invest. We highlight also cognitive practices in reducting biais and errors during the sensemaking process. The BA’s intuition atearly stage must be viewed as a processus of meaning construction through labelling and speech articulation. This thesis contributes to a better understanding ofventure capitalist behaviors at early stage as well as a better comprehension of how meaning can be created intuitively in uncertain context. Theoretical propositions are made for future researchs
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Nwankwo, Cosmas Chidozie. "Smart offshore structure for reliability prediction process." Thesis, Cranfield University, 2013. http://dspace.lib.cranfield.ac.uk/handle/1826/9335.
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A review of the developments within the field of structural reliability theory shows that
some gaps still exist in the reliability prediction process and hence there is an urgent
desire for improvements such that the estimated structural reliability will be capable of
expressing a physical property of the given structure. The current reliability prediction
process involves the continuous estimation and use of reliability index as a way of
estimating the safety of any given structure. The reliability index β depends on the
Probability Density Function (PDF) distribution for the wave force and the corresponding
PDF of resistance from respective structural members of the given structure. The PDF
for the applied wave force will depend on the PDF of water depth, wave angular velocity
and wave direction hence the reliability index as currently practiced is a statistical way
of managing uncertainties based on a general probabilistic model.
This research on Smart Offshore Structure for Reliability Prediction has proposed the
design of a measurement based reliability prediction process as a way of closing the
gap on structural reliability prediction process. Structural deflection and damping are
some of the measurable properties of an offshore structure and this study aims at
suggesting the use of these measurable properties for improvements in structural
reliability prediction process. A design case study has shown that a typical offshore
structure can deflect to a range of only a few fractions of a millimetre. This implies that if
we have a way of monitoring this level of deflection, we could use the results from such
measurement for the detection of a structural member failure. This advocated concept is
based on the hypothesis that if the original dynamic characteristics of a structure is
known, that measurement based modified dynamic properties can be used to determine
the onset of failure or failure propagation of the given structure.
This technology could reveal the location and magnitude of internal cracks or corrosion
effects on any given structure which currently is outside the current probability based
approach. A simple economic analysis shows that the recommended process shows a
positive net present value and that some $74mln is the Value of Information for any life
extension technology that could reveal the possibility of extending the life of a given
10,000bopd production platform from 2025 to 2028.
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Yin, Qi. "Prise en compte de la variabilité dans les calculs par éléments finis des structures composites en régime statique ou vibratoire." Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2304/document.
Full textAbstract:
La fabrication des structures composites conduit à une variabilité élevée de ses paramètres mécaniques. La thèse a comme objectif global de développer des méthodes économiques et robustes pour étudier la variabilité de la réponse statique ou dynamique des structures composites modélisées par éléments finis, prenant en compte les propriétés matériaux (modules d'élasticité, coefficients de Poisson, masses volumiques...) et physiques (épaisseurs et orientations des fibres) incertaines. Deux méthodes stochastiques : Certain Generalized Stresses Method (CGSM) et Modal Stability Procedure (MSP), sont développées. La méthode CGSM considère une hypothèse mécanique, les efforts généralisés sont supposés indépendants des paramètres incertains. Elle permet d'évaluer la variabilité de la réponse statique. La méthode MSP, proposée pour étudier la variabilité d'une structure en dynamique, est basée sur l'hypothèse considérant que les modes propres sont peu sensibles aux paramètres incertains. Les hypothèses mécaniques et une unique analyse par éléments finis permettent de construire un méta-modèle exploité dans une simulation de Monte Carlo. Le coût de calcul de ces méthodes stochastiques est donc réduit considérablement. De plus, elles présentent les avantages de ne pas limiter le nombre de paramètres incertains ou le niveau de variabilité d'entrée, et d'être compatibles avec tout code éléments finis standard. Quatre exemples académiques de plaque et coque composite sont traités avec la méthode CGSM, deux exemples académiques de plaque composite carrée et un exemple de plaque raidie sont traités avec la méthode MSP. La variabilité de la réponse statique (déplacement et critère de rupture) et dynamique (fréquence propre), soit la moyenne, l'écart-type, le coefficient de variation et la distribution, est évaluée. Les résultats statistiques obtenus par les méthodes proposées sont comparés avec ceux obtenus par une simulation de Monte Carlo directe, considérée comme la méthode de référence. La comparaison montre que les méthodes développées fournissent des résultats de bonne qualité et qu'elles sont très performantes en temps de calcul. Un indicateur d'erreur est également proposé, permettant de donner une estimation du niveau d'erreur des résultats obtenus par les méthodes CGSM ou MSP par rapport à la méthode de référence, sans réaliser un grand nombre d'analyses par éléments finisThe manufacture of composite structures leads to a high variability of mechanical parameters. The objective of this work is to develop economic and robust methods to study the variability of the static or dynamic response of composite structures modeled by finite elements, taking into account uncertain material (elastic moduli, Poisson's ratios, densities... ) and physical (thicknesses and fiber orientations) properties. Two methods are developed: the Certain Generalized Stresses Method (CGSM) and the Modal Stability Procedure (MSP). The CGSM considers a mechanical assumption, the generalized stresses are assumed to be independant of uncertain parameters. lt allows to evaluate the variability of static response. The MSP, proposed to study the variability of structures in dynamics, is based on the assumption that the modes shapes are insensitive to uncertain parameters. These mechanical assumptions and only one fïnite element analysis allow to construct a metamodel used in a Monte Carlo simulation. As a result, the computational cost is reduced considerably. Moreover, they are not limited by the number of considered parameters or the level of input variability, and are compatible with standard finite element software. Four academic examples of composite plate and shell are treated with the CGSM, while two academic examples of composite square plate and an example of stiffened plate are studied by the MSP. The variability of static response (displacement and failure criterion) and dynamic response (natural frequency), namely mean value, standard deviation, coefficient of variation and distribution, is evaluated. The results obtained by the proposed methods are compared with those obtained by the direct Monte Carlo simulation, considered as the reference method. The comparison shows that the proposed methods provide quite accurate results and highlights their high computational efficiency. An error indicator is also proposed, which allows to provide an estimation of the error level of the results obtained by the CGSM or MSP compared to the reference method, without performing a large number of finite element analyses
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Cherpeau, Nicolas. "Incertitudes structurales en géomodélisation : échantillonnage et approche inverse." Thesis, Université de Lorraine, 2012. http://www.theses.fr/2012LORR0141/document.
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La modélisation du sous-sol est un outil indispensable pour décrire, comprendre et quantifier les processus géologiques. L'accès au sous-sol et son observation étant limités aux moyens d'acquisition, la construction de modèles tridimensionnels du sous-sol repose sur l'interprétation de données éparses à résolution limitée. Dans ce contexte, de nombreuses incertitudes affectent la construction de tels modèles, dues aux possibles biais humains cognitifs lors de l'interprétation, à la variabilité naturelle des objets géologiques et aux incertitudes intrinsèques des données utilisées. Ces incertitudes altèrent la prédictibilité des modèles et leur évaluation est donc nécessaire afin de réduire les risques économiques et humains liés à l'utilisation des modèles. Le travail de thèse s'est déroulé dans le cadre plus spécifique des incertitudes sur les structures géologiques. Les réponses apportées sont multiples : (1) une méthode stochastique de génération de modèles structuraux à géométrie et topologie changeantes, combinant une connaissance a priori des structures géologiques aux données interprétées, a été développée ; (2) le réalisme géologique des structures modélisées est garanti grâce à la modélisation implicite, représentant une surface par une équipotentielle d'un champ scalaire volumique ; (3) la description des failles en un nombre restreint de paramètres incertains a permis d'aborder la modélisation inverse, ce qui ouvre la voie vers l'assimilation de données géophysiques ou d'écoulement fluides grâce à des méthodes bayesiennesSubsurface modeling is a key tool to describe, understand and quantify geological processes. As the subsurface is inaccessible and its observation is limited by acquisition methods, 3D models of the subsurface are usually built from the interpretation of sparse data with limited resolution. Therefore, uncertainties occur during the model building process, due to possible cognitive human bias, natural variability of geological objects and intrinsic uncertainties of data. In such context, the predictability of models is limited by uncertainties, which must be assessed in order to reduce economical and human risks linked to the use of models. This thesis focuses more specifically on uncertainties about geological structures. Our contributions are : (1) a stochastic method for generating structural models with various fault and horizon geometries as well as fault connections, combining prior information and interpreted data, has been developped ; (2) realistic geological objects are obtained using implicit modeling that represents a surface by an equipotential of a volumetric scalar field ; (3) faults have been described by a reduced set of uncertain parameters, which opens the way to the inversion of structural objects using geophysical or fluid flow data by baysian methods
Books on the topic "Uncertain structural processes"
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Sanderson, Benjamin Mark. Uncertainty Quantification in Multi-Model Ensembles. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.707.
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Long-term planning for many sectors of society—including infrastructure, human health, agriculture, food security, water supply, insurance, conflict, and migration—requires an assessment of the range of possible futures which the planet might experience. Unlike short-term forecasts for which validation data exists for comparing forecast to observation, long-term forecasts have almost no validation data. As a result, researchers must rely on supporting evidence to make their projections. A review of methods for quantifying the uncertainty of climate predictions is given. The primary tool for quantifying these uncertainties are climate models, which attempt to model all the relevant processes that are important in climate change. However, neither the construction nor calibration of climate models is perfect, and therefore the uncertainties due to model errors must also be taken into account in the uncertainty quantification.Typically, prediction uncertainty is quantified by generating ensembles of solutions from climate models to span possible futures. For instance, initial condition uncertainty is quantified by generating an ensemble of initial states that are consistent with available observations and then integrating the climate model starting from each initial condition. A climate model is itself subject to uncertain choices in modeling certain physical processes. Some of these choices can be sampled using so-called perturbed physics ensembles, whereby uncertain parameters or structural switches are perturbed within a single climate model framework. For a variety of reasons, there is a strong reliance on so-called ensembles of opportunity, which are multi-model ensembles (MMEs) formed by collecting predictions from different climate modeling centers, each using a potentially different framework to represent relevant processes for climate change. The most extensive collection of these MMEs is associated with the Coupled Model Intercomparison Project (CMIP). However, the component models have biases, simplifications, and interdependencies that must be taken into account when making formal risk assessments. Techniques and concepts for integrating model projections in MMEs are reviewed, including differing paradigms of ensembles and how they relate to observations and reality. Aspects of these conceptual issues then inform the more practical matters of how to combine and weight model projections to best represent the uncertainties associated with projected climate change.
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Rauh, Andreas, and Luise Senkel. Variable-Structure Approaches: Analysis, Simulation, Robust Control and Estimation of Uncertain Dynamic Processes. Springer, 2016.
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Rauh, Andreas, and Luise Senkel. Variable-Structure Approaches: Analysis, Simulation, Robust Control and Estimation of Uncertain Dynamic Processes. Springer, 2018.
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Schenk, Christian A., and Gerhart I. Schuëller. Uncertainty Assessment of Large Finite Element Systems. Springer, 2010.
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Uncertainty Assessment of Large Finite Element Systems (Lecture Notes in Applied and Computational Mechanics). Springer, 2005.
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Wikle, Christopher K. Spatial Statistics. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.710.
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The climate system consists of interactions between physical, biological, chemical, and human processes across a wide range of spatial and temporal scales. Characterizing the behavior of components of this system is crucial for scientists and decision makers. There is substantial uncertainty associated with observations of this system as well as our understanding of various system components and their interaction. Thus, inference and prediction in climate science should accommodate uncertainty in order to facilitate the decision-making process. Statistical science is designed to provide the tools to perform inference and prediction in the presence of uncertainty. In particular, the field of spatial statistics considers inference and prediction for uncertain processes that exhibit dependence in space and/or time. Traditionally, this is done descriptively through the characterization of the first two moments of the process, one expressing the mean structure and one accounting for dependence through covariability.Historically, there are three primary areas of methodological development in spatial statistics: geostatistics, which considers processes that vary continuously over space; areal or lattice processes, which considers processes that are defined on a countable discrete domain (e.g., political units); and, spatial point patterns (or point processes), which consider the locations of events in space to be a random process. All of these methods have been used in the climate sciences, but the most prominent has been the geostatistical methodology. This methodology was simultaneously discovered in geology and in meteorology and provides a way to do optimal prediction (interpolation) in space and can facilitate parameter inference for spatial data. These methods rely strongly on Gaussian process theory, which is increasingly of interest in machine learning. These methods are common in the spatial statistics literature, but much development is still being done in the area to accommodate more complex processes and “big data” applications. Newer approaches are based on restricting models to neighbor-based representations or reformulating the random spatial process in terms of a basis expansion. There are many computational and flexibility advantages to these approaches, depending on the specific implementation. Complexity is also increasingly being accommodated through the use of the hierarchical modeling paradigm, which provides a probabilistically consistent way to decompose the data, process, and parameters corresponding to the spatial or spatio-temporal process.Perhaps the biggest challenge in modern applications of spatial and spatio-temporal statistics is to develop methods that are flexible yet can account for the complex dependencies between and across processes, account for uncertainty in all aspects of the problem, and still be computationally tractable. These are daunting challenges, yet it is a very active area of research, and new solutions are constantly being developed. New methods are also being rapidly developed in the machine learning community, and these methods are increasingly more applicable to dependent processes. The interaction and cross-fertilization between the machine learning and spatial statistics community is growing, which will likely lead to a new generation of spatial statistical methods that are applicable to climate science.
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Davidson, Debra J., and Matthias Gross, eds. Oxford Handbook of Energy and Society. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190633851.001.0001.
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The Oxford Handbook of Energy and Society offers a timely and much-needed synthesis of recent developments in sociological analysis of energy-society relations, representing a wide breadth of contributors in sociology and related disciplines from across the globe. Regional case studies of different energy resources are featured, as are the roles of politics, markets, technology, social movements, and consumers, all contributing to a complex systems perspective on the uncertain future of energy-society relations. The volume is divided into seven sections. Section One includes chapters that highlight key contemporary dynamics and theoretical contributions in this field of scholarship. Following this is a section showcasing structural perspectives on energy-society relations, including chapters describing the persistent material and geopolitical relevance of fossil fuels. Section Three highlights research on consumers and consumption processes, while Section Four draws attention to emerging research on the inequitable distribution of energy access, and energy poverty. Section Five includes chapters that focus on the influence of publics and civil society in contemporary energy-society relations. Section Six offers chapters that focus on current trends in energy politics, and finally, in the concluding section we offer a selection of chapters that highlight some emerging trends that may have potential to generate—or constrain—significant shifts in energy-society relationships. While offering a diversity of perspectives and empirical research, contributors to this volume agree on a number of key issues that offer important insights into the future of energy-society relations, including the growing instability imposed by fossil-fuel dependence, and challenges and innovations associated with a renewable energy transition.
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Zúñiga, Fernando. Mapudungun. Edited by Michael Fortescue, Marianne Mithun, and Nicholas Evans. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199683208.013.40.
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Mapudungun, an unclassified language of southern Chile and south-central Argentina spoken by a somewhat uncertain but sizeable number of speakers, has word-formation phenomena that deserve to be called polysynthetic according to most of the (sometimes mutually exclusive) definitions of this term found in the descriptive and typological literature. Polypersonalism, productive nominal incorporation, a limited amount of lexical affixation, alongside significant grammatical affixation, and especially root-serializing/compounding processes lead to long and complex templatically structured verbal predicates that markedly contrast, not only with rather simple nouns in the same language, but also with predicates in many other languages of the region. This chapter describes the major word-formation processes of Mapudungun paying special attention to the typologies of polysynthesis that have been proposed in previous studies on the subject.
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Hilton-Jones, David. Muscle diseases. Oxford University Press, 2011. http://dx.doi.org/10.1093/med/9780198569381.003.0543.
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This chapter is concerned with those disorders in which the primary pathological process affects skeletal muscle, for which in everyday clinical practice the term myopathy is convenient shorthand. However, it must be stressed that diseases of the motor nerves and neuromuscular junction can produce an identical clinical picture to several of the myopathies, and this will be emphasized many times throughout the chapter when considering differential diagnosis. Indeed sometimes, despite one’s best efforts, one is left uncertain as to whether the primary disease process is in the nerves or muscles—it may be that in some conditions the disease process directly affects both nerves and muscles. The intimate relationship, both structural and functional, between nerves and the muscles they innervate means that disease of one may have a profound effect on the other—the most striking example is the change that occurs to skeletal muscle fibre-type distribution in denervation.
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Simon, Gleeson, and Guynn Randall. Part II The US Resolution Regime, 6 Resolution of Insured Depository Institutions. Oxford University Press, 2016. http://dx.doi.org/10.1093/law/9780199698011.003.0006.
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This chapter covers the resolution of US insured depository institutions, which is governed primarily by sections 11 and 13 of the Federal Deposit Insurance Act. It discusses certain background issues, including the chartering authorities of the institutions that are subject to resolution authority, the deposit insurance requirement, the structure of the FDIC’s resolution unit, the administrative nature of the FDIC resolution process, and the relatively high level of legal uncertainty in this area of US law. This chapter then describes the supervisory and other tools designed to prevent troubled banks and thrifts from failing, and also discusses the resolution process, the recapitalization (bail-in) within resolution strategy, and the ancillary claims process for claims left behind in the receivership.
Book chapters on the topic "Uncertain structural processes"
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Tonne, Jens, and Olaf Stursberg. "Constrained Model Predictive Control of Processes with Uncertain Structure Modeled by Jump Markov Linear Systems." In Variable-Structure Approaches, 335–61. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31539-3_12.
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Göcke, Lutz, and Robin Weninger. "Business Model Development and Validation in Digital Entrepreneurship." In Digital Entrepreneurship, 71–85. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53914-6_4.
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AbstractEvery venture is developed under high uncertainty and causal ambiguity. A large majority of digital startups leverage the lean startup approach to validate the attractiveness of their venture, to reduce avoidable investments of scarce resources, and to structure the venturing process. Digital entrepreneurs highlight that prioritization and the definition of MVPs are two challenges that entrepreneurs face when applying the lean startup approach. We provide support on these particular challenges through a structured approach—the venture pyramid—to (in)validate digital business models in the face of high uncertainty. Furthermore, we map different types of digital business models with patterns of minimum viable products to inspire digital entrepreneurs and scientists alike. To illustrate our thoughts, we have developed two case studies of German startups that applied a process of rigorous iteration and learning to their venturing processes.
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Matthies, Hermann G., and Adnan Ibrahimbegović. "Stochastic Multiscale Coupling of Inelastic Processes in Solid Mechanics." In Multiscale Modeling and Uncertainty Quantification of Materials and Structures, 135–57. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06331-7_9.
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Reintjes, Christian, Jonas Reuter, Michael Hartisch, Ulf Lorenz, and Bernd Engel. "Towards CAD-Based Mathematical Optimization for Additive Manufacturing – Designing Forming Tools for Tool-Bound Bending." In Lecture Notes in Mechanical Engineering, 12–22. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77256-7_2.
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AbstractThe trend towards flexible, agile, and resource-efficient production systems requires a continuous development of processes as well as of tools in the area of forming technology. To create load-adjusted and weight-optimized tool structures, we present an overview of a new algorithm-driven design optimization workflow based on mixed-integer linear programming. Loads and boundary conditions for the mathematical optimization are taken from numerical simulations. They are transformed into time-independent point loads generating physical uncertainty in the parameters of the optimization model. CAD-based mathematical optimization is used for topology optimization and geometry generation of the truss-like structure. Finite element simulations are performed to validate the structural strength and to optimize the shape of lattice nodes to reduce mechanical stress peaks. Our algorithm-driven design optimization workflow takes full advantage of the geometrical freedom of additive manufacturing by considering geometry-based manufacturing constraints. Depending on the additive manufacturing process, we use lower and upper bounds on the diameter of the members of a truss and the associated yield strengths. An additively manufactured flexible blank holder demonstrates the algorithm-driven topology design optimization.
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Russo, Giovanni, Vincenzo Capasso, Giuseppe Nicosia, and Vittorio Romano. "MS 27 MINISYMPOSIUM: ROBUST VARIABLE-STRUCTURE APPROACHES FOR CONTROL AND ESTIMATION OF UNCERTAIN DYNAMIC PROCESSES." In Mathematics in Industry, 655–57. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23413-7_90.
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Kleinfeller, Nikolai, Christopher M. Gehb, Maximilian Schaeffner, Christian Adams, and Tobias Melz. "Assessment of Model Uncertainty in the Prediction of the Vibroacoustic Behavior of a Rectangular Plate by Means of Bayesian Inference." In Lecture Notes in Mechanical Engineering, 264–77. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77256-7_21.
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AbstractDesigning the vibroacoustic properties of thin-walled structures is of particularly high practical relevance in the design of vehicle structures. The vibroacoustic properties of thin-walled structures, e.g., vehicle bodies, are usually designed using finite element models. Additional development effort, e.g., experimental tests, arises if the quality of the model predictions are limited due to inherent model uncertainty. Model uncertainty of finite element models usually occurs in the modeling process due to simplifications of the geometry or boundary conditions. The latter highly affect the vibroacoustic properties of a thin-walled structure. The stiffness of the boundary condition is often assumed to be infinite or zero in the finite element model, which can lead to a discrepancy between the measured and the calculated vibroacoustic behavior. This paper compares two different boundary condition assumptions for the finite element (FE) model of a simply supported rectangular plate in their capability to predict the vibroacoustic behavior. The two different boundary conditions are of increasing complexity in assuming the stiffness. In a first step, a probabilistic model parameter calibration via Bayesian inference for the boundary conditions related parameters for the two FE models is performed. For this purpose, a test stand for simply supported rectangular plates is set up and the experimental data is obtained by measuring the vibrations of the test specimen by means of scanning laser Doppler vibrometry. In a second step, the model uncertainty of the two finite element models is identified. For this purpose, the prediction error of the vibroacoustic behavior is calculated. The prediction error describes the discrepancy between the experimental and the numerical data. Based on the distribution of the prediction error, which is determined from the results of the probabilistic model calibration, the model uncertainty is assessed and the model, which most adequately predicts the vibroacoustic behavior, is identified.
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Hartisch, Michael, Christian Reintjes, Tobias Marx, and Ulf Lorenz. "Robust Topology Optimization of Truss-Like Space Structures." In Lecture Notes in Mechanical Engineering, 296–306. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77256-7_23.
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AbstractDue to the additional design freedom and manufacturing possibilities of additive manufacturing compared to traditional manufacturing, topology optimization via mathematical optimization gained importance in the initial design of complex high-strength lattice structures. We consider robust topology optimization of truss-like space structures with multiple loading scenarios. A typical dimensioning method is to identify and examine a suspected worst-case scenario using experience and component-specific information and to incorporate a factor of safety to hedge against uncertainty. We present a quantified programming model that allows us to specify expected scenarios without having explicit knowledge about worst-case scenarios, as the resulting optimal structure must withstand all specified scenarios individually. This leads to less human misconduct, higher efficiency and, thus, to cost and time savings in the design process. We present three-dimensional space trusses with minimal volume that are stable for up to 100 loading scenarios. Additionally, the effect of demanding a symmetric structure and explicitly limiting the diameter of truss members in the model is discussed.
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Vatchova, Boriana, and Alexander Gegov. "Production Rule and Network Structure Models for Knowledge Extraction from Complex Processes Under Uncertainty." In Recent Contributions in Intelligent Systems, 379–90. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41438-6_20.
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Tuncel, Doruk, Christian Körner, and Reinhold Plösch. "Setting the Scope for a New Agile Assessment Model: Results of an Empirical Study." In Lecture Notes in Business Information Processing, 55–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78098-2_4.
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AbstractAgile software development methods have been increasingly adopted by many organizations at different organizational levels. Whether named agile adoption, agile transition, agile transformation, digital transformation or new ways of working, the success of embracing this change process mostly remains uncertain. This is primarily because there are many ways of evaluating success. Based on the existing agile assessment models, we developed a model of principles with associated practice clusters that serves as a core for a new agile assessment model that is capable of assessing agile organizations at different scale. Towards our ultimate goal to establish a lightweight, context-sensitive agile maturity model, we validated our initial findings in an expert interview study to identify improvement points, and ensure the at hand model’s applicability, coherence and relevance. The results of the interview study show that the structure as well as the content of our assessment model fits with the experts’ expectations and experience.
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Ryburn, Megan. "¿El Sueño Chileno?" In Uncertain Citizenship, 72–90. University of California Press, 2018. http://dx.doi.org/10.1525/california/9780520298767.003.0004.
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This chapter is concerned with economic citizenship and the quest for el sueño chileno (the Chilean dream). Taking a transnational perspective on economic citizenship and comprehending it as more than just access to decent work, it seeks first to capture the economic marginalization in Bolivia that often acted as a catalyst for pursuing el sueño chileno. The latter part of the chapter reflects on the degree to which el sueño chileno was realized after crossing the border. It has a particular focus on employment experiences in wholesale garment retail, agriculture, and domestic work, and it includes reflections on a case of trafficking for labor exploitation uncovered in the course of fieldwork. The chapter considers the ways in which both structural processes and the agentic practices of migrants contribute to their ability to access the space of economic citizenship across borders—and thus contribute to their experiences of uncertainty.
Conference papers on the topic "Uncertain structural processes"
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Muscolino, Giuseppe, and Alba Sofi. "Stochastic Response of Structures With Uncertain-but-Bounded Parameters." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-10735.
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The uncertainty plays an important role in structural engineering. It is largely recognized that the uncertainty may affect both external excitations and structural parameters. However, while the numerous available data permit to model with good accuracy the excitations as stochastic processes, unfortunately the data about the structural parameters are quite limited. It follows that the probabilistic approach cannot be realistically applied to represent structural uncertainties; indeed, it requires a wealth of data, often unavailable, to define the probability distribution density of the fluctuating structural parameters. Non probabilistic approaches can be alternatively used to treat these uncertainties. In this framework, the interval model seems today the most suitable analytical tool. The aim of this paper is to evaluate the range of the random response of linear structural systems, with slight variation of the uncertain-but-bounded parameters, subjected to stochastic Gaussian excitations by applying the so-called interval perturbation method.
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Moan, Torgeir. "Integrity Management of Marine Structures; With Emphasis on Design for Structural Robustness." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-78109.
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Based on relevant accident experiences with oil and gas platforms, a brief overview of structural integrity management of offshore structures is given; including an account of adequate design criteria, inspection, repair and maintenance as well as quality assurance and control of the engineering processes. The focus is on developing research based design standards for Accidental Collapse Limit States to ensure robustness or damage tolerance in view damage caused by accidental loads due to operational errors and to some extent abnormal structural damage due to fabrication errors. Moreover, it is suggested to provide robustness in cases where the structural performance is sensitive to uncertain parameters. The use of risk assessment to aid decisions in lieu of uncertainties affecting the performance of novel and existing offshore structures, is briefly addressed.
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Freitag, Steffen, and Wolfgang Graf. "FE Analysis of Structures With Uncertain Model-Free Material Descriptions." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-63357.
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An artificial neural network concept is presented, which can be used to identify uncertain time-dependent material behavior. Dependencies between strain and stress processes obtained from uncertain results of experimental investigations are described by recurrent neural networks for fuzzy data. Direct and indirect approaches are presented for the computation of fuzzy stress and strain processes from experimental results. The identification of uncertain stress-strain-time dependencies with recurrent neural networks for fuzzy data is realized by a network training utilizing α-cuts and an α-level-optimization. After identification, the network can be applied instead of material models within the finite element method. An incremental finite element formulation is developed under consideration of different time scales for the material level and for the structural level. This enables the finite element analysis of structures with an uncertain model-free material description. An example is presented to demonstrated the applicability of the new approach.
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Liu, Yi, and Pingfeng Wang. "Probabilistic Modeling and Analysis of Fused Deposition Modeling Process Using Surrogate Models." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59603.
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Various sources of uncertain parameters at multiple levels, from design steps to manufacturing processes, are often involved in composite structures. Probabilistic modeling and analysis of the composite structure and its manufacturing processes can provide underlying information to assess uncertainties and improve the quality of the developed composite structures. This paper presents a stochastic multi-level modeling framework considering material, structural, modeling parameters as well as the manufacturing process based on a surrogate model. An enhanced laminate theory is employed to determine the elastic constants of the composite materials considering imperfect bonding among filaments in the manufacturing process. To improve the computational efficiency in simulation-based reliability approach, the evaluation of the structure properties is approximated by employing surrogate models based upon the physics model. To apply the present framework, a case study with a composite laminate beam under three-point bending, which is made through fused deposition modeling, is conducted, and the case study results demonstrate the efficacy of the presented modeling scheme and analysis methodology.
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Muscolino, Giuseppe, Roberta Santoro, and Alba Sofi. "Stochastic Sensitivity Analysis of Structural Systems With Interval Uncertainties." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-63482.
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Interval sensitivity analysis of linear discretized structures with uncertain-but-bounded parameters subjected to stationary multi-correlated Gaussian stochastic processes is addressed. The proposed procedure relies on the use of the so-called Interval Rational Series Expansion (IRSE), recently proposed by the authors as an alternative explicit expression of the Neumann series expansion for the inverse of a matrix with a small rank-r modification and properly extended to handle also interval matrices. The IRSE allows to derive approximate explicit expressions of the interval sensitivities of the mean-value vector and Power Spectral Density (PSD) function matrix of the interval stationary stochastic response. The effectiveness of the proposed method is demonstrated through numerical results pertaining to a seismically excited three-storey frame structure with interval Young’s moduli of some columns.
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Akram, Farooq, and Dimitri N. Mavris. "Uncertainty Propagation in Technology Valuation Process." In 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-0975.
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Theodosiou, C., P. Aichouh, S. Natsiavas, and C. Papadimitriou. "Reliability-Based Optimal Design of Fluid Filled Tanks Under Seismic Excitation." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/vib-48454.
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This work integrates developments in simplified dynamic modeling of unanchored fluid-filled tanks, stochastic modeling of earthquake loading time histories, probabilistic structural analysis tools, cost analyses, and multi-criteria optimal design formulations for the design optimization of unanchored fluid-filled cylindrical tanks operating in an uncertain seismic environment. An appropriate simplified mechanical model is developed which includes effects from the sloshing at the liquid free surface, the soil flexibility and the separation of the base plate of the tank from its foundation, which occurs during strong ground motion. A class of stochastic processes with frequency content and time-varying intensity characteristics determined by seismological parameters is used to model the earthquake loading time history. The reliability of the cylindrical tank against failure of the shell structure is computed using latest developments in efficient Monte Carlo simulation methods. A multi-criteria design optimization methodology is then used to obtain the optimal design characteristics of the tank system that meet cost and reliability constrains. Issues related to reliability estimation and optimization are addressed. The methodology is useful for optimally designing fluid-filled structures that operate in a seismic environment to withstand earthquakes with minimal social and economical losses during the lifetime of the structure.
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Hwang, Sungkun, and Seung-Kyum Choi. "Optimal Design of Thermo-Compression Bonding for Advanced Packaging System Under Uncertainty." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97988.
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Abstract As the trend of miniaturization of electronic components has grown, demands for advanced microelectronics packaging development have also increased. At the same time, however, this trend raises concerns of unreliable assembly processes that are caused by defective packaging interconnections. In particular, the defects can be induced by non-coplanarity and unpredictable structural deformation of interconnections. When a slope of the die exceeds a certain degree, connectivity between components in the package may fail, which results in warpage or electrical power loss. To control this issue, thermo-compression bonding has been developed to globally apply heat and pressure into the die while the substrate is maintained at a low stage temperature. Therefore, in order to effectively handle these issues, strongly coupled thermal and structural analysis is inevitable. In this research, a simulation-based optimal design of thermo-compression bonding is developed to achieve better packaging reliability in the time transient domain. The proposed framework clearly demonstrates how the multivariate uncertain parameters can be generated. Also, it suggests how the multivariate uncertainty can be propagated through the classification approach, i.e., artificial neural network. The classification approach is then utilized to estimate the reliability of the system. The efficacy of the proposed framework is demonstrated with a practical example of an advanced packaging system which is utilized in actual commercial products. Ultimately, this study demonstrates how the strong coupling optimization method can be utilized in the actual packaging system.
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Alpsten, Goran. "Causes of Structural Failures with Steel Structures." In IABSE Workshop, Helsinki 2017: Ignorance, Uncertainty, and Human Errors in Structural Engineering. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2017. http://dx.doi.org/10.2749/helsinki.2017.100.
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This paper is based on the experience from investigating over 400 structural collapses, incidents and serious structural damage cases with steel structures which have occurred over the past four centuries. The cause of the failures is most often a gross human error rather than a combination of “normal” variations in parameters affecting the load-carrying capacity, as considered in normal design procedures and structural reliability analyses. Human errors in execution are more prevalent as cause for the failures than errors in the design process, and the construction phase appears particularly prone to human errors. For normal steel structures with quasi-static (non-fatigue) loading, various structural instability phenomena have been observed to be the main collapse mode. An important observation is that welds are not as critical a cause of structural steel failures for statically loaded steel structures as implicitly understood in current regulations and rules for design and execution criteria.
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Tagade, Piyush M., and Han-Lim Choi. "A Polynomial Chaos Based Bayesian Inference Method With Uncertain Hyper-Parameters." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47632.
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This paper proposes stochastic spectral representation for Bayesian calibration of computer simulators with parametric and model structure uncertainty with unknown/poorly known prior hyper-parameters. The methodology is specifically developed for calibration of simulators with spatially/temporally varying parameters. Uncertainty in parameters and model structure is represented using independent stationary Gaussian processes with uncertain hyper-parameters. Gaussian processes are spectrally represented using Karhunnen-Loeve expansion. A methodology based on decomposition of parametric space and orthogonal polynomials defined on the decomposed space is developed for evaluating coefficients of Karhunnen-Loeve expansion of Gaussian process with uncertain hyper-parameters. Galerkin projection method is used to evaluate the resultant stochastic spectral decomposition of predicted system response. Bayesian inference is used to update the prior probability distribution of the polynomial chaos basis. The proposed method is demonstrated for calibration of a simulator of quasi-one dimensional flow through a convergent-divergent nozzle.
Reports on the topic "Uncertain structural processes"
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Biegler, L. T., I. E. Grossmann, and A. W. Westerberg. Life Cycle Analysis with Structured Uncertainty for the Synthesis of Process Flowsheets. Office of Scientific and Technical Information (OSTI), July 2002. http://dx.doi.org/10.2172/891779.
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Janowiak, Maria, Daniel Dostie, Michael Wilson, Michael Kucera, Howard Skinner, Jerry Hatfield, David Hollinger, and Christopher Swanston. Adaptation Resources for Agriculture: Responding to Climate Variability and Change in the Midwest and Northeast. United States Department of Agriculture, January 2018. http://dx.doi.org/10.32747/2018.6960275.ch.
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Changes in climate and extreme weather are already increasing challenges for agriculture nationally and
globally, and many of these impacts will continue into the future. This technical bulletin contains information and resources designed to help agricultural producers, service providers, and educators in the Midwest and Northeast regions of the United States integrate climate change considerations and action-oriented decisions into existing farm and conservation plans. An Adaptation Workbook provides producers a flexible, structured process to identify and assess climate change impacts, challenges, opportunities, and farm-level adaptation tactics and continuously evaluate adaptation actions for improving responses to extreme and uncertain conditions. A synthesis of Adaptation Strategies and Approaches serves as a “menu” of potential responses organized to provide a clear rationale for making decisions by connecting planned actions to broad adaptation concepts. Responses address both short- and long-range timeframes and extend from incremental adjustments of existing practices to major alterations that transform the entire farm operation. Example adaptation tactics—prescriptive actions for agricultural production systems common in the region—for each approach guide producers, service
providers, and educators to develop appropriate responses for their farms and location. Four Adaptation
Examples demonstrate how these adaptation process resources are used.