Статті в журналах: "Surrogate experiments"

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Holssaple, K. A. "Hypervelocity impact experiments in surrogate materials." International Journal of Impact Engineering 14, no. 1-4 (January 1993): 335–45. http://dx.doi.org/10.1016/0734-743x(93)90032-3.

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THOMAS, CHEVISE L., HARSHAVARDHAN THIPPAREDDI, SANJAY KUMAR, MACC RIGDON, ROBERT W. McKEE, and ALEXANDER M. STELZLENI. "Validation of Commonly Used Antimicrobial Interventions on Bob Veal Carcasses for Reducing Shiga Toxin–Producing Escherichia coli Surrogate Populations." Journal of Food Protection 84, no. 7 (February 9, 2021): 1114–21. http://dx.doi.org/10.4315/jfp-20-458.

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ABSTRACT Ruminants are natural reservoirs of Shiga toxin–producing Escherichia coli (STEC), and the STEC can be easily transferred to carcasses during the conversion of animals to meat. Three experiments were conducted to validate the efficacy of lactic acid (LA; 4%), peroxyacetic acid (PAA; 300 ppm), and hot water (HW; 80°C) for their individual or combined abilities to reduce STEC surrogates on bob veal carcasses pre- and postchill and through fabrication. In experiment 1, hot carcasses (n = 9) were inoculated with a five-strain cocktail (ca. 8 log CFU/mL) containing rifampin-resistant surrogate E. coli (BAA-1427, BAA-1428, BAA-1429, BAA-1430, and BAA-1431) and then treated with HW, LA, or PAA. Carcasses were then chilled (0 ± 1°C; 24 h) and split in half, and each side was treated with either LA or PAA. In experiment 2, hot carcasses (n = 3) were inoculated and chilled (24 h). After 24 h, the carcasses were split, and each side was treated with either LA or PAA. For experiment 3, carcasses (n = 3) were chilled for 24 h, split, inoculated, and treated with either LA or PAA. After chilling, carcasses from all three experiments were fabricated to subprimals and the cut surfaces were sampled to determine the translocation of bacteria. Experiment 1 showed that LA+LA was the most effective (P ≤ 0.05) treatment for reducing surrogate E. coli on veal. In experiments 2 and 3, LA and PAA were similar (P > 0.05) in their abilities to reduce E. coli on chilled veal carcasses. In experiments 1 and 2, all antimicrobial treatments resulted in undetectable levels (<0.2 log CFU/cm2) of surrogate E. coli on cut surfaces after fabrication, whereas low levels (1.7 and 1.0 log CFU/cm2 for LA and PAA, respectively) were observed in experiment 3. Of the antimicrobial interventions utilized, LA was more effective for reducing STEC surrogate populations on veal carcasses, pre- and/or postchill. HIGHLIGHTS

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Muharam, Yuswan, Danny Leonardi, and Alisya P. Ramadhania. "Simulation of the Oxidation and Combustion of Mixed Diesel-Biodiesel Fuel." MATEC Web of Conferences 156 (2018): 03008. http://dx.doi.org/10.1051/matecconf/201815603008.

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A comparative simulation-based research has been set up to obtain valid kinetic models of the oxidation and combustion of biodiesel surrogate and diesel surrogate, as well as mixed diesel-biodiesel surrogates which is used to predict their ignition delay times (IDT). The research consists of the development of the detailed kinetic models of the oxidation and combustion of biodiesel surrogate and diesel surrogate, the validation of the two models with the corresponding experimental IDT data, the merging and the validation of the two models for mixed diesel-biodiesel surrogates. The biodiesel surrogate kinetic model was validated with the experimental IDT data of methyl 9-decenoate at 20 atm and three equivalence ratios. The diesel surrogate kinetic model was validated with the experimental IDT data of n-hexadecane at the pressure ranging from 2 atm to 5 atm and the equivalence ratio of 1.0. The diesel-biodiesel surrogate kinetic model was validated with the experimental IDT data of real diesel-biodiesel fuels for four compositions and at 1.18 atm. The validation results of all models show that the models and the experiments are in good agreement.

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Adams-Selin, Rebecca D., Adam J. Clark, Christopher J. Melick, Scott R. Dembek, Israel L. Jirak, and Conrad L. Ziegler. "Evolution of WRF-HAILCAST during the 2014–16 NOAA/Hazardous Weather Testbed Spring Forecasting Experiments." Weather and Forecasting 34, no. 1 (January 4, 2019): 61–79. http://dx.doi.org/10.1175/waf-d-18-0024.1.

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Abstract Four different versions of the HAILCAST hail model have been tested as part of the 2014–16 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiments. HAILCAST was run as part of the National Severe Storms Laboratory (NSSL) WRF Ensemble during 2014–16 and the Community Leveraged Unified Ensemble (CLUE) in 2016. Objective verification using the Multi-Radar Multi-Sensor maximum expected size of hail (MRMS MESH) product was conducted using both object-based and neighborhood grid-based verification. Subjective verification and feedback was provided by HWT participants. Hourly maximum storm surrogate fields at a variety of thresholds and Storm Prediction Center (SPC) convective outlooks were also evaluated for comparison. HAILCAST was found to improve with each version due to feedback from the 2014–16 HWTs. The 2016 version of HAILCAST was equivalent to or exceeded the skill of the tested storm surrogates across a variety of thresholds. The post-2016 version of HAILCAST was found to improve 50-mm hail forecasts through object-based verification, but 25-mm hail forecasting ability declined as measured through neighborhood grid-based verification. The skill of the storm surrogate fields varied widely as the threshold values used to determine hail size were varied. HAILCAST was found not to require such tuning, as it produced consistent results even when used across different model configurations and horizontal grid spacings. Additionally, different storm surrogate fields performed at varying levels of skill when forecasting 25- versus 50-mm hail, hinting at the different convective modes typically associated with small versus large sizes of hail. HAILCAST was able to match results relatively consistently with the best-performing storm surrogate field across multiple hail size thresholds.

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Myšáková, Eva, and Matěj Lepš. "Surrogate Based Evaluation of the Design of Experiments." Advanced Materials Research 1144 (March 2017): 148–52. http://dx.doi.org/10.4028/www.scientific.net/amr.1144.148.

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Meta-modeling also known as Surrogate Modeling is one of the commonly used tools foranalysis of complex systems' behavior. The meta-model is constructed based on training data whichconsist of the training points generated via Design of Experiments (DoE) and responses of the originalmodel evaluated in these training points. The positioning of the points is crucial for the approximationquality of the meta-model. Therefore it is appropriate to assess the DoE's quality not only usingthe common geometrical or statistical criteria but also from the point of view of its actual particularpurpose. Such testing is able to recognize the appropriate set of training points and also the possibleability of the individual meta-models for actual problem.

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Melby, Jeffrey A., Fatima Diop, Norberto Nadal-Caraballo, Alex Taflanidis, and Victor Gonzalez. "HURRICANE WATER LEVEL PREDICTION USING SURROGATE MODELING." Coastal Engineering Proceedings, no. 36 (December 30, 2018): 57. http://dx.doi.org/10.9753/icce.v36.currents.57.

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For this study, the surrogate was constructed using kriging (Jia et al. 2015). The high fidelity coupled surge and wave numerical modelling for the Gulf of Mexico was used as the training set. The numerical model was either ADCIRC and STWAVE or ADCIRC and SWAN in the nearshore. The surrogate models were trained using tropical storm parameters (latitude, longitude, central pressure, radius to maximum wind speed, storm heading, and forward speed) at a specific location as inputs and individual responses (e.g. surge) as outputs. Tide was computed separately using ADCIRC and linearly superimposed with surge to get total water level. The regional surrogates accurately reproduced both peaks and time series of water levels for historical storms. An extensive validation was conducted to determine the optimal application of the kriging approach. In this paper we will report the efficient design-of-experiments approach, surrogate training and validation.

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Bauer, Benedikt, Felix Heimrich, Michael Kohler, and Adam Krzyżak. "On estimation of surrogate models for multivariate computer experiments." Annals of the Institute of Statistical Mathematics 71, no. 1 (November 2, 2017): 107–36. http://dx.doi.org/10.1007/s10463-017-0627-8.

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Tuo, Rui, C. F. Jeff Wu, and Dan Yu. "Surrogate Modeling of Computer Experiments With Different Mesh Densities." Technometrics 56, no. 3 (July 3, 2014): 372–80. http://dx.doi.org/10.1080/00401706.2013.842935.

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Latimer, G. D., W. R. Marcum, and W. F. Jones. "Dispersion of Surrogate LWR Fuel Experiments Under LOCA Conditions." Nuclear Technology 206, no. 9 (March 2, 2020): 1374–84. http://dx.doi.org/10.1080/00295450.2020.1712158.

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Liu, Bolin, and Liyang Xie. "Reliability Analysis of Structures by Iterative Improved Ensemble of Surrogate Method." Shock and Vibration 2019 (October 24, 2019): 1–13. http://dx.doi.org/10.1155/2019/6357104.

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Surrogate models have been widely adopted for reliability analysis. The common approach is to construct a series of surrogates based on a training set and then pick out the best one with the highest accuracy as an approximation of the time-consuming limit state function. However, the traditional method increases the risk of adopting an inappropriate model and does not take full advantage of the data devoted to constructing different surrogates. Furthermore, obtaining more samples is very expensive and sometimes even impossible. Therefore, to save the cost of constructing the surrogate and improve the prediction accuracy, an ensemble strategy is proposed in this paper for efficiently analyzing the structural reliability. The values of the weights are obtained by a recursive process and the leave-one-out technique, in which the values are updated in each iteration until a given prediction accuracy is achieved. Besides, a learning function is used to guide the selection of the next sampling candidate. Because the learning function utilizes the uncertainty estimator of the surrogate to guide the design of experiments (DoE), to accurately calculate the uncertainty estimator of the ensemble of surrogates, the concept of weighted mean square error is proposed. After the high-quality ensemble of surrogates of the limit state function is available, the Monte Carlo method is employed to calculate the failure probabilities. The proposed method is evaluated by three analytic problems and one engineering problem. The results show that the proposed ensemble of surrogates has better prediction accuracy and robustness than the stand-alone surrogates and the existing ensemble techniques.

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Woo, Mina, Kyunghoon Lee, Woojin Song, Beomsoo Kang, and Jeong Kim. "Numerical Estimation of Material Properties in the Electrohydraulic Forming Process Based on a Kriging Surrogate Model." Mathematical Problems in Engineering 2020 (June 10, 2020): 1–12. http://dx.doi.org/10.1155/2020/3219829.

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High-speed forming processes, such as electrohydraulic forming, have recently attracted attention with the development of forming technology. However, because the high-speed operation (above 100 m/s) raises safety concerns, most experiments are conducted in a closed die, which hides the forming process. Therefore, the experimental process can only be observed in a numerical simulation with accurate material properties. The conventional quasistatic material properties are improper for high-speed forming simulations with high strain rates (>102 s−1). In this study, the material properties of Al 6061-T6, which reflect the deformation behavior in the high-strain-rate region, were investigated in a numerical approach based on a reduced order model and a surrogate model in which the numerical results resemble the experimental results. The strain rate effect on the material was determined by the Cowper–Symonds constitutive equation, and two strain rate parameters were predicted. The surrogate model takes two material parameters as inputs and outputs a weighting coefficient calculated by the reduced order model. The surrogate model is based on the Kriging method to reduce the simulation cost. Next, the optimal material parameters that minimize the error between the surrogate model and the experiments are estimated by nonlinear least-squares optimization using a genetic algorithm and the constructed surrogate model. The predicted optimal parameters were verified by comparing the results of the experiment, numerical simulation, and surrogate model.

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Buyse, Marc, and Geert Molenberghs. "Criteria for the Validation of Surrogate Endpoints in Randomized Experiments." Biometrics 54, no. 3 (September 1998): 1014. http://dx.doi.org/10.2307/2533853.

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Correa, Juan, and Elias Bareinboim. "A Calculus for Stochastic Interventions:Causal Effect Identification and Surrogate Experiments." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (April 3, 2020): 10093–100. http://dx.doi.org/10.1609/aaai.v34i06.6567.

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Some of the most prominent results in causal inference have been developed in the context of atomic interventions, following the semantics of the do-operator and the inferential power of the do-calculus. In practice, many real-world settings require more complex types of interventions that cannot be represented by a simple atomic intervention. In this paper, we investigate a general class of interventions that covers some non-trivial types of policies (conditional and stochastic), which goes beyond the atomic class. Our goal is to develop general understanding and formal machinery to be able to reason about the effects of those policies, similar to the robust treatment developed to handle the atomic case. Specifically, in this paper, we introduce a new set of inference rules (akin to do-calculus) that can be used to derive claims about general interventions, which we call σ-calculus. We develop a systematic and efficient procedure for finding estimands of the effect of general policies as a function of the available observational and experimental distributions. We then prove that our algorithm and σ-calculus are both sound for the tasks of identification (Pearl, 1995) and z-identification (Bareinboim and Pearl, 2012) under this class of interventions.

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Gieschen, Rebekka, Christian Schwartpaul, Jannis Landmann, Lukas Fröhling, Arndt Hildebrandt, and Nils Goseberg. "Large-Scale Laboratory Experiments on Mussel Dropper Lines in Ocean Surface Waves." Journal of Marine Science and Engineering 9, no. 1 (December 30, 2020): 29. http://dx.doi.org/10.3390/jmse9010029.

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The rapid growth of marine aquaculture around the world accentuates issues of sustainability and environmental impacts of large-scale farming systems. One potential mitigation strategy is to relocate to more energetic offshore locations. However, research regarding the forces which waves and currents impose on aquaculture structures in such conditions is still scarce. The present study aimed at extending the knowledge related to live blue mussels (Mytilus edulis), cultivated on dropper lines, by unique, large-scale laboratory experiments in the Large Wave Flume of the Coastal Research Center in Hannover, Germany. Nine-months-old live dropper lines and a surrogate of 2.0 m length each are exposed to regular waves with wave heights between 0.2 and 1.0 m and periods between 1.5 and 8.0 s. Force time histories are recorded to investigate the inertia and drag characteristics of live mussel and surrogate dropper lines. The surrogate dropper line was developed from 3D scans of blue mussel dropper lines, using the surface descriptor Abbott–Firestone Curve as quality parameter. Pull-off tests of individual mussels are conducted that reveal maximum attachment strength ranges of 0.48 to 10.55 N for mussels that had medium 3.04 cm length, 1.60 cm height and 1.25 cm width. Mean drag coefficients of CD = 3.9 were found for live blue mussel lines and CD = 3.4 for the surrogate model, for conditions of Keulegan–Carpenter number (KC) 10 to 380, using regular wave tests.

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WHEELER, TOMMY L., and TERRANCE M. ARTHUR. "Novel Continuous and Manual Sampling Methods for Beef Trim Microbiological Testing." Journal of Food Protection 81, no. 10 (September 7, 2018): 1605–13. http://dx.doi.org/10.4315/0362-028x.jfp-18-197.

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ABSTRACT A sampling method that represents a greater proportion of the beef trimmings in a 900-kg combo bin should improve the current pathogen sampling and detection programs used by fresh beef processors. This study compared two novel, nondestructive sampling methodologies (a continuous sampling device [CSD] and a manual sampling device [MSD]) with the current industry methodologies, the N60 Excision (the “gold standard”) and N60 Plus, for collection of trim samples. Depending on the experiment, samples were analyzed for naturally occurring Escherichia coli O157:H7 or Salmonella, inoculated surrogates, or indicator organisms in multiple plants, on multiple days, across multiple lean percentage mixtures. Experiments 1A and 1B with natural contamination found no E. coli O157:H7 but similar (P > 0.05) prevalence of Salmonella (CSD 9.2% versus N60 Excision 6.0%) and similar (P > 0.05) levels of indicator organisms for CSD compared with both N60 methodologies. In experiments 2 and 3, CSD cloth sampling had the same or higher prevalence of naturally occurring E. coli O157:H7 and E. coli O157:H7 surrogate organisms, as well as similar levels of indicator organisms compared with the N60 methodologies. In experiment 4, MSD cloth sampling detected similar (P > 0.05) prevalence of E. coli O157:H7 surrogate organisms, as well as slightly lower (P < 0.05) levels of indicator organisms compared with N60 Plus. In experiment 5, the MSD found similar (P > 0.05) prevalence of naturally occurring E. coli O157:H7 and the same or slightly higher (P < 0.05) levels of naturally occurring indicator organisms compared with N60 Plus. In experiment 6, the MSD detected the same (P > 0.05) prevalence of naturally occurring Salmonella as did N60 Excision. The results of these experiments collectively demonstrate that sampling beef trim using either the CSD or MSD provides organism recovery that is similar to or better than the N60 Excision or the N60 Plus methodologies.

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Pacheco, Jorge E., Cristina H. Amon, and Susan Finger. "Bayesian Surrogates Applied to Conceptual Stages of the Engineering Design Process." Journal of Mechanical Design 125, no. 4 (December 1, 2003): 664–72. http://dx.doi.org/10.1115/1.1631580.

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During the conceptual design stages, designers often have incomplete knowledge about the interactions among design parameters. We are developing a methodology that will enable designers to create models with levels of detail and accuracy that correspond to the current state of the design process. Thus, designers can create a rough surrogate model when only a few data points are available and then refine the model as the design progresses and more information becomes available. These surrogates represent the system response when limited information is available and when few realizations of experiments or numerical simulations are possible. This paper presents a covariance-based approach for building multistage surrogates in the conceptual design stages when bounds for the response are not available a priori. We test the methodology using a one-dimensional analytical function and a heat transfer problem with an analytical solution, in order to obtain error measurements. We then illustrate the use of the methodology in a thermal design problem for wearable computers. The surrogate model enables the designer to understand the relationships among the design parameters.

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Gong, Xu, Zhengqi Gu, and Zhenlei Li. "Surrogate model for aerodynamic shape optimization of a tractor-trailer in crosswinds." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 226, no. 10 (May 9, 2012): 1325–39. http://dx.doi.org/10.1177/0954407012442295.

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A surrogate model-based aerodynamic shape optimization method applied to the wind deflector of a tractor-trailer is presented in this paper. The aerodynamic drag coefficient of the tractor-trailer with and without the wind deflector subjected to crosswinds is analyzed. The numerical results show that the wind deflector can decrease drag coefficient. Four parameters are used to describe the wind deflector geometry: width, length, height, and angle. A 30-level design of experiments study using the optimal Latin hypercube method was conducted to analyze the sensitivity of the design variables and build a database to set up the surrogate model. The surrogate model was constructed based on the Kriging interpolation technique. The fitting precision of the surrogate model was examined using computational fluid dynamics and certified using a surrogate model simulation. Finally, a multi-island genetic algorithm was used to optimize the shape of the wind deflector based on the surrogate model. The tolerance between the results of the computational fluid dynamics simulation and the surrogate model was only 0.92% when using the optimal design variables, and the aerodynamic drag coefficient decreased by 4.65% compared to the drag coefficient of the tractor-trailer installed with the original wind deflector. The effect of the optimal shape of the wind deflector was validated by computational fluid dynamics and wind tunnel experiment.

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Buyse, M., G. Molenberghs, T. Burzykowski, D. Renard, and H. Geys. "The validation of surrogate endpoints in meta-analyses of randomized experiments." Biostatistics 1, no. 1 (March 2000): 49–67. http://dx.doi.org/10.1093/biostatistics/1.1.49.

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Hazyuk, Ion, Marc Budinger, Florian Sanchez, and Christian Gogu. "Optimal design of computer experiments for surrogate models with dimensionless variables." Structural and Multidisciplinary Optimization 56, no. 3 (April 4, 2017): 663–79. http://dx.doi.org/10.1007/s00158-017-1683-7.

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de Almeida Filho, Adiel T., Thárcylla R. N. Clemente, Danielle Costa Morais, and Adiel Teixeira de Almeida. "Preference modeling experiments with surrogate weighting procedures for the PROMETHEE method." European Journal of Operational Research 264, no. 2 (January 2018): 453–61. http://dx.doi.org/10.1016/j.ejor.2017.08.006.

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Siddique, M. Hamid, Arshad Afzal, and Abdus Samad. "Design Optimization of the Centrifugal Pumps via Low Fidelity Models." Mathematical Problems in Engineering 2018 (June 21, 2018): 1–14. http://dx.doi.org/10.1155/2018/3987594.

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Low fidelity model assisted design optimization of turbomachines has reduced the total computational and experimental costs. These models are called surrogate models which mimic the actual experiments or simulations. The surrogate models can generate thousands of approximate results from a few samples, making it easy to locate the optimal solution. Ample articles reported surrogate assisted design optimization of centrifugal pumps. In this article, the authors try to give a brief overview of the surrogate based optimization technique along with its historical applications and trend of the recent use. The various key design parameters which affect the performance of the centrifugal pump have also been discussed. The effectiveness of the surrogate based optimization technique and corresponding performance metrics have been discussed.

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Epps, Robert W., Amanda A. Volk, Kristofer G. Reyes, and Milad Abolhasani. "Accelerated AI development for autonomous materials synthesis in flow." Chemical Science 12, no. 17 (2021): 6025–36. http://dx.doi.org/10.1039/d0sc06463g.

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A surrogate model is designed to represent a microfluidic material synthesis system using 1000 automatically conducted experiments. With this model, over 600 000 experiments are simulated to optimize an AI-guided material synthesis algorithm.

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Ganpule, Shailesh, Robert Salzar, Brandon Perry, and Namas Chandra. "Role of helmets in blast mitigation: insights from experiments on PMHS surrogate." International Journal of Experimental and Computational Biomechanics 4, no. 1 (2016): 13. http://dx.doi.org/10.1504/ijecb.2016.081745.

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Chandra, Namas, Shailesh Ganpule, Robert Salzar, and Brandon Perry. "Role of helmets in blast mitigation: insights from experiments on PMHS surrogate." International Journal of Experimental and Computational Biomechanics 4, no. 1 (2016): 13. http://dx.doi.org/10.1504/ijecb.2016.10002680.

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Kamali, M., K. Ponnambalam, and E. D. Soulis. "Computationally efficient calibration of WATCLASS Hydrologic models using surrogate optimization." Hydrology and Earth System Sciences Discussions 4, no. 4 (July 23, 2007): 2307–21. http://dx.doi.org/10.5194/hessd-4-2307-2007.

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Abstract. In this approach, exploration of the cost function space was performed with an inexpensive surrogate function, not the expensive original function. The Design and Analysis of Computer Experiments(DACE) surrogate function, which is one type of approximate models, which takes correlation function for error was employed. The results for Monte Carlo Sampling, Latin Hypercube Sampling and Design and Analysis of Computer Experiments(DACE) approximate model have been compared. The results show that DACE model has a good potential for predicting the trend of simulation results. The case study of this document was WATCLASS hydrologic model calibration on Smokey-River watershed.

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Preen, Richard J., and Larry Bull. "Design Mining Interacting Wind Turbines." Evolutionary Computation 24, no. 1 (March 2016): 89–111. http://dx.doi.org/10.1162/evco_a_00144.

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An initial study has recently been presented of surrogate-assisted evolutionary algorithms used to design vertical-axis wind turbines wherein candidate prototypes are evaluated under fan-generated wind conditions after being physically instantiated by a 3D printer. Unlike other approaches, such as computational fluid dynamics simulations, no mathematical formulations were used and no model assumptions were made. This paper extends that work by exploring alternative surrogate modelling and evolutionary techniques. The accuracy of various modelling algorithms used to estimate the fitness of evaluated individuals from the initial experiments is compared. The effect of temporally windowing surrogate model training samples is explored. A surrogate-assisted approach based on an enhanced local search is introduced; and alternative coevolution collaboration schemes are examined.

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Reynolds, L., T. P. Jones, K. A. Beérubeé, H. Wise, and R. Richards. "Toxicity of airborne dust generated by opencast coal mining." Mineralogical Magazine 67, no. 2 (April 2003): 141–52. http://dx.doi.org/10.1180/0026461036720091.

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Experiments were undertaken to determine the toxicity in the lung of dust generated by opencast coal mining. Since it was impractical to collect the large mass of actual opencast respirable dust required for the toxicity experiment, a surrogate dust with the same size distribution was manufactured (PSW). This surrogate dust had the same basic mineralogical composition as mineralogically characterized airborne dust collected from the welsh coal opencast pit at Park Slip West. Diesel exhaust particles (DEP), generated from mining vehicular and mechanical activity, contribute towards opencast particulate matter. Therefore, a second dust comprising of a 50/50 by weight mixture of the surrogate mineral dust and diesel soot was also examined (PSW + DEP). These dusts and DEP (weak biological reactivity) and a-quartz (high biological reactivity) were instilled into the lungs of healthy rats. The animals were sacrificed at 1, 6 and 11 weeks. Assessments of potential toxicity included lung to body weight ratios, acellular protein in lung lavage (markers of lung permeability) and total free cells (marker of inflammation). The surrogate opencast dust with or without DEP caused no significant increases in any of the parameters studied and as such was very similar to the weak biological effects of DEP alone. These effects contrasted sharply with those observed for the bioreactive mineral, quartz that induced rapid increases in permeability and a progressive inflammation. The use of a surrogate is less desirable then the real opencast mine dust, however, if as believed, the surrogate material is a representative mixture of the actual airborne dust around this opencast site, then these particles should show little or no short-term lung toxicity following inhalation.

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DOLAN, KEVIN T. "SURROGATE ANALYSIS OF MULTICHANNEL DATA WITH FREQUENCY DEPENDANT TIME LAG." Fluctuation and Noise Letters 04, no. 01 (March 2004): L75—L81. http://dx.doi.org/10.1142/s0219477504001677.

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Recently a new technique for generating linear surrogates of multichannel data was introduced. This technique, referred to as the coherent digitally filtered surrogate method, preserves both the individual power spectra, and the coherence function, of the original data. This method is somewhat limited in that it can only be applied to multichannel data in which the cross-spectrum is real. We present here an alteration to this algorithm that can be used to test any linear-correlation model, with arbitrary complex cross-spectra. This is of particular interest for experiments in which two channels are coupled with some time lag, and it is necessary to determine if the coupling is linear. We also demonstrate that this algorithm can be used along with the synchronization to provide a much better estimate for the degree of phase-locking between two signals than the coherence analysis techniques traditionally used in neuroscience.

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Wagner, John A. "Overview of Biomarkers and Surrogate Endpoints in Drug Development." Disease Markers 18, no. 2 (2002): 41–46. http://dx.doi.org/10.1155/2002/929274.

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There are numerous factors that recommend the use of biomarkers in drug development including the ability to provide a rational basis for selection of lead compounds, as an aid in determining or refining mechanism of action or pathophysiology, and the ability to work towards qualification and use of a biomarker as a surrogate endpoint. Examples of biomarkers come from many different means of clinical and laboratory measurement. Total cholesterol is an example of a clinically useful biomarker that was successfully qualified for use as a surrogate endpoint. Biomarkers require validation in most circumstances. Validation of biomarker assays is a necessary component to delivery of high-quality research data necessary for effective use of biomarkers. Qualification is necessary for use of a biomarker as a surrogate endpoint. Putative biomarkers are typically identified because of a relationship to known or hypothetical steps in a pathophysiologic cascade. Biomarker discovery can also be effected by expression profiling experiment using a variety of array technologies and related methods. For example, expression profiling experiments enabled the discovery of adipocyte related complement protein of 30 kD (Acrp30 or adiponectin) as a biomarker forin vivoactivation of peroxisome proliferator-activated receptors (PPAR)γactivity.

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Luo, Liheng, Dianzi Liu, Meiling Zhu, Yijie Liu, and Jianqiao Ye. "Maximum energy conversion from human motion using piezoelectric flex transducer: A multi-level surrogate modeling strategy." Journal of Intelligent Material Systems and Structures 29, no. 15 (July 9, 2018): 3097–107. http://dx.doi.org/10.1177/1045389x18783075.

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Conventional engineering design optimization requires a large amount of expensive experimental tests from prototypes or computer simulations, which may result in an inefficient and unaffordable design process. In order to overcome these disadvantages, a surrogate model may be used to replace the prototype tests. To construct a surrogate model of sufficient accuracy from limited number of tests/simulations, a multi-level surrogate modeling strategy is introduced in this article. First, a chosen number of points determined by optimal Latin Hypercube Design of Experiments are used to generate global-level surrogate models with genetic programming and the fitness landscape can be explored by genetic algorithms for near-optimal solutions. Local-level surrogate models are constructed then from the extended-optimal Latin Hypercube samples in the vicinity of global optimum on the basis of a much smaller number of chosen points. As a result, an improved optimal design is achieved. The efficiency of this strategy is demonstrated by the parametric optimization design of a piezoelectric flex transducer energy harvester. The optimal design is verified by finite element simulations and the results show that the proposed multi-level surrogate modeling strategy has the advantages of faster convergence and more efficiency in comparison with the conventional single-single level surrogate modeling technique.

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Yin, Peng, Wenfu Liu, Yong Yang, Haining Gao, and Chunhua Zhang. "An Experimental and Modeling Study on the Combustion of Gasoline-Ethanol Surrogates for HCCI Engines." Security and Communication Networks 2022 (February 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/5362928.

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As an effective clean fuel, ethanol has the characteristics of improving antiknock quality and reducing emissions. It is an ideal antiknock additive for Homogeneous Charge Compression Ignition (HCCI) engines. The oxidation of gasoline-ethanol surrogates in HCCI engines is a very complex process which is dominated by the reaction kinetics. This oxidation process directly determines the performance and emissions of HCCI engines. Coupling the computational fluid dynamic (CFD) model with the gasoline-ethanol surrogate mechanism can be used for fuel design, so the construction of a reduced mechanism with high accuracy is necessary. A mechanism (278 species, 1439 reactions) at medium and low temperatures and experiments in a HCCI engine for the oxidation of gasoline-ethanol surrogates were presented in this paper. Directed relation graph with error propagation (DRGEP) method and quasi-steady-state assumption (QSSA) method were used in order to get a reduced model. Then, the kinetics of the vital reactions related to the formation and consumption of H and OH were adjusted. To validate the model, the HCCI experiments for the oxidation of gasoline-ethanol surrogates were conducted under different operating conditions. The verification result indicated that the present model can predict the oxidation process of gasoline-ethanol effectively.

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Maike, Paul, and Pierre-Yves T. Henry. "Evaluation of the use of surrogate Laminaria digitata in eco-hydraulic laboratory experiments." Journal of Hydrodynamics 26, no. 3 (June 2014): 374–83. http://dx.doi.org/10.1016/s1001-6058(14)60042-1.

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Crombecq, Karel, Dirk Gorissen, Dirk Deschrijver, and Tom Dhaene. "A Novel Hybrid Sequential Design Strategy for Global Surrogate Modeling of Computer Experiments." SIAM Journal on Scientific Computing 33, no. 4 (January 2011): 1948–74. http://dx.doi.org/10.1137/090761811.

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Queipo, Néstor V., Carlos J. Arévalo, and Salvador Pintos. "The integration of design of experiments, surrogate modeling and optimization for thermoscience research." Engineering with Computers 20, no. 4 (October 6, 2004): 309–15. http://dx.doi.org/10.1007/s00366-004-0299-x.

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Poëtte, Gaël, Didier Lucor, and Hervé Jourdren. "A stochastic surrogate model approach applied to calibration of unstable fluid flow experiments." Comptes Rendus Mathematique 350, no. 5-6 (March 2012): 319–24. http://dx.doi.org/10.1016/j.crma.2012.01.018.

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Andrae, J. C. G., and R. A. Head. "HCCI experiments with gasoline surrogate fuels modeled by a semidetailed chemical kinetic model." Combustion and Flame 156, no. 4 (April 2009): 842–51. http://dx.doi.org/10.1016/j.combustflame.2008.10.002.

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Molenberghs, Geert, Helena Geys, and Marc Buyse. "Evaluation of surrogate endpoints in randomized experiments with mixed discrete and continuous outcomes." Statistics in Medicine 20, no. 20 (2001): 3023–38. http://dx.doi.org/10.1002/sim.923.

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Guo, Qinghua, Fuchu Dai, and Zhiqiang Zhao. "Comparison of Two Bayesian-MCMC Inversion Methods for Laboratory Infiltration and Field Irrigation Experiments." International Journal of Environmental Research and Public Health 17, no. 3 (February 10, 2020): 1108. http://dx.doi.org/10.3390/ijerph17031108.

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Bayesian parameter inversion approaches are dependent on the original forward models linking subsurface physical properties to measured data, which usually require a large number of iterations. Fast alternative systems to forward models are commonly employed to make the stochastic inversion problem computationally tractable. This paper compared the effect of the original forward model constructed by the HYDRUS-1D software and two different approximations: the Artificial Neural Network (ANN) alternative system and the Gaussian Process (GP) surrogate system. The model error of the ANN was quantified using a principal component analysis, while the model error of the GP was measured using its own variance. There were two groups of measured pressure head data of undisturbed loess for parameter inversion: one group was obtained from a laboratory soil column infiltration experiment and the other was derived from a field irrigation experiment. Strong correlations between the pressure head values simulated by random posterior samples indicated that the approximate forward models are reliable enough to be included in the Bayesian inversion framework. The approximate forward models significantly improved the inversion efficiency by comparing the observed and the optimized results with a similar accuracy. In conclusion, surrogates can be considered when the forward models are strongly nonlinear and the computational costs are prohibitive.

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TIAN, PENG, DAVID YANG, CHRISTINA QUIGLEY, MARISSA CHOU, and XI JIANG. "Inactivation of the Tulane Virus, a Novel Surrogate for the Human Norovirus." Journal of Food Protection 76, no. 4 (April 1, 2013): 712–18. http://dx.doi.org/10.4315/0362-028x.jfp-12-361.

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Human noroviruses (HuNoVs) are the major cause of nonbacterial gastroenteritis epidemics. The culturable feline calicivirus and murine norovirus have been used extensively as surrogates to study HuNoV biology, as HuNoV does not grow in vitro. Additional efforts to identify new surrogates are needed, because neither of these common surrogates are truly intestinal pathogens. The newly described Tulane virus (TV) is a typical calicivirus, it is isolated from macaque stools, is cultivable in vitro, and recognizes human histo-blood group antigens. Therefore, TV is a promising surrogate for HuNoVs. In this study, we evaluated the resistance or stability of TV under various physical and environmental conditions by measuring a 50% reduction of tissue culture infective dose (TCID50) by using a TV cell culture system. Due to the nature of this virus, it is hard to produce a high-titer stock through tissue culture. In our study, the maximal reduction in virus titers was 5 D (D = 1 log) in heat-denaturation and EtOH experiments, and 4 D in UV, chlorine, and pH-stability experiments. Therefore in this study, we defined the inactivation of TV as reaching a TCID50/ml of 0 (a 4- to 5-D reduction in TCID50, depending on the detection limit). TV was inactivated after incubation at 63°C for 5 min, incubation at 56°C for 30 min (5 D), exposure to 60 mJ/cm2 of UVC radiation (4 D), or incubation at 300 ppm of free chlorine for 10 min (4 D). TV was shown to be stable from pH 3.0 to 8.0, though an obvious reduction in virus titer was observed at pH 2.5 and 9.0, and was inactivated at pH 10.0 (4 D). TV was resistant to a low concentration of EtOH (40% or lower) but was fully inactivated (5 D) by 50 to 70% EtOH after a short exposure (20 s). In contrast, quantitative real-time PCR was unable to detect, or poorly detected, virus titer reductions between treated and untreated samples described in this study.

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Belyaev, Mikhail, Evgeny Burnaev, Ermek Kapushev, Stephane Alestra, Marc Dormieux, Antoine Cavailles, Davy Chaillot, and Eugenio Ferreira. "Building Data Fusion Surrogate Models for Spacecraft Aerodynamic Problems with Incomplete Factorial Design of Experiments." Advanced Materials Research 1016 (August 2014): 405–12. http://dx.doi.org/10.4028/www.scientific.net/amr.1016.405.

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This work concerns a construction of surrogate models for a specific aerodynamic data base. This data base is generally available from wind tunnel testing or from CFD aerodynamic simulations and contains aerodynamic coefficients for different flight conditions and configurations (such as Mach number, angle-of-attack, vehicle configuration angle) encountered over different space vehicles mission. The main peculiarity of aerodynamic data base is a specific design of experiment which is a union of grids of low and high fidelity data with considerably different sizes. Universal algorithms can’t approximate accurately such significantly non-uniform data. In this work a fast and accurate algorithm was developed which takes into account different fidelity of the data and special design of experiments.

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Roy, Mriganka, and Olga Wodo. "Feature Engineering for Surrogate Models of Consolidation Degree in Additive Manufacturing." Materials 14, no. 9 (April 27, 2021): 2239. http://dx.doi.org/10.3390/ma14092239.

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Surrogate models (SM) serve as a proxy to the physics- and experiment-based models to significantly lower the cost of prediction while providing high accuracy. Building an SM for additive manufacturing (AM) process suffers from high dimensionality of inputs when part geometry or tool-path is considered in addition to the high cost of generating data from either physics-based models or experiments. This paper engineers features for a surrogate model to predict the consolidation degree in the fused filament fabrication process. Our features are informed by the physics of the underlying thermal processes and capture the characteristics of the part’s geometry and the deposition process. Our model is learned from medium-size data generated using a physics-based thermal model coupled with the polymer healing theory to determine the consolidation degree. Our results demonstrate high accuracy (>90%) of consolidation degree prediction at a low computational cost (four orders of magnitude faster than the numerical model).

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Narasimhan, Harikrishna, and Shivani Agarwal. "Support Vector Algorithms for Optimizing the Partial Area under the ROC Curve." Neural Computation 29, no. 7 (July 2017): 1919–63. http://dx.doi.org/10.1162/neco_a_00972.

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The area under the ROC curve (AUC) is a widely used performance measure in machine learning. Increasingly, however, in several applications, ranging from ranking to biometric screening to medicine, performance is measured not in terms of the full area under the ROC curve but in terms of the partial area under the ROC curve between two false-positive rates. In this letter, we develop support vector algorithms for directly optimizing the partial AUC between any two false-positive rates. Our methods are based on minimizing a suitable proxy or surrogate objective for the partial AUC error. In the case of the full AUC, one can readily construct and optimize convex surrogates by expressing the performance measure as a summation of pairwise terms. The partial AUC, on the other hand, does not admit such a simple decomposable structure, making it more challenging to design and optimize (tight) convex surrogates for this measure. Our approach builds on the structural SVM framework of Joachims ( 2005 ) to design convex surrogates for partial AUC and solves the resulting optimization problem using a cutting plane solver. Unlike the full AUC, where the combinatorial optimization needed in each iteration of the cutting plane solver can be decomposed and solved efficiently, the corresponding problem for the partial AUC is harder to decompose. One of our main contributions is a polynomial time algorithm for solving the combinatorial optimization problem associated with partial AUC. We also develop an approach for optimizing a tighter nonconvex hinge loss–based surrogate for the partial AUC using difference-of-convex programming. Our experiments on a variety of real-world and benchmark tasks confirm the efficacy of the proposed methods.

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Sözen, Levent, Mehmet A. Guler, Deniz Bekar, and Erdem Acar. "Investigation and prediction of springback in rotary-draw tube bending process using finite element method." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 226, no. 12 (March 2, 2012): 2967–81. http://dx.doi.org/10.1177/0954406212440672.

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Rotary-draw tube bending operation is one of the most universal methods used for the tube forming processes. Similar to the other forming methods, some problems such as wall thinning, cross-section distortion, wrinkling, and springback can also be seen on the tubes formed by rotary-draw bending operations. Springback is a very common problem and its prediction plays a crucial role in increasing the efficiency of the tube bending operations and also to overcome the difficulties in the assembly processes. Tube diameter, wall thickness, bend radius, bend angle, and coefficient of friction can be considered as the most effective parameters that cause the variation of springback magnitude. In this study, not a simple one-at-a-time sensitivity analysis, but a thorough investigation of the springback phenomena involving interactions between the geometrical and mechanical parameters is done and surrogate models are developed via the data obtained from finite element analysis using a multi-purpose explicit and implicit finite element software LS-DYNA to analyze the non-linear response of structures. The constructed surrogate models can be utilized to perform fast prediction of springback for a given combination of parameters. Three different surrogate modeling techniques are exploited and it is found that the linear polynomial response surface approximations can provide acceptable accuracy. Finally, experiments are conducted to validate the accuracy of surrogate models. It is observed that the cross-validation error predictions are close to the errors observed in the experiments.

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Sadoughi, Mohammadkazem, Chao Hu, Behnam Moghadassian, Anupam Sharma, Joseph Beck, and Danielle Mathiesen. "Sequential Online Dispatch in Design of Experiments for Single- and Multiple-Response Surrogate Modeling." IEEE Transactions on Automation Science and Engineering 17, no. 4 (October 2020): 1674–88. http://dx.doi.org/10.1109/tase.2020.2969884.

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Wang, Weichung, Ray-Bing Chen, and Chia-Lung Hsu. "Using adaptive multi-accurate function evaluations in a surrogate-assisted method for computer experiments." Journal of Computational and Applied Mathematics 235, no. 10 (March 2011): 3151–62. http://dx.doi.org/10.1016/j.cam.2010.12.021.

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Wang, Yiping, Cheng Wu, Gangfeng Tan, and Yadong Deng. "Reduction in the aerodynamic drag around a generic vehicle by using a non-smooth surface." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 231, no. 1 (August 5, 2016): 130–44. http://dx.doi.org/10.1177/0954407016636970.

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Numerical investigations are carried out to investigate the reduction in the aerodynamic drag of a vehicle by employing a dimpled non-smooth surface. The computational scheme was validated by the experimental data reported in literature. The mechanism and the effect of the dimpled non-smooth surface on the drag reduction were revealed by analysing the flow field structure of the wake. In order to maximize the drag reduction performance of the dimpled non-smooth surface, an aerodynamic optimization method based on a Kriging surrogate model was employed to design the dimpled non-smooth surface. Four structure parameters were selected as the design variables, and a 16-level design-of-experiments method based on orthogonal arrays was used to analyse the sensitivities and the influences of the variables on the drag coefficient; a surrogate model was constructed from these. Then a multi-island genetic algorithm was employed to obtain the optimal solution for the surrogate model. Finally, the surrogate model and the simulation results showed that the optimal combination of design variables can reduce the aerodynamic drag coefficient by 5.20%.

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Wu, Jinglai, Zhen Luo, Nong Zhang, and Wei Gao. "A new sequential sampling method for constructing the high-order polynomial surrogate models." Engineering Computations 35, no. 2 (April 16, 2018): 529–64. http://dx.doi.org/10.1108/ec-05-2016-0160.

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Purpose This paper aims to study the sampling methods (or design of experiments) which have a large influence on the performance of the surrogate model. To improve the adaptability of modelling, a new sequential sampling method termed as sequential Chebyshev sampling method (SCSM) is proposed in this study. Design/methodology/approach The high-order polynomials are used to construct the global surrogated model, which retains the advantages of the traditional low-order polynomial models while overcoming their disadvantage in accuracy. First, the zeros of Chebyshev polynomials with the highest allowable order will be used as sampling candidates to improve the stability and accuracy of the high-order polynomial model. In the second step, some initial sampling points will be selected from the candidates by using a coordinate alternation algorithm, which keeps the initial sampling set uniformly distributed. Third, a fast sequential sampling scheme based on the space-filling principle is developed to collect more samples from the candidates, and the order of polynomial model is also updated in this procedure. The final surrogate model will be determined as the polynomial that has the largest adjusted R-square after the sequential sampling is terminated. Findings The SCSM has better performance in efficiency, accuracy and stability compared with several popular sequential sampling methods, e.g. LOLA-Voronoi algorithm and global Monte Carlo method from the SED toolbox, and the Halton sequence. Originality/value The SCSM has good performance in building the high-order surrogate model, including the high stability and accuracy, which may save a large amount of cost in solving complicated engineering design or optimisation problems.

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Berges, Julius Moritz, Kira van der Straeten, Georg Jacobs, Jörg Berroth, and Arnold Gillner. "Model-Based Estimation of the Strength of Laser-Based Plastic-Metal Joints Using Finite Element Microstructure Models and Regression Models." Materials 14, no. 17 (September 1, 2021): 5004. http://dx.doi.org/10.3390/ma14175004.

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Plastic-metal joints with a laser-structured metal surface have a high potential to reduce cost and weight compared to conventional joining technologies. However, their application is currently inhibited due to the absence of simulation methods and models for mechanical design. Thus, this paper presents a model-based approach for the strength estimation of laser-based plastic-metal joints. The approach aims to provide a methodology for the efficient creation of surrogate models, which can capture the influence of the microstructure parameters on the joint strength. A parametrization rule for the shape of the microstructure is developed using microsection analysis. Then, a parameterized finite element (FE) model of the joining zone on micro level is developed. Different statistical plans and model fits are tested, and the predicted strength of the FE model and the surrogate models are compared against experiments for different microstructure geometries. The joint strength is predicted by the FE model with a 3.7% error. Surrogate modelling using half-factorial experimental design and linear regression shows the best accuracy (6.2% error). This surrogate model can be efficiently created as only 16 samples are required. Furthermore, the surrogate model is provided as an equation, offering the designer a convenient tool to estimate parameter sensitivities.

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Reeping, K. W., J. A. Bohn, and R. A. Walker. "Chlorine-induced degradation in SOFCs operating with biogas." Sustainable Energy & Fuels 1, no. 6 (2017): 1320–28. http://dx.doi.org/10.1039/c7se00156h.

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Experiments described in this work examine degradation mechanisms of nickel-based anodes in solid oxide fuel cells (SOFCs) operating with a biogas surrogate and exposed to 110 ppm Cl (delivered either as CH₃Cl or HCl).

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Duan, Wen Rui, and Ling Tian. "Surrogate Modeling and Optimizing for CCP Etch Process." Applied Mechanics and Materials 670-671 (October 2014): 548–53. http://dx.doi.org/10.4028/www.scientific.net/amm.670-671.548.

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In order to analyze performance of the Capacitively Coupled Plasma (CCP) etcher, commercial software like OPTIMUS can be applied to approximate etch process model by Response Surface Method (RSM) or Radial Basis Functions (RBF). Multi-factor parameters are concerned in etch process, like frequencies of the dual Radio Frequency system (RF) and flow rate and flow ratio of the process gas. When facing the multi-dimensional problem, the algorithms would turned to be inefficiency and the optimization process may be trapped in local minimum area or cannot converge because of oscillation. To improve surrogate modeling for the CCP etcher, a self-optimizing RBF (SO-RBF) algorithm is proposed and a process modeling tool is developed. Experiments on a state-of-art dual station CCP etcher shows that based on the global approximation model generated by this algorithm, process parameter optimization can be easily implemented with less error than OPTIMUS.

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