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Literatura académica sobre el tema "Model Predictve Control"

Autor: Grafiati

Publicado: 10 de mayo de 2025

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Artículos de revistas sobre el tema "Model Predictve Control"

1

Gwon, Jun, Jong-Seok Kim, Young-Seok Lee, Oluleke Babayomi y Ki-Bum Park. "A Model-Free Predictive Control for IPMSM Drive Based on Finite Control Set". TRANSACTIONS OF KOREAN INSTITUTE OF POWER ELECTRONICS 30, n.º 2 (30 de abril de 2025): 165–72. https://doi.org/10.6113/tkpe.2025.30.2.165.

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Wieber, Pierre-Brice. "Model Predictive Control for Biped Walking Motion Generation". Journal of the Robotics Society of Japan 32, n.º 6 (2014): 503–7. http://dx.doi.org/10.7210/jrsj.32.503.

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白家納, 白家納 y 黃崇能 Pachara Opattrakarnkul. "以深度學習模式估測控制之駕駛輔助系統的研發". 理工研究國際期刊 12, n.º 1 (abril de 2022): 015–24. http://dx.doi.org/10.53106/222344892022041201002.

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<p>Adaptive cruise control (ACC) systems are designed to provide longitudinal assistance to enhance safety and driving comfort by adjusting vehicle velocity to maintain a safe distance between the host vehicle and the preceding vehicle. Generally, using model predictive control (MPC) in ACC systems provides high responsiveness and lower discomfort by solving real-time constrained optimization problems but results in computational load. This paper presents an architecture of deep learning based on model predictive control in ACC systems to avoid real-time optimization problems required by MPC, which in turn, reduces computational load. The learning dataset is acquired from the simulation data of the input/output of the MPC controller. We designed the proposed deep learning controller using long short-term memory networks (LSTMs) and simulated it in MATLAB/Simulink using the vehicle’s characteristics from the advanced vehicle simulator (ADVISOR). Finally, the safety and driving comfort are compared with the PID-based control to demonstrate the performance of the proposed deep-learning architecture.</p> <p> </p>

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Qin Shuo, 秦硕. "精密透镜系统的模型预测热控方法". Laser & Optoelectronics Progress 59, n.º 17 (2022): 1722006. http://dx.doi.org/10.3788/lop202259.1722006.

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SZABOLCSI, Róbert. "MODEL PREDICTIVE CONTROL APPLIED IN UAV FLIGHT PATH TRACKING MISSIONS". Review of the Air Force Academy 17, n.º 1 (24 de mayo de 2019): 49–62. http://dx.doi.org/10.19062/1842-9238.2019.17.1.7.

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G.S.S.S.S.V., Krishna Mohan. "Auto-tuning Smith-predictive Control of Delayed Processes based on Model Reference Adaptive Controller". Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (31 de marzo de 2020): 1224–30. http://dx.doi.org/10.5373/jardcs/v12sp4/20201597.

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G P, Athira y Riya Mary Francis. "Control of Totally Refluxed Reactive Distillation Column Using Model Predictive Controller". International Journal of Scientific Engineering and Research 3, n.º 8 (27 de agosto de 2015): 31–35. https://doi.org/10.70729/ijser15385.

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Muske, Kenneth R. y James B. Rawlings. "Model predictive control with linear models". AIChE Journal 39, n.º 2 (febrero de 1993): 262–87. http://dx.doi.org/10.1002/aic.690390208.

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Shamim, Nimat, Subrina Sultana Noureen, Argenis Bilbao, Anitha Sarah Subburaj y Stephen Bayne. "A Comparative Study of Vector Control and Model Predictive Control Technique for Grid Connected Battery System". International Journal of Research and Engineering 4, n.º 12 (5 de enero de 2018): 287–95. http://dx.doi.org/10.21276/ijre.2018.5.1.1.

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Rosolia, Ugo, Xiaojing Zhang y Francesco Borrelli. "Data-Driven Predictive Control for Autonomous Systems". Annual Review of Control, Robotics, and Autonomous Systems 1, n.º 1 (28 de mayo de 2018): 259–86. http://dx.doi.org/10.1146/annurev-control-060117-105215.

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In autonomous systems, the ability to make forecasts and cope with uncertain predictions is synonymous with intelligence. Model predictive control (MPC) is an established control methodology that systematically uses forecasts to compute real-time optimal control decisions. In MPC, at each time step an optimization problem is solved over a moving horizon. The objective is to find a control policy that minimizes a predicted performance index while satisfying operating constraints. Uncertainty in MPC is handled by optimizing over multiple uncertain forecasts. In this case, performance index and operating constraints take the form of functions defined over a probability space, and the resulting technique is called stochastic MPC. Our research over the past 10 years has focused on predictive control design methods that systematically handle uncertain forecasts in autonomous and semiautonomous systems. In the first part of this article, we present an overview of the approach we use, its main advantages, and its challenges. In the second part, we present our most recent results on data-driven predictive control. We show how to use data to efficiently formulate stochastic MPC problems and autonomously improve performance in repetitive tasks. The proposed framework is able to handle a large set of predicted scenarios in real time and learn from historical data.

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Tesis sobre el tema "Model Predictve Control"

1

Lu, Yaohui. "Scheduling quasi-min-max model predictve control". Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/11692.

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Hosseinkhan-Boucher, Rémy. "On Learning-Based Control of Dynamical Systems". Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPASG029.

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Les impératifs environnementaux suscitent un regain d’intérêt pour la recherche sur le contrôle de l’écoulement des fluides afin de réduire la consommation d’énergie et les émissions dans diverses applications telles que l’aéronautique et l’automobile. Les stratégies de contrôle des fluides peuvent optimiser le système en temps réel, en tirant parti des mesures des capteurs et des modèles physiques. Ces stratégies visent à manipuler le comportement d’un système pour atteindre un état souhaité (stabilité, performance, consommation d’énergie). Dans le même temps, le développement d’approches de contrôle pilotées par les données dans des domaines concurrents tels que les jeux et la robotique a ouvert de nouvelles perspectives pour le contrôle des fluides. Cependant, l’intégration du contrôle basé sur l’apprentissage en dynamique des fluides présente de nombreux défis, notamment en ce qui concerne la robustesse de la stratégie de contrôle, l’efficacité de l’échantillon de l’algorithme d’apprentissage, et la présence de retards de toute nature dans le système. Ainsi, cette thèse vise à étudier et à développer des stratégies de contrôle basées sur l’apprentissage en tenant compte de ces défis, dans lesquels deux classes principales de stratégies de contrôle basées sur les données sont considérées : l’apprentissage par renforcement (RL) et la commande prédictive basée sur l’apprentissage (LB-MPC). De multiples contributions sont apportées dans ce contexte. Tout d’abord, un développement étendu sur la connexion entre les domaines du contrôle stochastique (temps continu) et du processus de décision de Markov (temps discret) est fourni pour unifier les deux approches. Deuxièmement, des preuves empiriques sur les propriétés de régularisation de l’algorithme d’apprentissage par renforcement par maximum d’entropie sont présentées à travers des concepts d’apprentissage statistique pour mieux comprendre la caractéristique de robustesse de l’approche par maximum d’entropie. Troisièmement, la notion d’abstraction temporelle est utilisée pour améliorer l’efficacité de l’échantillonnage d’un algorithme de commande prédictive par modèle basé sur l’apprentissage et piloté par une règle d’échantillonnage de la théorie de l’information. Enfin, les modèles différentiels neuronaux sont introduits à travers le concept d’équations différentielles neuronales à retard pour modéliser des systèmes à temps continu avec des retards pour des applications en commande prédictive. Les différentes études sont développées à l’aide de simulations numériques appliquées à des systèmes minimalistes issus des théories des systèmes dynamiques et du contrôle afin d’illustrer les résultats théoriques. Les expériences de la dernière partie sont également menées sur des simulations d’écoulement de fluides en 2D
Environmental needs are driving renewed research interest in fluid flow control to reduce energy consumption and emissions in various applications such as aeronautics and automotive industries. Flow control strategies can optimise the system in real time, taking advantage of sensor measurements and physical models. These strategies aim at manipulating the behaviour of a system to reach a desired state (textit{e.g.}, stability, performance, energy consumption). Meanwhile, the development of data-driven control approaches in concurrent areas such as games and robotics has opened new perspectives for flow control. However, the integration of learning-based control in fluid dynamics comes with multiple challenges, including the robustness of the control strategy, the sample efficiency of the learning algorithm, and the presence of delays of any nature in the system. Thus, this thesis aims to study and develop learning-based control strategies with respect to these challenges where two main classes of data-driven control strategies are considered: Reinforcement Learning (RL) and Learning-based Model Predictive Control (LB-MPC). Multiple contributions are made in this context. First, an extended development on the connection between the ﬁelds of (continuous-time) Stochastic Control and (discrete-time) Markov Decision Process is provided to bridge the gap between the two approaches. Second, empirical evidence on the regularisation properties of the Maximum Entropy Reinforcement Learning algorithm is presented through statistical learning concepts to further understand the robustness feature of the Maximum Entropy approach. Third, the notion of temporal abstraction is used to improve the sample efficiency of a Learning-based Model Predictive Control algorithm driven by an Information Theoretic sampling rule. Lastly, neural differential models are introduced through the concept of Neural Delay Differential Equations to model continuous-time systems with delays for Model Predictive Control applications. The different studies are developed with numerical simulations applied on minimalistic systems from Dynamical Systems and Control theories to illustrate the theoretical results. The training experiments of the last part are also conducted on 2D fluid flow simulations

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Bacic, Marko. "Model predictive control". Thesis, University of Oxford, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400060.

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Borgesen, Jørgen Frenken. "Efficient optimization for Model Predictive Control in reservoir models". Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9959.

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The purpose of this thesis was to study the use of adjoint methods for gradient calculations in Model Predictive Control (MPC) applications. The goal was to find and test efficient optimization methods to use in MPC on oil reservoir models. Handling output constraints in the optimization problem has been studied closer since they deteriorate the efficiency of the MPC applications greatly. Adjoint- and finite difference approaches for gradient calculations was tested on reservoir models to determine there efficiency on this particular type of problem. Techniques for reducing the number of output constraints was also utilized to decrease the computation time further. The results of this study shows us that adjoint methods can decrease the computation time for reservoir simulations greatly. Combining the adjoint methods with techniques that reduces the number of output constraints can reduce the computation time even more. Adjoint methods require some more work in the modeling process, but the simulation time can be greatly reduced. The principal conclusion is that more specialized optimization algorithms can reduce the simulation time for reservoir models.

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Hanger, Martin Bøgseth. "Model Predictive Control Allocation". Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-13308.

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This thesis developes a control allocation method based on the Model Predictive Control algorithm, to be used on a missile in flight. The resulting Model Predictive Control Allocation (MPCA) method is able to account for actuator constraints and dynamics, setting it aside from most classical methods. A new effector configuration containing two groups of actuators with different dynamic authorities is also proposed. Using this configuration, the MPCA method is compared to the classical methods Linear Programming and Redistributed Pseudoinverse in various flight scenarios, highlighting performance differences aswell as emphasizing applications of the MPCA method. It is found to be superior to the two classical methods in terms of tracking performance and total cost. Nevertheless, some restrictions and weaknesses are revealed, but countermeasures to these are proposed. The newly developed convex optmization solver CVXGEN is utilized successfully in the method evaluation. Providing solve times in milliseconds even for large problems, CVXGEN makes real-time implementations of the MPCA method feasible.

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Qi, Kent Zhihua. "Dual-model predictive control". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq21621.pdf.

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Sriniwas, Ganti Ravi. "Nonlinear model predictive control". Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/10267.

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Couchman, Paul. "Stochastic model predictive control". Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442384.

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Wu, Xingjian. "Stochastic model predictive control". Thesis, University of Oxford, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.497157.

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Gormandy, Brent Anthony. "Fuzzy model predictive control". Thesis, University of Strathclyde, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248858.

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Libros sobre el tema "Model Predictve Control"

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Camacho, E. F. y C. Bordons. Model Predictive control. London: Springer London, 2007. http://dx.doi.org/10.1007/978-0-85729-398-5.

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Zhang, Ridong, Anke Xue y Furong Gao. Model Predictive Control. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-0083-7.

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Camacho, Eduardo F. y Carlos Bordons. Model Predictive Control. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-3398-8.

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Kouvaritakis, Basil y Mark Cannon. Model Predictive Control. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24853-0.

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Camacho, E. F. Model predictive control. London: Springer, 2003.

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Camacho, E. F. Model predictive control. 2^a ed. New York: Springer, 2004.

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1962-, Bordons C., ed. Model predictive control. Berlin: Springer, 1999.

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S, Han, ed. Receding horizon control: Model predictive control for state models. Berlin: Springer, 2005.

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Zheng, Tao. Advanced model predictive control. Rijeka, Croatia: InTech, 2011.

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Allgöwer, Frank. Nonlinear Model Predictive Control. Basel: Birkhäuser Basel, 2000.

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Capítulos de libros sobre el tema "Model Predictve Control"

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Camacho, Eduardo F. y Carlos Bordons. "Generalized Predictive Control". En Model Predictive Control, 51–83. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-3398-8_4.

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Camacho, E. F. y C. Bordons. "Generalized Predictive Control". En Model Predictive control, 47–79. London: Springer London, 2007. http://dx.doi.org/10.1007/978-0-85729-398-5_4.

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Zhang, Ridong, Anke Xue y Furong Gao. "Introduction". En Model Predictive Control, 1–14. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0083-7_1.

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Zhang, Ridong, Anke Xue y Furong Gao. "Industrial Application". En Model Predictive Control, 109–25. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0083-7_10.

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Zhang, Ridong, Anke Xue y Furong Gao. "Further Ideas on MPC and PFC Using Relaxed Constrained Optimization". En Model Predictive Control, 127–37. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0083-7_11.

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Zhang, Ridong, Anke Xue y Furong Gao. "Model Predictive Control Based on Extended State Space Model". En Model Predictive Control, 17–27. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0083-7_2.

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Zhang, Ridong, Anke Xue y Furong Gao. "Predictive Functional Control Based on Extended State Space Model". En Model Predictive Control, 29–35. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0083-7_3.

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Zhang, Ridong, Anke Xue y Furong Gao. "Model Predictive Control Based on Extended Non-minimal State Space Model". En Model Predictive Control, 37–50. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0083-7_4.

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Zhang, Ridong, Anke Xue y Furong Gao. "Predictive Functional Control Based on Extended Non-minimal State Space Model". En Model Predictive Control, 51–57. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0083-7_5.

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Zhang, Ridong, Anke Xue y Furong Gao. "Model Predictive Control Under Constraints". En Model Predictive Control, 59–63. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0083-7_6.

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Actas de conferencias sobre el tema "Model Predictve Control"

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Sooknah, Reeta, Sankara Papavinasam y R. Winston Revie. "Validation of a Prredictive Model for Microbiologically Influenced Corrosion". En CORROSION 2008, 1–17. NACE International, 2008. https://doi.org/10.5006/c2008-08503.

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Abstract Corrosion is one of the main threats to the integrity of oil and gas pipelines. It can occur both on the inside wall and outside surface of the pipelines. The control of corrosion is an ongoing challenge in pipeline operations. Predicting and assuring pipeline integrity and serviceability entail the use of sensors and monitoring tools as well as predictive models. Our aim is to develop an integrated internal pitting corrosion model, which incorporates the aspect of microbiologically influenced corrosion (MIC) in addition to non-MIC pitting corrosion. This paper presents the two components of this integrated model designed to predict internal corrosion in oil and gas pipelines. The first module, a pitting-corrosion model predicts when localized corrosion conditions (not related to MIC) will result in pipeline failures. The second module predicts the susceptibility of microbiologically influenced corrosion (MIC) inside pipelines. An evaluation of the MIC model based on four case histories of pipeline failures indicates that the occurrence of MIC can be predicted.

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Kopp, Fionna B. y Francesco Borrelli. "Data-Driven Multi-Modal Learning Model Predictive Control". En 2024 IEEE 63rd Conference on Decision and Control (CDC), 4905–10. IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886732.

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Sampathnarayanan, Balaji, Lorenzo Serrao, Simona Onori, Giorgio Rizzoni y Steve Yurkovich. "Model Predictive Control as an Energy Management Strategy for Hybrid Electric Vehicles". En ASME 2009 Dynamic Systems and Control Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/dscc2009-2671.

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The energy management strategy in a hybrid electric vehicle is viewed as an optimal control problem and is solved using Model Predictve Control (MPC). The method is applied to a series hybrid electric vehicle, using a linearized model in state space formulation and a linear MPC algorithm, based on quadratic programming, to find a feasible suboptimal solution. The significance of the results lies in obtaining a real-time implementable control law. The MPC algorithm is applied using a quasi-static simulator developed in the MATLAB environment. The MPC solution is compared with the dynamic programming solution (offline optimization). The dynamic programming algorithm, which requires the entire driving cycle to be known a-priori, guarantees the optimality and is used here as the benchmark solution. The effect of the parameters of the MPC (length of prediction horizon, type of prediction) is also investigated.

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Tarisciotti, Luca, Pericle Zanchetta, Alan Watson, Jon Clare, Marco Degano y Stefano Bifaretti. "Modulated model predictve control (M²PC) for a 3-phase active front-end". En 2013 IEEE Energy Conversion Congress and Exposition (ECCE). IEEE, 2013. http://dx.doi.org/10.1109/ecce.2013.6646821.

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Sadowska, Anna, Leo Steenson y Magnus Hedlund. "Model-Predictive Control of a Compliant Hydraulic System". En 2018 UKACC 12th International Conference on Control (CONTROL). IEEE, 2018. http://dx.doi.org/10.1109/control.2018.8516830.

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Dughman, S. S. y J. A. Rossiter. "Efficient robust feed forward model predictive control with tracking". En 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737593.

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Hernandez, Bernardo y Paul Trodden. "Distributed model predictive control using a chain of tubes". En 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737610.

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Kapernick, Bartosz, Sebastian Suss, Endric Schubert y Knut Graichen. "A synthesis strategy for nonlinear model predictive controller on FPGA". En 2014 UKACC 10th International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915218.

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Witheephanich, Kritchai, Luis Orihuela, Ramon A. Garcia y Juan M. Escano. "Min-max model predictive control with robust zonotope-based observer". En 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737613.

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Adelipour, Saeed, Mohammad Haeri y Gabriele Pannocchia. "Decentralized Robust Model Predictive Control for Multi-Input Linear Systems". En 2018 UKACC 12th International Conference on Control (CONTROL). IEEE, 2018. http://dx.doi.org/10.1109/control.2018.8516722.

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Informes sobre el tema "Model Predictve Control"

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Baum, C. C., K. L. Buescher, V. Hanagandi, R. Jones y K. Lee. Adaptive model predictive control using neural networks. Office of Scientific and Technical Information (OSTI), septiembre de 1994. http://dx.doi.org/10.2172/10178912.

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Castanon, David A. y Jerry M. Wohletz. Model Predictive Control for Dynamic Unreliable Resource Allocation. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 2002. http://dx.doi.org/10.21236/ada409519.

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B. Wayne Bequette y Priyadarshi Mahapatra. Model Predictive Control of Integrated Gasification Combined Cycle Power Plants. Office of Scientific and Technical Information (OSTI), agosto de 2010. http://dx.doi.org/10.2172/1026486.

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Li, Dinggen y Yang Ye. The Control of Air-Fuel Ratio of the Engine Based on Model Predictive Control. Warrendale, PA: SAE International, octubre de 2012. http://dx.doi.org/10.4271/2012-32-0050.

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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, marzo de 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines trajectory optimization, model predictive control (MPC), Lyapunov stability, and hierarchical RL for ensuring safe and robust control in complex environments. Through case studies in self-driving vehicles, autonomous drones, robotic manipulation, healthcare robotics, and multi-agent systems, this research highlights the trade-offs between model-based and model-free approaches, as well as the challenges of scalability, sample efficiency, hardware acceleration, and ethical AI deployment. The findings underscore the importance of hybrid RL-control frameworks, real-world RL training, and policy optimization techniques in advancing robotic intelligence and autonomous decision-making. Keywords: Optimal control, reinforcement learning, model-based RL, model-free RL, dynamic programming, policy optimization, Hamilton-Jacobi-Bellman equations, actor-critic methods, deep reinforcement learning, trajectory optimization, model predictive control, Lyapunov stability, hierarchical RL, multi-agent RL, robotics, self-driving cars, autonomous drones, robotic manipulation, AI-driven automation, safety in RL, hardware acceleration, sample efficiency, hybrid RL-control frameworks, scalable AI.

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Yates, Timothy y Kevin McNally. Uncertainty and sensitivity analysis of the HardSPEC environmental exposure model for pesticide regulatory assessments. HSE, septiembre de 2024. http://dx.doi.org/10.69730/hse.24rr1204.

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HardSPEC is an environmental fate model that predicts concentrations of pesticides in surface and groundwater following application on hard surfaces, such as roads, pavements and railway tracks, and a subsequent series of rainfall events; such pesticide use is typically of herbicides used for weed control. HardSPEC is used by HSE to assess the risk of, and take decisions related to, herbicide application. In this project we implemented the model in Matlab, a scientific programming language, and verified that the results precisely matched the results from the spreadsheet implementation. We developed a sensitivity analysis framework that varies the values of any of the parameters automatically and records the predicted concentrations

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Ollerenshaw, Douglas y Mark Costello. Model of Predictive Control of a Direct-Fire Projectile Equipped With Canards. Fort Belvoir, VA: Defense Technical Information Center, marzo de 2005. http://dx.doi.org/10.21236/ada432823.

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Witzig, Andreas, Camilo Tello, Franziska Schranz, Johannes Bruderer y Matthias Haase. Quantifying energy-saving measures in office buildings by simulation in 2D cross sections. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541623658.

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A methodology is presented to analyse the thermal behaviour of buildings with the goal to quantify energy saving measures. The solid structure of the building is modelled with finite elements to fully account for its ability to store energy and to accurately predict heat loss through thermal bridges. Air flow in the rooms is approximated by a lumped element model with three dynamical nodes per room. The dynamic model also contains the control algorithm for the HVAC system and predicts the net primary energy consumption for heating and cooling of the building for any time period. The new simulation scheme has the advantage to avoid U-values and thermal bridge coefficients and instead use well-known physical material parameters. It has the potential to use 2D and 3D geometries with appropriate automatic processing from BIM models. Simulations are validated by comparison to IDA ICE and temperature measurement. This work aims to discuss novel approaches to disseminating building simulation more widely.

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Christman. NR198704 Crack Initiation and Growth Modeling and Definition of Crack Growth Behavior in Line Pipe Steels. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), enero de 1987. http://dx.doi.org/10.55274/r0011199.

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The objective of the Crack Growth Modeling effort is to develop an understanding of the factors that control stress-corrosion crack growth. This effort has two main tasks: initiation and growth modeling; and definition of crack growth behavior. The model is used to predict crack growth based on determining when conditions are conducive to crack growth. Since the model deals with early crack growth, the properties of the metal nearest the surface must be considered. The second task, definition of crack growth behavior, deals with the growth of large cracks up to the point of failure. Of particular interest is the crack length-to-depth ratio because a crack with a small ratio gives a greater chance of leaking before breaking into a larger ratio. Also there is less chance of small crack linkage to form larger cracks when the lengthwise crack velocity is reduced. Thus a good understanding of the factors that control crack shape is essential for formulating a predictive model for long term crack growth.

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Shaver, Greg y Miles Droege. Develop and Deploy a Safe Truck Platoon Testing Protocol for the Purdue ARPA-E Project in Indiana. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317314.

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Hilly terrain poses challenges to truck platoons using fixed set speed cruise control. Driving the front truck efficiently on hilly terrain improves both trucks fuel economies and improves gap maintenance between the trucks. An experimentally-validated simulation model was used to show fuel savings for the platoon of 12.3% when the front truck uses long horizon predictive cruise control (LH-PCC), 8.7% when the front truck uses flexible set speed cruise control, and only 1.2% when the front truck uses fixed set speed cruise control. Purdue, Peloton, and Cummins have jointly configured two Peterbilt 579 trucks for relevant combinations of: (1) coordinated shifting, (2) constant or variable platoon gap controls, (3) flexible or constant speed setpoint cruise control of the front trucks, and (4) long-horizon predictive cruise control (LHPCC) of the front truck. Confirmation of this functionality during platooning was demonstrated at the Continental Test track in Uvalde, Texas. In Indiana, on-road experiments were limited to single truck operation with long-horizon predictive cruise control, flexible set speed cruise control, and constant setpoint cruise control. Data from all of the above was used to improve the fidelity of simulations used to arrive at the fuel savings and gap control findings for hilly terrain per what is summarized in the findings section. Additionally, in early summer 2020, Purdue submitted to, and received improvement from, INDOT for a safe truck platoon testing protocol (located in this report’s appendix), which could not be implemented in Indiana before the end of the project because of COVID-19. Presentations of the subject matter at COMVEC, MAASTO, Purdue Road School, and the Work Truck Show are listed in the appendix.

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