Computer Aided Water Management and Control - Colorado State University

Present modern computer-aided tools of systems analysis to planning, design, and operation of water resource systems. Topics covered include: optimal operation of multipurpose reservoir systems; optimal flood control system operations; coordinated unit commitment in hydropower systems; optimal multicrop allocation of seasonal and intraseasonal irrigation water; risk-based design of stochastic reservoir operating policies; economic evaluation of integrated design of water storage and conveyance systems; optimal conjunctive use of surface water and groundwater; optimal reservoir operations for water quality management; and optimal investment timing and selection of water resource projects. Several case studies are presented for river basins in the U.S., Dominican Republic, Brazil, Sri Lanka, India, Egypt, and Korea. Systems analysis tools studied include dynamic programming, stochastic optimization, network flow optimization, genetic algorithms, neural networks, agent-based reinforcement learning methods, multiobjective optimization, and fuzzy optimization. Course taught at Colorado State University

DESIGN AIDS FOR AIR VESSELS FOR TRANSIENT PROTECTION OF LARGE PIPE NETWORKS - A FRAMEWORK BASED ON PARAMETERIZATION OF KNOWLEDGE-BASE DERIVED FROM OPTIMIZED NETWORK MODELS

The need for optimal air vessel sizing tools, in protecting large pipe networks from undue transient pressures is well known. Graphical and other heuristic methods reported in literature are limited to sizing the air vessels for simple rising mains. Although attempts have been made to utilize optimization techniques, they have been largely unsuccessful due to their impractical computational requirements. This research work proposes a robust framework for developing surge protection design tools and demonstrates the usefulness of the framework through an example air vessel sizing tool. Efficiency and robustness of the proposed framework are demonstrated by developing a design aid for air vessel sizing for protecting large pipe network systems against excessive high pressures generated by rapid valve closures. The essence of the proposed framework is in identification of key transient response parameters influencing air vessel parameters from seemingly unmanageable transient response data. This parameterization helps in exploiting the similarity between transient responses of small pipe networks and sub-sections of large pipe networks. The framework employs an extensive knowledgebase of transient pressure and flow scenarios defined from several small network models and corresponding optimal air vessel sizes obtained from a genetic algorithm optimizer. A regression model based on an artificial neural network was used on this knowledgebase to identify key parameters influencing air vessel sizes. These key parameters were used as input variables and the corresponding air vessel parameters as output variables to train the neural network model. The trained neural network model was successfully applied for large complex pipe networks to obtain optimal air vessel sizes for transient protection. The neural network model predictions were compared with optimal air vessel parameters to assess the efficacy of the proposed framework. The validity and limitation of the design aid developed and areas in the framework that need further research are also presented. The proposed frame work requires generation of hundreds of optimization data for small and simple network systems which is a daunting task since genetic algorithm-based optimization is computationally expensive. Selection of a numerically efficient and sufficiently accurate transient analysis method for use inside a genetic algorithm based optimization scheme is crucial as any reduction in transient analysis time for a network system would tremendously reduce the computational costs of bi-level genetic algorithm optimization scheme. This research work also demonstrate that the Wave Plan Method is computationally more efficient than the Method of Characteristics for similar accuracies and the resulting savings in computational costs in the transient analysis of pipe networks and subsequently in the genetic algorithm based optimization schemes are significant

On the smoothness of nonlinear system identification

We shed new light on the \textit{smoothness} of optimization problems arising in prediction error parameter estimation of linear and nonlinear systems. We show that for regions of the parameter space where the model is not contractive, the Lipschitz constant and $\beta$-smoothness of the objective function might blow up exponentially with the simulation length, making it hard to numerically find minima within those regions or, even, to escape from them. In addition to providing theoretical understanding of this problem, this paper also proposes the use of multiple shooting as a viable solution. The proposed method minimizes the error between a prediction model and the observed values. Rather than running the prediction model over the entire dataset, multiple shooting splits the data into smaller subsets and runs the prediction model over each subset, making the simulation length a design parameter and making it possible to solve problems that would be infeasible using a standard approach. The equivalence to the original problem is obtained by including constraints in the optimization. The new method is illustrated by estimating the parameters of nonlinear systems with chaotic or unstable behavior, as well as neural networks. We also present a comparative analysis of the proposed method with multi-step-ahead prediction error minimization

Engineering Decision Support and Expert Systems - Colorado State University

The student is introduced to development of decision support systems (DSS) for application to complex engineering management and design problems under conflicting objectives and uncertainty. A number of techniques are introduced for aiding in the analysis of a wide range of complex multiobjective engineering problems. Several stochastic optimization methods are presented for including risk and reliability in engineering design. Basic concepts of expert systems (ES) are discussed to show an essential synergy between DSS and ES for development of decision support structures that allow inclusion of human domain knowledge, heuristics and fuzzy logic. Heuristic methods such as genetic algorithms and particle swarm optimization are offered as a means of solving complex engineering design and management problems that defy traditional techniques of mathematical programming and operations research. Machine learning methods using artificial neural networks are introduced for solving complex dynamic scheduling and control problems in engineering. Each student is required to present a final class project involving application of the tools and concepts presented in the class to a real-world engineering decision problem. Course taught at Colorado State University

On the Complexities of the Design of Water Distribution Networks

Water supply is one of the most recognizable and important public services contributing to quality of life. Water distribution networks WDNs are extremely complex assets. A number of complex tasks, such as design, planning, operation, maintenance, and management, are inherently associated with such networks. In this paper, we focus on the design of a WDN, which is a wide and open problem in hydraulic engineering. This problem is a large-scale combinatorial, nonlinear, nonconvex, multiobjective optimization problem, involving various types of decision variables and many complex implicit constraints. To handle this problem, we provide a synergetic association between swarm intelligence and multiagent systems where human interaction is also enabled. This results in a powerful collaborative system for finding solutions to such a complex hydraulic engineering problem. All the ingredients have been integrated into a software tool that has also been shown to efficiently solve problems from other engineering fields.This work has been developed with the support of the project IDAWAS, DPI2009-11591, of the Direccion General de Investigacion of the Ministerio de Educacion y Ciencia, and ACOMP/2010/146 of the Conselleria d'Educacio of the Generalitat Valenciana. The first author is also indebted to the Universitat Politecnica de Valencia for the sabbatical leave granted during the first semester of 2011. The use of English in this paper was revised by John Rawlins.Izquierdo Sebastián, J.; Montalvo Arango, I.; Pérez García, R.; Matías, A. (2012). On the Complexities of the Design of Water Distribution Networks. Mathematical Problems in Engineering. 2012:1-25. https://doi.org/10.1155/2012/9479611252012Goulter, I. C., & Coals, A. V. (1986). Quantitative Approaches to Reliability Assessment in Pipe Networks. 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Pressure-Driven Demand and Leakage Simulation for Water Distribution Networks. Journal of Hydraulic Engineering, 134(5), 626-635. doi:10.1061/(asce)0733-9429(2008)134:5(626)Montalvo, I., Izquierdo, J., Pérez, R., & Tung, M. M. (2008). Particle Swarm Optimization applied to the design of water supply systems. Computers & Mathematics with Applications, 56(3), 769-776. doi:10.1016/j.camwa.2008.02.006Montalvo, I., Izquierdo, J., Pérez, R., & Iglesias, P. L. (2008). A diversity-enriched variant of discrete PSO applied to the design of water distribution networks. Engineering Optimization, 40(7), 655-668. doi:10.1080/03052150802010607Montalvo, I., Izquierdo, J., Pérez-García, R., & Herrera, M. (2010). Improved performance of PSO with self-adaptive parameters for computing the optimal design of Water Supply Systems. Engineering Applications of Artificial Intelligence, 23(5), 727-735. doi:10.1016/j.engappai.2010.01.015Martínez, J. B. (2010). Cost and reliability comparison between branched and looped water supply networks. Journal of Hydroinformatics, 12(2), 150-160. doi:10.2166/hydro.2009.080Goulter, I. C. (1992). Systems Analysis in Water‐Distribution Network Design: From Theory to Practice. Journal of Water Resources Planning and Management, 118(3), 238-248. doi:10.1061/(asce)0733-9496(1992)118:3(238)Park, H., & Liebman, J. C. (1993). Redundancy‐Constrained Minimum‐Cost Design of Water‐Distribution Nets. Journal of Water Resources Planning and Management, 119(1), 83-98. doi:10.1061/(asce)0733-9496(1993)119:1(83)Khomsi, D., Walters, G. A., Thorley, A. R. D., & Ouazar, D. (1996). Reliability Tester for Water-Distribution Networks. Journal of Computing in Civil Engineering, 10(1), 10-19. doi:10.1061/(asce)0887-3801(1996)10:1(10)Tanyimboh, T. T., Tabesh, M., & Burrows, R. (2001). Appraisal of Source Head Methods for Calculating Reliability of Water Distribution Networks. Journal of Water Resources Planning and Management, 127(4), 206-213. doi:10.1061/(asce)0733-9496(2001)127:4(206)Kalungi, P., & Tanyimboh, T. T. (2003). Redundancy model for water distribution systems. Reliability Engineering & System Safety, 82(3), 275-286. doi:10.1016/s0951-8320(03)00168-6Morgan, D. R., & Goulter, I. C. (1985). Optimal urban water distribution design. Water Resources Research, 21(5), 642-652. doi:10.1029/wr021i005p00642Walters, G. A., & Knezevic, J. (1989). Discussion of « Reliability‐Based Optimization Model for Water Distribution Systems » by Yu‐Chun Su, Larry W. Mays, Ning Duan, and Kevin E. Lansey (December, 1987, Vol. 113, No. 12). Journal of Hydraulic Engineering, 115(8), 1157-1158. doi:10.1061/(asce)0733-9429(1989)115:8(1157)LOGANATHAN, G. V., SHERALI, H. D., & SHAH, M. P. (1990). A TWO-PHASE NETWORK DESIGN HEURISTIC FOR MINIMUM COST WATER DISTRIBUTION SYSTEMS UNDER A RELIABILITY CONSTRAINT. Engineering Optimization, 15(4), 311-336. doi:10.1080/03052159008941160Bouchart, F., & Goulter, I. (1991). Reliability Improvements in Design of Water Distribution Networks Recognizing Valve Location. Water Resources Research, 27(12), 3029-3040. doi:10.1029/91wr00590Gupta, R., & Bhave, P. R. (1994). Reliability Analysis of Water‐Distribution Systems. Journal of Environmental Engineering, 120(2), 447-461. doi:10.1061/(asce)0733-9372(1994)120:2(447)Xu, C., & Goulter, I. C. (1999). Reliability-Based Optimal Design of Water Distribution Networks. Journal of Water Resources Planning and Management, 125(6), 352-362. doi:10.1061/(asce)0733-9496(1999)125:6(352)Su, Y., Mays, L. W., Duan, N., & Lansey, K. E. (1987). Reliability‐Based Optimization Model for Water Distribution Systems. Journal of Hydraulic Engineering, 113(12), 1539-1556. doi:10.1061/(asce)0733-9429(1987)113:12(1539)Cullinane, M. J., Lansey, K. E., & Mays, L. W. (1992). Optimization‐Availability‐Based Design of Water‐Distribution Networks. Journal of Hydraulic Engineering, 118(3), 420-441. doi:10.1061/(asce)0733-9429(1992)118:3(420)Vamvakeridou-Lyroudia, L. S., Walters, G. A., & Savic, D. A. (2005). Fuzzy Multiobjective Optimization of Water Distribution Networks. Journal of Water Resources Planning and Management, 131(6), 467-476. doi:10.1061/(asce)0733-9496(2005)131:6(467)Montalvo, I., Izquierdo, J., Schwarze, S., & Pérez-García, R. (2010). Multi-objective particle swarm optimization applied to water distribution systems design: An approach with human interaction. Mathematical and Computer Modelling, 52(7-8), 1219-1227. doi:10.1016/j.mcm.2010.02.017Izquierdo, J., Montalvo, I., Pérez, R., & Fuertes, V. S. (2008). Design optimization of wastewater collection networks by PSO. Computers & Mathematics with Applications, 56(3), 777-784. doi:10.1016/j.camwa.2008.02.007Dong, Y., Tang, J., Xu, B., & Wang, D. (2005). An application of swarm optimization to nonlinear programming. Computers & Mathematics with Applications, 49(11-12), 1655-1668. doi:10.1016/j.camwa.2005.02.006Jin, Y.-X., Cheng, H.-Z., Yan, J., & Zhang, L. (2007). New discrete method for particle swarm optimization and its application in transmission network expansion planning. Electric Power Systems Research, 77(3-4), 227-233. doi:10.1016/j.epsr.2006.02.016Arumugam, M. S., & Rao, M. V. C. (2008). On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems. Applied Soft Computing, 8(1), 324-336. doi:10.1016/j.asoc.2007.01.010Izquierdo, J., Montalvo, I., Pérez, R., & Fuertes, V. S. (2009). Forecasting pedestrian evacuation times by using swarm intelligence. Physica A: Statistical Mechanics and its Applications, 388(7), 1213-1220. doi:10.1016/j.physa.2008.12.008Herrera, M., Izquierdo, J., Montalvo, I., García-Armengol, J., & Roig, J. V. (2009). Identification of surgical practice patterns using evolutionary cluster analysis. Mathematical and Computer Modelling, 50(5-6), 705-712. doi:10.1016/j.mcm.2008.12.026Molina, J., Santana, L. V., Hernández-Díaz, A. G., Coello Coello, C. A., & Caballero, R. (2009). g-dominance: Reference point based dominance for multiobjective metaheuristics. European Journal of Operational Research, 197(2), 685-692. doi:10.1016/j.ejor.2008.07.01510.1029/89WR02879. (2010). Water Resources Research. doi:10.1029/89wr02879Savic, D. A., & Walters, G. A. (1997). Genetic Algorithms for Least-Cost Design of Water Distribution Networks. Journal of Water Resources Planning and Management, 123(2), 67-77. doi:10.1061/(asce)0733-9496(1997)123:2(67)Zecchin, A. C., Simpson, A. R., Maier, H. R., & Nixon, J. B. (2005). Parametric Study for an Ant Algorithm Applied to Water Distribution System Optimization. IEEE Transactions on Evolutionary Computation, 9(2), 175-191. doi:10.1109/tevc.2005.844168Yurong Liu, Zidong Wang, Jinling Liang, & Xiaohui Liu. (2009). Stability and Synchronization of Discrete-Time Markovian Jumping Neural Networks With Mixed Mode-Dependent Time Delays. IEEE Transactions on Neural Networks, 20(7), 1102-1116. doi:10.1109/tnn.2009.2016210Jinling Liang, Zidong Wang, & Xiaohui Liu. (2009). State Estimation for Coupled Uncertain Stochastic Networks With Missing Measurements and Time-Varying Delays: The Discrete-Time Case. IEEE Transactions on Neural Networks, 20(5), 781-793. doi:10.1109/tnn.2009.2013240Zidong Wang, Yao Wang, & Yurong Liu. (2010). Global Synchronization for Discrete-Time Stochastic Complex Networks With Randomly Occurred Nonlinearities and Mixed Time Delays. 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Deep learning for the modeling and inverse design of radiative heat transfer

Deep learning is having a tremendous impact in many areas of computer science and engineering. Motivated by this success, deep neural networks are attracting increasing attention in many other disciplines, including the physical sciences. In this work, we show that artificial neural networks can be successfully used in the theoretical modeling and analysis of a variety of radiative-heat-transfer phenomena and devices. By using a set of custom-designed numerical methods able to efficiently generate the required training data sets, we demonstrate this approach in the context of three very different problems, namely (i) near-field radiative heat transfer between multilayer systems that form hyperbolic metamaterials, (ii) passive radiate cooling in photonic crystal slab structures, and (iii) thermal emission of subwavelength objects. Despite their fundamental differences in nature, in all three cases we show that simple neural-network architectures trained with data sets of moderate size can be used as fast and accurate surrogates for doing numerical simulations, as well as engines for solving inverse design and optimization in the context of radiative heat transfer. Overall, our work shows that deep learning and artificial neural networks provide a valuable and versatile toolkit for advancing the field of thermal radiatio

Optimal design of systems that evolve over time using neural networks

Thesis (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, September 2005.Includes bibliographical references (p. 124-126).Computational design optimization is challenging when the number of variables becomes large. One method of addressing this problem is to use pattern recognition to decrease the solution space in which the optimizer searches. Human "common sense" is used by designers to narrow the scope of search to a confined area defined by patterns conforming to likely solution candidates. However, computer-based optimization generally does not apply similar heuristics. In this thesis, a system is presented that recognizes patterns and adjusts its search for optimal solutions based on performance associations with these patterns. A design problem was selected that requires the optimization algorithm to assess designs that evolve over time. A small sensor network design is evolved into a larger sensor network design. Optimal design solutions for the small network do not necessarily lead to optimal design solutions for the larger network. Systems that are well-positioned to evolve have characteristics that distinguish themselves from systems that are not well-positioned to evolve. In this study, a neural network was able to recognize a pattern whereby flexible sensor networks evolved more successfully than less flexible networks.(cont.) The optimizing algorithm used this pattern to select candidate systems that showed promise for successful evolution. In this limited exploratory study, a genetic algorithm assisted by a neural network achieved better performance than an unassisted genetic algorithm did. In a Pareto front analysis, the assisted genetic algorithm yielded three times the number of optimal "non-dominated" solutions as the unassisted genetic algorithm did. It realized these results in one quarter the CPU time. This thesis uses a sensor network example to establish the merit of neural network use in multi-objective system design optimization and to lay a basis for future study.by Michael K. Nolan.S.M

Seismic Resilience-based Design and Optimization: A Deep Learning and Cyber-Physical Approach

With the growing acceptance and better understanding of the importance of uncertainties in seismic design, traditional design approaches with deterministic analysis are being replaced with more reliable approaches within a risk-based context. Recently, resilience has been increasingly studied as a comprehensive metric to assess the ability of a system to withstand and recover from disturbances with large uncertainties. For civil infrastructure systems susceptible to natural hazards, especially earthquakes as considered herein, seismic resilience could provide a measurement integrating both earthquake and post-earthquake performance. For structural engineers, improving infrastructure disaster resilience starts with the design of more resilient structures. This requires a quantitative approach to explicitly guild the design towards better resilience. However, when attempting to quantify the seismic resilience of a structure, large uncertainties lead to large computational costs associated with risk-based approaches. Additionally, the accuracy of numerical simulations under wide range of design scenarios is unknown. To address these challenges, this dissertation investigates the role of seismic resilience in structural design. This dissertation starts with a novel seismic protective device to improve structural resilience and follows with the development of a quantitative and efficient design, evaluation, and optimization framework for seismic resilience. This framework proposes metamodeling through deep neural networks for improved efficiency and cyber-physical systems for improved accuracy. Feedforward neural networks are adopted for fragility metamodeling, while online learning long-short term memory neural networks are developed for structural component metamodeling. Real-time hybrid simulation is used for the construction of cyber-physical systems. The proposed framework is demonstrated to have both improved accuracy and significantly reduced computational/experimental cost when compared to existing approaches. The applicability of the framework is illustrated through the optimization of structural systems for improved seismic resilience

Understanding plasticity in neural networks

Plasticity, the ability of a neural network to quickly change its predictions in response to new information, is essential for the adaptability and robustness of deep reinforcement learning systems. Deep neural networks are known to lose plasticity over the course of training even in relatively simple learning problems, but the mechanisms driving this phenomenon are still poorly understood. This paper conducts a systematic empirical analysis into plasticity loss, with the goal of understanding the phenomenon mechanistically in order to guide the future development of targeted solutions. We find that loss of plasticity is deeply connected to changes in the curvature of the loss landscape, but that it often occurs in the absence of saturated units. Based on this insight, we identify a number of parameterization and optimization design choices which enable networks to better preserve plasticity over the course of training. We validate the utility of these findings on larger-scale RL benchmarks in the Arcade Learning Environment.Comment: Accepted to ICML 2023 (oral presentation