Combining Skeletonization, Setpoint Curves, and Heuristic Algorithms to Define District Metering Areas in the Battle of Water Networks District Metering Areas

[EN] The problem presented in this edition of the Battle of the Water Networks is to define district metering areas (DMAs) in a large network. The problem is addressed in two phases. First, the complexity of the network is simplified by dividing it into three operational areas. Second, an optimization algorithm defines DMAs, looking for the best feasible solution. A preliminary simulation of the network is made. From this, engineering judgment allows for defining an initial set of elements suitable to change. In the second stage, a heuristic algorithm is used to search for the best DMA definition by selecting the locations and settings of the pressure-reducing valves and isolation valves. The network is then divided into two categories: the main pipes and the distribution pipes. Only the distribution pipes can be closed. With these restrictions and those described in the problem, the algorithm looks for the best DMA definition based on both the pressure and demand distribution among all the DMAs.This work was supported by the Program Fondecyt Regular (Project 1180660) of the Comision Nacional de Investigacion Cientifica y Tecnologica (Conicyt), Chile.Martínez-Solano, FJ.; Iglesias Rey, PL.; Mora Melia, D.; Ribelles-Aguilar, J. (2018). Combining Skeletonization, Setpoint Curves, and Heuristic Algorithms to Define District Metering Areas in the Battle of Water Networks District Metering Areas. Journal of Water Resources Planning and Management. 144(6):04018023-1-04018023-7. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000938S04018023-104018023-7144

Population size influence on the efficiency of evolutionary algorithms to design water networks

[EN] The optimal sizing in water distribution networks (WDN) is of great interest because it allows the selection of alternative economical solutions that ensure design requirements at nodes (demands and pressure) and at lines (velocities). Among all the available design methodologies, this work analyzes those based on evolutionary algorithms (EAs). EAs are a combination of deterministic and random approaches, and the performance of the algorithm depends on the searching process. Each EA features specific parameters, and a proper calibration helps to reduce the randomness factor and improves the effectiveness of the search for minima. More specifically, the only common parameter to all techniques is the initial size of the random population (P). It is well known that population size should be large enough to guarantee the diversity of solutions and must grow with the number of decision variables. However, the larger the population size, the slower the convergence process. This work attempts to determine the population size that yields better solutions in less time. In order to get that, the work applies a method based on the concept of efficiency (E) of an algorithm. This efficiency relates the quality of the obtained solution with the computational effort that every EA requires to find the final design solution. This ratio E also represents an objective indicator to compare the performance of different algorithms applied to WDN optimization. The proposed methodology is applied to the pipe-sizing problem of three medium-sized benchmark networks, such as Hanoi, New York Tunnel and GoYang networks. Thus, from the currently available algorithms, this work includes evolutionary methodologies based on a Pseudo-Genetic Algorithm (PGA), Particle Swarm Optimization (PSO) and Harmony Search (HS). First, the different algorithm parameters for each network are calibrated. The values used for every EA are those that have been calculated in previous works. Secondly, specific parameters remain constant and the population size is modified. After more than 500,000 simulations, the influence of the population size is statistically analyzed in the final solutions. Finally, the efficiency was analyzed for each network and algorithm. The results ensure the best possible configuration based on the quality of the solutions and the convergence speed of the algorithm, depending of the population size.Mora-Melia, D.; Martínez-Solano, FJ.; Iglesias Rey, PL.; Gutiérrez-Bahamondes, JH. (2017). Population size influence on the efficiency of evolutionary algorithms to design water networks. Procedia Engineering. 186:341-348. doi:10.1016/j.proeng.2017.03.209S34134818

The Efficiency of Setting Parameters in a Modified Shuffled Frog Leaping Algorithm Applied to Optimizing Water Distribution Networks

This paper presents a modified Shuffled Frog Leaping Algorithm (SFLA) applied to the design of water distribution networks. Generally, one of the major disadvantages of the traditional SFLA is the high number of parameters that need to be calibrated for proper operation of the algorithm. A method for calibrating these parameters is presented and applied to the design of three benchmark medium-sized networks widely known in the literature (Hanoi, New York Tunnel, and GoYang). For each of the problems, over 35,000 simulations were conducted. Then, a statistical analysis was performed, and the relative importance of each of the parameters was analyzed to achieve the best possible configuration of the modified SFLA. The main conclusion from this study is that not all of the original SFL algorithm parameters are important. Thus, the fraction of frogs in the memeplex q can be eliminated, while the other parameters (number of evolutionary steps Ns, number of memeplexes m, and number of frogs n) may be set to constant values that run optimally for all medium-sized networks. Furthermore, the modified acceleration parameter C becomes the key parameter in the calibration process, vastly improving the results provided by the original SFLA.This work was supported by the Program Initiation into research (Project 11140128) of the Comision Nacional de Investigacion Cientifica y Tecnologica (Conicyt), Chile. This work was also supported by the project DPI2009-13674 (OPERAGUA) of the Direccion General de Investigacion y Gestion del Plan Nacional de I + D + I del Ministerio de Ciencia e Innovacion, Spain.Mora Meliá, D.; Iglesias Rey, PL.; Martínez-Solano, FJ.; Muñoz-Velasco, P. (2016). The Efficiency of Setting Parameters in a Modified Shuffled Frog Leaping Algorithm Applied to Optimizing Water Distribution Networks. Water. 2016(8). https://doi.org/10.3390/w8050182S20168Alperovits, E., & Shamir, U. (1977). Design of optimal water distribution systems. Water Resources Research, 13(6), 885-900. doi:10.1029/wr013i006p00885Fujiwara, O., & Khang, D. B. (1990). A two-phase decomposition method for optimal design of looped water distribution networks. Water Resources Research, 26(4), 539-549. doi:10.1029/wr026i004p00539Su, 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)Chung, G., & Lansey, K. (2008). Application of the Shuffled Frog Leaping Algorithm for the Optimization of a General Large-Scale Water Supply System. Water Resources Management, 23(4), 797-823. doi:10.1007/s11269-008-9300-6Lansey, K. E., & Mays, L. W. (1989). Optimization Model for Water Distribution System Design. Journal of Hydraulic Engineering, 115(10), 1401-1418. doi:10.1061/(asce)0733-9429(1989)115:10(1401)Martínez-Solano, J., Iglesias-Rey, P. L., Pérez-García, R., & López-Jiménez, P. A. (2008). Hydraulic Analysis of Peak Demand in Looped Water Distribution Networks. Journal of Water Resources Planning and Management, 134(6), 504-510. doi:10.1061/(asce)0733-9496(2008)134:6(504)Artita, K. S., Kaini, P., & Nicklow, J. W. (2013). Examining the Possibilities: Generating Alternative Watershed-Scale BMP Designs with Evolutionary Algorithms. Water Resources Management, 27(11), 3849-3863. doi:10.1007/s11269-013-0375-3Iglesias-Rey, P. L., Martínez-Solano, F. J., Meliá, D. M., & Martínez-Solano, P. D. (2014). BBLAWN: A Combined Use of Best Management Practices and an Optimization Model Based on a Pseudo-Genetic Algorithm. Procedia Engineering, 89, 29-36. doi:10.1016/j.proeng.2014.11.156Cheng, C.-T., Feng, Z.-K., Niu, W.-J., & Liao, S.-L. (2015). Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China. Water, 7(12), 4477-4495. doi:10.3390/w7084477Huang, Y.-C., Lin, C.-C., & Yeh, H.-D. (2015). An Optimization Approach to Leak Detection in Pipe Networks Using Simulated Annealing. Water Resources Management, 29(11), 4185-4201. doi:10.1007/s11269-015-1053-4Casillas, M., Garza-Castañón, L., & Puig, V. (2015). Optimal Sensor Placement for Leak Location in Water Distribution Networks using Evolutionary Algorithms. Water, 7(11), 6496-6515. doi:10.3390/w7116496Geem, Z. (2015). Multiobjective Optimization of Water Distribution Networks Using Fuzzy Theory and Harmony Search. Water, 7(12), 3613-3625. doi:10.3390/w7073613Louati, M. H., Benabdallah, S., Lebdi, F., & Milutin, D. (2011). Application of a Genetic Algorithm for the Optimization of a Complex Reservoir System in Tunisia. Water Resources Management, 25(10), 2387-2404. doi:10.1007/s11269-011-9814-1SAVIC, D. A., & WALTERS, G. A. (1995). AN EVOLUTION PROGRAM FOR OPTIMAL PRESSURE REGULATION IN WATER DISTRIBUTION NETWORKS. Engineering Optimization, 24(3), 197-219. doi:10.1080/03052159508941190Nazif, S., Karamouz, M., Tabesh, M., & Moridi, A. (2009). Pressure Management Model for Urban Water Distribution Networks. Water Resources Management, 24(3), 437-458. doi:10.1007/s11269-009-9454-xCozzolino, L., Cimorelli, L., Covelli, C., Mucherino, C., & Pianese, D. (2015). An Innovative Approach for Drainage Network Sizing. Water, 7(12), 546-567. doi:10.3390/w7020546Iglesias, P. (2007). STUDY OF SENSITIVITY OF THE PARAMETERS OF A GENETIC ALGORITHM FOR DESIGN OF WATER DISTRIBUTION NETWORKS. Journal of Urban and Environmental Engineering, 1(2), 61-69. doi:10.4090/juee.2007.v1n2.061069Reca, J., & Martínez, J. (2006). Genetic algorithms for the design of looped irrigation water distribution networks. Water Resources Research, 42(5). doi:10.1029/2005wr004383Mora-Melia, D., Iglesias-Rey, P. L., Martinez-Solano, F. J., & Fuertes-Miquel, V. S. (2013). Design of Water Distribution Networks using a Pseudo-Genetic Algorithm and Sensitivity of Genetic Operators. Water Resources Management, 27(12), 4149-4162. doi:10.1007/s11269-013-0400-6Geem, Z. W. (2006). Optimal cost design of water distribution networks using harmony search. Engineering Optimization, 38(3), 259-277. doi:10.1080/03052150500467430Duan, Q. Y., Gupta, V. K., & Sorooshian, S. (1993). Shuffled complex evolution approach for effective and efficient global minimization. Journal of Optimization Theory and Applications, 76(3), 501-521. doi:10.1007/bf00939380Eusuff, M. M., & Lansey, K. E. (2003). Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm. Journal of Water Resources Planning and Management, 129(3), 210-225. doi:10.1061/(asce)0733-9496(2003)129:3(210)Montalvo, 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:10.1080/03052150802010607Marchi, A., Dandy, G., Wilkins, A., & Rohrlach, H. (2014). Methodology for Comparing Evolutionary Algorithms for Optimization of Water Distribution Systems. Journal of Water Resources Planning and Management, 140(1), 22-31. doi:10.1061/(asce)wr.1943-5452.0000321Mora-Melia, D., Iglesias-Rey, P. L., Martinez-Solano, F. J., & Ballesteros-Pérez, P. (2015). Efficiency of Evolutionary Algorithms in Water Network Pipe Sizing. Water Resources Management, 29(13), 4817-4831. doi:10.1007/s11269-015-1092-xElbeltagi, E., Hegazy, T., & Grierson, D. (2007). A modified shuffled frog-leaping optimization algorithm: applications to project management. Structure and Infrastructure Engineering, 3(1), 53-60. doi:10.1080/15732470500254535Elbeltagi, E., Hegazy, T., & Grierson, D. (2005). Comparison among five evolutionary-based optimization algorithms. Advanced Engineering Informatics, 19(1), 43-53. doi:10.1016/j.aei.2005.01.004Wang, Q., Guidolin, M., Savic, D., & Kapelan, Z. (2015). Two-Objective Design of Benchmark Problems of a Water Distribution System via MOEAs: Towards the Best-Known Approximation of the True Pareto Front. Journal of Water Resources Planning and Management, 141(3), 04014060. doi:10.1061/(asce)wr.1943-5452.0000460Eiben, A. E., Hinterding, R., & Michalewicz, Z. (1999). Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 3(2), 124-141. doi:10.1109/4235.771166Geem, Z. W., & Cho, Y.-H. (2011). Optimal Design of Water Distribution Networks Using Parameter-Setting-Free Harmony Search for Two Major Parameters. Journal of Water Resources Planning and Management, 137(4), 377-380. doi:10.1061/(asce)wr.1943-5452.0000130McClymont, K., Keedwell, E., & Savic, D. (2015). An analysis of the interface between evolutionary algorithm operators and problem features for water resources problems. A case study in water distribution network design. Environmental Modelling & Software, 69, 414-424. doi:10.1016/j.envsoft.2014.12.023Morgan, D. R., & Goulter, I. C. (1985). Optimal urban water distribution design. Water Resources Research, 21(5), 642-652. doi:10.1029/wr021i005p00642Savic, 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

Computational Determination of Air Valves Capacity Using CFD Techniques

[EN] The analysis of transient flow is necessary to design adequate protection systems that support the oscillations of pressure produced in the operation of motor elements and regulation. Air valves are generally used in pressurized water pipes to manage the air inside them. Under certain circumstances, they can be used as an indirect control mechanism of the hydraulic transient. Unfortunately, one of the major limitations is the reliability of information provided by manufacturers and vendors, which is why experimental trials are usually used to characterize such devices. The realization of these tests is not simple since they require an enormous volume of previously stored air to be used in such experiments. Additionally, the costs are expensive. Consequently, it is necessary to develop models that represent the behaviour of these devices. Although computational fluid dynamics (CFD) techniques cannot completely replace measurements, the amount of experimentation and the overall cost can be reduced significantly. This work approaches the characterization of air valves using CFD techniques, including some experimental tests to calibrate and validate the results. A mesh convergence analysis was made. The results show how the CFD models are an efficient alternative to represent the behavior of air valves during the entry and exit of air to the system, implying a better knowledge of the system to improve it.This research was funded by the Program Fondecyt Regular, grant number 1180660.García-Todolí, S.; Iglesias Rey, PL.; Mora Melia, D.; Martínez-Solano, FJ.; Fuertes-Miquel, VS. (2018). Computational Determination of Air Valves Capacity Using CFD Techniques. Water. 10(10):1-16. https://doi.org/10.3390/w10101433S1161010Liou, C. P., & Hunt, W. A. (1996). Filling of Pipelines with Undulating Elevation Profiles. Journal of Hydraulic Engineering, 122(10), 534-539. doi:10.1061/(asce)0733-9429(1996)122:10(534)Zhou, F., Hicks, F. E., & Steffler, P. M. (2002). Transient Flow in a Rapidly Filling Horizontal Pipe Containing Trapped Air. Journal of Hydraulic Engineering, 128(6), 625-634. doi:10.1061/(asce)0733-9429(2002)128:6(625)Laanearu, J., Annus, I., Koppel, T., Bergant, A., Vučković, S., Hou, Q., … van’t Westende, J. M. C. (2012). Emptying of Large-Scale Pipeline by Pressurized Air. Journal of Hydraulic Engineering, 138(12), 1090-1100. doi:10.1061/(asce)hy.1943-7900.0000631Apollonio, C., Balacco, G., Fontana, N., Giugni, M., Marini, G., & Piccinni, A. (2016). Hydraulic Transients Caused by Air Expulsion During Rapid Filling of Undulating Pipelines. Water, 8(1), 25. doi:10.3390/w8010025Zhou, F., Hicks, F. E., & Steffler, P. M. (2002). Observations of Air–Water Interaction in a Rapidly Filling Horizontal Pipe. Journal of Hydraulic Engineering, 128(6), 635-639. doi:10.1061/(asce)0733-9429(2002)128:6(635)Vasconcelos, J. G., Wright, S. J., & Roe, P. L. (2006). Improved Simulation of Flow Regime Transition in Sewers: Two-Component Pressure Approach. Journal of Hydraulic Engineering, 132(6), 553-562. doi:10.1061/(asce)0733-9429(2006)132:6(553)Li, J., & McCorquodale, A. (1999). Modeling Mixed Flow in Storm Sewers. Journal of Hydraulic Engineering, 125(11), 1170-1180. doi:10.1061/(asce)0733-9429(1999)125:11(1170)Ramezani, L., Karney, B., & Malekpour, A. (2015). The Challenge of Air Valves: A Selective Critical Literature Review. Journal of Water Resources Planning and Management, 141(10), 04015017. doi:10.1061/(asce)wr.1943-5452.0000530Stephenson, D. (1997). Effects of Air Valves and Pipework on Water Hammer Pressures. Journal of Transportation Engineering, 123(2), 101-106. doi:10.1061/(asce)0733-947x(1997)123:2(101)Bianchi, A., Mambretti, S., & Pianta, P. (2007). Practical Formulas for the Dimensioning of Air Valves. Journal of Hydraulic Engineering, 133(10), 1177-1180. doi:10.1061/(asce)0733-9429(2007)133:10(1177)De Martino, G., Fontana, N., & Giugni, M. (2008). Transient Flow Caused by Air Expulsion through an Orifice. Journal of Hydraulic Engineering, 134(9), 1395-1399. doi:10.1061/(asce)0733-9429(2008)134:9(1395)Bhosekar, V. V., Jothiprakash, V., & Deolalikar, P. B. (2012). Orifice Spillway Aerator: Hydraulic Design. Journal of Hydraulic Engineering, 138(6), 563-572. doi:10.1061/(asce)hy.1943-7900.0000548Iglesias-Rey, P. L., Fuertes-Miquel, V. S., García-Mares, F. J., & Martínez-Solano, J. J. (2014). Comparative Study of Intake and Exhaust Air Flows of Different Commercial Air Valves. Procedia Engineering, 89, 1412-1419. doi:10.1016/j.proeng.2014.11.467Martins, N. M. C., Soares, A. K., Ramos, H. M., & Covas, D. I. C. (2016). CFD modeling of transient flow in pressurized pipes. Computers & Fluids, 126, 129-140. doi:10.1016/j.compfluid.2015.12.002Zhou, L., Liu, D., & Ou, C. (2011). Simulation of Flow Transients in a Water Filling Pipe Containing Entrapped Air Pocket with VOF Model. Engineering Applications of Computational Fluid Mechanics, 5(1), 127-140. doi:10.1080/19942060.2011.11015357Davis, J. A., & Stewart, M. (2002). Predicting Globe Control Valve Performance—Part I: CFD Modeling. Journal of Fluids Engineering, 124(3), 772-777. doi:10.1115/1.1490108Stephens, D., Johnson, M. C., & Sharp, Z. B. (2012). Design Considerations for Fixed-Cone Valve with Baffled Hood. Journal of Hydraulic Engineering, 138(2), 204-209. doi:10.1061/(asce)hy.1943-7900.0000496Romero-Gomez, P., Ho, C. K., & Choi, C. Y. (2008). Mixing at Cross Junctions in Water Distribution Systems. I: Numerical Study. Journal of Water Resources Planning and Management, 134(3), 285-294. doi:10.1061/(asce)0733-9496(2008)134:3(285)Austin, R. G., Waanders, B. van B., McKenna, S., & Choi, C. Y. (2008). Mixing at Cross Junctions in Water Distribution Systems. II: Experimental Study. Journal of Water Resources Planning and Management, 134(3), 295-302. doi:10.1061/(asce)0733-9496(2008)134:3(295)Ho, C. K. (2008). Solute Mixing Models for Water-Distribution Pipe Networks. Journal of Hydraulic Engineering, 134(9), 1236-1244. doi:10.1061/(asce)0733-9429(2008)134:9(1236)Huang, J., Weber, L. J., & Lai, Y. G. (2002). Three-Dimensional Numerical Study of Flows in Open-Channel Junctions. Journal of Hydraulic Engineering, 128(3), 268-280. doi:10.1061/(asce)0733-9429(2002)128:3(268)Weber, L. J., Schumate, E. D., & Mawer, N. (2001). Experiments on Flow at a 90° Open-Channel Junction. Journal of Hydraulic Engineering, 127(5), 340-350. doi:10.1061/(asce)0733-9429(2001)127:5(340)Chanel, P. G., & Doering, J. C. (2008). Assessment of spillway modeling using computational fluid dynamics. Canadian Journal of Civil Engineering, 35(12), 1481-1485. doi:10.1139/l08-094Li, S., Cain, S., Wosnik, M., Miller, C., Kocahan, H., & Wyckoff, R. (2011). Numerical Modeling of Probable Maximum Flood Flowing through a System of Spillways. Journal of Hydraulic Engineering, 137(1), 66-74. doi:10.1061/(asce)hy.1943-7900.0000279Castillo, L., García, J., & Carrillo, J. (2017). Influence of Rack Slope and Approaching Conditions in Bottom Intake Systems. Water, 9(1), 65. doi:10.3390/w9010065Regueiro-Picallo, M., Naves, J., Anta, J., Puertas, J., & Suárez, J. (2016). Experimental and Numerical Analysis of Egg-Shaped Sewer Pipes Flow Performance. Water, 8(12), 587. doi:10.3390/w812058

Methodology for Pumping Station Design Based on Analytic Hierarchy Process (AHP)

[EN] Pumping station (PS) designs in water networks basically contemplate technical and economic aspects. Technical aspects could be related to the number of pumps in PS and the operational modes of PS. Meanwhile, economic aspects could be related to all the costs that intervene in a PS design, such as investment, operational and maintenance costs. In general, water network designs are usually focused on optimizing operational costs or investment costs, However, some subjective technical aspects have not been approached, such as determining the most suitable pump model, the most suitable number of pumps and the complexity of control system operation in a PS design. Therefore, the present work aims to select the most suitable pump model and determine the prior-ities that technical and economic factors could have in a PS design by a multi-criteria analysis, such as an analytic hierarchy process (AHP). The proposed work will contemplate two main criteria, and every criterion will be integrated by sub-criteria to design a PS. In this way, technical factors (number of pumps and complexity of the operating system) and economic factors (investment, operational and maintenance costs) will be considered for a PS design. The proposed methodology consists of realizing surveys to a different group of experts that determines the importance of one criterion over each other criterion in a PS design through pairwise comparisons. Finally, this methodology will provide importance weight for the criteria and sub-criteria on the PS. Besides, this work will perform a rating of the considered alternatives of pump models in every case study, evaluating quantitatively every alternative with every criterion in the PS design. The main objective of this work will select the most adequate pump model according to the obtained rating, considering technical and economic aspects in every case study.This research was funded by the Program Fondecyt Regular, grant number 1210410.Briceño-León, CX.; Sanchez-Ferrer, DS.; Iglesias Rey, PL.; Martínez-Solano, FJ.; Mora-Melia, D. (2021). Methodology for Pumping Station Design Based on Analytic Hierarchy Process (AHP). Water. 13(20):1-35. https://doi.org/10.3390/w13202886S135132

The High-Density Ionized Gas in the Central Parsecs of the Galaxy

We report the results from observations of H30$\alpha$ line emission in Sgr A West with the Submillimeter Array at a resolution of 2\arcsec and a field of view of about 40\arcsec. The H30$\alpha$ line is sensitive to the high-density ionized gas in the minispiral structure. We compare the velocity field obtained from H30$\alpha$ line emission to a Keplerian model, and our results suggest that the supermassive black hole at Sgr A* dominates the dynamics of the ionized gas. However, we also detect significant deviations from the Keplerian motion, which show that the impact of strong stellar winds from the massive stars along the ionized flows and the interaction between Northern and Eastern arms play significant roles in the local gas dynamics.Comment: 4 pages, 2 figure

A Methodology for the Optimization of Flow Rate Injection to Looped Water Distribution Networks through Multiple Pumping Stations

[EN] The optimal function of a water distribution network is reached when the consumer demands are satisfied using the lowest quantity of energy, maintaining the minimal pressure required at the same time. One way to achieve this is through optimization of flow rate injection based on the use of the setpoint curve concept. In order to obtain that, a methodology is proposed. It allows for the assessment of the flow rate and pressure head that each pumping station has to provide for the proper functioning of the network while the minimum power consumption is kept. The methodology can be addressed in two ways: the discrete method and the continuous method. In the first method, a finite set of combinations is evaluated between pumping stations. In the continuous method, the search for the optimal solution is performed using optimization algorithms. In this paper, Hooke Jeeves and Nelder Mead algorithms are used. Both the hydraulics and the objective function used by the optimization are solved through EPANET and its Toolkit. Two case studies are evaluated, and the results of the application of the different methods are discussed.This study was supported by the Program Initiation into research (Project 11140128) of the Comision Nacional de Investigacion Cientifica y Tecnologica (Conicyt), Chile.León Celi, C.; Iglesias Rey, PL.; Martínez-Solano, FJ.; Mora Meliá, D. (2016). A Methodology for the Optimization of Flow Rate Injection to Looped Water Distribution Networks through Multiple Pumping Stations. Water. 8(12):1-16. https://doi.org/10.3390/w8120575S11681

Efficiency Criteria as a Solution to the Uncertainty in the Choice of Population Size in Population-Based Algorithms Applied to Water Network Optimization

[EN] Different Population-based Algorithms (PbAs) have been used in recent years to solve all types of optimization problems related to water resource issues. However, the performances of these techniques depend heavily on correctly setting some specific parameters that guide the search for solutions. The initial random population size P is the only parameter common to all PbAs, but this parameter has received little attention from researchers. This paper explores P behaviour in a pipe-sizing problem considering both quality and speed criteria. To relate both concepts, this study applies a method based on an efficiency ratio E. First, specific parameters in each algorithm are calibrated with a fixed P. Second, specific parameters remain fixed, and the initial population size P is modified. After more than 600,000 simulations, the influence of P on obtaining successful solutions is statistically analysed. The proposed methodology is applied to four well-known benchmark networks and four different algorithms. The main conclusion of this study is that using a small population size is more efficient above a certain minimum size. Moreover, the results ensure optimal parameter calibration in each algorithm, and they can be used to select the most appropriate algorithm depending on the complexity of the problem and the goal of optimization.This study was supported by the Program Initiation into research (Project 11140128) of the Comision Nacional de Investigacion Cientifica y Tecnologica (Conicyt), Chile.Mora Meliá, D.; Gutiérrez Bahamondes, JH.; Iglesias Rey, PL.; Martínez-Solano, FJ. (2016). Efficiency Criteria as a Solution to the Uncertainty in the Choice of Population Size in Population-Based Algorithms Applied to Water Network Optimization. Water. 2016(8). https://doi.org/10.3390/w8120583S5832016

US Cosmic Visions: New Ideas in Dark Matter 2017: Community Report

This white paper summarizes the workshop "U.S. Cosmic Visions: New Ideas in Dark Matter" held at University of Maryland on March 23-25, 2017.Comment: 102 pages + reference