Caracterización de vertederos hidráulicos mediante técnicas cfd

[EN] We use ANSYS Fluent R15.0 Academic to caracterize sharp crested weirs of 3 differents geometries: triangular, rectangular and trapezoidal. A laboratory experiment and a SWMM 5 Application Model have been realized. (inglés)[ES] Usamos ANSYS Fluent R15.0 Academic para caracterizar los vertederos hidráulicos de 3 formas diferentes: triangular, rectangular y trapezoidal. Una experimentación de laboratorio y una aplicación al modelo SWMM 5 están realizadasNgamalieu Nengoue, UA. (2015). Caracterización de vertederos hidráulicos mediante técnicas cfd. http://hdl.handle.net/10251/67819Archivo delegad

Multi-Objective Optimization for Urban Drainage or Sewer Networks Rehabilitation through Pipes Substitution and Storage Tanks Installation

[EN] Drainage networks are civil constructions which do not generally attract the attention of decision-makers. However, they are of crucial importance for cities; this can be seen when a city faces floods resulting in extensive and expensive damage. The increase of rain intensity due to climate change may cause deficiencies in drainage networks built for certain defined flows which are incapable of coping with sudden increases, leading to floods. This problem can be solved using different strategies; one is the adaptation of the network through rehabilitation. A way to adapt the traditional network approach consists of substituting some pipes for others with greater diameters. More recently, the installation of storm tanks makes it possible to temporarily store excess water. Either of these solutions can be expensive, and an economic analysis must be done. Recent studies have related flooding with damage costs. In this work, a novel solution combining both approaches (pipes and tanks) is studied. A multi-objective optimization algorithm based on the NSGA-II is proposed for the rehabilitation of urban drainage networks through the substitution of pipes and the installation of storage tanks. Installation costs will be o set by damage costs associated with flooding. As a result, a set of optimal solutions that can be implemented based on the objectives to be achieved by municipalities or decisions makers. The methodology is finally applied to a real network located in the city of Bogotá, Colombia.This work was supported by the Program Fondecyt Regular (Project 1180660) of the Comision Nacional de Investigacion Cientifica y Tecnologica (Conicyt), Chile.Ngamalieu-Nengoue, UA.; Martínez-Solano, FJ.; Iglesias Rey, PL.; Mora-Meliá, D. (2019). Multi-Objective Optimization for Urban Drainage or Sewer Networks Rehabilitation through Pipes Substitution and Storage Tanks Installation. Water. 11(5). https://doi.org/10.3390/w11050935S115Kordana, S. (2018). The identification of key factors determining the sustainability of stormwater systems. E3S Web of Conferences, 45, 00033. doi:10.1051/e3sconf/20184500033Yazdi, J., Lee, E. H., & Kim, J. H. (2015). Stochastic Multiobjective Optimization Model for Urban Drainage Network Rehabilitation. Journal of Water Resources Planning and Management, 141(8), 04014091. doi:10.1061/(asce)wr.1943-5452.0000491Starzec, M., Dziopak, J., Słyś, D., Pochwat, K., & Kordana, S. (2018). Dimensioning of Required Volumes of Interconnected Detention Tanks Taking into Account the Direction and Speed of Rain Movement. Water, 10(12), 1826. doi:10.3390/w10121826Mailhot, A., & Duchesne, S. (2010). Design Criteria of Urban Drainage Infrastructures under Climate Change. Journal of Water Resources Planning and Management, 136(2), 201-208. doi:10.1061/(asce)wr.1943-5452.0000023Gulizia, C., & Camilloni, I. (2014). Comparative analysis of the ability of a set of CMIP3 and CMIP5 global climate models to represent precipitation in South America. International Journal of Climatology, 35(4), 583-595. doi:10.1002/joc.4005Ma, M., He, B., Wan, J., Jia, P., Guo, X., Gao, L., … Hong, Y. (2018). Characterizing the Flash Flooding Risks from 2011 to 2016 over China. Water, 10(6), 704. doi:10.3390/w10060704Kirshen, P., Caputo, L., Vogel, R. M., Mathisen, P., Rosner, A., & Renaud, T. (2015). Adapting Urban Infrastructure to Climate Change: A Drainage Case Study. Journal of Water Resources Planning and Management, 141(4), 04014064. doi:10.1061/(asce)wr.1943-5452.0000443Moselhi, O., & Shehab-Eldeen, T. (2000). Classification of Defects in Sewer Pipes Using Neural Networks. Journal of Infrastructure Systems, 6(3), 97-104. doi:10.1061/(asce)1076-0342(2000)6:3(97)Driessen, P., Hegger, D., Kundzewicz, Z., van Rijswick, H., Crabbé, A., Larrue, C., … Wiering, M. (2018). Governance Strategies for Improving Flood Resilience in the Face of Climate Change. Water, 10(11), 1595. doi:10.3390/w10111595Reyna, S. M., Vanegas, J. A., & Khan, A. H. (1994). Construction Technologies for Sewer Rehabilitation. Journal of Construction Engineering and Management, 120(3), 467-487. doi:10.1061/(asce)0733-9364(1994)120:3(467)Abraham, D. M., Wirahadikusumah, R., Short, T. J., & Shahbahrami, S. (1998). Optimization Modeling for Sewer Network Management. Journal of Construction Engineering and Management, 124(5), 402-410. doi:10.1061/(asce)0733-9364(1998)124:5(402)Sebti, A., Fuamba, M., & Bennis, S. (2016). Optimization Model for BMP Selection and Placement in a Combined Sewer. Journal of Water Resources Planning and Management, 142(3), 04015068. doi:10.1061/(asce)wr.1943-5452.0000620Zahmatkesh, Z., Burian, S. J., Karamouz, M., Tavakol-Davani, H., & Goharian, E. (2015). Low-Impact Development Practices to Mitigate Climate Change Effects on Urban Stormwater Runoff: Case Study of New York City. Journal of Irrigation and Drainage Engineering, 141(1), 04014043. doi:10.1061/(asce)ir.1943-4774.0000770Mora-Melià, D., López-Aburto, C., Ballesteros-Pérez, P., & Muñoz-Velasco, P. (2018). Viability of Green Roofs as a Flood Mitigation Element in the Central Region of Chile. Sustainability, 10(4), 1130. doi:10.3390/su10041130Ugarelli, R., & Di Federico, V. (2010). Optimal Scheduling of Replacement and Rehabilitation in Wastewater Pipeline Networks. Journal of Water Resources Planning and Management, 136(3), 348-356. doi:10.1061/(asce)wr.1943-5452.0000038Ngamalieu-Nengoue, U., Iglesias-Rey, P., Martínez-Solano, F., Mora-Meliá, D., & Saldarriaga Valderrama, J. (2019). Urban Drainage Network Rehabilitation Considering Storm Tank Installation and Pipe Substitution. Water, 11(3), 515. doi:10.3390/w11030515Lee, E., & Kim, J. (2017). Development of Resilience Index Based on Flooding Damage in Urban Areas. Water, 9(6), 428. doi:10.3390/w9060428Iglesias-Rey, P. L., Martínez-Solano, F. J., Saldarriaga, J. G., & Navarro-Planas, V. R. (2017). Pseudo-genetic Model Optimization for Rehabilitation of Urban Storm-water Drainage Networks. Procedia Engineering, 186, 617-625. doi:10.1016/j.proeng.2017.03.278Fadel, A. W., Marques, G. F., Goldenfum, J. A., Medellín-Azuara, J., & Tilmant, A. (2018). Full Flood Cost: Insights from a Risk Analysis Perspective. Journal of Environmental Engineering, 144(9), 04018071. doi:10.1061/(asce)ee.1943-7870.0001414Duan, H.-F., Li, F., & Yan, H. (2016). Multi-Objective Optimal Design of Detention Tanks in the Urban Stormwater Drainage System: LID Implementation and Analysis. Water Resources Management, 30(13), 4635-4648. doi:10.1007/s11269-016-1444-1Starzec, M. (2018). A critical evaluation of the methods for the determination of required volumes for detention tank. E3S Web of Conferences, 45, 00088. doi:10.1051/e3sconf/20184500088Pochwat, K. B., & Słyś, D. (2018). Application of Artificial Neural Networks in the Dimensioning of Retention Reservoirs. Ecological Chemistry and Engineering S, 25(4), 605-617. doi:10.1515/eces-2018-0040Cunha, M. C., Zeferino, J. A., Simões, N. E., & Saldarriaga, J. G. (2016). Optimal location and sizing of storage units in a drainage system. Environmental Modelling & Software, 83, 155-166. doi:10.1016/j.envsoft.2016.05.015Martino, G. D., De Paola, F., Fontana, N., Marini, G., & Ranucci, A. (2011). Pollution Reduction in Receivers: Storm-Water Tanks. Journal of Urban Planning and Development, 137(1), 29-38. doi:10.1061/(asce)up.1943-5444.0000037Andrés-Doménech, I., Montanari, A., & Marco, J. B. (2012). Efficiency of Storm Detention Tanks for Urban Drainage Systems under Climate Variability. Journal of Water Resources Planning and Management, 138(1), 36-46. doi:10.1061/(asce)wr.1943-5452.0000144Wang, M., Sun, Y., & Sweetapple, C. (2017). Optimization of storage tank locations in an urban stormwater drainage system using a two-stage approach. Journal of Environmental Management, 204, 31-38. doi:10.1016/j.jenvman.2017.08.024Cunha, M. C., Zeferino, J. A., Simões, N. E., Santos, G. L., & Saldarriaga, J. G. (2017). A decision support model for the optimal siting and sizing of storage units in stormwater drainage systems. International Journal of Sustainable Development and Planning, 12(01), 122-132. doi:10.2495/sdp-v12-n1-122-132Dziopak, J. (2018). A wastewater retention canal as a sewage network and accumulation reservoir. E3S Web of Conferences, 45, 00016. doi:10.1051/e3sconf/20184500016Słyś, D. (2018). An innovative retention canal – a case study. E3S Web of Conferences, 45, 00084. doi:10.1051/e3sconf/20184500084Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. doi:10.1109/4235.996017Martínez-Solano, F., Iglesias-Rey, P., Saldarriaga, J., & Vallejo, D. (2016). Creation of an SWMM Toolkit for Its Application in Urban Drainage Networks Optimization. Water, 8(6), 259. doi:10.3390/w8060259Wang, Q., Zhou, Q., Lei, X., & Savić, D. A. (2018). Comparison of Multiobjective Optimization Methods Applied to Urban Drainage Adaptation Problems. Journal of Water Resources Planning and Management, 144(11), 04018070. doi:10.1061/(asce)wr.1943-5452.0000996Mora-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-xMora-Melià, D., Martínez-Solano, F. J., Iglesias-Rey, P. L., & Gutiérrez-Bahamondes, J. H. (2017). Population Size Influence on the Efficiency of Evolutionary Algorithms to Design Water Networks. Procedia Engineering, 186, 341-348. doi:10.1016/j.proeng.2017.03.20

Urban Drainage Networks Rehabilitation Using Multi-Objective Model and Search Space Reduction Methodology

[EN] The drainage network always needs to adapt to environmental and climatic conditions to provide best quality services. Rehabilitation combining pipes substitution and storm tanks installation appears to be a good solution to overcome this problem. Unfortunately, the calculation time of such a rehabilitation scenario is too elevated for single-objective and multi-objective optimization. In this study, a methodology composed by search space reduction methodology whose purpose is to decrease the number of decision variables of the problem to solve and a multiobjective optimization whose purpose is to optimize the rehabilitation process and represent Pareto fronts as the result of urban drainage networks optimization is proposed. A comparison between different model results for multi-objective optimization is made. To obtain these results, Storm Water Management Model (SWMM) is first connected to a Pseudo Genetic Algorithm (PGA) for the search space reduction and then to a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization. Pareto fronts are designed for investment costs instead of flood damage costs. The methodology is applied to a real network in the city of Medellin in Colombia. The results show that search space reduction methodology provides models with a considerably reduced number of decision variables. The multi-objective optimization shows that the models¿ results used after the search space reduction obtain better outcomes than in the complete model in terms of calculation time and optimality of the solutions.Ngamalieu-Nengoue, UA.; Iglesias Rey, PL.; Martínez-Solano, FJ. (2019). Urban Drainage Networks Rehabilitation Using Multi-Objective Model and Search Space Reduction Methodology. Infrastructures. 4(2). https://doi.org/10.3390/infrastructures4020035S4

Urban Drainage Network Rehabilitation Considering Storm Tank Installation and Pipe Substitution

[EN] The drainage networks of our cities are currently experiencing a growing increase in runoff flows, caused mainly by the waterproofing of the soil and the effects of climate change. Consequently, networks originally designed correctly must endure floods with frequencies much higher than those considered in the design phase. The solution of such a problem is to improve the network. There are several ways to rehabilitate a network: conduit substitution as a former method or current methods such as storm tank installation or combined use of conduit substitution and storm tank installation. To find an optimal solution, deterministic or heuristic optimization methods are used. In this paper, a methodology for the rehabilitation of these drainage networks based on the combined use of the installation of storm tanks and the substitution of some conduits of the system is presented. For this, a cost-optimization method and a pseudo-genetic heuristic algorithm, whose efficiency has been validated in other fields, are applied. The Storm Water Management Model (SWMM) model for hydraulic analysis of drainage and sanitation networks is used. The methodology has been applied to a sector of the drainage network of the city of Bogota in Colombia, showing how the combined use of storm tanks and conduits leads to lower cost rehabilitation solutions.This work was supported by the Program Fondecyt Regular (Project 1180660) of the Comision Nacional de Investigación Científica y Tecnológica (Conicyt), Chile.Ngamalieu-Nengoue, UA.; Iglesias Rey, PL.; Martínez-Solano, FJ.; Mora-Meliá, D.; Saldarriaga, J. (2019). Urban Drainage Network Rehabilitation Considering Storm Tank Installation and Pipe Substitution. Water. 11(3):515-537. https://doi.org/10.3390/w11030515S51553711