Applications of Optimal Hydraulic Control to Ground‐Water Systems

Ahlfeld, David P.; Heidari, Manoutchehr

doi:10.1061/(asce)0733-9496(1994)120:3(350)

Cited by 83 publications

(36 citation statements)

References 27 publications

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“…#!/bin/csh -f ################################################################ # the following arguments are called for from script in APPSPACK # argv [1] = input file name # argv [2] = output file name # argv [3] = tag # argv [4] = message (yes or no) # argv [5] = fidelity # argv [6] = number of variables # argv [7..n] = tag name for each variable ################################################################…”

Section: Begin Run Mfo Scriptmentioning

confidence: 99%

“…cp -r templatedir workdir.$num mv $argv [1] workdir.$num/dakota_vars cd workdir.$num # run a different executable if least squares option is passed # into this script (argv [3] == "ls") set executable = APPSfbhc if (($#argv > 2) && ($argv [3] == "ls")) \\ set executable = APPSfbhc_ls [2] # NOTE: moving $argv [2] at the end of the script avoids any # problems with read race conditions # (although master-slave does not have this problem). mv $argv [2] ../.…”

Section: Begin Low Fi Scriptmentioning

confidence: 99%

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Developing a computationally efficient dynamic multilevel hybrid optimization scheme using multifidelity model interactions.

Hough

Gray

Castro

et al. 2006

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Many engineering application problems use optimization algorithms in conjunction with numerical simulators to search for solutions. The formulation of relevant objective functions and constraints dictate possible optimization algorithms. Often, a gradient based approach is not possible since objective functions and constraints can be nonlinear, nonconvex, non-differentiable, or even discontinuous and the simulations involved can be computationally expensive. Moreover, computational efficiency and accuracy are desirable and also influence the choice of solution method.With the advent and increasing availability of massively parallel computers, computational speed has increased tremendously. Unfortunately, the numerical and model complexities of many problems still demand significant computational resources. Moreover, in optimization, these expenses can be a limiting factor since obtaining solutions often requires the completion of numerous computationally intensive simulations. Therefore, we propose a multifidelity optimization algorithm (MFO) designed to improve the computational efficiency of an optimization method for a wide range of applications.In developing the MFO algorithm, we take advantage of the interactions between multifidelity models to develop a dynamic and computational time saving optimization algorithm. First, a direct search method is applied to the high fidelity model over a reduced design space. In conjunction with this search, a specialized oracle is employed to map the design space of this high fidelity model to that of a computationally cheaper low fidelity model using space mapping techniques. Then, in the low fidelity space, an optimum is obtained using gradient or non-gradient based optimization, and it is mapped back to the high fidelity space.In this paper, we describe the theory and implementation details of our MFO algorithm. We also demonstrate our MFO method on some example problems and on two applications: earth penetrators and groundwater remediation. 4 AcknowledgmentsThanks to Paul Demmie and Donald Longcope for valuable discussions and help with earth penetrator models used within this document. Thanks to Katherine Fowler for providing the groundwater remediation problem and promoting our optimization method to the groundwater community. Thanks to Mike Eldred for valuable discussions and suggestions about space mapping.5

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Section: Begin Run Mfo Scriptmentioning

confidence: 99%

Section: Begin Low Fi Scriptmentioning

confidence: 99%

Developing a computationally efficient dynamic multilevel hybrid optimization scheme using multifidelity model interactions.

Hough

Gray

Castro

et al. 2006

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“…Large-scale constrained nonlinear optimization [Gill et al, 2002] is used to find optimal management strategies within the constraints of the physical and agricultural systems represented by the integrated simulation models. Simulation-optimization methods have been used extensively in groundwater management [e.g., Gorelick, 1983;Yeh, 1992;Ahlfeld and Heidari, 1994;Wagner, 1995;Bredehoeft et al, 1995;Freeze and Gorelick, 1999;Feyen and Gorelick, 2004] and in conjunctive use [e.g., Bredehoeft and Young, 1983;Matsukawa et al, 1992;Reichard, 1995;Rao et al, 2004;Vedula et al, 2005]. This paper builds on these previous simulation-optimization studies, and features three particular strengths: (1) A complex spatially distributed groundwater model is directly incorporated into the optimization procedure, (2) an efficient methodology is used for solving the resulting CPUintensive problem, i.e., using analytical Jacobians and a sequential solution procedure, and (3) the resulting integrated water management model is applied to a large-scale realworld problem in a developing country, generating insights that are also relevant to other irrigated systems.…”

Section: Introductionmentioning

confidence: 99%

Sustainable conjunctive water management in irrigated agriculture: Model formulation and application to the Yaqui Valley, Mexico

Schoups

Addams

Minjares³

et al. 2006

Water Resources Research

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[1] This paper investigates strategies to alleviate the effects of droughts on the profitability and sustainability of irrigated agriculture. These strategies include conjunctive management of surface water and groundwater resources, and engineered improvements such as lining of irrigation canals and addition of regional pumping well capacity. A spatially distributed simulation-optimization model was developed for an irrigated system consisting of multiple surface water reservoirs and an alluvial aquifer. The simulation model consists of an agronomic component and simulators describing the hydrologic system. The physical models account for storage and flow through the reservoirs, routing through the irrigation canals, and regional groundwater flow. The agronomic model describes crop productivity as a function of irrigation quantity and salinity, and determines agricultural profit. A profit maximization problem was formulated and solved using large-scale constrained gradient-based optimization. The model was applied to a real-world conjunctive surface water/groundwater management problem in the Yaqui Valley, an irrigated agricultural region in Sonora, Mexico. The model reproduces recorded reductions in agricultural production during a historical drought. These reductions were caused by a decline in surface water availability and limited installed pumping capacity. Results indicate that the impact of the historical 8-year drought could have been significantly reduced without affecting profit in wet years by better managing surface water and groundwater resources. Namely, groundwater could have been more heavily relied upon and surface water allocation capped at a sustainable level as an operating rule. Lining the irrigation canals would have resulted in water savings of 30% of historical reservoir releases during wet years, which could have been used in subsequent drier years to increase agricultural production. The benefits of a greater reliance on groundwater pumping by installing additional wells are limited due to pumping restrictions near the coast to avoid seawater intrusion and due to increased pumping costs.

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“…For this particularly difficult kind of problem, discrete optimization algorithms, such as dynamic programming, branch and bound, local search, and evolutionary algorithms (e.g., genetic algorithms), have been applied successfully. For sites in which complex hydrogeological conditions obscure an obvious intuitive design, simulation-optimization techniques help decision makers in shedding light over alternate feasible options (Ahlfeld and Heidari 1994).…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization for Sustainable Groundwater Management in Semiarid Regions

McPhee

Yeh

2004

J. Water Resour. Plann. Manage.

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Applications of Optimal Hydraulic Control to Ground‐Water Systems

Cited by 83 publications

References 27 publications

Developing a computationally efficient dynamic multilevel hybrid optimization scheme using multifidelity model interactions.

Developing a computationally efficient dynamic multilevel hybrid optimization scheme using multifidelity model interactions.

Sustainable conjunctive water management in irrigated agriculture: Model formulation and application to the Yaqui Valley, Mexico

Multiobjective Optimization for Sustainable Groundwater Management in Semiarid Regions

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