Solving iTOUGH2 simulation and optimization problems using the PEST protocol

被引:58
|
作者
Finsterle, Stefan [1 ]
Zhang, Yingqi [1 ]
机构
[1] Lawrence Berkeley Natl Lab, Div Earth Sci, Berkeley, CA 94720 USA
关键词
Optimization; Sensitivity analysis; Inverse modeling; Uncertainty quantification; iTOUGH2; PEST; UNSATURATED FLOW; AQUIFER PARAMETERS; MODEL; SEEPAGE; INVERSE; FIELD; ALGORITHM; TRANSIENT; SITE;
D O I
10.1016/j.envsoft.2011.02.008
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The PEST protocol has been implemented into the iTOUGH2 code, allowing the user to link any simulation program (with ASCII-based inputs and outputs) to iTOUGH2's sensitivity analysis, inverse modeling, and uncertainty quantification capabilities. These application models can be pre- or post-processors of the TOUGH2 non-isothermal multiphase flow and transport simulator, or programs that are unrelated to the TOUGH suite of codes. PEST-style template and instruction files are used, respectively, to pass input parameters updated by the iTOUGH2 optimization routines to the model, and to retrieve the model-calculated values that correspond to observable variables. We summarize the iTOUGH2 capabilities and demonstrate the flexibility added by the PEST protocol for the solution of a variety of simulation optimization problems. In particular, the combination of loosely coupled and tightly integrated simulation and optimization routines provides both the flexibility and control needed to solve challenging inversion problems for the analysis of multiphase subsurface flow and transport systems. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:959 / 968
页数:10
相关论文
共 50 条
  • [41] SOLVING DISTRIBUTED CONSTRAINT OPTIMIZATION PROBLEMS USING ANT COLONY OPTIMIZATION
    Yang Xiaolei
    Yuan Xiujiu
    Feng Youqian
    Zhao Xuejun
    JOURNAL OF THE BALKAN TRIBOLOGICAL ASSOCIATION, 2016, 22 (03): : 2931 - 2941
  • [42] Merging crow search into ordinal optimization for solving equality constrained simulation optimization problems
    Horng, Shih-Cheng
    Lin, Shieh-Shing
    JOURNAL OF COMPUTATIONAL SCIENCE, 2017, 23 : 44 - 57
  • [43] Simulation of an analogue circuit solving NP-hard optimization problems
    Babicz, Dora
    Tihanyi, Attila
    Koller, Miklos
    Rekeczky, Csaba
    Horvath, Andras
    2019 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), 2019,
  • [44] Solving Strategic Military Workforce Planning Problems with Simulation-Optimization
    Turan, Hasan Huseyin
    Elsawah, Sondoss
    Jalalvand, Fatemeh
    Ryan, Michael J.
    2020 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), 2020, : 1620 - 1625
  • [45] 2 ALGORITHMS FOR SOLVING VECTOR-OPTIMIZATION PROBLEMS
    WILHELM, J
    FANDEL, G
    AUTOMATION AND REMOTE CONTROL, 1976, 37 (11) : 1721 - 1727
  • [46] Solving constrained optimization problems using a novel genetic algorithm
    Tsoulos, Ioannis G.
    APPLIED MATHEMATICS AND COMPUTATION, 2009, 208 (01) : 273 - 283
  • [47] Using Long and Short Term Memories in Solving Optimization Problems
    Nagamatu, Masahiro
    Weerasinghe, Jagath
    NEURAL INFORMATION PROCESSING, PT 2, PROCEEDINGS, 2009, 5864 : 457 - 464
  • [48] Solving multiobjective optimization problems using an artificial immune system
    Coello C.A.C.
    Cortés N.C.
    Genetic Programming and Evolvable Machines, 2005, 6 (2) : 163 - 190
  • [49] Solving the Energy Management Problems Using Thermal Exchange Optimization
    Djeblahi, Zahia
    Mahdad, Belkacem
    Srairi, Kamel
    ELECTRICA, 2024, 24 (01): : 67 - 86
  • [50] Solving Optimization Problems in Nimrod/OK using a Genetic Algorithm
    Lim, Yu Hua
    Tan, Jefferson
    Abramson, David
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, ICCS 2012, 2012, 9 : 1647 - 1656