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The design of simulation/optimizatio...

Kalwij, Ineke Margot.

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The design of simulation/optimization modeling techniques for nonlinear dynamic groundwater systems.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	The design of simulation/optimization modeling techniques for nonlinear dynamic groundwater systems./
Author:	Kalwij, Ineke Margot.
Description:	148 p.
Notes:	Major Professor: Richard C. Peralta.
Contained By:	Dissertation Abstracts International66-04B.
Subject:	Engineering, Agricultural. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3170296
ISBN:	9780542069932

The design of simulation/optimization modeling techniques for nonlinear dynamic groundwater systems.
Kalwij, Ineke Margot.

The design of simulation/optimization modeling techniques for nonlinear dynamic groundwater systems. - 148 p.

Major Professor: Richard C. Peralta.

Thesis (Ph.D.)--Utah State University, 2004.

Three new simulation/optimization (S/O) modeling techniques are developed and tested: (i) the Intelligent Space Tube Optimizer (ISTO); (ii) the Global Converger (GC); and (iii) the Robustness Enhancing Optimizer (REO). The main objective of developing the ISTO and the GC is to improve optimization efficiency for large and complex transport optimization problems, and the main objective of developing the REO is to develop robust optimal pumping strategy designs.

ISBN: 9780542069932Subjects--Topical Terms:

1019504
Engineering, Agricultural.

The design of simulation/optimization modeling techniques for nonlinear dynamic groundwater systems.
LDR:03385nam 2200313 a 45 001 971801
005 20110927
008 110927s2004 eng d
020 $a 9780542069932
035 $a (UnM)AAI3170296
035 $a AAI3170296
040 $a UnM $c UnM
100 1 $a Kalwij, Ineke Margot. $3 1295830
245 1 4 $a The design of simulation/optimization modeling techniques for nonlinear dynamic groundwater systems.
300 $a 148 p.
500 $a Major Professor: Richard C. Peralta.
500 $a Source: Dissertation Abstracts International, Volume: 66-04, Section: B, page: 2250.
502 $a Thesis (Ph.D.)--Utah State University, 2004.
520 $a Three new simulation/optimization (S/O) modeling techniques are developed and tested: (i) the Intelligent Space Tube Optimizer (ISTO); (ii) the Global Converger (GC); and (iii) the Robustness Enhancing Optimizer (REO). The main objective of developing the ISTO and the GC is to improve optimization efficiency for large and complex transport optimization problems, and the main objective of developing the REO is to develop robust optimal pumping strategy designs.
520 $a ISTO includes multiple cycles and for each cycle the multi-dimensional space tube is defined, strategies are generated, and genetic algorithm (GA) optimization is performed and calls trained artificial neural networks (ANNs) as a substitute simulator. A space tube consists of overlapping multi-dimensional subspaces, and lengthens in the direction of the optimal solution. Its radius is automatically adjusted between cycles based on optimization performance. Application to an assumed problem demonstrates that the ISTO generally converges within 0.5% of the globally optimal solution. However, specifying an inappropriate initial space tube radius or too few simulations for ANN training degrades performance. Further, there is a trade-off between computational time and desired degree of convergence. For the Blain Naval Ammunition Depot (NAD), ISTO develops optimal pumping strategies generally more efficiently than a hybrid heuristic optimization approach, which uses GA and tabu seach (TS).
520 $a The GC uses GA and TS, and dynamically adapts TS parameters affecting solution space size, search coarseness, TS probability, and tabu list size, based on optimization performance. Applying the CG to a hypothetical problem shows that the GC yields better results within 1000 simulations than GA-TS and GA alone. Applying the GC to NAD demonstrates that the GC is more efficient than the GA-TS or GA alone for solving a large-scale and complex transport optimization problem (NAD).
520 $a The REO couples GA and TS as optimizers and incorporates aquifer parameter sensitivity analysis to guide multiple-realization optimization. This combined approach maximizes strategy robustness for a pumping strategy that is optimal for the objective function. The REO is applied to develop robust least cost strategies for the Umatilla Chemical Depot model. Results show that the REO efficiently develops robust optimal pumping strategies while maintaining least cost values.
590 $a School code: 0241.
650 4 $a Engineering, Agricultural. $3 1019504
650 4 $a Engineering, Environmental. $3 783782
690 $a 0539
690 $a 0775
710 2 0 $a Utah State University. $3 960049
773 0 $t Dissertation Abstracts International $g 66-04B.
790 $a 0241
790 1 0 $a Peralta, Richard C., $e advisor
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3170296