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Comprehensive Optimal ManagementComprehensive Optimal ManagementComprehensive Optimal Management Planning of Integrated Aquifer and Surface Water Resource Systems
Comprehensive Optimal Management Planning of Integrated Aquifer and Surface Water Resource SystemsWater Resource SystemsWater Resource Systems
Larry Deschaine (HydroGeologic), Varut Guvanasen (HydroGeologic), Don DeMarco (HydroGeologic), Xinyu Wei (HydroGeologic), Janos D Pinter (Ozyegin University), Kirk Nelson y), (USBR), George Matanga (USBR)
Larry Deschaine (HydroGeologic), Varut Guvanasen (HydroGeologic), Don DeMarco (HydroGeologic), Xinyu Wei (HydroGeologic), Janos D Pinter (Ozyegin University), Kirk Nelson y), (USBR), George Matanga (USBR)(USBR)
March 1, 2011 CWEMF Conference
(USBR)
March 1, 2011 CWEMF Conference
011 Overview of Project TasksOverview of Project Tasks
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Task 1 – Literature Review: Task 1 – Literature Review: Task 1 Literature Review: Perform literature review of existing water-allocation models and methodologies for linking
hydrologic and water-allocation. Formulate the methodology that will be implemented for linking CALSIM and HGS.
Task 2 – Code Modification for CALSIM-HGS Linkage:
Task 1 Literature Review: Perform literature review of existing water-allocation models and methodologies for linking
hydrologic and water-allocation. Formulate the methodology that will be implemented for linking CALSIM and HGS.
Task 2 – Code Modification for CALSIM-HGS Linkage:g Modify code in CALSIM and HGS to facilitate implementation of the linkage methodology
formulated in Task 1. Task 3 – Verification and Validation of CALSIM-HGS Linked Model:
Separate and combined CALSIM and HGS modules will be verified against field data. The
g Modify code in CALSIM and HGS to facilitate implementation of the linkage methodology
formulated in Task 1. Task 3 – Verification and Validation of CALSIM-HGS Linked Model:
Separate and combined CALSIM and HGS modules will be verified against field data. The verified CALSIM-HGS model shall be validated against a management scenario undertaken in the past. If a past management scenario is not available, a sensitivity analysis based on possible management scenarios shall be performed.
Task 4 – Collaborative Work in Construction of Historical and Prediction of Future Meteorological Data for Input into the Linked CALSIM-HGS Model
verified CALSIM-HGS model shall be validated against a management scenario undertaken in the past. If a past management scenario is not available, a sensitivity analysis based on possible management scenarios shall be performed.
Task 4 – Collaborative Work in Construction of Historical and Prediction of Future Meteorological Data for Input into the Linked CALSIM-HGS ModelMeteorological Data for Input into the Linked CALSIM-HGS Model This proposed Task 4 shall be performed during the year (October 2008 to September, 2009) in
the northern sector (Sacramento River Valley) of the Central Valley of California.
Meteorological Data for Input into the Linked CALSIM-HGS Model This proposed Task 4 shall be performed during the year (October 2008 to September, 2009) in
the northern sector (Sacramento River Valley) of the Central Valley of California.
2CGS
Main ContentMain Content
Formulation of the linked simulation – optimization methodology
Formulation of the linked simulation – optimization methodology
Development of the optimization toolbox Development of the linkage utilities Development of the optimization toolbox Development of the linkage utilities Development of the testing cases Development of the Graphical User Interface (GUI) Development of the testing cases Development of the Graphical User Interface (GUI)p p ( )p p ( )
3
Geospatial RepresentationGeospatial Representationp pp p
CalSim configurationCalSim configuration Geographical mappingGeographical mapping
Shasta Lake
Trinity Lake
Lewiston Lake
Keswick R i
Whiskeytown Lake
Reservoir
Clear CreekCreek
Communication Point
Decision Point
4
Temporal RepresentationTemporal Representationp pp p
When a decision is affected by the formal time step condition and When a decision is affected by the formal time step condition and
Current Past Past & Future
y pit will influence the future, how should we formulate the transient optimization problem?
y pit will influence the future, how should we formulate the transient optimization problem?
HGS
Current Past Past & Future
Interface
Optimization
Decisions
t t t
5
Optimization ToolboxOptimization Toolboxpp
Linear SolverLP Solve
Linear SolverLP SolveSequential Linear Programming
Nonlinear Solver - LGOSequential Linear Programming
Nonlinear Solver - LGOLinear ProgrammingBranch-and-bound global search
method (BB)Global adaptive random search (GARS)
Linear ProgrammingBranch-and-bound global search method (BB)
Global adaptive random search (GARS)Global adaptive random search (GARS)Multi-start based global random search (MS)Constrained local search (LS) by the reduced gradient
th d
Global adaptive random search (GARS)Multi-start based global random search (MS)Constrained local search (LS) by the reduced gradient
th d
6
methodmethod
011
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LGO Optimization Capabilities LGO Optimization Capabilities
LGO will easily handle complex mixed integer, non-liner programming (MINLP) problems
LGO will easily handle complex mixed integer, non-liner programming (MINLP) problemsliner programming (MINLP) problems100+ integer variablesThousands of continuous variables
liner programming (MINLP) problems100+ integer variablesThousands of continuous variablesNo assumption of uni-modality, convexity, concavity nor is it
restricted to just mildly non-linear problemD i d f l h i b d d l ith
No assumption of uni-modality, convexity, concavity nor is it restricted to just mildly non-linear problem
D i d f l h i b d d l ithDesigned for use on complex physics-based models with long run times
LGO constraint handling – penalty multiplier
Designed for use on complex physics-based models with long run times
LGO constraint handling – penalty multiplier
7CGS
g p y p Adjust the multiplier to handle both hard and soft constraints
g p y p Adjust the multiplier to handle both hard and soft constraints
7
011 Illustration of LGOIllustration of LGO
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ObjectiveSt ti P i t
jStarting Points
Removed e o edSolution Space
Decision Variable
Solution
8CGS
Solution
011
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HGL-Optimization Linkage Schematic PlotsHGL-Optimization Linkage Schematic Plots
Linear Problems100 i i bl
Linear Problems100 i i bl100+ integer variables
Nonlinear Problems Adjust the multiplier to handle both hard and soft constraints
100+ integer variablesNonlinear Problems
Adjust the multiplier to handle both hard and soft constraints Adjust the multiplier to handle both hard and soft constraints Adjust the multiplier to handle both hard and soft constraints
9CGS9
WrapperInitialize LoopControl file providing
information to each stepLinearHGS -
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Wrapper Control file
Create Input Files Create Input Files Create Input Files
Base Forward Perturbation Backward Perturbation (Optional)
HGS LPsolve
Run Model Run Model Run Model
Extract SimulatedValue
Extract SimulatedValue
Extract SimulatedValue
Sensitivity Matrix(Jacobian) Predictive Results
HGS-Sensi
Linkage with LP_solve
HGS Sensi
Sensi-
LP Control andformulation file
10CGS Optimal Solution
Run LP_Solve
Update WrapperControl File If Final Time Step?No Yes
SensiLPsolve
Nonlinear – HGS-LGO linkage
LGO Create New Decision Variables, Call Wrapper
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Create Input Files from3
Wrapper C eate put es o
LGO Output Format
Run Model LGO –
Extract SimulatedValue
If Optimal?
Optimization Process
NoEvaluate Objective
Functions and ConstraintsWrite to LGO input File
If Optimal? Or If Stopping
Criteria Satisfied?
Yes
11CGS Optimal Solution
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Linkage Utilities - JUPITER APILinkage Utilities - JUPITER APID
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An Application Programming Interface
An Application Programming InterfaceProgramming Interface (API) to facilitate building of model-analysis software
Programming Interface (API) to facilitate building of model-analysis softwareof model analysis software.
Current version is 1.3.1 (8/17/2009)
of model analysis software.Current version is 1.3.1
(8/17/2009)(8/17/2009)Coded in FORTRAN-90
(8/17/2009)Coded in FORTRAN-90
12CGS
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Introduction to JUPITER APIIntroduction to JUPITER APID
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Authors Edward R. Banta, U.S. Geological Survey, Lakewood, Colorado, USA Eileen P. Poeter, International Ground Water Modeling Center of the Colorado
School of Mines Golden Colorado USA
Authors Edward R. Banta, U.S. Geological Survey, Lakewood, Colorado, USA Eileen P. Poeter, International Ground Water Modeling Center of the Colorado
School of Mines Golden Colorado USASchool of Mines, Golden, Colorado, USA John E. Doherty, University of Queensland, Brisbane, and Watermark Numerical
Computing, Corinda, Queensland, Australia Mary C. Hill, U.S. Geological Survey, Boulder, Colorado, USA
A li ti B ilt ith JUPITER API
School of Mines, Golden, Colorado, USA John E. Doherty, University of Queensland, Brisbane, and Watermark Numerical
Computing, Corinda, Queensland, Australia Mary C. Hill, U.S. Geological Survey, Boulder, Colorado, USA
A li ti B ilt ith JUPITER API Applications Built with JUPITER API UCODE_2005 — UCODE_2005 and six post-processors are included. These
programs can be used with existing process models to perform sensitivity analysis, data needs assessment, calibration, prediction, and uncertainty analysis. OPR PPR A f i d i d l di i
Applications Built with JUPITER API UCODE_2005 — UCODE_2005 and six post-processors are included. These
programs can be used with existing process models to perform sensitivity analysis, data needs assessment, calibration, prediction, and uncertainty analysis. OPR PPR A f i d i d l di i OPR-PPR — A computer program for assessing data importance to model predictions using linear statistics
A widely used parameter estimation software PEST (SSPA) uses similar concept, approach, structure and conventions.
OPR-PPR — A computer program for assessing data importance to model predictions using linear statistics
A widely used parameter estimation software PEST (SSPA) uses similar concept, approach, structure and conventions.
13CGS
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Model Analysis Applications by JUPITER APIModel Analysis Applications by JUPITER API
Dec
em JUPITER APIJUPITER API
Sensitivity analysisSensitivity analysisCalibrationData assessmentCalibrationData assessmentEvaluating alternative modelsUncertainty evaluationEvaluating alternative modelsUncertainty evaluationUncertainty evaluationOptimizationUncertainty evaluationOptimization
14CGS
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Capabilities and Limitations of JUPITER APICapabilities and Limitations of JUPITER API
Dec
em JUPITER-APIJUPITER-API
Flexible communications with process models –Flexible communications with process models –(requires ascii format of input/output)
Parallel computations – (requires network (requires ascii format of input/output)
Parallel computations – (requires network p ( qread/write access between computers)
Compressed storage of matrices
p ( qread/write access between computers)
Compressed storage of matricesCompressed storage of matricesCompressed storage of matrices
15CGS
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Main Modules in JUPITER APIMain Modules in JUPITER API
Dec
em
Name Function LevelGDT Gl b l D t M d l IGDT Global Data Module ITYP Data Type Module IUTL Utilities Module IBAS Basic Module IIMIO Model Input-Output Module IIEQN I l E i M d l IIEQN Internal Equation Module IIDEP Dependents Module IIPRI Prior Information Module IIPLL Parallel Processing IISEN Sensitivity Module III
16CGS
STA Statistics Module IIICustomized Modules, Higher Level Applications II, III, IV
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Update Input - Using Template FilesUpdate Input - Using Template Files
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em
A template file is needed for an input file that A template file is needed for an input file that contain parameters manipulated by Jupiter
The template file .tpl is a replica of a input file contain parameters manipulated by Jupiter
The template file .tpl is a replica of a input file p p p pexcept that spaces occupied by parameters are replaced by a specific character string
p p p pexcept that spaces occupied by parameters are replaced by a specific character stringp y p g
N input files manipulated need N template filesp y p g
N input files manipulated need N template files
17CGS
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Example Input file: WEL.tplExample Input file: WEL.tplD
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p p pp p p
Template file:Template file: Actual WEL file:
Begin with jtf+space+parameter delimiter
Parameter delimiter: ~ is the parameter delimiter in this template file between two s is the parameter
Begin with jtf+space+parameter delimiter
Parameter delimiter: ~ is the parameter delimiter in this template file between two s is the parameter
3 0 0 0 022 6 9 -500.0 0 02 10 9 -900.0 0 0
file, between two ~s is the parameter space, instead of using ~, you can use #, @, !, etc.(not letters, not numbers)
Parameter space: wider parameter
file, between two ~s is the parameter space, instead of using ~, you can use #, @, !, etc.(not letters, not numbers)
Parameter space: wider parameter
-1-1-1
p pspace allows higher precision
When dealing with free format input files, remember to leave space, or comma between the parameter
p pspace allows higher precision
When dealing with free format input files, remember to leave space, or comma between the parameter
Template File:
jtf ~3 0 0 0 02comma between the parameter
delimiter and adjacent parameterscomma between the parameter delimiter and adjacent parameters
22 6 9~ p1 ~ 0 02 10 9~ p2 ~ 0 0 -1-1
18CGS
-1
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Read Output – Using Instruction FileRead Output – Using Instruction FileD
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p gp g
An instruction file is needed for an output file that i d d ( b i ) i d b
An instruction file is needed for an output file that i d d ( b i ) i d bcontain dependents (observations) interested by
JupiterN fil i i d d d N
contain dependents (observations) interested by JupiterN fil i i d d d NN output files containing dependents need N instruction filesT h i fil
N output files containing dependents need N instruction filesT h i filTwo ways to construct the .ins file:
Instruction set optionTwo ways to construct the .ins file:
Instruction set option
19CGS
Standard file optionStandard file option
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Example – Standard fileExample – Standard fileD
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pp
Jif @
Begin with jif+space+marker delimiter
Begin with jif+space+marker delimiter
@
StandardFile 0 1 3
Obs1
Marker delimiter: defines the beginning and ending of a marker, can not be letters, numbers any brackets ! : &;
Marker delimiter: defines the beginning and ending of a marker, can not be letters, numbers any brackets ! : &;
Obs2
Obs3
numbers, any brackets, !, :, &; recommend to use @, $, ~, #, %
Marker: a search string for locating the desired information
numbers, any brackets, !, :, &; recommend to use @, $, ~, #, %
Marker: a search string for locating the desired information
Jif marker delimiter(not used for standard, but needs to be defined)
StandardFile Nskip (lines to skip)locating the desired information in an output file
Only search from top to bottom, left to right can not reverse
locating the desired information in an output file
Only search from top to bottom, left to right can not reverse
StandardFile Nskip (lines to skip)Readcolumn (white space delimited column in a line) nread (number of observations to be read)
20CGS
left to right, can not reverseleft to right, can not reverseDependent names (correspond to the control file)
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Example – Using Instruction File to Read An HGS Observation Output FileExample – Using Instruction File to Read An HGS Observation Output File
Dec
em Read An HGS Observation Output FileRead An HGS Observation Output File
21CGS
HGS-Optimization Linkage Test CasesHGS-Optimization Linkage Test Casesp gp g
Linear ProblemDewater in a confined groundwater aquifer – adapted from
Linear ProblemDewater in a confined groundwater aquifer – adapted from g q p
the Sample problem 1 in MODFLOW2000-GWM manual (USGS, 2005)
Nonlinear Problem
g q pthe Sample problem 1 in MODFLOW2000-GWM manual (USGS, 2005)
Nonlinear ProblemNonlinear ProblemRedding basin integrated surface / groundwater model –
model created based on the central valley model but this is a h h i l
Nonlinear ProblemRedding basin integrated surface / groundwater model –
model created based on the central valley model but this is a h h i lhypothetical casehypothetical case
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011 Case 1: Dewater ProblemCase 1: Dewater Problem
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Objective: Objective: Objective: Minimizing total pumping rate
Decision variables
Objective: Minimizing total pumping rate
Decision variablesPumping rate at 7
wells Constraints: Head at * locations less
Pumping rate at 7 wells
Constraints: Head at * locations lessHead at * locations less
than 50ft (15.24m)All pumping rates > 0
Head at * locations less than 50ft (15.24m)
All pumping rates > 0
23CGS
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Standard Linear Programming FormulationStandard Linear Programming Formulation
Dec
em FormulationFormulation
Objective: Objective: nn xcxcxcz ...min 2211 “Minimizing” objective
Subject toSubject to
nn RHSxaxaxa 111212111 ...
objective
“Less than”
mnmnmm RHSxaxaxa 12211 ...
......
and
constraints
0...1 nxxand
“Non-negative” decision variables
24CGS
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Linear Programming Formulation of the Dewatering ProblemLinear Programming Formulation of the Dewatering Problem
Dec
em of the Dewatering Problemof the Dewatering Problem
Objective:Objective: npppz ...min 21Objective: Subject toObjective: Subject to
nppp 21
n
iiinn qeHHqeqeqe
1010111212111 ...
n
iiinn qeHHqeqeqe
1010111212111 ...
......
0...1 nqqand
Where, eij = (-1)*response matrix coefficientWhere, eij ( 1) response matrix coefficient
qi = pumping rate (positive means pumping)
Hj =simulated heads at observation locations
25CGS
q0i = base-case pumping rate
H0j = base-case simulated heads at observation locations
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Model Output from LP SolveModel Output from LP SolveD
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pp
26CGS
011
GWM solutionHGS-Lpsolve HGS-LGO HGS-LGO HGS-LGO HGS-LGO
Min = 1000 Min = 0 Min = 0Use HGS-Lpsolve Results as starting
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OptTime 300 sec 600 sec 600 sec 600 sec
Opt mode MS+LS MS+LS LS LS
P1 11144 11863 4020.999 4020.999 4020.999 12091.81
P2 809 424 3761 875 3761 875 3761 875 0P2 809 424 3761.875 3761.875 3761.875 0
P3 0 0 4155.006 4155.006 4155.006 0
P4 7954 5327 3861.316 3861.316 3861.316 6353.816
P5 0 0 4068.939 4068.939 4068.939 0
6 0 0 3865 115 3865 115 3865 115 0p6 0 0 3865.115 3865.115 3865.115 0
p7 9734 9610 5490.252 5490.252 5490.252 9720.567
Obj=Total Pump 29641 27224 29223.5 29223.5 29223.5 28166.2
Penalized Obj 32891 28166.2
Constraints <=15.24
h1 14.68916 13.50109
h2 14.68916 13.50109
h3 15.3484 14.20763
h4 14.83316 13.54729
h5 15.10641 13.9395
h6 15.53084 14.24998
h7 14.79638 13.52235
27CGS
h7 14.79638 13.52235
h8 14.92268 13.70699
h9 15.10058 13.92421
h10 15.40749 14.24474
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OPT-Results ComparisonOPT-Results ComparisonD
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MODFLOW-GWM HGS-LP_solveWell 1 11144 11863Well 1 11144 11863
Well 2 809 424
Well 3 0 0
Well 4 7954 5327
Well 5 0 0
Well 6 0 0
Well 7 9734 9610
28CGS
Case 2: Reservoir Discharge to Redding BasinCase 2: Reservoir Discharge to Redding Basin
011
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Original HydroGeosphere Central Valley Model
Trimmed and Refined for the Redding Basin
4.5E+064.5E+06
Y
4.46E+06
4.48E+06
Y
4.46E+06
4.48E+06
4.44E+064.44E+06
29CGS
X540000 560000 580000 600000
X540000 560000 580000 600000
011 Problem FormulationProblem Formulation
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DV1: Q2•5 Days with storm•5 Days with storm event at day 1
•Objective:Min[(Q1+Q2)+Penalty]
•Constraints:Reservior storage
DV2: Q1
S(t) = S(t-1) + Inflow –Q1 – Q2 Smin<S(t)<Smax, t=2,4
River MaxFL and MFLMin(Qr(t))> QminMax(Qr(t)) < Qmax
30CGS
Constraint: Qmin<Qr<Qmax
Problem ConceptualizationDecision Variables (Control)
Problem ConceptualizationDecision Variables (Control)01
1
- Decision Variables (Control)- Decision Variables (Control)
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To reduce the # of control variables, assuming the same discharge rates over the 4 days
To reduce the # of control variables, assuming the same discharge rates over the 4 days
Lake discharge to Clear Creek Q1(t)Q1(0)=Q1(1), …. =Q1(4)
L k di h t K i k D
Lake discharge to Clear Creek Q1(t)Q1(0)=Q1(1), …. =Q1(4)
L k di h t K i k D Lake discharge to Keswick DamQ2(0)=Q2(1), …. Q2(4)
Initial storage in Whiskeytown Lake
Lake discharge to Keswick DamQ2(0)=Q2(1), …. Q2(4)
Initial storage in Whiskeytown Lake Initial storage in Whiskeytown LakeS(0)
Total 3 decision variables
Initial storage in Whiskeytown LakeS(0)
Total 3 decision variables
31CGS
ConstraintsConstraints01
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Evaluated without Hydrogeosphere simulationL k St S(1) S(2) S(3) S(4)
Evaluated without Hydrogeosphere simulationL k St S(1) S(2) S(3) S(4)Lake Storage: S(1), S(2), S(3), S(4)
Evaluated with Hydrogeosphere simulation
Lake Storage: S(1), S(2), S(3), S(4)
Evaluated with Hydrogeosphere simulationEvaluated with Hydrogeosphere simulationAt clear creek min(Qr,1~4days) >= MFLAt clear creek max(Qr,1~4days) <=MaxFL
Evaluated with Hydrogeosphere simulationAt clear creek min(Qr,1~4days) >= MFLAt clear creek max(Qr,1~4days) <=MaxFL
32CGS
Problem FormulationProblem Formulation01
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Step1: Assuming linear response, using the reservoir module without considering model simulated stream flow (likely to be
Step1: Assuming linear response, using the reservoir module without considering model simulated stream flow (likely to bewithout considering model simulated stream flow (likely to be a nonlinear response).
Step 2: Use the results from step1 as starting condition, feed
without considering model simulated stream flow (likely to be a nonlinear response).
Step 2: Use the results from step1 as starting condition, feed p p g ,that to the simulation-optimization module.
p p g ,that to the simulation-optimization module.
33CGS
Model SolutionModel Solution01
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Starts unfeasible, ends with feasible solution Starts unfeasible, ends with feasible solution
700000 00
800000.00
Objective Function vs Interation Numbers
400000.00
500000.00
600000.00
700000.00
0.00
100000.00
200000.00
300000.00
34CGS
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Objective Function Value
Model SolutionModel Solution01
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Starts unfeasible, ends with improved but unfeasible solution
Starts unfeasible, ends with improved but unfeasible solution
Objective
20000003000000400000050000006000000
Objective
35CGS
010000002000000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Issues & Challenges with Global OptimizationIssues & Challenges with Global Optimization
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LGO rule of thumb for the number of function evaluations:
LGO rule of thumb for the number of function evaluations:
Global search: 1000*(nvars+ncons+1) or 1000*(3+6+1) or 10000
Global search: 1000*(nvars+ncons+1) or 1000*(3+6+1) or 100001000 (3+6+1), or 10000
Local search mode: (nvars+ncons+1)**2 or (3+6+1)**2 or 100
1000 (3+6+1), or 10000Local search mode: (nvars+ncons+1)**2 or
(3+6+1)**2 or 100(3+6+1)**2 or 100For a total of ~10,000
(3+6+1)**2 or 100For a total of ~10,000
36CGS
Proposed SolutionProposed Solution01
1 pp
i h f i fil f hi h i hi h fid lii h f i fil f hi h i hi h fid li
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Write out the ML function file from which to write a high fidelity approximation of the response surface (which executes in milliseconds) and plug that back into the model. A data set with an
Write out the ML function file from which to write a high fidelity approximation of the response surface (which executes in milliseconds) and plug that back into the model. A data set with an ) p gaspect ratio of 10:1, so for 20 variables, make 200 runs.
Developing a design of experiment (DoE) approach where we tl h th 200 ( i LGO)
) p gaspect ratio of 10:1, so for 20 variables, make 200 runs.
Developing a design of experiment (DoE) approach where we tl h th 200 ( i LGO)smartly chose the 200 runs (using LGO)
Developing a “hot-start” option, so we can read in the existing solutions to initialize the LGO array, and;
smartly chose the 200 runs (using LGO) Developing a “hot-start” option, so we can read in the existing
solutions to initialize the LGO array, and;y, ; A parallel run mode for multi-core machines.
y, ; A parallel run mode for multi-core machines.
37CGS
011 Current Development -1Current Development -1
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pp
A sensitivity/response module for user to evaluate which A sensitivity/response module for user to evaluate which A sensitivity/response module for user to evaluate which optimizer to use: i.e. linear or nonlinear; , and range of Decision variables etc
A sensitivity/response module for user to evaluate which optimizer to use: i.e. linear or nonlinear; , and range of Decision variables etc
Initial sampling procedure: A random, or some form of such as design of experiment, etc.
Initial sampling procedure: A random, or some form of such as design of experiment, etc.
Generation module for providing user a feeling about how objectives/constraints response to the change in decision
Generation module for providing user a feeling about how objectives/constraints response to the change in decision variables – i.e. the response surface, also a starting condition for the nonlinear optimizervariables – i.e. the response surface, also a starting condition for the nonlinear optimizer
38CGS
Current Development -2Current Development -201
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An LP module to evaluate linear or slightly An LP module to evaluate linear or slightly nonlinear problems
A SLP module with iterative procedures to handle nonlinear problems
A SLP module with iterative procedures to handle pmild nonlinear problems
pmild nonlinear problems
39CGS
Current Development - 3Current Development - 301
1 pp3/
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An nonlinear module with efficient global search An nonlinear module with efficient global search procedures to handle nonlinear problems, such as LGO for handling nonlinear problemprocedures to handle nonlinear problems, such as LGO for handling nonlinear problem
Optimizer for integer programming: on/off condition
Optimizer for integer programming: on/off condition
40CGS
Current Development - 4Current Development - 401
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Using trained responses/machine-learning type of Using trained responses/machine-learning type of technique to create a fast executing high fidelity approximation of the physics model to serve as a technique to create a fast executing high fidelity approximation of the physics model to serve as a surrogate to replace the expensive function calls to the comprehensive physical model, or while surrogate to replace the expensive function calls to the comprehensive physical model, or while iteratively working with it to reduce computation burden.iteratively working with it to reduce computation burden.
41CGS
011
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HGS-OPT Graphical User InterfaceHGS-OPT Graphical User InterfaceHGS-OPT Graphical User InterfaceHGS-OPT Graphical User Interface
•To streamline the process•To improve usability•To streamline the process•To improve usabilityTo improve usability•Developed in C#To improve usability
•Developed in C#
42CGS
011
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011
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011
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011
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011
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011
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011
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011
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011
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63CGS
Future DevelopmentFuture Development01
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Build more linkage components using Jupiter API and h l f ili h i i b
Build more linkage components using Jupiter API and h l f ili h i i bother tools to facilitate the communication between
Hydrogeosphere and the optimization modulesD l GUI it t t li R d t t
other tools to facilitate the communication between Hydrogeosphere and the optimization modulesD l GUI it t t li R d t t Develop GUI, use it to streamline Random start, Sensitivity, LP, SLP, Nonlinear OPT processes
Using trained responses/machine learning t pe of
Develop GUI, use it to streamline Random start, Sensitivity, LP, SLP, Nonlinear OPT processes
Using trained responses/machine learning t pe of Using trained responses/machine-learning type of technique
Using trained responses/machine-learning type of technique
64CGS
011
3/17
/20
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