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

3/17

/20

jj

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

3/17

/20

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

3/17

/20

ObjectiveSt ti P i t

jStarting Points

Removed e o edSolution Space

Decision Variable

Solution

8CGS

Solution

011

3/17

/20

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 -

3/17

/201

1

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

3/17

/201

1

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

mbe

r 22,

200

9

Linkage Utilities - JUPITER APILinkage Utilities - JUPITER APID

ecem

gg

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

mbe

r 22,

200

9

Introduction to JUPITER APIIntroduction to JUPITER APID

ecem

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

mbe

r 22,

200

9

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

mbe

r 22,

200

9

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

mbe

r 22,

200

9

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

mbe

r 22,

200

9

Update Input - Using Template FilesUpdate Input - Using Template Files

Dec

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

mbe

r 22,

200

9

Example Input file: WEL.tplExample Input file: WEL.tplD

ecem

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

mbe

r 22,

200

9

Read Output – Using Instruction FileRead Output – Using Instruction FileD

ecem

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

mbe

r 22,

200

9

Example – Standard fileExample – Standard fileD

ecem

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)

mbe

r 22,

200

9

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

22

011 Case 1: Dewater ProblemCase 1: Dewater Problem

3/17

/20

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

mbe

r 22,

200

9

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

mbe

r 22,

200

9

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

mbe

r 22,

200

9

Model Output from LP SolveModel Output from LP SolveD

ecem

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

3/17

/20 g

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

mbe

r 22,

200

9

OPT-Results ComparisonOPT-Results ComparisonD

ecem

pp

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

3/17

/20

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

3/17

/20

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)

3/17

/20

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

13/

17/2

0

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

13/

17/2

0

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

13/

17/2

0

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

13/

17/2

0

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

011

3/17

/20

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

3/17

/20

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

3/17

/20

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

1 pp3/

17/2

0

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/

17/2

0

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

1 pp3/

17/2

0

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

3/17

/20

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

3/17

/20

52CGS

011

3/17

/20

54CGS

011

3/17

/20

57CGS

011

3/17

/20

58CGS

011

3/17

/20

59CGS

011

3/17

/20

60CGS

011

3/17

/20

61CGS

011

3/17

/20

62CGS

011

3/17

/20

63CGS

Future DevelopmentFuture Development01

1 pp3/

17/2

0

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

THANK YOU !THANK YOU !THANK YOU !THANK YOU !

FeedbacksQuestionsFeedbacksQuestionsQuestions

RecommendationsQuestions

Recommendations

65CGS