Discussion on Modeling
Stefan FinsterleEarth Sciences Division
Lawrence Berkeley National Laboratory
29. Task Force MeetingLund, Sweden
November 29-29 , 2012
Model DevelopmentProblem
Conceptual Model
Mathematical Model
Numerical Model
Verification
Calibration
Validation
Prediction
Abstraction
Quantification
Discretization
Analytical Solution
Data
Data
Modeling Success Criteria
• Captures salient features of system behavior (expert judgment)• Acceptable match
(goodness-of-fit criteria)• Acceptable estimation uncertainty
(determinant of estimation covariance matrix)• Ability to make acceptable predictions
(validation acceptance criteria)• Combination
(model identification criteria) Depends on study objectives Use as criteria for test design!
Overall Objectives Task 8• Joint effort between Task Forces
on:– Engineered Barrier Systems– Groundwater Flow and Transport• Focuses on: – interface between engineered and
natural systems– understanding of hydraulic
interaction between bentonite backfill and near-field host rock
– on scale of deposition hole– wetting of bentonite buffer– deposition hole characterization and
criteria development– interplay between model
development and site characterization data from field testing (BRIE) (test design and blind predictions)
In Patrick’s Words…
• scientific understanding of the exchange of water across the bentonite-rock interface
• better predictions of the wetting of the bentonite buffer
• better characterization methods of the canister boreholes
• better methods for establishing deposition hole criteria
Discussion on Key Features and Processes• Relative importance of:
– Features• bentonite or rock?• fractures or matrix?• geometry or properties?• random fractures vs.
deterministic features?
– Assumptions and conceptualizations• gap• Richards vs. two-phase
– Parameter values• Correlations• Impact of gap (closure)
– change in void ratio– capillary barrier effect
Discussion on Uncertainties
• How to quantify epistemic and aleatory uncertainties?
• How to model uncertainty and variability?
• Relation to experimental design and data needs?
• Role of calibration?
Discussion on Uncertainty Quantification• Which relevant
prediction is most uncertain?
• Which uncertain factor is responsible for high prediction uncertainty?
• Which data should be collected to reduce prediction uncertainty?
• How to do a formal UQ analysis?
Next Steps• Make models more stable• Refine models to include
more characterization data• Add deterministic structures• Calibration• Perform sensitivity analyses
for parameters, conceptual models, and scenarios
• Add coupled THMC processes
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