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

Groundwater EBSdisscussion

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

Discussion of Proposed Next Steps

• Are the proposed next steps rigorously justified by the analyses done so far?

• Which objectives will be addressed by the proposed next steps?

• Do the proposed analyses reduce prediction uncertainty?

• Can next steps be prioritized?