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Solution Verification Linked to Model Validation, Reliability, and Confidence

Description: The concepts of Verification and Validation (V&V) can be oversimplified in a succinct manner by saying that 'verification is doing things right' and 'validation is doing the right thing'. In the world of the Finite Element Method (FEM) and computational analysis, it is sometimes said that 'verification means solving the equations right' and 'validation means solving the right equations'. In other words, if one intends to give an answer to the equation '2+2=', then one must run the resulting code to assure that the answer '4' results. However, if the nature of the physics or engineering problem being addressed with this code is multiplicative rather than additive, then even though Verification may succeed (2+2=4 etc), Validation may fail because the equations coded are not those needed to address the real world (multiplicative) problem. We have previously provided a 4-step 'ABCD' quantitative implementation for a quantitative V&V process: (A) Plan the analyses and validation testing that may be needed along the way. Assure that the code[s] chosen have sufficient documentation of software quality and Code Verification (i.e., does 2+2=4?). Perform some calibration analyses and calibration based sensitivity studies (these are not validated sensitivities but are useful for planning purposes). Outline the data and validation analyses that will be needed to turn the calibrated model (and calibrated sensitivities) into validated quantities. (B) Solution Verification: For the system or component being modeled, quantify the uncertainty and error estimates due to spatial, temporal, and iterative discretization during solution. (C) Validation over the data domain: Perform a quantitative validation to provide confidence-bounded uncertainties on the quantity of interest over the domain of available data. (D) Predictive Adequacy: Extend the model validation process of 'C' out to the application domain of interest, which may be outside the domain of available data in one or more planes ...
Date: June 16, 2004
Creator: Logan, R W & Nitta, C K
Partner: UNT Libraries Government Documents Department

Estimating Parametric, Model Form, and Solution Contributions Using Integral Validation Uncertainty Quantification

Description: One of the final steps in building a numerical model of a physical, mechanical, thermal, or chemical process, is to assess its accuracy as well as its sensitivity to input parameters and modeling technique. In this work, we demonstrate one simple process to take a top-down or integral view of the model, one which can implicitly reflect any couplings between parameters, to assess the importance of each aspect of modeling technique. We illustrate with an example of a comparison of a finite element model with data for violent reaction of explosives in accident scenarios. We show the relative importance of each of the main parametric inputs, and the contributions of model form and grid convergence. These can be directly related to the importance factors for the system being analyzed as a whole, and help determine which factors need more attention in future analyses and tests.
Date: February 28, 2006
Creator: Logan, R W; Nitta, C K & Chidester, S K
Partner: UNT Libraries Government Documents Department