This report presents a perspective on the role of code comparison activities in verification and validation. We formally define the act of code comparison as the Code Comparison Principle (CCP) and investigate its application in both verification and validation. One of our primary conclusions is that the use of code comparisons for validation is improper and dangerous. We also conclude that while code comparisons may be argued to provide a beneficial component in code verification activities, there are higher quality code verification tasks that should take precedence. Finally, we provide a process for application of the CCP that we believe is minimal for achieving benefit in verification processes.
Several major issues associated with model validation are addressed here. First, we extend the application-based, model validation metric presented in Hills and Trucano (2001) to the Maximum Likelihood approach introduced in Hills and Trucano (2002). This method allows us to use the target application of the code to weigh the measurements made from a validation experiment so that those measurements that are most important for the application are more heavily weighted. Secondly, we further develop the linkage between suites of validation experiments and the target application so that we can (1) provide some measure of coverage of the target application and, (2) evaluate the effect of uncertainty in the measurements and model parameters on application level validation. We provide several examples of this approach based on steady and transient heat conduction, and shock physics applications.
In this report we describe the performance of the ALEGRA shock wave physics code on a set of gas dynamic shock reflection problems that have associated experimental pressure data. These reflections cover three distinct regimes of oblique shock reflection in gas dynamics--regular, Mach, and double Mach reflection. For the selected data, the use of an ideal gas equation of state is appropriate, thus simplifying to a considerable degree the task of validating the shock wave computational capability of ALEGRA in the application regime of the experiments. We find good agreement of ALEGRA with reported experimental data for sufficient grid resolution. We discuss the experimental data, the nature and results of the corresponding ALEGRA calculations, and the implications of the presented experiment--calculation comparisons.
This report describes the results of the FY01 Level 1 Peer Reviews for the Verification and Validation (V&V) Program at Sandia National Laboratories. V&V peer review at Sandia is intended to assess the ASCI (Accelerated Strategic Computing Initiative) code team V&V planning process and execution. The Level 1 Peer Review process is conducted in accordance with the process defined in SAND2000-3099. V&V Plans are developed in accordance with the guidelines defined in SAND2000-3 101. The peer review process and process for improving the Guidelines are necessarily synchronized and form parts of a larger quality improvement process supporting the ASCI V&V program at Sandia. During FY00 a prototype of the process was conducted for two code teams and their V&V Plans and the process and guidelines updated based on the prototype. In FY01, Level 1 Peer Reviews were conducted on an additional eleven code teams and their respective V&V Plans. This report summarizes the results from those peer reviews, including recommendations from the panels that conducted the reviews.
This report presents general concepts in a broadly applicable methodology for validation of Accelerated Strategic Computing Initiative (ASCI) codes for Defense Programs applications at Sandia National Laboratories. The concepts are defined and analyzed within the context of their relative roles in an experimental validation process. Examples of applying the proposed methodology to three existing experimental validation activities are provided in appendices, using an appraisal technique recommended in this report.
Verification and validation (V and V) are the primary means to assess accuracy and reliability in computational simulations. This paper presents an extensive review of the literature in V and V in computational fluid dynamics (CFD), discusses methods and procedures for assessing V and V, and develops a number of extensions to existing ideas. The review of the development of V and V terminology and methodology points out the contributions from members of the operations research, statistics, and CFD communities. Fundamental issues in V and V are addressed, such as code verification versus solution verification, model validation versus solution validation, the distinction between error and uncertainty, conceptual sources of error and uncertainty, and the relationship between validation and prediction. The fundamental strategy of verification is the identification and quantification of errors in the computational model and its solution. In verification activities, the accuracy of a computational solution is primarily measured relative to two types of highly accurate solutions: analytical solutions and highly accurate numerical solutions. Methods for determining the accuracy of numerical solutions are presented and the importance of software testing during verification activities is emphasized.
Two major issues associated with model validation are addressed here. First, we present a maximum likelihood approach to define and evaluate a model validation metric. The advantage of this approach is it is more easily applied to nonlinear problems than the methods presented earlier by Hills and Trucano (1999, 2001); the method is based on optimization for which software packages are readily available; and the method can more easily be extended to handle measurement uncertainty and prediction uncertainty with different probability structures. Several examples are presented utilizing this metric. We show conditions under which this approach reduces to the approach developed previously by Hills and Trucano (2001). Secondly, we expand our earlier discussions (Hills and Trucano, 1999, 2001) on the impact of multivariate correlation and the effect of this on model validation metrics. We show that ignoring correlation in multivariate data can lead to misleading results, such as rejecting a good model when sufficient evidence to do so is not available.
This report describes the underlying principles and goals of the Sandia ASCI Verification and Validation Program Validation Metrics Project. It also gives a technical description of two case studies, one in structural dynamics and the other in thermomechanics, that serve to focus the technical work of the project in Fiscal Year 2001.
This report describes the initial definition of the Verification and Validation (V and V) Plan Peer Review Process at Sandia National Laboratories. V and V peer review at Sandia is intended to assess the ASCI code team V and V planning process and execution. Our peer review definition is designed to assess the V and V planning process in terms of the content specified by the Sandia Guidelines for V and V plans. Therefore, the peer review process and process for improving the Guidelines are necessarily synchronized, and form parts of a larger quality improvement process supporting the ASCI V and V program at Sandia.