Predictive Computational Fluid Dynamics Development and its Verification and Validation: An Overview

Abstract

A major focus for the development of computational fluid dynamics (CFD) technology is the attainment of an acceptable level of credibility of CFD analysis and simulations for the range of fluid flows of interest. Toward this end, a systematic program for CFD verification and validation (V&V) is being developed and implemented to enable Predictive-CFD (P-CFD) capability for use in the industrial design process. This paper provides an overview of a practical approach for CFD V&V that can support the initial use of CFD in the industrial design process and lays the foundation for providing a true predictive capability for CFD in the future. The approach emphasizes a bottom up view of CFD validation. In particular, validation assessments of fundamental, unit and separate-effects physics, flow configurations are performed to develop the large body of knowledge required to implement knowledge-based tools and procedures, e.g., best practices and design guidelines, for managing uncertainty and improving reliability of CFD analysis and simulations. In this approach, the flow field data obtained from validation experiments require a higher level of fidelity, resolution and documentation, including a complete and thorough description of the boundary and initial conditions driving the flows, the as-built geometry of the validation experiment and an appropriate uncertainty analysis of the experimental data. Datasets which meet these standards are termed validation-level datasets. It is expected that over time, the amount of validation-level data in the CFD V&V archives and the knowledge gained from CFD V&V assessments will be sufficient to assure the accuracy of the associated CFD predictions over a wide range of applications with a minimal amount of additional, confirmatory physical testing. In time, automated and standardized P-CFD methodology with associated best practices, design guidelines, and uncertainty quantification methods will provide a predictive capability in which sufficient confidence can be placed in CFD predictions that CFD analysis can replace large, semi-scale physical testing and allow for designs to be developed using CFD up front in the design process.