Review of CFD Guidelines for Dispersion Modeling

Abstract

This is the review of CFD (Computational Fluid Dynamics) guidelines for dispersion modeling in the USA, Japan and Germany. Most parts of this review are based on the short report of the special meeting on CFD Guidelines held at the International Symposium on Computational Wind Engineering (CWE2014), University of Hamburg, June 2014. The objective of this meeting was to introduce and discuss the action program to make worldwide guidelines of CFD gas-dispersion modeling. The following six gas-dispersion guidelines including Verification and Validation (V&V) schemes are introduced by each author; (1) US CFD guidelines; (2) COST/ES1006; (3) German VDI (Verein Deutscher Ingenieure) guidelines; (4) Atomic Energy Society of Japan; (5) Japan Society of Atmospheric Environment; (6) Architectural Institute of Japan. All guidelines were summarized in the same format table shown in the main chapters in order to compare them with each other. In addition to the summary of guidelines, the overview of V&V schemes and many guidelines of CFD modeling in the USA are explained.

A Review of Methodology for Evaluating the Performance of Atmospheric Transport and Dispersion Models and Suggested Protocol for Providing More Informative Results

“…However, the Atomic Energy Society of Japan has developed a set of criteria for use in assessing CFD model predictions in comparison to wind tunnel data for neutral/slightly unstable conditions. These require FAC2 > 0.89 for ground level concentration along the plume axis, FAC2 > 0.54 for total spatial concentration, a correlation factor > 0.9 and a regression line slope of 0.9-1.1 [9]. The difference between these criteria and those in Table 2 suggest that there is no universal definition of acceptable model performance.…”

“…This means that CFD solutions cannot be Although both RANS and the LES approaches appear innately superior to Gaussian methods, both rely on a series of modelling assumptions, commonly including the eddy-viscosity and gradient-diffusion hypotheses. The limitations of these approximations, even in the simplest flow fields, are well known and solutions are less accurate for low wind speed regions [8,9]. Britter and Hanna [10] have observed that although predictions from RANS methods may produce reasonable qualitative results for mean flows, their actual performance may be little better than that of simple Gaussian models when compared to experimental data.…”

“…Britter and Hanna [10] have observed that although predictions from RANS methods may produce reasonable qualitative results for mean flows, their actual performance may be little better than that of simple Gaussian models when compared to experimental data. This means that CFD solutions cannot be said to have a known accuracy and can only be evaluated in relation to verification and validation benchmarks [9]. The pros and cons of Gaussian and CFD modelling approaches are summarised in Table 1.…”

“…Much remains to be learned about the fluid dynamical processes that control dispersion in such areas. said to have a known accuracy and can only be evaluated in relation to verification and validation benchmarks [9]. The pros and cons of Gaussian and CFD modelling approaches are summarised in Table 1.…”

Application of CFD modelling to air quality in Kuwait City

“…The requirements set out in three recent guidelines [32][33][34] were followed in the present work. The governing Eqs.…”