The use of Taguchi, ANOVA, and GRA methods to optimize CFD analyses of ventilation performance in buildings

We present a method that can be used to optimize CFD analyses of ventilation performance in buildings; the method was successfully applied in other disciplines. In the method, three statistical methods are used -the Taguchi method, ANOVA, and Grey Relational Analysis (GRA) to achieve optimization. We then model the risk of draft and the mean age of air in a two-dimensional benchmark test case to exemplify the application of the method. We use five independent variables for modeling: temperature, air velocity, and three room dimensions, each with five levels. In short, the method is implemented as follows: At first, using an L25 orthogonal array in the Taguchi method, we decreased the total number of cases to be modeled from 3125 (55) to 25. Then, we modeled the 25 cases with CFD and used the Taguchi method and ANOVA analyses to find the best solutions for draught risk or the age of air, while with the GRA method we found the best solution for both of them considered jointly. Finally, new CFD models for the variables describing the best solutions were run to confirm their su-periority within the 25 cases selected for modeling within the orthogonal L25 array. They were not compared against all possible 3125 cases because the computational time would have been prohibitive. Nevertheless, we believe, that the results are credible in identifying the best solutions. This is based on experience in other dis-ciplines. We, therefore, propose that the presented method is considered when complex problems are analyzed related to ventilation performance requiring large computational power.

Automated summary

The study introduces a method to optimize building ventilation performance through statistical methods and CFD analysis. The results show that this method can effectively reduce modeling computational time and find the best solutions.