Investigation and optimization of forced convective heat transfer around a tall building using experimental results

Babakhani, J and Veysi, F (2024) Investigation and optimization of forced convective heat transfer around a tall building using experimental results. International Journal of Building Pathology and Adaptation, 42(5), pp. 893-913. ISSN 2398-4708

Abstract

Purpose: The purpose of this article is to investigate the variables affecting heat transfer from the surfaces of a tall building and also the extent of the impact of each of them. Another purpose of this paper is to provide a suitable model for estimating the heat transfer coefficient of the external surfaces of the building according to the impact of variables. Design/methodology/approach: In this study, the Taguchi's approach in the design of the experiments was used to reduce the number of experiments. Percent contributions factors into the overall and surface-averaged Nu of a square prism were obtained by the (ANOVA). The change in Nu by changing either of T, P, angle of attack and V were investigated by the (ANOM). The most significant factors affecting the value Nu were also identified to facilitate the design of thermal systems by eliminating the factors imposing no significant effect on the response in the molding phase. The set of conditions under which the air properties remained unchanged was identified. Five correlations were formulated to predict Nu. Findings: Models used in BES, in which the effects of T, P, A and geometrical effects are not accounted for, are not reliable. The air pressure was found to impose no significant effect on the overall Nu of the considered square prism. Studied in the range of 274–303 K, the air temperature imposed a significant effect on the overall Nu. The results of ANOVA show the significant role of Re to predict Nu of tall buildings. Originality/value: This article is taken from a doctoral dissertation.

Item Type: Article
Uncontrolled Keywords: analysis of variance; building; convective heat transfer; optimization; regression; Taguchi
Date Deposited: 11 Apr 2025 16:00
Last Modified: 11 Apr 2025 16:00