Advances in Fluid Dynamics and Building Ventilation

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Fluid Science and Technology".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 1056

Special Issue Editor


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Guest Editor
Tianjin Key Lab of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
Interests: indoor air quality; building ventilation; air purification

Special Issue Information

Dear Colleagues,

Most time of people is spent inside the building, so indoor air quality is crucial. Ventilation is an important way to ensure indoor air quality. The computational simulation technology has become an indispensable technology for indoor ventilation design. In recent years, new methods have continuously developed. This special issue mainly focuse on the latest new technologies and simulation methods related to indoor ventilation, hoping to further reveal the physical characteristics of indoor air flow and future technological development trends.

Prof. Dr. Zhengwei Long
Guest Editor

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Keywords

  • indoor air quality
  • building ventilation
  • CFD

Published Papers (1 paper)

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Research

24 pages, 7001 KiB  
Article
A New Optimization Design Method of Multi-Objective Indoor Air Supply Using the Kriging Model and NSGA-II
by Yu Guo, Yukun Wang, Yi Cao and Zhengwei Long
Appl. Sci. 2023, 13(18), 10465; https://doi.org/10.3390/app131810465 - 19 Sep 2023
Viewed by 854
Abstract
When using meta-heuristic optimization approaches for optimization, a large number of samples are required. In particular, when generating a subgeneration, the utilization of existing samples is low and the number of individuals is high. Therefore, surrogate-based optimization has been developed, which greatly reduces [...] Read more.
When using meta-heuristic optimization approaches for optimization, a large number of samples are required. In particular, when generating a subgeneration, the utilization of existing samples is low and the number of individuals is high. Therefore, surrogate-based optimization has been developed, which greatly reduces the number of individuals in the subgeneration and the cost of optimization. In complex air supply scenarios, single-objective optimization results may not be comprehensive; therefore, this paper developed a double-objective air supply optimization method based on the Kriging surrogate model and Non-dominated Sorting Genetic Algorithms-II. And it proposed the infill criteria based on clustering to advance the Pareto Frontier. The method was validated with an inverse prediction case, and in particular, the problems when based on 3D steady-state simulations were analyzed. The results showed that the method can quickly achieve an approximate prediction of the boundary conditions (when predictions were made based on experimental data, the number of simulations was 82 and the average error was 6.8%). Finally, the method was used to optimize the air supply parameters of a dual-aisle, single-row cabin, with only 118 samples used in the optimization process. The Pareto set suggested that an airflow organization with dual circulation may be optimal. Full article
(This article belongs to the Special Issue Advances in Fluid Dynamics and Building Ventilation)
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