Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A New Optimization Design Method of Multi-Objective Indoor Air Supply Using Kriging Model and NSGA-II

Version 1 : Received: 18 August 2023 / Approved: 18 August 2023 / Online: 21 August 2023 (03:23:07 CEST)

A peer-reviewed article of this Preprint also exists.

Guo, Y.; Wang, Y.; Cao, Y.; Long, Z. A New Optimization Design Method of Multi-Objective Indoor Air Supply Using the Kriging Model and NSGA-II. Appl. Sci. 2023, 13, 10465. Guo, Y.; Wang, Y.; Cao, Y.; Long, Z. A New Optimization Design Method of Multi-Objective Indoor Air Supply Using the Kriging Model and NSGA-II. Appl. Sci. 2023, 13, 10465.

Abstract

When using meta-heuristic optimization approaches for optimization, a large number of samples are required. In particular, when generating 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 proposed the infill criteria based on clustering to advance the Pareto Frontier. The method was validated by 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 prediction 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. The Pareto set suggested that an airflow organization with dual circulation may be optimal.

Keywords

air supply optimization; double-objective optimization; surrogate-based optimization; Kriging model; genetic algorithm

Subject

Engineering, Architecture, Building and Construction

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