Particle Swarm Optimization–Support Vector Regression (PSO-SVR)-Based Rapid Prediction Method for Radiant Heat Transfer for a Spacecraft Vacuum Thermal Test

• DOI: 10.3390/app14209407
• Publication Date: 2024-10-17T08:42:15Z
• Authors: Xinming Su; Hongsen Jiang; Taichun Qin; Guiping Lin
• Keywords: Technology; QH301-705.5; T; Physics; QC1-999; infrared heating cage; rapid prediction method; PSO-SVR model; radiant heat transfer; Engineering (General). Civil engineering (General); Chemistry; TA1-2040; Biology (General); QD1-999

The simulation of external heat flow has a pivotal role in the vacuum thermal test spacecraft. key to simulating spacecraft through an infrared heating cage lies calculation radiative transfer, and existing Monte Carlo methods for have disadvantages complicated modeling slow speed. In this paper, we consider spacing, partition height, arc length, curvature, circumferential relationship, radial other variables. particle swarm optimization–support vector regression (PSO-SVR) method is used establish angular coefficient relationship model between cages with different shapes, which realizes rapid prediction cage. coefficients obtained by prognostic are essentially same as those simulation, while efficiency improved 29,750 times. Taking small control star case study, error gradually decreases increase flow, maximum 6.1%.