Particle Swarm Optimization–Support Vector Regression (PSO-SVR)-Based Rapid Prediction Method for Radiant Heat Transfer for a Spacecraft Vacuum Thermal Test
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
DOI:
10.3390/app14209407
Publication Date:
2024-10-17T08:42:15Z
AUTHORS (4)
ABSTRACT
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%.
SUPPLEMENTAL MATERIAL
Coming soon ....
REFERENCES (26)
CITATIONS (0)
EXTERNAL LINKS
PlumX Metrics
RECOMMENDATIONS
FAIR ASSESSMENT
Coming soon ....
JUPYTER LAB
Coming soon ....