Open Access System for Information Sharing

Login Library

 

Article
Cited 5 time in webofscience Cited 6 time in scopus
Metadata Downloads

Artificial neural network-based temperature prediction of a lunar orbiter in thermal vacuum test: Data-driven reduced-order models SCIE SCOPUS

Title
Artificial neural network-based temperature prediction of a lunar orbiter in thermal vacuum test: Data-driven reduced-order models
Authors
Jang, ByungkwanLee, WoojinLee, Jang-JoonJin, Hyungyu
Date Issued
2024-02
Publisher
Elsevier BV
Abstract
This study presents data-driven reduced-order models (ROMs) of a lunar orbiter based on principal component analysis (PCA) and artificial neural networks (ANNs) for a ground thermal vacuum test to simulate space thermal environments. We employed a radial basis function network (RBFN) and deep neural network (DNN) from among the various types of ANNs. PCA extracts features from high-dimensional data, such as thermal analysis data. It is utilized in machine learning algorithms as a preprocessing step before inputting the data into neural networks. This process improves the convergence speed and training performances compared to using neural networks alone. The coefficients of the extracted principal component modes were regressed using the RBFN and DNN. Twenty thermal design parameters comprising infrared emissivity, effective thermal conductivity, thermal contact conductance coefficients, and thermal conductance were used to train the ROMs. We conducted training and test of the proposed models during the cold and hot balance phases of the ground test. Consequently, the temperature map can be estimated in seconds for the new design parameters, and the model results are consistent with thermal analysis and measurement data. © 2023
URI
https://oasis.postech.ac.kr/handle/2014.oak/120783
DOI
10.1016/j.ast.2023.108867
ISSN
1270-9638
Article Type
Article
Citation
Aerospace Science and Technology, vol. 145, page. 108867, 2024-02
Files in This Item:
There are no files associated with this item.

qr_code

  • mendeley

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher

진현규JIN, HYUNGYU
Dept of Mechanical Enginrg
Read more

Views & Downloads

Browse