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Proper Orthogonal Decomposition을 활용한 극저온 수직 평판의 열전달 예측
류익현(Ikhyun Ryu),류동흠(Dongheum Ryu),이용빈(Yongbin Lee) 대한기계학회 2023 대한기계학회 춘추학술대회 Vol.2023 No.11
In this research, heat transfer prediction of cryogenic vertical flat plate subject to frosting was conducted using Proper Orthogonal Decomposition (POD). Experiments were conducted using a heat flux sensor from varying the air temperature, air humidity, air velocity, and wall temperature. With POD, one of the popular Reduced Order Modeling (ROM) techniques, dominant behaviors of the heat flux variation were extracted to generate a predictive model together with Polynomial Regression (PR). To compare with the previous work of the correlation equation built from the common heat transfer characteristics, two performance metrics were calculated: Mean Absolute Error (MAE) and the relative error of the total heat transfer prediction. As a result, the predictive model built using POD showed better prediction performance compared to the correlation equations, showing great potential in predicting time-series outputs of the heat transfer of cryogenic vertical flat plate under forced convection.
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박준혁(Joon-Hyuk Park),류동흠(Dongheum Ryu),이용빈(Yongbin Lee) 대한기계학회 2023 대한기계학회 춘추학술대회 Vol.2023 No.11
Latin Hypercube Design (LHD) is one of the most widely used design of experiment methods in computer simulation, not involving random errors. However, because of randomness in LHD, its space-filling property is not robust. Although optimal LHD (OLHD) has been developed to resolve the robustness issue, the increase in the size of samples makes it practically impossible to find the optimal solution, due to rapid increases in computational time and the number of design variables for optimization problems of OLHD generation. Translational Propagation Latin Hypercube Design (TPLHD), creating near-optimal LHD based on rules without optimization, handled these shortcomings. TPLHD, however, has poor space-filling property compared to OLHD. Therefore, this paper proposed a method to efficiently create a large set of samples with space-filling property comparable to OLHD, through optimizing seed design of TPLHD instead of entire experimental points. Its performance was proved effective by comparing it with TPLHD and OLHD.
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