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Machine Learning-Based Prediction on...

Oh, Youngsuk,

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Machine Learning-Based Prediction on Heat Transfer Enhancement in Complex Thermal Systems /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning-Based Prediction on Heat Transfer Enhancement in Complex Thermal Systems // Youngsuk Oh.
作者:	Oh, Youngsuk,
面頁冊數:	1 electronic resource (136 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-05, Section: B.
Contained By:	Dissertations Abstracts International86-05B.
標題:	Mechanical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31564123
ISBN:	9798342749138

Machine Learning-Based Prediction on Heat Transfer Enhancement in Complex Thermal Systems /
Oh, Youngsuk,

Machine Learning-Based Prediction on Heat Transfer Enhancement in Complex Thermal Systems /Youngsuk Oh. - 1 electronic resource (136 pages)

Source: Dissertations Abstracts International, Volume: 86-05, Section: B.

The complexity of interactions between multiple factors and heat transfer enhancement creates a strong desire to investigate new approaches as an alternative to traditional numerical and experimental methods. This study aims to evaluate the applicability of machine learning techniques in identifying the complicated nonlinear patterns in huge dataset and predicting the performance of heat transfer enhancement systems, including microscale pin fin heat sinks and nanofluids. Various types of machines learning algorithms, including multilayer perceptron, random forest, light gradient boosting machine, extreme gradient boosting and stacking methods are employed to develop a universal predictor applicable under various operating conditions. Experimental data acquired from the literature were employed to train the prediction model. The performance of the trained machine learning models was tested using the mean squared error and the coefficient of determination. The influence of each input variable on model development was examined to identify key features. Information gain is introduced and calculated for determining the importance of parameters in prediction. External validation is performed with an additional unseen dataset to further assess the applicability of the selected models across different experimental data points. The results demonstrate that machine learning methods can provide broad applicability and excellent predictions of heat transfer enhancement of microscale pin fin heat sinks and complex nanofluids with minimal prediction error.

English

ISBN: 9798342749138Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Heat enhancement

Machine Learning-Based Prediction on Heat Transfer Enhancement in Complex Thermal Systems /
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520 $a The complexity of interactions between multiple factors and heat transfer enhancement creates a strong desire to investigate new approaches as an alternative to traditional numerical and experimental methods. This study aims to evaluate the applicability of machine learning techniques in identifying the complicated nonlinear patterns in huge dataset and predicting the performance of heat transfer enhancement systems, including microscale pin fin heat sinks and nanofluids. Various types of machines learning algorithms, including multilayer perceptron, random forest, light gradient boosting machine, extreme gradient boosting and stacking methods are employed to develop a universal predictor applicable under various operating conditions. Experimental data acquired from the literature were employed to train the prediction model. The performance of the trained machine learning models was tested using the mean squared error and the coefficient of determination. The influence of each input variable on model development was examined to identify key features. Information gain is introduced and calculated for determining the importance of parameters in prediction. External validation is performed with an additional unseen dataset to further assess the applicability of the selected models across different experimental data points. The results demonstrate that machine learning methods can provide broad applicability and excellent predictions of heat transfer enhancement of microscale pin fin heat sinks and complex nanofluids with minimal prediction error.
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