An Expert Model Based on Physics-Aware Neural Network for the Prediction of Thermal Conductivity of Nanofluids

Bhaumik Bivas; Changdar Satyasaran; De Soumen

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An Expert Model Based on Physics-Aware Neural Network for the Prediction of Thermal Conductivity of Nanofluids

机译：An Expert Model Based on Physics-Aware Neural Network for the Prediction of Thermal Conductivity of Nanofluids

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摘要

Operating fluids are always a significant factor for not achieving a good enough performance of heat transfer equipment and also for growing the energy costs. To resolve this issue, nanofluids are considered a potential choice for conventional heat transfer fluids due to their efficiency for the improvement of overall thermal performance. The aim of this research is to propose a physics-guided machine learning approach by incorporating physics-based relations at the initial stage and into traditional loss functions for predicting the thermal conductivity of water-based nanofluids using a wide range of both experimental and simulated data of nanoparticles Al2O3, CuO, and TiO2. Further, smart connectionist methods, viz., ridge regression, lasso regression, random forest, extreme gradient boosting (mBoosr (XGB)), and black-box multilayer perceptron (MLP) are applied to compare the present physics-aware MLP model based on different statistical indicators. The accuracy analyses reveal that the use of physical views to monitor the learning of neural networks shows better results with mean absolute percentage error (MAPE) = 0.7075%, root-mean-squared error (RMSE) = 0.0042 W/mK, and R-2 = 0.9525. The temperature and volume concentration variations are discussed graphically. Furthermore, the outcomes of applied algorithms confirm that the well-known theoretical and computer-aided models show substandard results than the proposed model.

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《Journal of heat transfer: Transactions of the ASME》 |2022年第10期|共12页
作者
Bhaumik Bivas; Changdar Satyasaran; De Soumen;
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作者单位

Univ Calcutta;

Univ Copenhagen;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类热力工程、热机;
关键词
neural network; hybrid physics data; physics-guided loss; multilayer perceptron; nanofluids; thermal conductivity; DYNAMIC VISCOSITY; PARTICLE-SIZE; HEAT-TRANSFER; DIFFUSIVITY; AL2O3/WATER; GLYCOL;

An Expert Model Based on Physics-Aware Neural Network for the Prediction of Thermal Conductivity of Nanofluids

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