TY - JOUR

T1 - An Expert Model Based on Physics-Aware Neural Network for the Prediction of Thermal Conductivity of Nanofluids

AU - Bhaumik, Bivas

AU - Changdar, Satyasaran

AU - De, Soumen

N1 - Publisher Copyright: Copyright © 2022 by ASME.

PY - 2022

Y1 - 2022

N2 - 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 (XGBOOST (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) ¼ 0.7075%, root-mean-squared error (RMSE) ¼ 0.0042 W/mK, and R2 ¼ 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.

AB - 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 (XGBOOST (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) ¼ 0.7075%, root-mean-squared error (RMSE) ¼ 0.0042 W/mK, and R2 ¼ 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.

KW - hybrid physics data

KW - multilayer perceptron

KW - nanofluids

KW - neural network

KW - physics-guided loss

KW - thermal conductivity

U2 - 10.1115/1.4055116

DO - 10.1115/1.4055116

M3 - Journal article

AN - SCOPUS:85138773255

VL - 144

JO - Journal of Heat Transfer

JF - Journal of Heat Transfer

SN - 0022-1481

IS - 10

M1 - 103501

ER -