Physics-based smart model for prediction of viscosity of nanofluids containing nanoparticles using deep learning. Issue 2 (30th January 2021)

• Record Type: Journal Article
• Authors: Changdar, Satyasaran; Bhaumik, Bivas; De, Soumen
• Is Part Of: Journal of computational design and engineering. Volume 8:Issue 2(2021)
• Page Start: 600
• Page End: 614
• Publication Date: 2021-01-30
• Subjects: deep learning -- hybrid physics data -- neural network -- nanoparticles -- nanofluids -- viscosity; Engineering -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Computer-aided design; Engineering -- Data processing; Electronic journals; Electronic journals; Periodicals; 620.0042
• DOI: 10.1093/jcde/qwab001
• Languages: English
• ISSNs: 2288-4300

Abstract

Abstract: The traditional model-driven methods are not much efficient to predict the viscosity of nanofluids accurately. This study presents a novel approach of using physics-guided deep learning technique for predicting viscosity of water-based nanofluids from large dataset containing both experimental and simulated data of spherical oxide nanoparticles $\rm{Al2O3}$, $\rm{CuO}$, $\rm{SiO2}$, and $\rm{TiO2}$ . Further, this study introduces a novel methodology of combining deep learning methods and physics-based models to leverage their complementary strengths. To the best of the author's knowledge, theory-guided deep learning prediction model was never used to predict viscosity before. The theory-guided deep neural networks (TGDNN) model is trained by minimizing the mean square error (MSE) and regularization terms using Adam optimization technique. The investigations reveal that the values of R 2, RMSE, and AARD% are, respectively, 0.999868, 0.001143, and 2.198887 on experimental testing dataset. The TGDNN model learns non-linear relationship among the input variables from the training data. Additionally, the results show that the proposed method performed better than the other well-known existing theoretical and computer-aided models to predict the viscosity in wide range with high level of accuracy. Graphical Abstract: