APPLICABILITY OF MACHINE LEARNING TECHNIQUES IN PREDICTING SPECIFIC HEAT CAPACITY OF COMPLEX NANOFLUIDS
-
Published:2024
Issue:3
Volume:55
Page:39-60
-
ISSN:1064-2285
-
Container-title:Heat Transfer Research
-
language:en
-
Short-container-title:Heat Trans Res
Author:
Oh Youngsuk,Guo Zhixiong
Abstract
The complexity of the interaction between base fluids and nano-sized particles makes the prediction of nanofluid
thermophysical properties difficult. However, machine learning techniques can be utilized as an alternative approach due to their ability to identify complex nonlinear patterns in data and make accurate forecasts. This paper presents intuitive predictions of specific heat of various types of nanofluids using machine learning models based on experimental data obtained from 47 different studies, comprising 5009 data points. Three machine learning algorithms, namely, artificial neural network (ANN), support vector regression (SVR), and extreme gradient boosting (XGBoost), were tested to develop a universal predictor for nanofluid specific heat. To enhance the performance of the machine learning models, the best set of input variables was selected, and hyperparameter optimization was conducted to maximize the prediction accuracy. The accuracy of three selected machine learning models [i.e., MLP (a type of ANN), SVR, and XGBoost] and their unseen data prediction capability were compared with existing complicated empirical models, and the results showed that the machine learning-based predictions were more accurate. The machine learning models demonstrated excellent agreement with experimental nanofluid specific heat data. Particularly, the extreme gradient boosting method (i.e., XGBoost) showed the best nanofluid specific heat forecast results with minimal prediction error and presented broad range of applicability.
Subject
Fluid Flow and Transfer Processes,Mechanical Engineering,Condensed Matter Physics
Reference74 articles.
1. Adun, H., Kavaz, D., Wole-Osho, I., and Dagbasi, M., Synthesis of Fe3O4-Al2O3-ZnO/Water Ternary Hybrid Nanofluid: Investigating the Effects of Temperature, Volume Concentration and Mixture Ratio on Specific Heat Capacity, and Development of Hybrid Machine Learning for Prediction, J. Energy Storage, vol. 41, Article ID 102947, 2021. 2. Akilu, S., Baheta, A.T., Said, M.A.M., Minea, A.A., and Sharma, K.V., Properties of Glycerol and Ethylene Glycol Mixture Based SiO2–CuO/C Hybrid Nanofluid for Enhanced Solar Energy Transport, Sol. Energy Mater. Sol. Cells, vol. 179, pp. 118-128, 2018. 3. Akilu, S., Baheta, A.T., Sharma, K.V., and Said, M.A., Experimental Determination of Nanofluid Specific Heat with SiO2 Nanoparticles in Different Base Fluids, AIP Conf. Proc., vol. 1877, Article ID 090001, 2017. 4. Ali, N., Graphene-Based Nanofluids: Production Parameter Effects on Thermophysical Properties and Dispersion Stability, Nanomaterials, vol. 12, no. 357, 2022. 5. Andreu-Cabedo, P., Mondragon, R., Hernandez, L., Martinez-Cuenca, R., Cabedo, L., and Julia, J., Increment of Specific Heat Capacity of Solar Salt with SiO2 Nanoparticles, Nanoscale Res. Lett., vol. 9, Article ID 582, 2014.
|
|