Thermal conductivity prediction of titania-water nanofluid: A case study using different machine learning algorithms
Published in Case Studies in Thermal Engineering, 2021
This study compares five machine learning algorithms (ANN, GBR, SVR, DTR, and RFR) for predicting the thermal conductivity of TiO2-water nanofluids using a dataset of 228 data points. The gradient boosting regression (GBR) algorithm performed best, achieving 99% accuracy on both training and test sets, with the study also finding that nanoparticle shape significantly influences thermal conductivity predictions.
Recommended citation: Sharma, Palash, K. Ramesh, R. Parameshwaran, and Sandip S. Deshmukh. "Thermal conductivity prediction of titania-water nanofluid: A case study using different machine learning algorithms." Case Studies in Thermal Engineering 30 (2022): 101658.
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