Details
| Original language | English |
|---|---|
| Article number | 045537 |
| Number of pages | 18 |
| Journal | Engineering Research Express |
| Volume | 6 |
| Issue number | 4 |
| Publication status | Published - 5 Nov 2024 |
Abstract
In thermal engineering, predicting nanofluid thermophysical properties is essential for efficient cooling systems and improved heat transfer. Traditional methods often fall short in handling complex datasets. This study leverages machine learning (ML) and metaheuristic algorithms to predict key nanofluid properties, such as specific heat capacity (SHC), thermal conductivity (TC), and viscosity. By utilizing Artificial Neural Networks (ANN), Support Vector Regression (SVR), Gradient Boosting (GB), and Linear Regression (LR), alongside metaheuristic models like Differential Evolution (DE) and Particle Swarm Optimization (PSO),we achieve superior prediction accuracy compared to traditional models. The integration of these computational techniques with empirical data demonstrates their effectiveness in capturing the complex dynamics of thermofluids. Our results validate the precision ofMLand metaheuristic models in predicting nanofluid properties and underscore their potential as robust tools for researchers and practitioners in thermal engineering. This work paves the way for future exploration ofMLalgorithms in thermal management, marking a significant advancement in optimizing nanofluid applications in industry and research.
Keywords
- heat transfer, machine learning, nanofluids
ASJC Scopus subject areas
- Engineering(all)
- General Engineering
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In: Engineering Research Express, Vol. 6, No. 4, 045537, 05.11.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Enhancing thermal management systems
T2 - a machine learning and metaheuristic approach for predicting thermophysical properties of nanofluids
AU - Saha, Aritra
AU - Basu, Ankan
AU - Banerjee, Sumanta
N1 - Publisher Copyright: © 2024 The Author(s).
PY - 2024/11/5
Y1 - 2024/11/5
N2 - In thermal engineering, predicting nanofluid thermophysical properties is essential for efficient cooling systems and improved heat transfer. Traditional methods often fall short in handling complex datasets. This study leverages machine learning (ML) and metaheuristic algorithms to predict key nanofluid properties, such as specific heat capacity (SHC), thermal conductivity (TC), and viscosity. By utilizing Artificial Neural Networks (ANN), Support Vector Regression (SVR), Gradient Boosting (GB), and Linear Regression (LR), alongside metaheuristic models like Differential Evolution (DE) and Particle Swarm Optimization (PSO),we achieve superior prediction accuracy compared to traditional models. The integration of these computational techniques with empirical data demonstrates their effectiveness in capturing the complex dynamics of thermofluids. Our results validate the precision ofMLand metaheuristic models in predicting nanofluid properties and underscore their potential as robust tools for researchers and practitioners in thermal engineering. This work paves the way for future exploration ofMLalgorithms in thermal management, marking a significant advancement in optimizing nanofluid applications in industry and research.
AB - In thermal engineering, predicting nanofluid thermophysical properties is essential for efficient cooling systems and improved heat transfer. Traditional methods often fall short in handling complex datasets. This study leverages machine learning (ML) and metaheuristic algorithms to predict key nanofluid properties, such as specific heat capacity (SHC), thermal conductivity (TC), and viscosity. By utilizing Artificial Neural Networks (ANN), Support Vector Regression (SVR), Gradient Boosting (GB), and Linear Regression (LR), alongside metaheuristic models like Differential Evolution (DE) and Particle Swarm Optimization (PSO),we achieve superior prediction accuracy compared to traditional models. The integration of these computational techniques with empirical data demonstrates their effectiveness in capturing the complex dynamics of thermofluids. Our results validate the precision ofMLand metaheuristic models in predicting nanofluid properties and underscore their potential as robust tools for researchers and practitioners in thermal engineering. This work paves the way for future exploration ofMLalgorithms in thermal management, marking a significant advancement in optimizing nanofluid applications in industry and research.
KW - heat transfer
KW - machine learning
KW - nanofluids
UR - http://www.scopus.com/inward/record.url?scp=85209353597&partnerID=8YFLogxK
U2 - 10.1088/2631-8695/ad8536
DO - 10.1088/2631-8695/ad8536
M3 - Article
AN - SCOPUS:85209353597
VL - 6
JO - Engineering Research Express
JF - Engineering Research Express
IS - 4
M1 - 045537
ER -