article · Scientific Reports
Designing efficient heat transfer systems using nanofluids requires accurate knowledge of fluid flow and thermal behaviour. Machine learning models, specifically Support Vector Regression and Gradient Boosting, were trained on seventy-five experimental data samples to predict key thermal and hydraulic parameters of fly ash and copper hybrid nanofluids under turbulent conditions. These parameters include heat transfer coefficient, Nusselt number, pressure drop, and friction factor. The evaluated dataset covered a range of temperatures, concentrations up to two percent by volume, and varying flow conditions. Both computational models delivered high predictive accuracy, with average absolute percentage errors below four percent and strong correlation coefficients across all targets. Analysis indicated that flow conditions, represented by the Reynolds number, exerted the greatest influence on performance, followed by particle concentration and temperature. These models serve as computationally rapid tools to predict behaviour within the studied range.
Evaluating novel heat transfer fluids normally demands time-consuming and expensive physical experiments. Using validated machine learning models allows engineers to rapidly forecast heat transfer efficiency and pressure drops under turbulent flow. This computational approach speeds up the preliminary evaluation of hybrid nanofluids, helping researchers identify promising operating conditions without conducting exhaustive laboratory testing for every scenario.
The models could serve as digital design-support tools for thermal engineers and system designers working on nanofluid-based heat exchange systems. Because the models were trained on a modest dataset of seventy-five samples representing a single fluid mixture, the technology is at an early stage of development and functions strictly as an interpolative tool within its tested parameters rather than an off-the-shelf, general-purpose software product.
AI-generated from the published abstract. Always read the original work before citing.
Accurate prediction of thermal–hydraulic characteristics is essential for the efficient design and optimization of nanofluid (NF)-based heat transfer systems. In this study, Support Vector Regression (SVR) and Gradient Boosting (GB) models were developed to simultaneously predict the heat transfer coefficient (HTC), Nusselt number (Nu), pressure drop (ΔP), and friction factor (f) of fly ash–Cu hybrid nanofluids under turbulent forced-convection conditions. A total of 75 data samples derived from the experimental study of Kanti et al. served as the basis for training and evaluating the predictive models. This data set covering Reynolds number (Re) from 6,807 to 45,155, nanofluid concentrations from 0 to 2.0 vol.%, and temperatures of 30, 45, and 60 °C was utilized for model development and validation. Both machine-learning approaches exhibited excellent predictive capability. SVR achieved regression coefficient (R 2 ) values of 0.9931, 0.9929, 0.9913, and 0.9727 for HTC, Nu, ΔP, and f, respectively, with an average mean absolute percentage error (MAPE) of 3.13%. Correspondingly, the GB model produced R 2 values of 0.9934, 0.9926, 0.9660, and 0.9826, with an average MAPE of 3.62%. Five-fold cross-validation yielded mean R 2 values of 0.9590 for SVR and 0.9691 for GB, confirming strong model robustness. Feature importance analysis revealed that Reynolds number contributed 76.7% to the predictive performance, followed by nanoparticle concentration (18.9%) and temperature (4.4%). The results demonstrate that both models can accurately predict the thermal–hydraulic behavior of fly ash–Cu hybrid nanofluids, providing efficient alternatives to extensive experimental testing. These models offer a computationally efficient design-support tool for the investigated operating range; however, given the modest 75-sample dataset restricted to a single fly ash–Cu formulation, they should be regarded as interpolative within the investigated range rather than broadly generalizable.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1038/s41598-026-66669-w
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
