Machine Learning Applications in Nanofluid Thermal Analysis

The union of nanofluid science with machine learning has transformed the prediction, optimisation and control of heat transfer in advanced fluid systems. Nanofluids—colloidal suspensions of nanoparticles in base liquids—offer enhanced thermal conductivity and tailored rheological properties. Machine learning techniques, notably artificial neural networks, support vector machines and adaptive neuro-fuzzy inference systems, have been applied to predict key performance metrics such as Nusselt number, thermal conductivity and dynamic viscosity. Integration with computational fluid dynamics has yielded hybrid workflows that accelerate multiphysics simulations and reduce experimental burden. Across porous media, heat exchangers and micro-scale channels, data-driven models have identified non-linear dependencies on particle concentration, size and temperature, enabling rapid optimisation of energy-efficient designs. This interdisciplinary approach underpins global efforts in sustainable thermal management, from renewable energy systems to electronics cooling, by providing accurate, cost-effective tools for system design and real-time control.

Research from Nature Portfolio

Recent studies have introduced multilayer perceptron neural networks with advanced training algorithms to simulate slip flows of electrically conducting and non-Newtonian nanofluids over porous stretching surfaces with thermal and velocity slip conditions. These intelligent solvers accurately predict skin friction coefficients and Nusselt numbers, matching traditional numerical methods while offering substantial computational speed-ups. Complementary work has employed an adaptive neuro-fuzzy inference system to learn flow and temperature distributions in a square cavity containing copper-based nanofluid. By treating CFD outputs as training data, the model achieves meshless prediction of velocity and thermal fields at untested locations, delivering high correlation and reduced computational cost compared to ant colony and conventional fuzzy logic methods.

Machine Learning Applications in Nanofluid Thermal Analysis publication trend

The graph below shows the total number of articles in machine learning applications in nanofluid thermal analysis across all publications each year (not limited to Nature Index journals).

Technical terms

Nanofluid: Colloidal suspension of nanoparticles in a base liquid to enhance thermal and flow properties.

Artificial neural network (ANN): Computational model of interconnected neurons for regression and classification tasks.

Support vector machine (SVM): Supervised learning algorithm that defines optimal hyperplanes for classification or regression.

Adaptive neuro-fuzzy inference system (ANFIS): Hybrid framework combining neural networks with fuzzy logic to approximate complex functions.

Computational fluid dynamics (CFD): Numerical analysis of fluid flow and heat transfer via discretised governing equations.

Nusselt number: Dimensionless measure of convective to conductive heat transfer across a boundary.

References

1. Nanofluid heat transfer and machine learning: Insightful review of machine learning for nanofluid heat transfer enhancement in porous media and heat exchangers as sustainable and renewable energy solutions. Results in Engineering (2024).
2. Thermal Conductivity Modeling of Nanofluids Contain MgO Particles by Employing Different Approaches. Symmetry (2020).
3. Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network. Nanomaterials (2020).