article · Journal of Radiation Research and Applied Sciences
Industrial thermal management systems require accurate predictions of advanced fluid motion across various geometries and operating conditions. A computational study modelled ternary hybrid nanofluid flow across a curved Riga surface while incorporating chemical reactions and activation energy. Governing flow equations were converted into ordinary differential equations using similarity transformations and resolved numerically using a Runge Kutta Fehlberg fourth to fifth order method paired with a shooting technique. The resulting data trained an artificial neural network to estimate critical engineering coefficients. Findings show that increasing the solid volume fraction enhances the thermal profile while reducing the concentration profile. Furthermore, the reaction rate parameter reduces fluid concentration, whereas the activation energy parameter produces the opposite outcome. The trained neural network model demonstrated a high degree of precision across all tested parameters.
Efficient heat dissipation is essential for reliable operations in chemical processing, biomedical equipment, and broader industrial manufacturing. Modelling complex fluids with artificial intelligence provides engineers with rapid, precise insights into heat transfer behaviour over curved electromagnetic surfaces. This predictive capability helps improve the design, construction, and optimisation of next-generation thermal management systems without the continuous need for resource-intensive physical testing or slow numerical solvers.
This work represents early-stage computational research applicable to thermal management systems in chemical, biomedical, and environmental engineering. Potential end-users include industrial design engineers and thermal software developers seeking faster predictive models for hybrid nanofluid heat transfer. Because the findings are based entirely on mathematical formulations and neural network training rather than physical prototypes, substantial laboratory testing and real-world validation are required before any industrial deployment or commercialisation can occur.
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To improve the thermal management systems in industrial operations, there is a need for the prediction and complexity of advanced fluid motion across various physical conditions on different geometries. Further, ternary nanofluid flow across curved Riga surfaces plays a significant role in thermal management systems, chemical industries, thermal management systems, biomedical, environmental engineering, and more. Based on the above importance the current study focuses artificial neural network (ANN) model on the ternary nanofluid flow over a curved Riga surface in the presence of chemical reaction and activation energy. Proper assumptions and boundary layer approximation were used to develop the model. Using appropriate similarity variables, the governing equations are further simplified to ordinary differential equations (ODEs). Runge Kutta Fehlberg's 4th-5th order and shooting process are applied to solve the simplified equations. The primary objective is to improve the construction and optimization of thermal administration systems and other manufacturing procedures by gaining a deeper knowledge of the fluid motion and characteristics of heat transfer involved. Graphs are used to provide additional context for the important dimensionless constraints. The obtained data was utilized to train the ANN model, which was then verified towards numerical values of important engineering coefficients. The results reveal that the addition of solid volume fraction will enhance the thermal profile while declining the concentration profile. The reaction rate parameter will decline the concentration, and a reverse trend is seen for the activation energy parameter. The constructed model exhibits an outstanding degree of precision throughout the procedure, spanning all phases of the research.
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DOI: 10.1016/j.jrras.2024.101078
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