Analysis and Modeling of Heat and Mass Transfer in Alumina-water Nanofluids using Levenberg-Marquardt Backpropagation Neural Networks (LMT-ABPNN)
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Abstract
This work examines the utilisation of artificial intelligence, particularly soft computing and machine learning, to augment robustness, enhance modelling precision, and facilitate swift assessment in nanofluid-based heat transfer systems. This research utilizes the Levenberg-Marquardt algorithm alongside Artificial Backpropagation Neural Networks to assess the efficacy of nanoparticles in convective heat transfer mechanisms.
The emphasis is on creating a comparison between Al2O3-H2O nanoparticles and g-Al2O3 in diverse base fluids, such as ethylene glycol and water, across an extensive surface area.The boundary layer flow is analysed under the effects of magnetohydrodynamics (MHD), incorporating slip boundary conditions and g-Al2O3 nanofluids. This research examines a subject that has not been before investigated. The viscosity and thermal conductivity models for g-Al2O3 nanofluids are established using empirical data, whereas thermal radiation effects are included into the Brinkman viscosity and Maxwell thermal conductivity models for Al2O3 nanofluids.The governing partial differential equations of magnetohydrodynamics are converted into ordinary differential equations using a suitable transformation. The dataset for the LMT-ABPNN model is produced using the Shooting method, which alters physical parameters across several situations, functioning as benchmarks for model training, validation, and testing. The efficacy of the LMT-ABPNN is assessed using measures like Mean Squared Error (MSE), error histograms, and regression analysis. The research examines the impact of many parameters on temperature, concentration, and velocity profiles. The Mean Squared Error (MSE) of the LMT-ABPNN model is assessed for several configurations of the Local Nusselt number in the Al2O3 -water system, with the Modified Local Nusselt number yielding the most precise prediction. The Sherwood number is employed to evaluate the model's efficacy in forecasting the power generation of waste heat recovery systems. The model has significant adaptability, with its gradient and learning rate underscoring its efficacy. The error histogram reveals negligible mistakes, suggesting possible avenues for additional optimization. The regression analysis of the alumina-water combination is illustrated in four graphs, which not only exhibit the model's present effectiveness but also highlight its potential for further enhancement.
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