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AbstractCrystallization deposition within micro-scale heat exchangers poses significant challenges to their efficiency and functionality, stemming from the accumulation of crystalline residues on heat transfer surfaces. This study employs advanced machine learning methodologies, including GRU, LSTM, RNN, and CNN models, to explore the underlying factors influencing micro heat exchanger performance. Through meticulous analysis of key parameters such as Reynolds number, sedimentation coefficient, flow rate, and channel dimensions, the study aims to delineate the foundational factors shaping heat exchanger performance at the microscopic level. Results reveal the exceptional accuracy of CNN model in forecasting experimental outcomes, surpassing 99% accuracy and demonstrating superior performance compared to traditional numerical methods. Temperature emerges as a pivotal determinant, profoundly influencing crystallization dynamics, with its intricate interplay with solute solubility elucidated through rigorous analysis. Furthermore, comparative assessment of training times highlights the CNN model’s efficiency, attributed to its specialized architecture suited for spatial data processing. This study provides valuable insights into the impact of crystallization deposition on microscale heat exchangers, showcasing the transformative potential of machine learning in optimizing heat exchanger performance and addressing operational challenges in industrial applications. [ABSTRACT FROM AUTHOR]

AbstractThe utilization of molten salts in heat transfer applications, specifically within shell-and-tube heat exchangers, has garnered significant attention for its potential in sustainable energy solutions. this study employs advanced machine learning algorithms, including decision tree regressor, support vector regressor, extreme gradient boosting, and random forest, to not only predict the heat transfer behavior of molten salts but also unravel the complex mechanisms underlying this process. Achieving a remarkable accuracy score of 0.985, the Support Vector Regressor leads the predictive models, closely followed by random forest (0.982), Decision Tree Regressor (0.974), and Extreme Gradient Boosting (0.965). The incorporation of Shapley Additive exPlanations values accentuates the Reynolds number’s pivotal role, elucidating a robust correlation with the Nusselt value. These insights transcend mere prediction, offering a profound understanding that can significantly impact the design and optimization of molten salt heat exchangers. The applications of molten salts extend across various sectors, including concentrated solar energy and thermal storage, solidifying their position as a versatile and effective solution in the pursuit of sustainable and efficient energy systems. [ABSTRACT FROM AUTHOR]

AbstractThis study underscores the critical role of heat transfer modeling in the design and optimization of heat exchangers, recognizing the challenges posed by traditional numerical methods. To address the need for more efficient alternatives, machine learning emerges as a promising solution, leveraging its capacity to unravel intricate relationships in heat transfer processes. The research focuses on forced convection heat transfer involving nanoparticles (specifically TiO2, Al2O3, and Cu) dispersed in water, employing the Lagrangian–Eulerian approach under turbulent flow conditions with a constant surface heat flux. Machine-learning techniques, namely extreme gradient boosting, support vector regression, and tree regression are employed to predict heat transfer outcomes based on collected data. The achieved results exhibit promising accuracy levels, with eXtreme gradient boosting, support vector regression, and tree regression algorithm models demonstrating impressive accuracies of 91%, 91%, and 94%, respectively. The mean absolute errors for these machine-learning models were recorded as 1.07, 7.24, and 3.74. This research serves as a demonstration of the potential of machine-learning methodologies in modeling complex physical systems, as evidenced by their efficiency in capturing the behavior of nanofluids under various conditions. The obtained accuracy and low mean absolute error values indicate the robustness of these machine-learning models in predicting heat transfer results. The implications of these findings are substantial for the field of heat exchanger design and optimization. The study suggests that machine-learning techniques offer a more efficient and cost-effective approach compared to traditional numerical simulations. By leveraging the capabilities of these models, there is potential for enhanced accuracy and efficiency in the design and optimization processes of heat exchangers, contributing to advancements in thermal engineering. [ABSTRACT FROM AUTHOR]