<ul><li>Researchers from IMT Atlantique developed a novel neural-accelerated dynamic model for heat pumps' heat exchangers, combining numerical modeling and machine learning for efficiency and precision.</li><li>The NAD method simplifies the heat exchanger model and integrates a neural network to improve accuracy in predicting outlet values, forming a universal differential equation.</li><li>Testing against a regular FV model, the NAD model showed significant speed improvements, being 194-343 times faster in simulations across different time spans with errors less than 0.4% and an R2 score of 0.95 indicating high accuracy and reliability.</li><li>The study published in Applied Thermal Engineering highlights the NAD model's computational efficiency, accuracy, and robustness, offering a promising approach for optimizing heat exchangers in heat pumps.</li></ul>

How to optimize heat exchangers in heat pumps

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