A reverse design method for cryocooler regenerator based on artificial neural network

In conventional cryocooler regenerator design, researchers typically employ specialized software to traverse numerous parameter combinations to assess performance, comparing the results against predetermined requirements to determine the optimal structural dimensions and operating parameters. However, this approach suffers from low research and development efficiency, and the optimal solution may not be included in the tested cases. To address these issues, this study proposes a novel reverse design methodology that directly calculates the regenerator structural dimensions and operating parameters based on predefined target performance. The method centers around the deep learning artificial neural network (ANN) model as its core, effectively addressing the challenge of setting input and output parameters during the model’s learning process using the performance dataset obtained through forward calculation of the regenerator. Additionally, a wide-range and multi-step input parameter adjustment mechanism is incorporated, thereby ensuring the method’s adaptability and practical applicability. The optimized ANN model demonstrates promising results, with an average relative error of 2.36% for regenerator length prediction, 4.79% for cold end mass flow prediction, and 4.73% for coefficient of performance (COP) prediction. Extensive model accuracy assessments confirm the model’s predictive capabilities across various intervals of the predicted variables. By utilizing the rapid computational speed of deep learning models and their proficiency in parallel processing, the method proposed in this study dramatically shortens the regenerator design time to mere seconds, thereby introducing a pioneering approach for rapid and precise regenerator design.