Basem Ali, Yasiri M, Ghodratallah Pooya, Sharma Kamal, Raj Nimesh, Ameer S Abdul, Abdullah Mohammed Yaseen, Abdul Hussein Abbas Hameed, Al-Khuzaie Mohammed Y, Ahmedi M
Faculty of Engineering, Warith Al-Anbiyaa University, Karbala, 56001, Iraq.
Departmet of Chemical Engineering, Al-Amarah University, Maysan, Iraq.
Sci Rep. 2025 Jan 28;15(1):3516. doi: 10.1038/s41598-025-87926-4.
In this paper, the usage of a predictive surrogate model for the estimate of flow variables in the transient phase of coolant injection from the nose cone by combining the Long Short-Term Memory (LSTM) and Proper Orthogonal Decomposition (POD) technique. The velocity, pressure, and mass fraction of the counterflow jet is evaluated via this hybrid technique and the source of discrepancy of a predictive surrogate model with Full order model is explained in this study. The POD modes for the efficient prediction of the different flow variables are defined. The performance of the POD + LSTM for different ranges of training and test is evaluated and it is found that the performance of this hybrid technique is acceptable when 80% of the available data is training test. The predictive errors of coolant mass fraction and axial velocity is higher due to the complexity of the vortex in the recirculation region.
本文通过结合长短期记忆(LSTM)和本征正交分解(POD)技术，使用预测性替代模型来估计从鼻锥注入冷却剂瞬态阶段的流动变量。通过这种混合技术评估了逆流射流的速度、压力和质量分数，并解释了预测性替代模型与全阶模型之间差异的来源。定义了用于有效预测不同流动变量的POD模式。评估了POD+LSTM在不同训练和测试范围内的性能，发现当80%的可用数据用于训练测试时，这种混合技术的性能是可以接受的。由于再循环区域中涡旋的复杂性，冷却剂质量分数和轴向速度的预测误差较高。
