Incremental learning for flow field reconstruction in variable-geometry nozzles

The internal flow of nozzles governs the performance and stability of aerospace and high-speed systems, making accurate and efficient flow prediction essential. However, traditional computational fluid dynamics (CFD) approaches are still limited by prohibitive computational costs and inflexibility under variable geometric configurations. To address these challenges, this study proposes a dual-end convolutional squeeze-and-excitation (DE-ConvSE) adapter U-Net for rapid nozzle flow prediction, in which a U-Net backbone is augmented with DE-ConvSE adapters. The network integrates geometric topology encoding and boundary condition mapping to accurately reconstruct velocity, pressure, and temperature distributions. To enable efficient adaptation to previously unseen nozzle geometries, an adapter-based incremental learning strategy is introduced, where the pretrained backbone is frozen and only a small set of adapter parameters is updated. This strategy substantially improves the generalization capability of the model while maintaining high computational efficiency and mitigating catastrophic forgetting. Validation on a convergent–divergent nozzle benchmark demonstrates that the proposed approach achieves high reconstruction accuracy, with maximum symmetric relative errors below 9.14% and mean absolute errors below 5.21%. Compared with CFD solvers, the proposed method is mesh-free and provides orders-of-magnitude acceleration, highlighting its potential for real-time flow prediction and design optimization in advanced fluid dynamic systems.