Mixer Temperature Prediction via Decomposed Transformer and xLSTM with Physical Constraints

During high-altitude simulation tests, the accurate reproduction of environmental conditions directly affects the ability to reliably evaluate the engine’s performance under simulated high-altitude conditions. Traditional physical models, though interpretable, often fall short in handling nonlinear dynamics and time-delayed effects caused by disturbances such as abrupt flow rate fluctuations. For this problem, this work proposes a novel hybrid modeling framework that integrates physical principles with deep learning architectures. The proposed approach incorporates three key innovations. First, a physics-guided residual learning scheme is introduced, where the theoretical outlet temperature derived from energy conservation laws and assumptions of symmetric inlet mixing serves as a prior, and a data-driven model corrects residual deviations. Second, a multiscale feature extraction module is constructed by combining a Transformer with DWT, capturing both long-term regularities and short-term fluctuations that often exhibit quasi-symmetric patterns in structured systems. Third, a perturbation-aware memory structure is designed, fusing a Transformer branch for long-term dependency modeling with an extended LSTM (xLSTM) branch for short-term dynamic sensitivity. The experimental results on real-world test datasets demonstrate that the proposed hybrid model significantly outperforms traditional physical models. Specifically, it achieves an MAPE of 0.0156 and a R2 of 0.9867, indicating high predictive accuracy. The model not only achieves superior prediction accuracy but also preserves physical interpretability, making it a promising solution for intelligent control in industrial mixing systems.