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Aero-Engine Installation Performance Evaluation Method Based on Pre-trained Model
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DOI：10.1016/j.ijmecsci.2025.111083.png)
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Abstract
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• A STN-MLP-based ML framework is proposed for aero-engine installation performance evaluation. • This approach enables precise, unsupervised installed thrust assessment. • Mechanism-data hybrid modeling effectively improves the forecasting model accuracy. • STN structure avoids the effect of flight boundary conditions. • Mechanism modification offers a significant benefit under small data samples.
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