Prediction and optimization of gas turbine secondary air system cooling efficiency based on deep learning
Secondary-air systems (SASs) are critical for maintaining material integrity and optimizing thermal performance in gas turbines (GTs) and related energy equipment. This work introduces an end-to-end framework that couples high-fidelity numerical simulation (NS) with an attention-augmented 1D-CNN (AM...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2025.2547997 |
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