Abstract
Hypersonic vehicles represent a new strategic frontier in aerospace technology, with the scramjet as their mainstream propulsion system. However, the scramjet presents significant challenges in thermal protection and electricity supply due to the long-term high-Mach-number operation and the lack of power drive shaft. Designing integrated cooling and power generation systems (ICPGSs) based on the supercritical CO2 Brayton cycle can effectively solve these problems, with the advantages of high efficiency, compactness and non-coking. Nonetheless, its long-term operation under off-design conditions deviates from the rated-design condition. A novel integrated design approach is proposed by combining ensemble learning with heuristic algorithms to ensure a truly optimal all-condition performance. Different from existing studies that treat off-design analysis as a post-hoc validation, this design approach embeds a feedback mechanism into the rated-design optimization through ensemble-learning-based surrogate models and a synchronous iterative optimization of design and operation through heuristic algorithms, resulting in an improvement of up to 21.59%, which yields both a lower heat dissipation load and a larger electricity supply capacity. To reduce the unacceptable time consumption of numerical solutions in the optimization process, six surrogate models are considered and compared. A rapid design method based on a Bayesian-optimized XGBoost model is proposed, which enables a rapid and accurate prediction of system performance under diverse conditions, with the reduction in time consumption reaching up to 96.88%, and the mean absolute percentage error being only 1.59%. The findings provide theoretical support for the optimal design of ICPGSs and the performance enhancement of scramjets.
| Original language | English |
|---|---|
| Article number | 100172 |
| Journal | Innovation Energy |
| Volume | 3 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 24 Jul 2026 |
| Externally published | Yes |
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