Integrated cooling and power generation system addressing thermal protection and long-term power supply demands for scramjets: A novel design approach
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PUBLIC SUMMARY
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An integrated cooling and power generation system is designed for the scramjet.
A novel design approach is proposed to achieve the optimal all-condition performance.
A new rapid design method is developed based on Bayesian-optimized XGBoost model.
Novel design approach enhances the average power generation efficiency by 21.59%.
New rapid design method reduces the time use by 96.88% with an acceptable accuracy.
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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. -
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REFERENCES
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Yu M., Li J., Zhang Y., et al. (2026). Integrated cooling and power generation system addressing thermal protection and long-term power supply demands for scramjets: A novel design approach. The Innovation Energy 3:100172. https://doi.org/10.59717/j.xinn-energy.2026.100172
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Yu M., Li J., Zhang Y., et al. (2026). Integrated cooling and power generation system addressing thermal protection and long-term power supply demands for scramjets: A novel design approach. The Innovation Energy 3:100172. https://doi.org/10.59717/j.xinn-energy.2026.100172
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Figure 1.
Flowchart of the main research content in this study.
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Figure 2.
(A) An integrated cooling and power generation system based on supercritical CO2 Brayton cycle. (B) Calculation flowchart of off-design performance.
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Figure 3.
(A) Variations of wall temperature and heat flux density at the outlet of the combustor with time. (B-E) All-condition operating parameters of the integrated cooling and power generation system. Off-design performance of integrated cooling and power generation system of (F) Heat absorption pressure and (G) Heat release pressure. (H) All-condition performance of the integrated cooling and power generation system.
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Figure 4.
(A) Dataset used for the surrogate model. (B) Process of obtaining the surrogate model. (C & D) Mapping performance of average power generation efficiency based on Bayesian-optimized XGBoost model. (E) Time consumption for prediction of average power generation efficiency based on the Bayesian-optimized XGBoost model.
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Figure 5.
(A) Radar charts of surrogate models’ performance comparison. (B) Learning curve of the surrogate model as the sample size changes.
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Figure 6.
(A) Flowchart of the novel integrated design approach. Surrogate-assisted optimization process and robust analysis to different data splits of (B) Optimization process and (C) Robust analysis. Comparison of (D) power generation efficiency under all operating conditions and corresponding (E) power output of the novel integrated design approach and the baseline. Relative improvement in power generation efficiency of the novel integrated design approach compared to the baseline. (D) Relative improvement under all operating conditions. (E) Relative improvement in average power generation efficiency.
