Optimization design method for accelerated control law of engine life extension considering combustion chamber outlet temperature distribution
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Wei Liu
und
Haibo Zhang
Abstract
This paper proposes an accelerated optimization control law for extending the service life of aero-engine that takes into account the outlet temperature distribution factor (OTDF) of the combustion chamber. Based on the CFD numerical simulation method, a component-level model of a turbofan engine applicable to the entire flight envelope and all states and capable of real-time representation of the thermal pattern temperature of turbine blades is established. A thermal-mechanical fatigue life model of the high-pressure turbine guide vanes is established. The optimization principle of the accelerated control law for extending life is verified through simulation. The method proposed in this paper reduces the maximum temperature of the hot spot in front of the turbine by 15 K and increases the mechanical fatigue life of the turbine guide vanes by nearly 9.23 %. Compared with the traditional accelerated control law optimization method that does not consider extending life, it further reduces the highest temperature of the hot spot in front of the turbine, and the mechanical fatigue life of the turbine guide vanes increases from 4,793 cycles to 5,422 cycles, an increase of nearly 13.12 %.
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Research ethics: This study did not involve human or animal subjects; thus, no ethical approval was required. The research adhered to institutional policies and academic standards to ensure research integrity.
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Informed consent: This study did not involve human participants or personally identifiable information; therefore, informed consent was not required.
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Author contributions: The first author was responsible for study design, experiments/simulations, and data analysis. Co-authors contributed to methodology validation and manuscript revision. All authors have reviewed and approved the final version and agreed to be accountable for all aspects of the work.
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Use of Large Language Models, AI and Machine Learning Tools: No large language models (e.g., ChatGPT or similar tools) were used for data generation, analysis, or manuscript writing in this study. Artificial intelligence or machine learning tools were not used in the writing, analysis, or editing of this manuscript.
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Conflict of interest: The authors declare no financial, commercial, or personal conflicts of interest.
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Research funding: This research was supported in part by the General Project of National Natural Science Foundation of China (No. 52372389), the National Science and Technology Major Project (J2022-1-0003-0003), the Industry-University Research Cooperation Project of Aero Engine Corporation of China (No. HFZL2023CXY013), and the Forward Layout Research Special Key Cultivation Project of Nanjing University of Aeronautics and Astronautics (ILB240101A24).
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Data availability: The data supporting the findings of this study are available from the corresponding author upon reasonable request.
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