Minimum specific fuel consumption performance-seeking control for variable cycle engine based on CHOA-SQP hybrid algorithm and active disturbance rejection control
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Tianfu Fan
und
Songlin Li
Abstract
To reduce specific fuel consumption (SFC) of aero-engine, increase the combat radius of fighter jets, improve economic performance, and contribute to energy conservation and emission reduction, this paper proposes a hybrid optimization algorithm combining the chimp optimization algorithm (ChOA) and sequential quadratic programming (SQP). The algorithm ensures effective optimization while significantly reducing solving time. Focusing on a double bypass variable cycle engine, a sensitivity analysis is conducted to select optimal control variables. An intelligent optimization control system integrates ChOA-SQP with active disturbance rejection control (ADRC) for minimum SFC optimization. Results indicate that ChOA achieves a 7.28 % average SFC reduction, SQP achieves 6.71 %, and ChOA-SQP achieves 7.47 %, with ChOA-SQP reducing SFC an additional 0.57 % compared to SQP and 0.19 % compared to ChOA. Convergence time is only 24.25 % of ChOA and 109.75 % of SQP, with a convergence speed faster than ChOA and similar to SQP. The system transitions seamlessly to the minimum SFC point while ensuring safe engine operation, reducing SFC by 7.58 % at the subsonic cruise design point and 5.69 % at the supersonic cruise design point, with maximum thrust fluctuations of 0.87 % and 0.95 %, respectively.
Acknowledgment
This study was co-supported by the National Science and Technology Major Project of China (No. 2019-Ⅲ-0001-0044) and the Science Center for Gas Turbine Project of China (No. 2022-DB-V-002-001).
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: This study was co-supported by the National Science and Technology Major Project of China (No. 2019-Ⅲ-0001-0044) and the Science Center for Gas Turbine Project of China (No. 2022-DB-V-002-001).
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Data availability: Not applicable.
Nomenclature
- C p
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pressure recovery coefficient
- A
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area (m2)
- a
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coefficient vector (−)
- Chaotic
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value of the chaotic mapping (−)
- c
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coefficient vector (−)
- d
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distance between the chimpanzees and the prey (−)
- F
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thrust (kN)
- f
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coefficient vector (−)
- H
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altitude (m)
- Ma
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Mach number (−)
- m
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chaotic vector (−)
- m 0
-
air flow (kg/s)
- N
-
spool rotational speed (%)
- r
-
random vector (−)
- SM
-
surge margin (%)
- SFC
-
specific fuel consumption (kg/(N*h))
- T
-
temperature (K)
- t
-
number of iteration (−)
- W f
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fuel flow (kg/s)
- x
-
current position of chimpanzees (−)
- α
-
guide vane angle (°)
- β
-
injector opening (%)
- μ
-
random number (−)
- π
-
compression ratio or expansion ratio (−)
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