A Study on Aeroengine Conceptual Design Considering Multi-Mission Performance Reliability
Owing to the realization of multi-mission adaptability requires more complex mechanical structure, the candidates of future aviation propulsion are confronted with more overall reliability problems than that of the conventional gas turbine engine. This situation is challenging to a traditional aeroe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/13/4668 |
| _version_ | 1827713542396051456 |
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| author | Dalu Cao Guangchen Bai |
| author_facet | Dalu Cao Guangchen Bai |
| author_sort | Dalu Cao |
| collection | DOAJ |
| description | Owing to the realization of multi-mission adaptability requires more complex mechanical structure, the candidates of future aviation propulsion are confronted with more overall reliability problems than that of the conventional gas turbine engine. This situation is challenging to a traditional aeroengine deterministic design method. To overcome this challenge, the Reliability-based Multi-Design Point Methodology is proposed for aeroengine conceptual design. The presented methodology adopted an unconventional approach of engaging the reliability prediction by artificial neural network (ANN) surrogate models rather than the time-consuming Monte Carlo (MC) simulation. Based on the Adaptive Particle swarm optimization, the utilization of the pre-training technique optimizes the initial network parameters to acquire better-conditioned initial network, which is sited closer to designated optimum so that contributes to the convergence property. Moreover, a new hybrid algorithm is presented to integrate the pre-training technique into neural network training procedure in order to enhance the ANN performance. The proposed methodology is applied to the cycle design of a turbofan engine with uncertainty component performance. The testing results certify that the prediction accuracy of pre-trained ANN is improved with negligible computational cost, which only spent nearly one-millionth as much time as the MC-based probabilistic analysis (0.1267 s vs. 95,262 s, for 20 testing samples). The MC simulation results substantiate that optimal cycle parameters precisely improve the engine overall performance to simultaneously reach expected reliability (≥98.9%) in multiple operating conditions without unnecessary performance redundancy, which verifies the efficiency of the presented methodology. The presented efforts provide a novel approach for aeroengine cycle design, and enrich reliability design theory as well. |
| first_indexed | 2024-03-10T18:38:24Z |
| format | Article |
| id | doaj.art-4d9d1b677f4849bba756ddb6cbd93bed |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T18:38:24Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4d9d1b677f4849bba756ddb6cbd93bed2023-11-20T06:01:53ZengMDPI AGApplied Sciences2076-34172020-07-011013466810.3390/app10134668A Study on Aeroengine Conceptual Design Considering Multi-Mission Performance ReliabilityDalu Cao0Guangchen Bai1School of Energy and Power Engineering, Beihang University, Beijing 102206, ChinaSchool of Energy and Power Engineering, Beihang University, Beijing 102206, ChinaOwing to the realization of multi-mission adaptability requires more complex mechanical structure, the candidates of future aviation propulsion are confronted with more overall reliability problems than that of the conventional gas turbine engine. This situation is challenging to a traditional aeroengine deterministic design method. To overcome this challenge, the Reliability-based Multi-Design Point Methodology is proposed for aeroengine conceptual design. The presented methodology adopted an unconventional approach of engaging the reliability prediction by artificial neural network (ANN) surrogate models rather than the time-consuming Monte Carlo (MC) simulation. Based on the Adaptive Particle swarm optimization, the utilization of the pre-training technique optimizes the initial network parameters to acquire better-conditioned initial network, which is sited closer to designated optimum so that contributes to the convergence property. Moreover, a new hybrid algorithm is presented to integrate the pre-training technique into neural network training procedure in order to enhance the ANN performance. The proposed methodology is applied to the cycle design of a turbofan engine with uncertainty component performance. The testing results certify that the prediction accuracy of pre-trained ANN is improved with negligible computational cost, which only spent nearly one-millionth as much time as the MC-based probabilistic analysis (0.1267 s vs. 95,262 s, for 20 testing samples). The MC simulation results substantiate that optimal cycle parameters precisely improve the engine overall performance to simultaneously reach expected reliability (≥98.9%) in multiple operating conditions without unnecessary performance redundancy, which verifies the efficiency of the presented methodology. The presented efforts provide a novel approach for aeroengine cycle design, and enrich reliability design theory as well.https://www.mdpi.com/2076-3417/10/13/4668aircraft engineperformance reliabilitycycle designartificial neural networkpre-training technique |
| spellingShingle | Dalu Cao Guangchen Bai A Study on Aeroengine Conceptual Design Considering Multi-Mission Performance Reliability Applied Sciences aircraft engine performance reliability cycle design artificial neural network pre-training technique |
| title | A Study on Aeroengine Conceptual Design Considering Multi-Mission Performance Reliability |
| title_full | A Study on Aeroengine Conceptual Design Considering Multi-Mission Performance Reliability |
| title_fullStr | A Study on Aeroengine Conceptual Design Considering Multi-Mission Performance Reliability |
| title_full_unstemmed | A Study on Aeroengine Conceptual Design Considering Multi-Mission Performance Reliability |
| title_short | A Study on Aeroengine Conceptual Design Considering Multi-Mission Performance Reliability |
| title_sort | study on aeroengine conceptual design considering multi mission performance reliability |
| topic | aircraft engine performance reliability cycle design artificial neural network pre-training technique |
| url | https://www.mdpi.com/2076-3417/10/13/4668 |
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