Özet
This paper presents risk-based design optimization of a supersonic axisymmetric outwardturning engine inlet with geometric uncertainties by exploiting a convolutional neural network. Due to the exorbitant computational burden, a convolutional neural network-based surrogate model is implemented to be used in the prediction of engine performance parameters which are total pressure recovery and mass flow ratio. To generate a dataset, 256 unique configurations for the inlet are parametrically designed in Engineering Sketch Pad while the SU2 Suite is used to obtain a solution of supersonic flow domains. For sought-after optimum reliable design, gradient-free particle swarm optimization is incorporated with a first-order reliability method. Inlet buzz is considered as the critical phenomenon in computations of the reliability of the engine while the maximum probability of failure is limited with 10−7 in the optimum inlet configuration.
Orijinal dil | İngilizce |
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Ana bilgisayar yayını başlığı | AIAA Propulsion and Energy Forum, 2021 |
Yayınlayan | American Institute of Aeronautics and Astronautics Inc, AIAA |
ISBN (Basılı) | 9781624106118 |
DOI'lar | |
Yayın durumu | Yayınlandı - 2021 |
Etkinlik | AIAA Propulsion and Energy Forum, 2021 - Virtual, Online Süre: 9 Ağu 2021 → 11 Ağu 2021 |
Yayın serisi
Adı | AIAA Propulsion and Energy Forum, 2021 |
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???event.eventtypes.event.conference??? | AIAA Propulsion and Energy Forum, 2021 |
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Şehir | Virtual, Online |
Periyot | 9/08/21 → 11/08/21 |
Bibliyografik not
Publisher Copyright:© 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.