{"title":"Axial Compressor Map Generation Leveraging Autonomous Self-Training Artificial Intelligence. Phase 2","authors":"Maksym Burlaka, Sascha Podlech, Leonid Moroz","doi":"10.1115/1.4063779","DOIUrl":null,"url":null,"abstract":"Abstract This paper discusses a study performed by SoftInWay as part of a Phase II SBIR project funded by NASA. In contrast with the Phase I project (published in paper GTP-22-1328) where three discrete compressors were considered, the Phase II study was focused on addressing the problem of axial compressor long development time and cost with the use of AI models capable of predicting the geometry and performance of various multi-stage axial compressors with multiple variable vanes. The applicability of the AI models to various compressors enables the opportunity to avoid iterations between engine cycle analysis and compressor design. In this paper, automated compressor design and performance generation workflows are described. The approach for autonomous selection of the architectures and hyperparameters of Machine Learning (ML) models is explained. The uncertainty quantification techniques are considered. The developed ML-powered methods for compressor geometry prediction are discussed. The ML models' accuracy values and representations of typical geometry and performance predictions are given. The utilization of the ML models in engine cycle analysis is discussed.","PeriodicalId":15685,"journal":{"name":"Journal of Engineering for Gas Turbines and Power-transactions of The Asme","volume":"1 1","pages":"0"},"PeriodicalIF":1.4000,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Engineering for Gas Turbines and Power-transactions of The Asme","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/1.4063779","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract This paper discusses a study performed by SoftInWay as part of a Phase II SBIR project funded by NASA. In contrast with the Phase I project (published in paper GTP-22-1328) where three discrete compressors were considered, the Phase II study was focused on addressing the problem of axial compressor long development time and cost with the use of AI models capable of predicting the geometry and performance of various multi-stage axial compressors with multiple variable vanes. The applicability of the AI models to various compressors enables the opportunity to avoid iterations between engine cycle analysis and compressor design. In this paper, automated compressor design and performance generation workflows are described. The approach for autonomous selection of the architectures and hyperparameters of Machine Learning (ML) models is explained. The uncertainty quantification techniques are considered. The developed ML-powered methods for compressor geometry prediction are discussed. The ML models' accuracy values and representations of typical geometry and performance predictions are given. The utilization of the ML models in engine cycle analysis is discussed.
期刊介绍:
The ASME Journal of Engineering for Gas Turbines and Power publishes archival-quality papers in the areas of gas and steam turbine technology, nuclear engineering, internal combustion engines, and fossil power generation. It covers a broad spectrum of practical topics of interest to industry. Subject areas covered include: thermodynamics; fluid mechanics; heat transfer; and modeling; propulsion and power generation components and systems; combustion, fuels, and emissions; nuclear reactor systems and components; thermal hydraulics; heat exchangers; nuclear fuel technology and waste management; I. C. engines for marine, rail, and power generation; steam and hydro power generation; advanced cycles for fossil energy generation; pollution control and environmental effects.