Konstantinos I. Papadopoulos, Christos P. Nasoulis, Vasilis Gkoutzamanis, Anestis I. Kalfas
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The best performing application decreases computational time by two orders of magnitude, while retaining equal accuracy and consistency as the original model. It is employed for creating a dataset for training an artificial neural network against random mission patterns. The trained network is integrated into a surrogate model. The latter part of the analysis evaluates optimized mission profile characteristics with respect to energy consumption, against a benchmark flight-path. The combined optimization process decreases the multi-hour-scale timeframe by two orders of magnitude to a 3-minute sequence. Using the novel framework, a 12% average energy consumption benefit is calculated for short, medium and long regional missions, against equivalent benchmark profiles.","PeriodicalId":15685,"journal":{"name":"Journal of Engineering for Gas Turbines and Power-transactions of The Asme","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Flight-Path Optimization for a Hybrid-Electric Aircraft\",\"authors\":\"Konstantinos I. Papadopoulos, Christos P. Nasoulis, Vasilis Gkoutzamanis, Anestis I. Kalfas\",\"doi\":\"10.1115/1.4063707\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract This study aims to illustrate a sequence that optimizes the flight-path trajectory for a hybrid-electric propulsion system at mission level, in addition to identifying the respective optimum power management strategy. An in-house framework for hybrid-electric propulsion system modeling is utilized. A hybrid-electric commuter aircraft serves as a virtual test-bench. Vectorized calculations, decision variable count and optimization algorithms are considered for reducing the computational time of the framework. Performance improvements are evaluated for the aircraft's design mission profile. Total energy consumption is set as the objective function. Emphasis lies on minimizing the average value and standard deviation of the energy consumption and timeframe metrics. The best performing application decreases computational time by two orders of magnitude, while retaining equal accuracy and consistency as the original model. It is employed for creating a dataset for training an artificial neural network against random mission patterns. The trained network is integrated into a surrogate model. 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Flight-Path Optimization for a Hybrid-Electric Aircraft
Abstract This study aims to illustrate a sequence that optimizes the flight-path trajectory for a hybrid-electric propulsion system at mission level, in addition to identifying the respective optimum power management strategy. An in-house framework for hybrid-electric propulsion system modeling is utilized. A hybrid-electric commuter aircraft serves as a virtual test-bench. Vectorized calculations, decision variable count and optimization algorithms are considered for reducing the computational time of the framework. Performance improvements are evaluated for the aircraft's design mission profile. Total energy consumption is set as the objective function. Emphasis lies on minimizing the average value and standard deviation of the energy consumption and timeframe metrics. The best performing application decreases computational time by two orders of magnitude, while retaining equal accuracy and consistency as the original model. It is employed for creating a dataset for training an artificial neural network against random mission patterns. The trained network is integrated into a surrogate model. The latter part of the analysis evaluates optimized mission profile characteristics with respect to energy consumption, against a benchmark flight-path. The combined optimization process decreases the multi-hour-scale timeframe by two orders of magnitude to a 3-minute sequence. Using the novel framework, a 12% average energy consumption benefit is calculated for short, medium and long regional missions, against equivalent benchmark profiles.
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.