Airfoil sweepback deformation optimization method based on aircraft fuel economy, electronic equipment and storage medium
By using the DDPG algorithm to train the agent to optimize the sweep angle of the wing surface, the problems of insufficient fuel economy and range in traditional aircraft design are solved, and more efficient fuel use and longer range are achieved.
CN119740324AActive Publication Date: 2025-04-01HARBIN INST OF TECH
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Patent Information
- Application Number
- CN202411937778.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-26
- Publication Date
- 2025-04-01
- Estimated Expiration
- 2044-12-26
AI Technical Summary
Technical Problem
In traditional aircraft design, fixed wing surface sweep angles cannot provide the best fuel economy and range under different flight conditions, and the prior art is difficult to effectively solve this problem.
Method used
The deep deterministic policy gradient (DDPG) algorithm is used to train the agent, and optimize the sweep angle of the wing surface through intelligent decision-making to optimize the aircraft fuel economy.
Benefits of technology
Through intelligent decision-making, the aircraft's range and fuel economy are significantly improved, adapting to complex and changing flight conditions, and handling the needs of continuous action space.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses an airfoil sweepback deformation optimization method based on aircraft fuel economy, electronic equipment and a storage medium, and belongs to the technical field of aerospace engineering. In order to improve the voyage by intelligently deciding the optimal airfoil sweepback angle, an intelligent agent network is constructed and comprises an actor network and a commentator network, the input of the actor network is a state, and the output of the actor network is an action; setting an initial environment of the hypersonic aircraft, defining states corresponding to the hypersonic aircraft, including flight height, speed, voyage and mass, defining an action corresponding to the hypersonic aircraft as an airfoil sweepback angle, and defining an award corresponding to the hypersonic aircraft as fuel consumption and an influence relationship thereof; training the obtained actor network and the commentator network based on the hypersonic flight vehicle by using a DDPG algorithm improved by a successful sample playback method to obtain an airfoil sweepback deformation optimization model based on the fuel economy of the flight vehicle; and performing an airfoil sweepback optimization decision under the flight condition.
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