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Wind tunnel test scheduling method and system based on deep reinforcement learning

A technology of wind tunnel test and reinforcement learning, applied in the field of wind tunnel test, can solve the problems of lack of predictability in wind tunnel test scheduling and inability to minimize power consumption.

Active Publication Date: 2020-10-02
LOW SPEED AERODYNAMIC INST OF CHINESE AERODYNAMIC RES & DEV CENT
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Problems solved by technology

[0008] The purpose of the present invention is to provide a wind tunnel test scheduling method and system based on deep reinforcement learning, aiming to solve the technical problems that the wind tunnel test scheduling in the prior art lacks predictability and cannot minimize power consumption

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  • Wind tunnel test scheduling method and system based on deep reinforcement learning
  • Wind tunnel test scheduling method and system based on deep reinforcement learning
  • Wind tunnel test scheduling method and system based on deep reinforcement learning

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Embodiment Construction

[0050] Aspects of the invention will be described more fully hereinafter with reference to the accompanying drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to any specific structure or function presented throughout this disclosure. Rather, these aspects are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Based on the teachings herein, one skilled in the art will appreciate that the scope of the invention is intended to encompass any aspect disclosed herein, whether implemented alone or in combination with any other aspect of the invention. For example, it may be implemented using any number of means or performing methods set forth herein. Additionally, in addition to the aspects of the invention set forth herein, the scope of the invention is intended to cover apparatus or methods implemented using other structures, fu...

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Abstract

The invention is applicable to the technical field of wind tunnel tests, and provides a wind tunnel test scheduling method and system based on deep reinforcement learning. In the wind tunnel test scheduling method and system, the utilization rate of the branch pipeline and the valve opening delay time of the branch pipeline are considered, wherein one variable of the target function is the utilization rate of the branch pipeline, therefore, the distribution of the wind tunnel test scheduling pipelines can be globally considered; in the distribution, the selection of the next execution action is obtained by maximizing the target function, so that the scheduling method has predictability, the utilization rate of power resources can be maximized, the opening / closing times of branch pipelinescan be minimized, and the loss of power equipment is reduced; and the other variable of the target function is the valve opening delay time of the branch pipeline, so that the prediction accuracy canbe improved, and the smooth experiment can be ensured.

Description

technical field [0001] The invention belongs to the technical field of wind tunnel tests, and in particular relates to a wind tunnel test scheduling method and system based on deep reinforcement learning. Background technique [0002] The wind tunnel test is to fix the aircraft model or physical object in a pipe-shaped ground artificial environment (ie wind tunnel), according to the principle of relativity of motion, artificially create air flow to simulate the various complexities of aircraft or other objects in the air. An aerodynamic experimental method for obtaining experimental data to understand the aerodynamic characteristics of actual aircraft or other objects. [0003] The scheduling problem is usually defined as the problem of correspondingly allocating a set of resources in order to complete a set of work within a period of time. It exists widely in the fields of energy, transportation, production, computing, emergency medical care, security, etc. Optimization. ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06
CPCG06Q10/0631G06Q10/067
Inventor 明丽洪熊建军王桂芝罗昌俊王小飞何福袁海文侯昱珂
Owner LOW SPEED AERODYNAMIC INST OF CHINESE AERODYNAMIC RES & DEV CENT
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