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基于深度学习技术的激波风洞智能测力系统研究

汪运鹏, 杨瑞鑫, 聂少军, 姜宗林

汪运鹏, 杨瑞鑫, 聂少军, 姜宗林. 基于深度学习技术的激波风洞智能测力系统研究[J]. 力学学报, 2020, 52(5): 1304-1313. DOI: 10.6052/0459-1879-20-190
引用本文: 汪运鹏, 杨瑞鑫, 聂少军, 姜宗林. 基于深度学习技术的激波风洞智能测力系统研究[J]. 力学学报, 2020, 52(5): 1304-1313. DOI: 10.6052/0459-1879-20-190
Wang Yunpeng, Yang Ruixin, Nie Shaojun, Jiang Zonglin. DEEP-LEARNING-BASED INTELLIGENT FORCE MEASUREMENT SYSTEM USING IN A SHOCK TUNNEL[J]. Chinese Journal of Theoretical and Applied Mechanics, 2020, 52(5): 1304-1313. DOI: 10.6052/0459-1879-20-190
Citation: Wang Yunpeng, Yang Ruixin, Nie Shaojun, Jiang Zonglin. DEEP-LEARNING-BASED INTELLIGENT FORCE MEASUREMENT SYSTEM USING IN A SHOCK TUNNEL[J]. Chinese Journal of Theoretical and Applied Mechanics, 2020, 52(5): 1304-1313. DOI: 10.6052/0459-1879-20-190
汪运鹏, 杨瑞鑫, 聂少军, 姜宗林. 基于深度学习技术的激波风洞智能测力系统研究[J]. 力学学报, 2020, 52(5): 1304-1313. CSTR: 32045.14.0459-1879-20-190
引用本文: 汪运鹏, 杨瑞鑫, 聂少军, 姜宗林. 基于深度学习技术的激波风洞智能测力系统研究[J]. 力学学报, 2020, 52(5): 1304-1313. CSTR: 32045.14.0459-1879-20-190
Wang Yunpeng, Yang Ruixin, Nie Shaojun, Jiang Zonglin. DEEP-LEARNING-BASED INTELLIGENT FORCE MEASUREMENT SYSTEM USING IN A SHOCK TUNNEL[J]. Chinese Journal of Theoretical and Applied Mechanics, 2020, 52(5): 1304-1313. CSTR: 32045.14.0459-1879-20-190
Citation: Wang Yunpeng, Yang Ruixin, Nie Shaojun, Jiang Zonglin. DEEP-LEARNING-BASED INTELLIGENT FORCE MEASUREMENT SYSTEM USING IN A SHOCK TUNNEL[J]. Chinese Journal of Theoretical and Applied Mechanics, 2020, 52(5): 1304-1313. CSTR: 32045.14.0459-1879-20-190

基于深度学习技术的激波风洞智能测力系统研究

基金项目: 1)国家自然科学基金资助项目(11672357)
详细信息
    通讯作者:

    汪运鹏

  • 中图分类号: V211.751

DEEP-LEARNING-BASED INTELLIGENT FORCE MEASUREMENT SYSTEM USING IN A SHOCK TUNNEL

  • 摘要: 高焓条件气动力测量试验对高超声速飞行器气动外形设计和优化起决定性作用. 通常采用脉冲风洞(如激波风洞)产生高温、高压驱动气体以模拟高超声速高焓试验气流. 在脉冲风洞对高超飞行器模型进行测力试验时, 测力天平输出信号结果无法摆脱惯性载荷的干扰影响, 其导致的测力模型低频振动问题基本无法通过滤波彻底解决, 尤其对试验时间只有几毫秒的情况, 六分量测力天平的结构设计研究受到了极大挑战. 因此, 对实现短试验时间条件高性能测力的深入研究发现, 天平动态校准凸显重要性和必要性. 本研究提出一种新的基于人工智能深度学习技术的单矢量动态自校准方法和智能测力系统概念, 并应用于目前激波风洞测力试验中. 该动校方法的最主要特点之一是对整体测力系统的校准, 而非仅仅针对天平, 并且保证校准的测力系统即为风洞试验对象, 确保校准与应用的一致性. 在测试评估中, 测试样本和风洞试验验证均得到了较为理想的效果, 大幅度低频振动干扰基本被消除, 脉冲风洞测力的精度和可靠性得到了大幅提高.
    Abstract: Aerodynamic force measurement in high-enthalpy flow is very important for the design and optimization of hypersonic vehicles. Currently, impulse facilities are used for generating high-temperature and high-pressure driving gas to simulate the high-enthalpy flow with hypersonic flight-conditions, such as a shock tunnel. However, when force tests are conducted in an impulse facility, the inertial force has a large influence on the measuring results, which creates low-frequency vibrations of the test model and its motion cannot be addressed through digital filtering. In the case of a few milliseconds of test time, the structural design of the six-component balance is greatly challenged. Therefore, dynamic calibration becomes very important for improving the precision and accuracy of force measurement during short-duration. A new method, deep-learning-based single-vector dynamic self-calibration of the force measurement system, and intelligent force measurement system are proposed for obtaining high-accurate aerodynamic force in impulse facilities. One of the main features of this dynamic calibration method is the calibration of the overall force measurement system, not just the balance. Applying this method, the calibrated force measurement system is the wind tunnel test object, which ensures the consistency of calibration and application. In the evaluation, the test verification has achieved relatively ideal results, the large-scale low-frequency vibration interference has been basically eliminated, and the accuracy and reliability of the force measurement in impulse facility have been greatly improved.
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    其他类型引用(7)

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  • 被引次数: 23
出版历程
  • 收稿日期:  2020-06-05
  • 刊出日期:  2020-10-09

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