电子测量与仪器学报

陈志,席隆.全程可控的微重力试验装置应变传感器布局优化研究[J].电子测量与仪器学报,2021,35(8):62-69

全程可控的微重力试验装置应变传感器布局优化研究

Study on the placement optimization of strain sensors in theCSU electromagnetic microgravity tower

DOI：

英文关键词:quantum particle swarm optimization deformation reconstruction optimal sensor placement strain sensor

基金项目:载人航天工程空间应用系统项目、中科院科研仪器研制项目（YJKYYQ20180017）资助

作者	单位
陈志	1. 中国科学院空间应用工程与技术中心，2. 中国科学院大学，3. 中国科学院太空应用重点实验室
席隆	1. 中国科学院空间应用工程与技术中心，3. 中国科学院太空应用重点实验室

Author	Institution
Chen Zhi	1. Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences,2. University of Chinese Academy of Sciences,3. Key Laboratory of Space Utilization, Chinese Academy of Sciences
Xi Long	1. Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences,3. Key Laboratory of Space Utilization, Chinese Academy of Sciences

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中文摘要:

在建的全程可控的地基微重力试验设备是一种采用直线电机驱动实验舱体,以上抛下落方式来产生微重力环境的新型落塔装置,其支撑结构上的微小形变是决定实验舱能否在轨道上平顺运行的关键因素。为了更加科学有效地布置应变传感器对其进行监测,将改进后的量子粒子群算法用于传感器布局优化。以有限元模型作为实际算例,比较和验证了粒子群算法、量子粒子群算法以及改进量子粒子群 3 种算法布局优化策略在形变重构上的有效性和优劣性,改进后的量子粒子群算法得到的重构形变平均绝对误差为最大形变的 1. 2%。该结果表明对量子粒子群算法的改进方法是有效的,同时也说明了随机算法用于形变重构的传感器优化布置是可行的。

英文摘要:

The CSU Electromagnetic Microgravity Tower under construction is a new type of drop tower which uses the linear motor to drive the experimental cabin and uses the upthrow and drop method to generate the microgravity environment. The tiny deformation on the supporting structure is the key factor to determine whether the experimental cabin can run smoothly on the track. In order to arrange the strain sensors more scientifically and effectively to monitor it, the improved quantum particle swarm optimization algorithm is applied to the sensor placement optimization. The finite element model is taken as an example to compare and verify the validity and superiority of the three placement optimization strategies of particle swarm optimization algorithm, quantum particle swarm optimization algorithm and improved quantum particle swarm optimization algorithm in the deformation reconstruction. The average absolute error of the improved quantum particle swarm optimization algorithm is only 1. 2% of the maximum deformation. The result shows that the improved method of quantum particle swarm optimization algorithm is effective, and it also confirms that the stochastic algorithm is feasible to optimize the sensor placement for deformation reconstruction.

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