全程可控的微重力试验装置应变传感器布局优化研究

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。

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

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