Serial and Parallel Fast-Floquet Analyses in Rotorcraft Aeroelasticity

S. Subramanian, S. Venkataratnam, G. Gaonkar
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Abstract

Rotorcraft stability investigation involves a nonlinear trim analysis for the control inputs and periodic responses, and, as a follow-up, a linearized stability analysis for the Floquet transition matrix (FTM), and its eigenvalues and eigenvectors. The trim analysis is based on a shooting method with damped Newton iteration, which gives the FTM as a byproduct, and the eigenanalysis on the QR method; the corresponding trim and stability analyses are collectively referred to as the Floquet analysis. A rotor with Q blades that are identical and equally spaced has Q planes of symmetry. Exploiting this symmetry, the fast-Floquet analysis, in principle, reduces the run time and frequency indeterminacy of the conventional Floquet analysis by a factor of Q. It is implemented on serial computers and on all three types of mainstream parallel-computing hardware: SIMD and MIMD computers, and a distributed computing system of networked workstations; large models with hundreds of states are treated. A comprehensive database is presented on computational reliability such as the eigenvalue condition number and on parallel performance such as the speedup and efficiency, which show, respectively, how fast a job can be completed with a set of processors and how well their idle times are minimized. Despite the Q-fold reduction, the serial run time is excessive and grows between quadratically and cubically with the number of states. By contrast, the parallel run time can be reduced dramatically and its growth can be controlled by a judicious combination of speedup and efficiency. Moreover, the parallel implementation on a distributed computing system is as routine as the serial implementation.
旋翼机气动弹性的串行和并行快速小波分析
旋翼机稳定性研究包括对控制输入和周期响应的非线性修整分析,以及对Floquet转移矩阵(FTM)及其特征值和特征向量的线性化稳定性分析。修剪分析基于阻尼牛顿迭代的射击法,该方法将FTM作为副产物,并在QR法上进行特征分析;相应的修剪和稳定性分析统称为Floquet分析。一个转子有Q个相同且等距的叶片,有Q个对称面。利用这种对称性,快速Floquet分析在原则上将传统Floquet分析的运行时间和频率不确定性降低了1倍。它在串行计算机和所有三种主流并行计算硬件上实现:SIMD和MIMD计算机,以及网络化工作站的分布式计算系统;处理具有数百个状态的大型模型。给出了计算可靠性(如特征值条件数)和并行性能(如加速和效率)的综合数据库,分别显示了使用一组处理器完成作业的速度和最小化空闲时间的程度。尽管减少了q倍,串行运行时间还是过多,并且随着状态的数量在二次和三次之间增长。相比之下,并行运行时间可以大大减少,并且可以通过明智地结合加速和效率来控制其增长。此外,在分布式计算系统上并行实现与串行实现一样常规。
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