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Using HPC in Gas Turbines Blade Fault Diagnosis

机译:在涡轮机叶片故障诊断中使用HPC

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摘要

Parallelization of two approaches used for identification of faults in blades of a gas turbine is presented. The first approach, termed as direct simulation, is aiming to populate a fault diagnosis database allowing early identification of faults. The second approach, termed as the inverse one, gives a more focused solution to fault identification, by using the direct approach in an iterative way which permits the estimation of the blade geometry alterations. By using state of the art aprallel tools such as ScaLAPACK library and exploiting inherent coarse-grained parallelism in calculating the elements of the Jacobian needed in the iterative method encouraging speedups have been obtained. The test cases presented include theoretically produced fault signals as well as experimental cases, where actual measurement data are shown to produce in quasi real time the geometrical deformations which existed in the test engine.
机译:提出了两种用于识别燃气轮机叶片故障的方法的并行化。第一种方法称为直接仿真,旨在填充故障诊断数据库,以便尽早识别故障。第二种方法称为逆方法,它通过以迭代方式使用直接方法为故障识别提供更集中的解决方案,该方法允许估计叶片几何形状的变化。通过使用最先进的工具,例如ScaLAPACK库,并利用固有的粗粒度并行性来计算迭代方法所需的Jacobian元素,从而获得了加速效果。给出的测试案例包括理论上产生的故障信号以及实验案例,其中实际测量数据显示为准实时生成测试引擎中存在的几何变形。

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