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APS -70th Annual Meeting of the APS Division of Fluid Dynamics- Event - Extreme sensitivity in Thermoacoustics

机译:APS-流体动力学APS部门第70届年会-事件-热声中的极度敏感性

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In rocket engines and gas turbines, fluctuations in the heat release rate can lock in to acoustic oscillations and grow catastrophically. Nine decades of engine development have shown that these oscillations are difficult to predict but can usually be eliminated with small ad hoc design changes. The difficulty in prediction arises because the oscillations' growth rate is exceedingly sensitive to parameters that cannot always be measured or simulated reliably, which introduces severe systematic error into thermoacoustic models of engines. Passive control strategies then have to be devised through full scale engine tests, which can be ruinously expensive. For the Apollo F1 engine, for example, 2000 full-scale tests were required. Even today, thermoacoustic oscillations often re-appear unexpectedly at full engine test stage. Although the physics is well known, a novel approach to design is required. In this presentation, the parameters of a thermoacoustic model are inferred from many thousand automated experiments using inverse uncertainty quantification. The adjoint of this model is used to obtain cheaply the gradients of every unstable mode with respect to the model parameters. This gradient information is then used in an optimization algorithm to stabilize every thermoacoustic mode by subtly changing the geometry of the model.
机译:在火箭发动机和燃气轮机中,放热率的波动会锁定于声波振荡,并急剧增长。九十年的发动机开发表明,这些振荡很难预测,但通常可以通过进行小规模的临时设计更改而消除。由于振荡的增长率对无法始终可靠测量或模拟的参数极其敏感,因此出现了预测困难,这将严重的系统误差引入了发动机的热声模型中。然后必须通过全面的发动机测试来设计被动控制策略,这可能会非常昂贵。例如,对于Apollo F1发动机,需要进行2000次全面测试。即使在今天,在整个发动机测试阶段,热声振荡也经常会意外地再次出现。虽然物理学是众所周知的,但仍需要一种新颖的设计方法。在此演示中,使用逆不确定性量化从数千个自动实验中推导出热声模型的参数。该模型的伴随物用于廉价地获得每个不稳定模式相对于模型参数的梯度。然后,此梯度信息将用于优化算法中,通过巧妙地更改模型的几何形状来稳定每个热声模式。

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