Multi-fidelity Methods in Aerodynamic Robust Optimization
Multi-fidelity Methods in Aerodynamic Robust Optimization
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DOI：
10.2514/6.2016-0680
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发表时间：
2016-01
期刊：
影响因子：
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通讯作者：
A. S. Padron;J. Alonso;M. Eldred
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文献类型：
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作者：
A. S. Padron;J. Alonso;M. Eldred
为了设计鲁棒可靠的航空航天系统，有必要对不确定性对系统行为的影响进行适当的量化。使用最高保真度的方法执行鲁棒优化是需要的，尽管这是不可行的，因为在优化迭代中需要进行许多模拟来计算系统性能的统计数据，这将导致禁止的计算成本。本文描述了一种多保真度方法来实现高保真鲁棒优化。我们的多保真度方法使用由低保真度模型和模型修正相结合构建的多项式混沌展开来近似每次优化迭代中使用的高保真统计量和统计量的梯度。模型修正解释了高保真度（计算流体动力学RANS）模型和低保真度（CFD欧拉）模型之间的差异。多保真度方法的一个关键特点是它结合了CFD的解析梯度（伴随）来获得统计量的梯度。将多保真度方法应用于RAE2822翼型在不确定流动条件下的鲁棒优化，结果表明，与高保真度优化相比，该方法可节省60% ~ 90%的计算量。
In order to design robust and reliable aerospace systems it is necessary to properly quantify the effect of uncertainties on the systems’ behavior. Performing a robust optimization with the highest fidelity method is desired albeit not feasible because of the prohibited computational cost associated with the many simulations needed in the optimization iterations to compute statistics of the system’s performance. Here we describe a multi-fidelity method to enable high-fidelity robust optimization. Our multi-fidelity method uses a polynomial chaos expansion constructed from the combination of a low-fidelity model and a model correction to approximate the high-fidelity statistics and the gradients of the statistics used in each optimization iteration. The model correction accounts for the difference between the high-fidelity (Computational Fluid Dynamics RANS) model and the low-fidelity (CFD Euler) model. A key feature of the multi-fidelity method is its incorporation of analytic gradients (adjoints) from the CFD to obtain the gradients of the statistics. The application of the multi-fidelity method to the robust optimization of an RAE2822 airfoil subject to uncertain flow conditions shows that 60% to 90% computational savings can be achieved when compared to the high-fidelity optimization.