Agile SBO Framework Exploiting Multisimulation Data: Optimising Efficiency and Stall Margin of a Transonic Compressor
Agile SBO Framework Exploiting Multisimulation Data: Optimising Efficiency and Stall Margin of a Transonic Compressor
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敏捷 SBO 框架利用多重仿真数据：优化 Transonic 压缩机的效率和失速裕度
DOI：
10.1115/gt2018-76639
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
I. Lepot
中科院分区：
文献类型：
--
作者：
L. Baert;Christophe Dumeunier;M. Leborgne;C. Sainvitu;I. Lepot
涡轮机械设计已经成为一个仿真驱动的过程，永远面临着减少周期时间和进一步整合复杂性和多种物理的双重需求。这种二元性推动了高维设计空间，并有利于多仿真环境，评估不同的操作点，不同的学科，甚至不同的保真度水平。为了管理CPU成本，基于代理的优化（SBO）已成为一种既定的方法。高效SBO的关键使能因素之一是能够避免在设计空间中发生仿真失败的区域，这在多仿真环境中特别重要，因为单个计算的失败并不一定意味着其他计算的系统失败。
目前的工作提出了创新的自适应代理，利用插值/回归和分类的混合，已在集成优化平台Minamo中实现。基于NASA Rotor 37的多模拟演示器已经建立，以执行气动机械多点优化。由于优化的目标之一是提高失速裕度，气动模拟被迫与数值稳定性极限相联系。结果表明，不同的成功模型的引入有一个有益的影响的过程中的多模拟优化。通过使用部分信息获得的改进的模型质量允许更有效的搜索过程，同时获得了更好的全局成功率。
Turbomachinery design has become a simulation-driven process, permanently confronted to the dual need to reduce the cycle time and to further integrate complexity and multiple physics. This duality pushes towards high-dimensional design spaces and favours a multisimulation environment that assesses different operating points, different disciplines, or even different fidelity levels. To manage CPU cost, surrogate-based optimisation (SBO) has become an established approach. One of the key enablers for efficient SBO is being able to avoid the regions in the design space where simulation failures occur, and this is of a particular interest in a multisimulation environment since the failure of a single computation does not necessarily imply the systematic failing of the other computations.
The current work presents innovative auto-adaptive surrogates, exploiting a blend of interpolation/regression and classification, that have been implemented in the integrated optimisation platform Minamo. A multisimulation demonstrator, based on NASA Rotor 37, has been set up to perform an aero-mechanical multi-point optimisation. With one of the objectives of the optimisation to improve the stall margin, the aerodynamic simulations are forced to flirt with the numerical stability limits. It is shown that the introduction of different success models has a beneficial impact on the course of the multisimulation optimisation. The improved model quality, obtained by using partial information, has allowed for a more efficient search process, and at the same time a better global success rate has been obtained.