The design process of centrifugal compressors requires applying an optimization process which is computationally expensive due to complex analytical equations underlying the compressor's dynamical equations. Although the regression surrogate models could drastically reduce the computational cost of such a process, the major challenge is the scarcity of data for training the surrogate model. Aiming to strategically exploit the labeled samples, we propose the Active-CompDesign framework in which we combine a thermodynamics-based compressor model (i.e., our internal software for compressor design) and Gaussian Process-based surrogate model within a deployable Active Learning (AL) setting. We first conduct experiments in an offline setting and further, extend it to an online AL framework where a real-time interaction with the thermodynamics-based compressor's model allows the deployment in production. ActiveCompDesign shows a significant performance improvement in surrogate modeling by leveraging on uncertainty-based query function of samples within the AL framework with respect to the random selection of data points. Moreover, our framework in production has reduced the total computational time of compressor's design optimization to around 46% faster than relying on the internal thermodynamics-based simulator, achieving the same performance.

翻译：离心压缩机的设计过程需要进行优化，而由于压缩机动力学方程背后复杂的解析公式，这一优化的计算成本高昂。尽管回归代理模型能够大幅降低此类过程的计算成本，但主要挑战在于训练代理模型的数据稀缺。为了战略性地利用标记样本，我们提出了Active-CompDesign框架，该框架将基于热力学的压缩机模型（即我们用于压缩机设计的内部软件）与基于高斯过程的代理模型相结合，部署在主动学习设置中。我们首先在离线环境下进行实验，随后将其扩展到在线主动学习框架，在该框架中，与基于热力学的压缩机模型的实时互动使其能够部署于生产中。ActiveCompDesign通过利用主动学习框架中基于不确定性的样本查询函数（相较于随机选择数据点），在代理建模方面展现出显著的性能提升。此外，我们的生产框架将压缩机设计优化的总计算时间缩短了约46%（相较于依赖内部基于热力学的模拟器），同时实现了相同的性能。