Combining Thermodynamics-based Model of the Centrifugal Compressors and
  Active Machine Learning for Enhanced Industrial Design Optimization
Shadi Ghiasi Guido Pazzi Concettina Del Grosso Giovanni De Magistris Giacomo Veneri
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.