An Aero-Thermodynamic Physics-Informed Neural Network for Small-Sample Performance Prediction of Variable-Speed Centrifugal Chillers

Accurate performance prediction of variable-speed centrifugal chillers is important for building energy optimization and the development of digital twins in HVAC systems. In practice, obtaining extensive operational data is costly, creating a prevalent “small-sample” dilemma under which conventional data-driven models are prone to overfitting with poor extrapolation capability. While recent Physics-Informed Neural Networks (PINNs) incorporate system-level thermodynamic constraints (e.g., COP definitions), they typically treat the centrifugal compressor as a thermodynamic black box, neglecting its inherent fluid dynamic characteristics; consequently, extrapolated predictions may be physically inconsistent or fall into unsafe operating regions such as compressor surge. To address this gap, this paper proposes an Aero-thermodynamic Physics-Informed Neural Network (Aero-PINN) that introduces three mechanisms into the PINN loss function: (1) dimensionless aerodynamic similarity mapping governed by affinity laws, (2) a surge boundary constraint that prevents non-physical extrapolations, and (3) an aerodynamic–electrical energy coupling validation. Experimental validation on 420 real-world variable-speed test records shows that the Aero-PINN achieves a COP RMSE of 0.04 and a COP MAPE of 0.3%, outperforming standard MLP and polynomial baselines. Moreover, 100% of the extrapolated operating points satisfy all fluid dynamic safety and energy efficiency constraints. This framework provides a reliable, physics-constrained small-sample learning approach, facilitating factory calibration and reduced-test digital modeling for chiller plants.