Physics-based Approximation and Prediction of Speedlines in Compressor Performance Maps

Abstract

Speedlines in compressor performance maps (CPMs) are critical for understanding and predicting compressor behavior under various operating conditions. We investigate a physics-based method for reconstructing compressor performance maps from sparse measurements by fitting each speedline with a superellipse and encoding it as a compact, interpretable vector (surge, choke, curvature, and shape parameters). Building on the formulation of Llamas et al., we develop a robust two-stage fitting pipeline that couples global search with local refinement. The approach is validated on industrial data-sets for different turbocharger types. We discuss prediction quality for inter- and extrapolation, metric sensitivities and outline opportunities for physics-informed constraints, alternative function families, and hybrid physics-ML mappings to improve boundary behavior and, ultimately, enable full CPM reconstruction from limited data.