Physics-based Approximation and Prediction of Speedlines in Compressor Performance Maps
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911583620300800 |
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| author | Akiev, Abdul-Malik Ergür, Danyal Schirger, Alexander Müller, Matthias Hinterleitner, Alexander Bartz-Beielstein, Thomas |
| author_facet | Akiev, Abdul-Malik Ergür, Danyal Schirger, Alexander Müller, Matthias Hinterleitner, Alexander Bartz-Beielstein, Thomas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_11317 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Physics-based Approximation and Prediction of Speedlines in Compressor Performance Maps Akiev, Abdul-Malik Ergür, Danyal Schirger, Alexander Müller, Matthias Hinterleitner, Alexander Bartz-Beielstein, Thomas Numerical Analysis 65K99 G.1.6 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. |
| title | Physics-based Approximation and Prediction of Speedlines in Compressor Performance Maps |
| topic | Numerical Analysis 65K99 G.1.6 |
| url | https://arxiv.org/abs/2603.11317 |