Abstract

The study presents a detailed analysis of a contemporary method for fitting and extrapolation of turbine performance maps of automotive turbochargers. The complete algorithm is implemented in a MATLABTM based application with graphical user interface, the ultimate goal of which is to facilitate turbocharger performance data postprocessing. Guidelines are given on how to generate extrapolated maps from a fitted model considering the output data resolution. As a key validity criterion, fit quality of a sample measured turbine map is assessed using statistical and analytical methods. The report is concluded by a discussion of possible ways to improve the robustness of the algorithm with respect to input data of different origin and quality.

Highlights

  • The most appropriate turbocharger for a combustion engine is selected by the matching process

  • A typical turbine map is obtained as a product of hot gas stand measurement [5, 7], where a complete turbocharger is mounted on the testing device and characterised under steady conditions

  • A turbine efficiency map consists of speed lines, which are made of operating points of constant corrected speed

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Summary

Introduction

The most appropriate turbocharger for a combustion engine is selected by the matching process. It must confirm that target engine power and torque can be achieved based on thermodynamic properties of applied turbine and compressor. These are described by performance maps, which include information about the interrelationship between rotating speed, pressure ratio, mass flow rate and efficiency. GT-SUITETM [1, 2, 3] can be mentioned as an example of the most widely used application for engine thermodynamics simulation and turbocharger matching (among other). The aim of this work is to reproduce the industry-standard turbine map fitting method and analyse its properties, so it can be considered for a potential further use in post-processing of larger amounts of experimental data. MATLABTM [4] environment has been selected for implementation of the algorithm for reasons of its availability and easy handling of input data files with help of built-in functions

Implementation of turbine map fitting method
Pre-processing of turbine map
Fitting the optimum blade speed ratio
Fitting the maximum efficiency
Fitting the normalized efficiency
Fitting the optimum corrected mass flow rate
Fitting the normalized mass flow rate
Extrapolated efficiency map
Fit quality assessment
Statistical approach
Robustness check
Conclusion
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