Compressor Blade Vibrations of Next Generation of Turbocharger With Focus on Damping: Simplified Prediction and AI-Based Evaluation

Recently [1], Accelleron announced the X300-L series, marking the beginning of a new concept in turbocharger technology. This innovative platform-based approach simplifies service procedures but also provides adaptability to respond to diverse demands caused by a certain variety of future fuels. The integration of enhanced power densities, standardized components, and a modular design offers fewer frame sizes and variants. Consequently, individual core components (i.e., compressor wheel or turbine impeller) are necessarily not only more compact and more powerful but also extend their utility across a wider range of applications. Accurate blade vibration analysis is essential to identify potential performance issues, mitigate fatigue and wear, and prevent disastrous failures. The complete variety of numerical and experimental methods that are involved during the modern development process of new turbochargers will easily extend the scope of this contribution. To allow a more detailed insight, this paper presents a comprehensive study on compressor blade vibrations; with a particular focus on damping as one of the most important aspects of high-cycle fatigue. First, a blade vibration measurement is carried out employing both strain gauges and blade tip timing, two cutting-edge methodologies that demonstrate strong mutual consistency. The dynamic stress of five chosen resonance crossings is investigated to primarily underline the robustness against high-cycle fatigue and show the agreement between strain gauge and tip timing measurements. Second, a simplified method is adopted to predict critical damping ratios of specific blade vibration resonances. This approach incorporates inputs as modal parameters (like natural frequency and mode shape) and operational conditions (fluid density and speed of sound), all of which are conveniently accessible during the preliminary design phase. Third, experimental data from several resonance crossings recorded with strain gauges are fed into an AI-driven methodology utilizing Deep Learning, enabling the assessment of genuine damping ratios for these resonances. Subsequently, a comparison is presented, contrasting the new damping evaluation (AI based on strain gauge measurement) with a state-of-the-art solution (SDOF-Fit of tip timing data) as well as the predicted damping ratios of the first part. In conclusion, this paper not only highlights the good performance of the compressor in terms of HCF strength but also underscores its accelerated development through the use of reliable data from the early design phase. Modern measurement techniques offer remarkable precision, validate numerical predictions, and finally increase the trust in such methods. The proposed AI-based damping estimator validates damping predictions as a practical tool for engineers in the field of turbocharger development, facilitating the development of more reliable and efficient turbocharging systems for future applications.