This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Automotive Turbocharger Rotor Optimization Using Machine Learning Technique
Technical Paper
2022-01-0216
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
Turbochargers are widely employed in internal combustion engines, in both, diesel and gasoline vehicle, to boost the power without any extra fuel usage. Turbocharger comes in different sizes based upon the boost pressure to increase. Capacity of turbocharger are available in great range in the market which are designed to match the requirement. From structural point of view, key component of an automotive turbocharger is rotor. This rotor consists of compressor wheel, turbine wheel, shaft and bearing (journal/ball) mainly. In industries, design & development of turbocharger rotor for its dynamic characteristics is done using virtual engineering technique (Computer Aided Engineering). Multibody dynamic (MBD) analysis simulation is one of the best approaches which is used to study the rotor in great details. In this current MBD procedure fluid-structure interaction problem is solved by modelling oil film in the journal bearing and solving it using “Reynolds equation”. Shaft displacement is provided to oil film which eventually output the pressure development in the bearing. This pressure then acts upon the shaft and modal transient analysis is performed for the structure analysis.
As current approach is a quite complex, time require to complete the simulation is in days. Multiple simulation is required to study the design sensitivity and reach to an optimum design of turbocharger rotor. So, any essential design study takes this huge time to carry out, hence it is one of the challenges in the development cycle.
Over the period of last few years, a lot of such simulation has happened for variety of turbocharger. It provides a huge data set which could be utilized to find a pattern or prediction of rotor dynamic characteristics. One of such effort has been made and a deep learning-based rotor dynamics model has been developed. This model further with non-linear Optimization technique is very promising for optimizing the rotor design parameters. This method not only predicts the outcomes with given design parameter but also minimize the outcomes by providing the most optimized set of design parameters within hours.
Recommended Content
Magazine Issue | Automotive Engineering International 2013-06-04 |
Technical Paper | Physical Modeling of a Turbocharger Electric Waste-Gate Actuator for Control Purpose |
Technical Paper | Circular Systems with Non-Linear Stiffnesses |
Authors
Citation
Shrivastava, S., Sinha, A., Ray, S., Du, I. et al., "Automotive Turbocharger Rotor Optimization Using Machine Learning Technique," SAE Technical Paper 2022-01-0216, 2022, https://doi.org/10.4271/2022-01-0216.Also In
References
- Chen , W.J. Rotordynamics and Bearing Design of Turbochargers Mechanical Systems and Signal Processing 29 2012 77 89
- Liu , Z. , Wang , R. , Cao , F. , and Shi , P. Dynamic Behaviour Analysis of Turbocharger Rotor-Shaft System in Thermal Environment Based on Finite Element Method Shock and Vibration 2020 2020 18
- Shah , D.S. and V. N. Patel A Dynamic Model For Vibration Studies Of Dry And Lubricated Deep Groove Ball Bearings Considering Local Defects On Races Measurement 137 2019 535 555
- Nguyen-Schafer , H. Rotor Dynamics of Automotive Turbochargers 2nd 2015
- Acar , E. Effect of Error Metrics on Optimum Weight Factor Selection for Ensemble of Metamodels Expert Systems with Applications 42 5 2015 2703 2709
- Bashiri , M. and Farshbaf , G.A. Tuning the Parameters of an Artificial Neural Network Using Central Composite Design and Genetic Algorithm Scientia Iranica 18 2011 1600 1608
- Clarke , S.M. , Griebsch , J.H. , and Simpson , T.W. Analysis of Support Vector Regression for Approximation of Complex Engineering Analyses Journal of Mechanical Design, Transactions of the ASME 127 2005 1077 1087
- Draper , N. and Smith , H. Applied Regression Analysis 2nd ed. John Wiley & Sons 1998
- Dyn , N. , Levin , D. , and Rippa , S. Numerical Procedures for Surface Fitting of Scattered Data by Radial Functions SIAM Journal on Scientific and Statistical Computing 7 1986 639 659