Smith_0954407020928665.pdf (2.19 MB)
Numerical continuation applied to internal combustion engine models
journal contribution
posted on 2020-06-18, 13:24 authored by Shaun Smith, James KnowlesJames Knowles, Byron MasonByron MasonThis paper proposes tools from bifurcation theory, specifically numerical continuation, as a complementary method for efficiently mapping the state-parameter space of an internal combustion engine model. Numerical continuation allows a steady-state engine response to be traced directly through the state-parameter space, under the simultaneous variation of one or more model parameters. By applying this approach to two nonlinear engine models (a physics-based model and a data-driven model), this work determines how input parameters ‘throttle position’ and ‘desired load torque’ affect the engine’s dynamics. Performing a bifurcation analysis allows the model’s parameter space to be divided into regions of different qualitative types of the dynamic behaviour, with the identified bifurcations shown to correspond to key physical properties of the system in the physics-based model: minimum throttle angles required for steady-state operation of the engine are indicated by fold bifurcations; regions containing self-sustaining oscillations are bounded by supercritical Hopf bifurcations. The bifurcation analysis of a data-driven engine model shows how numerical continuation could be used to evaluate the efficacy of data-driven models.
Funding
This research is funded through the EPSRC Centre for Doctoral Training in Embedded Intelligence under grant reference EP/L014998/1, with industrial support from Jaguar Land Rover.
History
School
- Aeronautical, Automotive, Chemical and Materials Engineering
Department
- Aeronautical and Automotive Engineering
Published in
Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile EngineeringVolume
234Issue
14Pages
3458-3475Publisher
SAGE PublicationsVersion
- VoR (Version of Record)
Rights holder
© IMechEPublisher statement
This is an Open Access Article. It is published by Sage under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/Acceptance date
2020-04-23Publication date
2020-06-12Copyright date
2020ISSN
0954-4070eISSN
2041-2991Publisher version
Language
- en