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Numerical continuation applied to internal combustion engine models

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journal contribution
posted on 18.06.2020, 13:24 by Shaun SmithShaun Smith, James KnowlesJames Knowles, Byron MasonByron Mason
This 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 Engineering

Volume

234

Issue

14

Pages

3458-3475

Publisher

SAGE Publications

Version

VoR (Version of Record)

Rights holder

© IMechE

Publisher 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

23/04/2020

Publication date

2020-06-12

Copyright date

2020

ISSN

0954-4070

eISSN

2041-2991

Language

en

Depositor

Dr James Knowles. Deposit date: 17 June 2020

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