Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.496859
Title: Neural network modelling of automotive dampers for variable temperature operation and suspension system tuning
Author: Alghafir, Mohammed Najib
Awarding Body: UNIVERSITY OF SUSSEX
Current Institution: University of Sussex
Date of Award: 2009
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Abstract:
This thesis focuses on modelling of passive hydraulic automotive dampers for use in computationally-fast vehicle-dynamic simulation. An extended version of the Duym and Reybrouck 1998 physical model is examined to enable work with high frequency input displacements. This computationally-expensive model is verified with real damper data under both isothermal and variable temperature regular, and random (Pave) input displacement conditions. Initially the extension includes just additional input kinematics to account for inertial effects, with an imposed temperature profile. Subsequently a heat generation model is developed to include appropriate energy losses. When the heat generation model is coupled to the damper model, naturally-generated transient-temperature operation of the damper can be accounted for.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.496859  DOI: Not available
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