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Details of Grant 

EPSRC Reference: GR/R53784/01
Title: Investigating the Homogeneously Charged Compression Ignition Engine (HCCI) using a novel stochastic model approach
Principal Investigator: Kraft, Professor M
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Lund Institute of Technology University of Cambridge
Department: Chemical Engineering and Biotechnology
Organisation: University of Cambridge
Scheme: Standard Research (Pre-FEC)
Starts: 01 December 2001 Ends: 30 November 2004 Value (£): 60,357
EPSRC Research Topic Classifications:
Combustion
EPSRC Industrial Sector Classifications:
Information Technologies Transport Systems and Vehicles
No relevance to Underpinning Sectors
Related Grants:
Panel History:  
Summary on Grant Application Form
The Homogeneously Charged Compression Ignition (HCCI) engine exhibits much better emission characteristics than a Diesel engine whilst having similar efficiency. HCCI engines have very low nitrogen oxide (NOx) and soot-particle emissions and have a lower capital cost than conventional engines because they do not require a high-pressure injection system. Nevertheless, there are unresolved issues such as engine control andemissions of unburnt hydrocarbons and carbon monoxide. This research programme is concerned with the development of a novel stochastic'model for the combustion process in a HCCI engine. This model is based on previous work by the author and his co-workers (as detailed in theCase for Support) and aims at supporting experimentalists in academia and industry by providing the sensitivities of the parameters of interest. Aspecial feature of this model is the ability to predict quantitatively the levels of emissions such as carbon monoxide and unburnt hydrocarbons aswell as the progress of the combustion process itself, which in turn makes the stochastic model attractive as a simulation tool for engine control.Three main objectives are identified. First, the computational cost of the current algorithm will be reduced by adaptive time stepping, higher orderoperator splitting, and a predictor corrector technique. A weighted particle method will increase the accuracy of the algorithm. Second, models formore realistic mixing, exhaust gas recycling, and the intake and expansion stroke will be implemented. Third, a comprehensive sensitivity study oftemperature and pressure of the inlet fuel-air mixture, exhaust gas recycling, fuel composition, and valve timing will be performed.
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Organisation Website: http://www.cam.ac.uk