Research

Dynamic Identification of thermodynamic parameters for Turbocharger Compressor Models


Reference:

Burke, R., Olmeda, P. and Serrano, J. R., 2015. Dynamic Identification of thermodynamic parameters for Turbocharger Compressor Models. Journal of Engineering for Gas Turbines and Power: Transactions of the ASME

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Official URL:

http://dx.doi.org/10.1115/1.4030092

Abstract

A novel experimental procedure is presented which allows simultaneous identification of heat and work transfer parameters for turbocharger compressor models. The method introduces a thermally transient condition and uses temperature measurements to extract the adiabatic efficiency and internal convective heat transfer coefficient simultaneously, thus capturing the aerodynamic and thermal performance. The procedure has been implemented both in simulation and experimentally on a typical turbocharger gas stand facility. Under ideal conditions, the new identification predicted adiabatic efficiency to within 1%point1 and heat transfer coefficient to within 1%. A sensitivity study subsequently showed that the method is particularly sensitive to the assumptions of heat transfer distribution pre and post compression. If 20% of the internal area of the compressor housing is exposed to the low pressure intake gas, and this is not correctly assumed in the identification process, errors of 7-15%points were observed for compressor efficiency. This distribution in heat transfer also affected the accuracy of heat transfer coefficient which increased to 20%. Thermocouple sensors affect the transient temperature measurements and in order to maintain efficiency errors below 1%, probes with diameter of less than 1.5mm should be used. Experimentally, the method was shown to reduce the adiabatic efficiency error at 90krpm and 110krpm compared to industry standard approach from 6% to 3%. However at low speeds, where temperature differences during the identification are small, the method showed much larger errors.

Details

Item Type Articles
CreatorsBurke, R., Olmeda, P. and Serrano, J. R.
DOI10.1115/1.4030092
DepartmentsFaculty of Engineering & Design > Mechanical Engineering
Research CentresPowertrain & Vehicle Research Centre
EPSRC Centre for Doctoral Training in Statistical Mathematics (SAMBa)
RefereedYes
StatusPublished
ID Code43756

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