Transcript No Slide Title

ICAT, November 13-14 2008
Outline
 Background, motivation and goals
 Heat Transfer in Engines
 Heat Transfer Correlations: Woschni, Assanis and Hohenberg HTCs
 Double-wiebe function for HCCI models
 Results
 Conclusions
Background and problem statement
 The ultimate aim of this work is to model the effect of combustion chamber
deposits on the thermal environment of the engine and to validate the
results by our experimental results.
 Combustion models are generally developed for conventional engines.
 These models need to be modified to be able to model HCCI conditions.
 Combustion in HCCI engines is a controlled auto-ignition of well-mixed, air
and residual gas.
 Thermal conditions of the combustion chamber are governed by chemical
kinetics strongly coupled with heat transfer from the hot gas to the walls.
 The heat losses have a critical effect on HCCI ignition timing and burning
rate, thus it is essential to understand heat transfer process in the
combustion chamber in the modelling of HCCI engines.
Heat Transfer from gas to walls
Heat Transfer correlations
Woschni:
Heat Transfer correlations
Characteristic velocity in Woschni:
Heat Transfer correlations
Assanis (Modified Woschni)
Heat Transfer correlations
Hohenberg
Heat Transfer correlations
Generic Form
Heat Transfer correlations
Generic Form
Heat Transfer correlations
Single Generic Form
Heat Transfer correlations
Single Generic Form
Results
Single Generic Form
Normalized charasteristic length scale profiles
Normalized charasteristic velocity profiles
Normalized temperature profiles
Normalized pressure profiles
Variation of velocity term for each model
Effect of C2 on heat transfer coefficient for Assanis model
Variation of heat transfer coefficient traces
Heat transfer coefficient traces for different heat transfer models
Heat flux traces for different heat transfer models
Measured and predicted cylinder pressure traces for different conditions
Wiebe function
Double-Wiebe function
Results- The Differences
Wiebe functions with different m values
for a burn duration of 10 CAD and 50% heat release at TDC
Results- The Differences
Late Combustion :
Results- The Differences
Mass fraction burned traces
Results- Experimental validation
Measured and predicted cylinder pressure traces
Results- Experimental validation
Net heat release traces
Conclusions – Wiebe function
 with the standart Wiebe function, parameters can be adjusted to fit either
the time or the value of maximum pressure if 100% combustion is
assumed.
 It is possible to match both quantities if it is allowed less than 100%
combustion.
 It becomes possible to fit both quantities without specifying an unrealistic
proportion of unburned fuel if double-Wiebe definition is used.
 double-Wiebe function gives a lower peak heat-release rate than the
standart-Wiebe.
 Result of the slower combustion of part of the mixture in double-Wiebe
function agrees with experimental results as well.
Conclusions – Heat transfer correlations
 Woschni correlation includes a term representing the combustion
compression velocity, which is the bulk gas movement due to
compression of the unburned gas by an advancing flame front that is not
applicable to HCCI engines. This exaggerates heat transfer rates during
combustion and expansion.
 Assanis correlation is a modified type of Woschni HTC for HCCI engines.
Here has movement issue is changed emprically by reducing the
magnitude of the combustion velocity. It give very low heat transfer rates
for whole engine cycle in our HCCI engine, thus overestimates peak
pressures.
 Hohenberg heat transfer model which has no explicit combustion velocity
term, give better aggrement with our experiments than the others.
Conclusions – Heat transfer correlations
 Although there are differences in the magnitudes obtained by using
Assanis and Hohenberg correlations, it is possible to match with our
experimental pressure data by adjusting the scaling coefficient as it is
done by Assanis.
 However, it is rather unsatisfactory to adjust coefficients substantially for
each different HCCI engine.
 We prefer not to do any empirical re-adjustment of the model coefficients.
 Hohenberg is the simplest correlation which needs little adjustment,
therefore it may be advantageous to use Hohenberg correlation in HCCI
simulations.
 The existing correlations are developed for SI conditions. It is likely that
better correlations could be derived directly by making structural changes
that represent more closely the nature of the HCCI process.
Thanks for your attention. I would like to invite you to
International Conference on Fuels and Combustion in Engines
in Istanbul, September 2009
For more information
[email protected]
www.fce.sakarya.edu.tr