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ENCIT 2020
18th Brazilian Congress of Thermal Sciences and Engineering
IDENTIFICATION OF WIEBE AND WOSCHNI PARAMETERS USING STOCHASTIC METHOD OF DIFFERENTIAL EVOLUTION
Submission Author:
Lucas Peixoto , PR
Co-Authors:
Lucas Peixoto, Stephan Hennings Och, Carlos augusto Henning Laurindo, Paulo Philippi, Luís Mauro Moura
Presenter: Lucas Peixoto
doi://10.26678/ABCM.ENCIT2020.CIT20-0563
Abstract
Engine testing is an expensive process, and in order to better understand the engine behavior at different conditions simulations are performed, making its research less expensive. Most simulations use empirical correlations to describe complex phenomena associated with the fast and transient nature of the combustion process, two of the most well know correlations are the Wiebe and Woschni correlation, that describe the fuel burn rate and heat transfer between gas and cylinder walls respectively. Both correlations are dependent on constants that can be adjusted to a specific engine, therefore accounting for its geometry and operation conditions used during testing. Consequently, the accurate identification of these parameters is an important step for reliable result when simulating combustion processes. This paper proposes a parameter identification of the Wiebe and Woschni correlations with the use of a stochastic method algorithm called differential evolution. The objective function used is the sum of the squared errors that compares the results of simulations using the correlations to standard test data from an engine in order to minimize variation with the test data and identify the parameters values. Results show that the algorithm can reliably reach a good solution within few iterations in a given set of predetermined domain of solutions. The found objective function value for the Wiebe correlation is 0.0255 [(mburned,gas/mtotal)²] with R² of 0.9995, and for the Woschni formulae the values are 6.38x10-9[Bar²] for the objective function and 0.999 for R², therefore accurately describing the test data, used for both correlations, using the differential evolution method. Furthermore, a sensibility analysis was performed for the Woschni correlation, revealing that these parameters are more easily identifiable at high engine speed, when analyzing the condition number of the coefficient matrix used.
Keywords
Differential Evolution, Woschni, Wiebe function, Parameter identification
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