A Correlation Methodology between AVL Mean Value Engine Model and Measurements with Concept Analysis of Mean Value Representation for Engine Transient Tests

Paper #:
  • 2017-24-0053

Published:
  • 2017-09-04
DOI:
  • 10.4271/2017-24-0053
Citation:
Pinamonti, S., Brancale, D., Meister, G., and Mendoza, P., "A Correlation Methodology between AVL Mean Value Engine Model and Measurements with Concept Analysis of Mean Value Representation for Engine Transient Tests," SAE Technical Paper 2017-24-0053, 2017.
Pages:
21
Abstract:
The use of state of the art simulation tools for effective front-loading of the calibration process is essential to support the additional efforts required by the new Real Driving Emission (RDE) legislation. The process needs a critical model validation where the correlation in dynamic conditions is used as a preliminary insight into the bounds of the representation domain of engine mean values.This paper focuses on the methodologies for correlating dynamic simulations with emissions data measured during dynamic vehicle operation (fundamental engine parameters and gaseous emissions) obtained using dedicated instrumentation on a diesel vehicle, with a particular attention for oxides of nitrogen NOx specie. This correlation is performed using simulated tests run within AVL’s mean value engine and engine aftertreatment (EAS) model MoBEO (Model Based Engine Optimization).A conceptual analysis is dedicated to the intrinsic uncertainties of a mean value representation (measurements and simulations) with respect to an ideal high-resolution dynamic representation; this is carried out for two purposes: (i) to understand the intrinsic uncertainties of a mean value representation domain and (ii) to understand how to correlate at best the simulated value with the measurements during transient cycles, particularly when the fundamental parameters (e.g. emission mass flow rate, temperatures or the EGR rate calculated from CO2 measurements) depend on factors characterized by heterogeneous dynamics and different transport/propagation times. Furthermore, elementary methods to compensate the time lag/delays and the sensors response time are discussed to obtain a proper correlation between measurements and simulations.Using these methods, the objective is to explain how the correlated engine values and the small differences between simulated and measured results can be sourced by specific dynamic phenomena and how they impact the final results. The analysis concludes with a global assessment of model to measurements correlation and with the expected level of confidence for the model based calibration process based on the achieved level of correlation.
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