Gasoline PPC: A Parametric Study of Late Cycle Mixing Conditions using a Predictive Two-zone SRM Modeling Tool

Lundgren, Marcus; Tunér, Martin; Johansson, Bengt; Bjerkborn, Simon; Frojd, Karin; Andersson, Arne; Mauß, Fabian; Jiang, Bincheng

doi:10.4271/2013-01-2621

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…PRESS RMSE and Validation RMSE [41]; 6. If the model is not validated, further test points are needed, so return to step (2); 7. Analyse and evaluate trends from the modelcomparing against engineering judgement, and extract information for model calibration.…”

Section: Design Of Experiments Methodology For Srm Sensitivity Studymentioning

confidence: 99%

“…Prominent approaches and capability now available include the commercially available package developed by CMCL Innovations, referred to herein as CMCL stochastic reactor model (SRM), which has been tested and validated in applications including Diesel engine entire speed-load domain simulation, 1 Diesel engine optimization 2 and simulation of homogeneous charge compression ignition (HCCI) engines. 3–5 Building on collaborative work with several European Universities, Loge AB has provided an alternative SRM, which achieves simulation times reduction 6 and allows for in-depth study of the mixing time profiles impact and two-zone thermodynamic models application, 7 and use of Eulerian field probability density function (PDF) methods 8 and tabulation strategy. 9…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of zero-dimensional stochastic reactor modelling for a Diesel engine application

Korsunovs

Campean

Pant

et al. 2019

International Journal of Engine Research

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Prediction of engine-out emissions with high fidelity from in-cylinder combustion simulations is still a significant challenge early in the engine development process. This article contributes to this fast evolving body of knowledge by focusing on the evaluation of NO x emission prediction capability of a probability density function–based stochastic reactor engine models for a Diesel engine. The research implements a systematic approach to the study of the stochastic reactor engine model performance, underpinned by a detailed space-filling design of experiments (DoE)-based sensitivity analysis of both external and internal parameters, evaluating their effects on the accuracy in matching physical measurements of both in-cylinder conditions and NO x output. The approach proposed in this article introduces an automatic stochastic reactor engine model calibration methodology across the engine operating envelope, based on a multi-objective optimization approach. This aims to exploit opportunities for internal stochastic reactor engine model parameters tuning to achieve good overall modelling performance as a trade-off between physical in-cylinder measurements accuracy and the output NO x emission predictions error. The results from the case study provide a valuable insight into the effectiveness of the stochastic reactor engine model, showing good capability for NO x emissions prediction and trends, while pointing out the critical sensitivity to the external input parameters and modelling conditions.

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Section: Design Of Experiments Methodology For Srm Sensitivity Studymentioning

confidence: 99%