Yes / This paper introduces a hybrid dynamic modelling approach for the prediction of NOx emissions for a Diesel engine, based on a multi-physics simulation platform coupling a 1-D air path model (GT-Suite) with in-cylinder combustion model (CMCL Stochastic Reactor Model Engine Suite). The key motivation for this research was the requirement to establish a real time stochastic simulation capability for emissions predictions early in engine development, which required the replacement of the slow combustion chemistry solver (SRM) with an appropriate surrogate model. The novelty of the approach in this research is the introduction of a hybrid approach to metamodeling that combines dynamic experiments for the gas path model with a zonal optimal space-filling design of experiments (DoEs) for the combustion model. The dynamic experiments run on the virtual Diesel engine model (GT- Suite) was used to fit a dynamic model for the parameters required as input to the SRM. Optimal Latin Hypercubes (OLH) DoE run on the SRM model was used to fit a response surface model for the NOx emissions. This surrogate NOx model was then used to replace the computationally expensive SRM simulation, enabling real time simulations of transient drive cycles to be executed. The performance of the proposed approach was validated on a simulated NEDC drive cycle against experimental data collected for the engine case study, which proved the capability of methodology to capture the transient trends for the NOx emissions. The significance of this work is that it provided an efficient approach to the development of a global model with real time transient modelling capability based on the integration of dynamic and local DoE metamodeling experiments.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/18376 |
| Date | 23 February 2021 |
| Creators | Pant, Gaurav, Campean, Felician, Korsunovs, Aleksandrs, Neagu, Daniel, Garcia-Afonso, Oscar |
| Publisher | SAE |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Technical paper, Accepted manuscript |
| Rights | (c) 2021 SAE. Full-text reproduced in accordance with the publisher's self-archiving policy. |
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