Development of Model-Based Combustion Control to Ensure Robustness of Emissions, Fuel Consumption and Noise Performances in New Generation Diesel Engine

Sahara, Hiroya; Kakuda, Yoshie; Kim, Sang-kyu; Shimo, Daisuke; Maruyama, Keiji; Morinaga, Shinichi; Hashimoto, Takayoshi; Hayashibara, Hiroshi; Tetsuno, Masayuki

doi:10.3182/20130904-4-jp-2042.00118

Cited by 4 publications

(1 citation statement)

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“…The accuracy of the empirical model or a fit is a vital factor to the design of searching a system optimum. Here, physicsbased models not only offer higher level of accuracy, but also take into account a wide range of input variables [8]. Multi-dimensional Computational Fluid Dynamic (CFD) modelling can be applied to describe in detail the physical processes for the in-cylinder turbulent combustion and flow fields within the engine air and combustion systems.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Emissions in a Modern Compression Ignition Engine Using Multi-Dimensional PDF-Based Stochastic Simulations and Statistical Surrogate Generation

Lai¹,

Parry²,

Mosbach³

et al. 2018

SAE Technical Paper Series

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Digital engineering workflows, involving physico-chemical simulation and advanced statistical algorithms, offer a robust and cost-effective methodology for model-based internal combustion engine development. In this paper, a modern Tier 4 capable Cat ® C4.4 engine is modelled using a digital workflow that combines the probability density function (PDF)-based Stochastic Reactor Model (SRM) Engine Suite with the statistical Model Development Suite (MoDS). In particular, an advanced multi-zonal approach is developed and applied to simulate fuels, in-cylinder combustion and gas phase as well as particulate emissions characteristics, validated against measurements and benchmarked with respect to the predictive power and computational costs of the baseline model. The multizonal SRM characterises the combustion chamber on the basis of different multi-dimensional PDFs dependent upon the bulk or the thermal boundary layer in contact with the cylinder liner. In the boundary layer, turbulent mixing is significantly weaker and heat transfer to the liner alters the combustion process. The integrated digital workflow is applied to perform parameter estimation based on the in-cylinder pressure profiles and engine-out emissions (i.e. NOx, CO, soot and unburnt hydrocarbons; uHCs) measurements. Four DoE (design-of-experiments) datasets are considered, each comprising measurements at a single load-speed point with various other operating conditions, which are then used to assess the capability of the calibrated models in mimicking the impact of the input variable space on the combustion characteristics and emissions. Both model approaches predict in-cylinder pressure profiles, NOx, and soot emissions satisfactorily well across all four datasets. Capturing the physics of emission formation near the cylinder liner enables the multi-zonal SRM approach to provide improved predictions for intermediates, such as CO and uHCs, particularly at low load operating points. Finally, fast-response surrogates are generated using the High Dimensional Model Representation (HDMR) approach, and the associated global sensitivities of combustion metrics and emissions are also investigated.

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Section: Introductionmentioning

confidence: 99%