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Technical Paper

Multi-Objective Optimization of Fuel Consumption and NOx Emissions with Reliability Analysis Using a Stochastic Reactor Model

2019-04-02
2019-01-1173
The introduction of a physics-based zero-dimensional stochastic reactor model combined with tabulated chemistry enables the simulation-supported development of future compression-ignited engines. The stochastic reactor model mimics mixture and temperature inhomogeneities induced by turbulence, direct injection and heat transfer. Thus, it is possible to improve the prediction of NOx emissions compared to common mean-value models. To reduce the number of designs to be evaluated during the simulation-based multi-objective optimization, genetic algorithms are proven to be an effective tool. Based on an initial set of designs, the algorithm aims to evolve the designs to find the best parameters for the given constraints and objectives. The extension by response surface models improves the prediction of the best possible Pareto Front, while the time of optimization is kept low.
Technical Paper

Development of a Computationally Efficient Progress Variable Approach for a Direct Injection Stochastic Reactor Model

2017-03-28
2017-01-0512
A novel 0-D Probability Density Function (PDF) based approach for the modelling of Diesel combustion using tabulated chemistry is presented. The Direct Injection Stochastic Reactor Model (DI-SRM) by Pasternak et al. has been extended with a progress variable based framework allowing the use of a pre-calculated auto-ignition table. Auto-ignition is tabulated through adiabatic constant pressure reactor calculations. The tabulated chemistry based implementation has been assessed against the previously presented DI-SRM version by Pasternak et al. where chemical reactions are solved online. The chemical mechanism used in this work for both, online chemistry run and table generation, is an extended version of the scheme presented by Nawdial et al. The main fuel species are n-decane, α-methylnaphthalene and methyl-decanoate giving a size of 463 species and 7600 reactions.
Technical Paper

Advanced Predictive Diesel Combustion Simulation Using Turbulence Model and Stochastic Reactor Model

2017-03-28
2017-01-0516
Today numerical models are a major part of the diesel engine development. They are applied during several stages of the development process to perform extensive parameter studies and to investigate flow and combustion phenomena in detail. The models are divided by complexity and computational costs since one has to decide what the best choice for the task is. 0D models are suitable for problems with large parameter spaces and multiple operating points, e.g. engine map simulation and parameter sweeps. Therefore, it is necessary to incorporate physical models to improve the predictive capability of these models. This work focuses on turbulence and mixing modeling within a 0D direct injection stochastic reactor model. The model is based on a probability density function approach and incorporates submodels for direct fuel injection, vaporization, heat transfer, turbulent mixing and detailed chemistry.
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