Werner Willems

Computational Optimization of a Down-Scaled Diesel Engine Operating in the Conventional Diffusion Combustion Regime Using a Multi-Objective Genetic Algorithm

Computational optimization of a high-speed diesel engine, combined with diesel engine size-scaling, is presented. A multi-objective genetic algorithm was employed to simultaneously optimize fuel consumption and engine-out emissions of the down-scaled version of a previously optimized baseline engine. By separating the design parameters into hardware parameters (e.g., the piston bowl geometry) and controllable parameters (e.g., injection pressure and timings), multiple operating conditions were optimized simultaneously. A new variable was introduced to evaluate the convergence of the optimization, defined as the ratio of the number of Pareto designs and the number of valid designs in each generation. Particular interest was placed on the effect of injection pressure on the optimization of the engine and whether the previously optimized baseline engine design holds for different engine sizes. For 32 generations, totaling 1024 designs, no better design than the initial optimum, which was generated for the baseline engine, was found. This indicates that the current engine size-scaling model works well.

Combustion system development for the new diesel engines in light and medium commercial vehicles from Ford and PSA

This is the first engine family specifically designed for light and medium commercial vehicles to be produced under the joint Ford Motor Company/PSA Peugeot Citroen Diesel engine co-operation agreement. Although the new engine is based on an existing engine architecture, it introduces a number of technical innovations, raising the bar even higher for engine technology in the commercial vehicle sector. Part of the engine development has focused on optimising the combustion system and was led by the engineers of the Ford Research and Advanced Engineering Centre FFA in Aachen, Germany. The new engines produce engine-out emissions that are well within the stringent limits given by the EU4 Light Duty Truck (LDT) emissions standard for all of the vehicle applications, whilst maintaining good fuel economy.

Brennverfahrens-Entwicklung für die neuen Dieselmotoren in leichten Nutzfahrzeugen von Ford und PSA

Erstmals wurde im Rahmen der Zusammenarbeit zwischen Ford Motor Company und PSA Peugeot Citroen eine Dieselmotorenfamilie speziell fur leichte Nutzfahrzeuganwendungen entwickelt. Trotz Ubernahme der Grundarchitektur des Vorgangermotors konnte eine Reihe technischer Neuheiten eingefuhrt werden, die den technischen Status im Nutzfahrzeugbereich nochmals anheben. Die Entwicklung des Brennverfahrens bildete den Schwerpunkt der Arbeiten, die unter Leitung des Ford Forschungszentrums Aachen durchgefuhrt wurden. Die Motoren erfullen die Auflagen der EU4-Abgasgesetzgebung fur leichte Nutzfahrzeuge bei gleichzeitig niedrigem Kraftstoffverbrauch.

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

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. The advantage of the probability density function approach compared to mean value models is its capability to account for temperature and mixture inhomogeneities. Therefore, notional particles are introduced each with its own temperature and composition. The particle condition is changed by mixing, injection, vaporization, chemical reaction and heat transfer. Mixing is modeled using the one-dimensional Euclidean minimum spanning tree mixing model, which requires the scalar mixing frequency as input. Therefore, a turbulence model is proposed to calculate the mixing time depending on turbulent kinetic energy and its dissipation. The turbulence model accounts for density, swirl, squish and injection effects on turbulent kinetic energy within the combustion chamber. Finally, the 0D stochastic reactor model is tested for 40 different operating points distributed over the whole engine map. The results show a close match of experimental heat release rate and NOx emissions. The trends of measured CO and HC concentrations are captured qualitatively. Additionally, the 0D simulation results are compared to more detailed 3D CFD combustion simulation results for three operating points differing in engine speed and load. The comparison shows that the 0D stochastic reactor model is able to capture turbulence effects on local temperature and mixture distribution, which in turn affect NOx, CO and HC emission formation. Overall, the 0D stochastic reactor model has proven its predictive capability for the investigated diesel engine and can be assigned to tasks concerning engine map simulation and parameter sweeps.

Well-to-Wheels Emissions of Greenhouse Gases and Air Pollutants of Dimethyl Ether from Natural Gas and Renewable Feedstocks in Comparison with Petroleum Gasoline and Diesel in the United States and Europe

Dimethyl ether (DME) is an alternative to diesel fuel for use in compression-ignition engines with modified fuel systems and offers potential advantages of efficiency improvements and emission redu ...

Computational methods for diesel combustion system development

Simulation methods have become an indispensable tool in the engine development process, thanks to the substantial increases in computational power and the continuous improvements to physical models of recent years. Development costs and turnaround times can be significantly reduced by using a combination of computational and experimental methods.