Search Results

Modeling of internal combustion engines is a multidisciplinary activity that is continuously growing and is more and more present during the development phase of an engine, mostly today when the time to production is reduced. There are different levels of complexity when thinking about engine modeling, from the high level of details of the 3D models to a much simpler approach of the 1D models. The last is the most adequate to analyze the whole engine system with multiple components together, looking for its performance. It is also interesting and important to observe the advances of the combustion modeling and prediction of emissions that these models allow. In this article the possibilities of 1D modeling is presented. A quick overview on available simulation codes is done and some models of combustion are studied. Finally, 1D engine models are built to verify their advantages, drawbacks and results in terms of engine performance.

During the development of a air intake manifold simulation is necessary to verify the component characteristics in terms of flow considering runner in-balance as focus. Results can also present exhaust gases recirculation (EGR) distribution affecting each of the different runners in order to verify if those recirculated exhaust gases were been equally distributed among all the runner outlets or, at least, presenting a percentual difference of EGR concentration in each runner inside a defined tolerance range. Aiming these results some calculation techniques were used as follow: 1D modeling as well as 3D modeling was used 1D model was built using GT-Power and 3D modeling was done using computational fluid dynamics based on ANSYS FLUENT.

Recent advances in hardware and software have allowed to development teams to use their time not only solving common CAE analyses but also thinking about the development of CAE optimization environments. Optimization comes like a tool capable to drive design parameters towards regions where selected characteristics of the project can be further improved. This work presents one case that serves to illustrate the application of a optimization technique well known as multi-objective optimization. In the example a Multi-Objective Genetic Algorithm is used to define the thicknesses of several regions over a valve cover looking not only for lower flexibility but also to lower mass. In this case, the commercial code PERMAS was used to verify the structural behavior of the valve cover, as well as to calculate its mass.