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The compression ratio is an important engine design parameter. It determines to a large extend engine properties like the achievable efficiency, the heat losses from the combustion chamber and the exhaust losses. The same properties are affected by insulation of the combustion chamber. It is therefore especially important to know the compression ratio when doing experiments with thermal barrier coatings (TBC). In case of porous TBCs, the standard methods to measure the compression ratio can give wrong results. When measuring the compression ratio by volume, using a liquid, it is uncertain if the liquid fills the total porous volume of the coating. And for a thermodynamic compression ratio estimation, a model for the heat losses is needed, which is not available when doing experiments with insulation. The subject of this paper is the evaluation of an alternative method to assess the compression ratio.

Due to an ongoing trend of high injection pressures in the realm of internal combustion engines, the role of cavitation that typically happens inside the injector nozzle has become increasingly important. In this work, a large Eddy Simulation (LES) with cavitation modeled on the basis of an Eulerian Stochastic Field (ESF) method and a homogeneous mixture model is performed to investigate the role of cavitation on the Engine Combustion Network (ECN) spray G2. The Eulerian stochastic field cavitation model is coupled to a pressure based solver for the flow, which lowers the computational cost, thereby making the methodology highly applicable to realistic injector geometries. Moreover, the nature of the Eulerian stochastic field method makes it more convenient to achieve a high scalability when applied to parallel cases, which gives the method the edge over cavitation models that are based on Lagrangian tracking.

Many new combustion concepts are currently being investigated to further improve engines in terms of both efficiency and emissions. Examples include homogeneous charge compression ignition (HCCI), lean stratified premixed combustion, stratified charge compression ignition (SCCI), and high levels of exhaust gas recirculation (EGR) in diesel engines, known as low temperature combustion (LTC). All of these combustion concepts have in common that the temperatures are lower than in traditional spark ignition or diesel engines. To further improve and develop combustion concepts for clean and highly efficient engines, it is necessary to develop new computational tools that can be used to describe and optimize processes in nonstandard conditions, such as low temperature combustion.