Project 1:   NHR@KIT - Collaboration with University of Rostock

Project title:
Development and validation of a hybrid grid/particle method for turbulent flows supported by high performance computations with OpenFOAM

Cooperation with:
Prof. Nikolai Kornev (University of Rostock), Faculty of Mechanical Engineering and Marine Technology, Chair of Modelling and Simulation

Duration:
3 years

Coordination within SCC:
Dr.-Ing. J.A. Denev (SCC-PI: Prof. Martin Frank)

Short description:
The main goal of the present project is the further development and validation of a new computational fluid dynamics (CFD) method using a combination of grid-free (particles) and grid-based techniques. A fundamental assumption of this novel approach is the decomposition of any physical quantity into the grid based (large scale) and the fine scale parts, whereas large scales are resolved on the grid and fine scales are represented by particles. Dynamics of large and fine scales is calculated from two coupled transport equations one of which is solved on the grid whereas the second one utilizes the Lagrangian grid free Vortex Particle Method (VPM).

Application areas:
These problems include external flow problems, e.g. flows around vehicles like ships, cars, trucks or airplanes with a strong relation to energy (energy savings) and mobility. Particularly, the new hybrid method will be utilized for the prediction of efficiency and design of new energy saving devices (ESD) for model and real scale ships. ESD allow to reduce the delivered power up to ten percent.

Open source software:
OpenFOAM

 

Project 2:   NHR@KIT - Collaboration with Engler-Bunte Institute and Institute for Piston Engines at KIT  

Project title:
GPU-based machine learning methods to develop predictive models for estimation of exhaust gas properties from internal combustion engines during cold starts from real-world experimental data

Cooperation with:
Prof. Dr.-Ing. Dimosthenis Trimis  (KIT, Engler-Bunte Institute, Combustion Division) and Prof. Dr. sc.-techn. Thomas Koch (KIT, Institute for Piston Engines)    

Duration:
3 years

Coordination within SCC:
Dr.-Ing. J.A. Denev (SCC-PI: Prof. Martin Frank)

Short description:
Through extensive experiments large data sets have been obtained recently on internal combustion engine performance at cold starts and first few driving kilometers. The data sets contain measurements of more than 1000 parameters recorded simultaneously at steps of 0.1 seconds comprising among other engine performance parameters, detailed multi-component composition of the exhaust gas as well as corresponding properties of nanometer-sized particles compiled from electron microscopic  images. The objective of the project is to apply machine learning (ML) methods to this inexhaustible amount of data to develop models capable of predicting the exhaust gas composition including particulate matter concentrations and nanostructural/molecular properties during variable non-stationary cold start conditions. 

Application areas:
Bringing together the data from the different experimental and analytical techniques of both the engine parameters and the exhaust gas composition from the investigated engines will result in ML-derived predictive models and lead to a better understanding of the fundamental influencing parameters on pollutant formation. This will help to design intelligent engine operating strategies that can be employed for future biofuel and reFuel based engines.

Open source software:
HeAT (Helmholtz Analytics Toolkit) library

The software OpenFOAM models fluid flows and heat ans mass transfer problems for Engineering. It belongs to the group of Computational Fluid Dynamics (CFD) software. The SDL EEM installs and updates OpenFOAM on the SCC-supercomputers. It uses it actively for numerical modelling of engineering applications as well as for basic scientific research and steadily contributes to its development.