Authors:
Lars Frank Große
and
Franz Joos
Affiliation:
Helmut-Schmidt-University and University of the Federal Armed Forces Hamburg, Germany
Keyword(s):
Computational Fluid Dynamics (CFD), Combustion simulation, Furnace, Finite-volume-model.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
Enterprise Information Systems
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial, Financial and Medical Applications
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
The design process of commercially available combustion engines is often based on real experiments which is expensive concerning to fuel consumption, men power and environmental pollution. It is possible to replace complex experiments by computer simulations. The prediction of the velocity field, the mixing process of fuel and oxidiser and the temperature field is a wide range of research subjects. In case of turbulent flow simulations with combustion the chemical reactions and the coupling have to be calculated at the same time. With regard to computer time the used chemical reaction mechanism has a big influence on the performance of the whole simulation. Therefore optimisation procedures often improve the representation of the chemistry. The suggestion made in this paper, is the use of artificial neuronal networks for approximation of complex chemistry in turbulent combustion simulations.