An investigation into automation of fire field modelling techniques
The research described in this thesis has produced a prototype system based on fire field modelling techniques for use by members of the Fire Safety Engineering community who are not expert in modelling techniques. The system captures the qualitative reasoning of an experienced modeller in the assessment of room geometries in order to setup important initial parameters of the problem. The prototype system is based on artificial intelligence techniques, specifically expert system technology. It is implemented as a case based reasoning (CBR) system, primarily because it was discovered that the expert uses case based reasoning when manually dealing with such problems. The thesis answers three basic research questions. These are organised into a primary question and two subsidiary questions. The primary question is: how can CFD setup for fire modelling problems be automated? From this, the two subsidiary questions are concerned with how to represent the qualitative and quantitative knowledge associated with fire modelling; and selection of the most appropriate method of knowledge storage and retrieval. The thesis describes how knowledge has been acquired and represented for the system, pattern recognition issues, the methods of knowledge storage and retrieval chosen, the implementation of the prototype system and validation. Validation has shown that the system models the expert’s knowledge in a satisfactory way and that the system performs competently when faced with new problems. The thesis concludes with a section regarding new research questions arising from the research, and the further work these questions entail.