Computational fluid dynamics (CFD) based Fire Field Modelling (FFM) codes offer powerful tools for fire safety engineers but their operation requires a high level of skill and an understanding of the mode of operation and limitations, in order to obtain meaningful results in complex scenarios. This problem is compounded by the fact that many FFM cases are barely stable and poor quality set-up can lead to solution failure. There are considerable dangers of misuse of FFM techniques if they are used without adequate knowledge of both the underlying fire science and the associated numerical modelling. CFD modelling can be difficult to set up effectively since there are a number of potential problems: it is not always clear what controls are needed for optimal solution performance, typically there will be no optimal static set of controls for the whole solution period to cover all stages of a complex simulation, there is the generic problem of requiring a high quality mesh - which cannot usually be ascertained until the mesh is actually used for the particular simulation for which it is intended and there are potential handling issues, e.g. for transitional events (and extremes of physical behaviour) which are likely to break the solution process. In order to tackle these key problems, the research described in this thesis has identified and investigated a methodology for analysing, applying and automating a CFD Expert user's knowledge to support various stages of the simulation process - including the key stages of creating a mesh and performing the simulation. This research has also indicated an approach for the control of a FFM CFD simulation which is analogous to the way that a FFM CFD Expert would approach the modelling of a previously unseen scenario. These investigations have led to the identification of a set of requirements and appropriate knowledge which have been instantiated as the, so called, Experiment Engine (EE). This prototype component (which has been built and tested within the SMARTFIRE FFM environment) is capable, both of emulating an Expert users' ability to produce a high quality and appropriate mesh for arbitrary scenarios, and is also able to automatically adjust a key control factor of the solution process.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:485952 |
| Date | January 2007 |
| Creators | Wang, Yanbo |
| Contributors | Ewer, John ; Patel, Mayur |
| Publisher | University of Greenwich |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://gala.gre.ac.uk/8472/ |
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