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An investigation of techniques to assist with reliable specification and successful simulation of fire field modelling scenarios

An investigation of techniques to assist with reliable specification and successful simulation of fire field modelling scenarios

Wang, Yanbo (2007) An investigation of techniques to assist with reliable specification and successful simulation of fire field modelling scenarios. PhD thesis, University of Greenwich.

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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.

This research has demonstrated that it is possible to emulate an Experts' ability to analyse a series of simulation trials (starting from a simplified, coarse mesh test run) in order to improve subsequent modelling attempts and to improve the scenario specification and/or meshing solution in order to allow the software to recover from a complete solution failure. The research has also shown that it is possible to emulate an Expert user's ability to provide continual run-time control of a simulation and to provide significant benefits in terms of performance, overall reliability and accuracy of the results.

The instantiation and testing of the Experiment Engine concept, on a chosen FFM environment - SMARTFIRE, has demonstrated significant performance and stability gains when compared to non Experiment Engine controlled simulations, for a range of complex "real world" fire scenarios. Preliminary tests have shown that the Experiment Engine controlled simulation was generally able to finish the simulations successfully without experiencing any difficulty, even for very complex scenarios, and that the run-time solution control adjustments, made to the time step size by both the Experiment Engine and by the Expert, showed similar trends and responses in reacting to the physical and/or numerical changes in the solution. This was also noticed for transitional events seen during the simulation. It has also been shown that the Experiment Engine (EE) controlled simulation demonstrates a saving of up to 40% of simulation sweeps for complex fire scenarios when compared with non-EE controlled simulations. Analysis of the results has demonstrated that the control technique, deployed by the EE, have no significant impact on the final solution results - hence, the Experiment Engine controlled simulations are able to produce physically sound results, which are almost identical to Expert controlled simulations.

The research has investigated a number of new methods and algorithms (e.g. case categorisation, case recognition, block-wise mesh justification, local adaptive mesh refinements, etc.) that are combined into a novel approach to enhance the robustness, efficiency and the ease-of-use of the existing FFM software package. The instantiation of these methods as a prototype control system (within the target FFM environment - SMARTFIRE) has enhanced the software with a valuable tool-set and arguably will make the FFM techniques more accessible and reliable for novice users.

The component based design and implementation of the Experiment Engine has proved to be highly robust and flexible. The Experiment Engine (EE) provides a bi­directional communication channel between the existing SMARTFIRE Case Specification Environment and the solution module (the CFD Engine). These key components can now communicate directly via status- and control- messages. In this way, it is possible to maintain the original Case Specification Environment and the CFD Engine processes completely independently. The two components interact with each other when the EE is operating. This componentization has enabled rapid prototyping and implementation of new development requirements (as well as the integration of other support techniques) as they have been identified.

Item Type: Thesis (PhD)
Additional Information:
Uncontrolled Keywords: fire field modelling scenarios, SMARTFIRE, fire simulation, mathematical modelling,
Subjects: H Social Sciences > HD Industries. Land use. Labor
Q Science > QA Mathematics
Q Science > QC Physics
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Department of Mathematical Sciences
Last Modified: 16 Jan 2019 17:37

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