Using artificial intelligence to control fluid flow computations

Computational simulation is an essential tool for the prediction of fluid flow. Many powerful simulation programs exist today. However, using these programs to reliably analyze fluid flow and other physical situations requires considerable human effort and expertise to set up a simulation, determine whether the output makes sense, and repeatedly run the simulation with different inputs until a satisfactory result is achieved. Automating this process is not only of considerable practical importance but will also significantly advance basic artificial intelligence (AI) research in reasoning about the physical world

An investigation into the feasibility, problems and benefits of re-engineering a legacy procedural CFD code into an event driven, object oriented system that allows dynamic user interaction

This research started with questions about how the overall efficiency, reliability and ease-of-use of Computational Fluid Dynamics (CFD) codes could be improved using any available software engineering and Human Computer Interaction (HCI) techniques. Much of this research has been driven by the difficulties experienced by novice CFD users in the area of Fire Field Modelling where the introduction of performance based building regulations have led to a situation where non CFD experts are increasingly making use of CFD techniques, with varying degrees of effectiveness, for safety critical research. Formerly, such modelling has not been helped by the mode of use, high degree of expertise required from the user and the complexity of specifying a simulation case. Many of the early stages of this research were channelled by perceived limitations of the original legacy CFD software that was chosen as a framework for these investigations. These limitations included poor code clarity, bad overall efficiency due to the use of batch mode processing, poor assurance that the final results presented from the CFD code were correct and the requirement for considerable expertise on the part of users. The innovative incremental re-engineering techniques developed to reverse-engineer, re-engineer and improve the internal structure and usability of the software were arrived at as a by-product of the research into overcoming the problems discovered in the legacy software. The incremental reengineering methodology was considered to be of enough importance to warrant inclusion in this thesis. Various HCI techniques were employed to attempt to overcome the efficiency and solution correctness problems. These investigations have demonstrated that the quality, reliability and overall run-time efficiency of CFD software can be significantly improved by the introduction of run-time monitoring and interactive solution control. It should be noted that the re-engineered CFD code is observed to run more slowly than the original FORTRAN legacy code due, mostly, to the changes in calling architecture of the software and differences in compiler optimisation: but, it is argued that the overall effectiveness, reliability and ease-of-use of the prototype software are all greatly improved. Investigations into dynamic solution control (made possible by the open software architecture and the interactive control interface) have demonstrated considerable savings when using solution control optimisation. Such investigations have also demonstrated the potential for improved assurance of correct simulation when compared with the batch mode of processing found in most legacy CFD software. Investigations have also been conducted into the efficiency implications of using unstructured group solvers. These group solvers are a derivation of the simple point-by-point Jaccobi Over Relaxation (JOR) and Successive Over Relaxation (SOR) solvers [CROFT98] and using group solvers allows the computational processing to be more effectively targeted on regions or logical collections of cells that require more intensive computation. Considerable savings have been demonstrated for the use of both static- and dynamic- group membership when using these group solvers for a complex 3-imensional fire modelling scenario. Furthermore the improvements in the system architecture (brought about as a result of software re-engineering) have helped to create an open framework that is both easy to comprehend and extend. This is in spite of the underlying unstructured nature of the simulation mesh with all of the associated complexity that this brings to the data structures. The prototype CFD software framework has recently been used as the core processing module in a commercial Fire Field Modelling product (called "SMARTFIRE" [EWER99-1]). This CFD framework is also being used by researchers to investigate many diverse aspects of CFD technology including Knowledge Based Solution Control, Gaseous and Solid Phase Combustion, Adaptive Meshing and CAD file interpretation for ease of case specification