PhD Thesis - staffweb - University of Greenwich

PhD Thesis by John Ewer.The figure (See Figure 2.2.1-1) shows a typical run time simulation life cycle that shows userinteraction, processing and outputs. Generally the set-up / configuration, meshing and postprocessing data analysis tools are all completely separate from the numerical CFD engine. Theuser cannot easily make use of data visualisation to continuously monitor the solution becausethe user would have to force the system to output frequent complete result file dumps. Similarlythe user is generally unable to "interact" with the solution controls because this generallyrequires saving a complete restart dump, going back to the set-up tool in order to re-configurea restart using modified control parameters and finally reloading the case and restarting tocontinue processing.There are a wide range of CFD codes that fall into this category, including:Phoenics [CHAM], Flow 3D [FLOW3D91], Easyflow [EASYFLOW90], Fluent, and Astec.2.2.2 Other approaches to improve batch mode CFD codesSeveral attempts have been made at developing Intelligent Front Ends [WILLIAMS88][JAMBUNATHAN91] for CFD systems. Whilst this research has some scope for improving theCFD simulation process, because of the importance having a high quality case specification, itis likely that the problems facing CFD research (described in Chapter 1) will still apply.Unfortunately the research only used Knowledge Based techniques to support the set-up andspecification of a case and did not modify the, essentially, batch mode numerical CFD engines.Phoenics with rules was an attempt to control the CFD processing centred on an experimentalroutine build into the Phoenics commercial CFD code. This module contained a fairly simplealgorithm to modify the relaxation parameters based on the latest values of residuals for someof the solved variables. A paper by Spalding [SPALDING92] presented simple implementationdetails of the production rules without validation of the potential benefits.2-16