A physical modeling assistant for the preliminary stages of finite element analysis

Abstract

This paper describes work in progress aimed at developing an interactive modeling tool that assists engineers with the task of physical modeling in finite element analysis. Physical modeling precedes the numerical simulation phase of finite element analysis and involves applying modeling idealizations to real world physical systems so that complex engineering problems are more amenable to numerical computation. In the paper, the nature of physical modeling is explored, a cognitive model of how engineers are thought to model complex problems is described and based on this model a knowledge-based modeling assistant is proposed. The AI approach taken is based on Chandrasekaran's propose-critique-modify design model adapted for the task of physical modeling. Within this framework, the AI paradigms of case-based reasoning, derivational analogy and model-based reasoning are exploited. By representing fundamental thermal modeling scenarios as cases, complex physical systems can be modeled in a piecewise fashion. Derivational analogy permits generative adaptation of retrieved cases by using model-based engineering traces thereby providing a basis for critiquing case solutions. An initial prototype is described which has been implemented for the domain of convection heat transfer analysis.

Reusability-based selection of parametric finite element analysis models

“…Routine reuse involves using new inputs in existing models or anticipated variations of the problems they model. Several researchers have successfully applied knowledge-based inference methods toward automation of routine reuse for narrowly defined sets of problems representing specific application domains of the finite element method (FEM; Finn, 1993; Shephard & Wentorf, 1994; Fenves & Turkiyyah, 1996). Support for automated routine reuse of FEA models has also been achieved by modeling associativity between design and analysis model representations (Peak, 1993; Peak et al, 1999).…”

Knowledge engineering requirements in derivational analogy

“…The objective for modelling is to create a model that is computationally realistic to solve, but, at the same time retains the important features of the physical system. This task forms an important initial stage in real world engineering analysis problems and for the analyst it involves making a series of assumptions and justifications to produce the simplified model [10,11,12]. Figure 4 illustrates a typical modelling scenario associated with the analysis of an electronic cooling fin.…”

“…Modelling strategies are based on experiential modelling episodes and therefore do not have a strong domain theory [11]. The reasoning process involved in deciding which strategy to apply requires the use of fundamental heat transfer knowledge in the form of formulae, approximations, correlations and assumptions.…”

“…Furthermore, the application of such problem solving knowledge is highly dependent on the physical system being analysed and the strategy being assessed. The implication of this is that the use of model based or generalised planning techniques have proven to be problematic in model generation [11]. This has led us to examine the use of derivational analogy techniques in this domain which has been tackled using planning and first principles approaches hitherto.…”

The management of intelligence-assisted finite element analysis technology

“…Each paradigm has its advantages and limitations and so any future FEA package/design tool that wishes to achieve the ideals of a perfect FE implementation [10] should try to take into account the feasibility of incorporating such methods based on their limitations rather than their advantages [32]. [15], [16], [22], [24], [28], [33], [39], [40], [49], [50], [54], [[57], [59], [77], [82]} Quicker convergence of solutions based on "learnt" Mesh patterns { [1], [3], [15], [16], [18], [38], [42], [58], [71] A cognitive mapping for the FEM is now presented, which entails detailing the nature of how each AI technique has been and could be applied to FEMG in particular. This is done so as to develop a framework for incorporating AI within the FEA process (see Section IIIB).…”