MSG: A computer system for automated modeling of heat transfer

Ling, Sui-ky Ringo; Steinberg, Louis I.; Jaluria, Yogesh

doi:10.1017/s0890060400000378

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…We believe our methodology of identifying key physical features of the domain and of reasoning about how they interact with the geometry is quite general and extensible. For example, in the ingot casting problem of heat transfer, the temperature profile seems to be the key feature [see Ling et al (1993)]. Temget-grid-lines Input: coordinate, domain Output: {coordinate = (constants)} 1.…”

Section: Future Workmentioning

confidence: 99%

Intelligent automated grid generation for numerical simulations

Yao¹,

Gelsey²

1996

AIEDAM

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Numerical simulation of partial differential equations (PDEs) plays a crucial role in predicting the behavior-of physical systems and in modern engineering design. However, to produce reliable results with a PDE simulator, a human expert must typically expend considerable time and effort in setting up the simulation. Most of this effort is spent in generating the grid, the discretization of the spatial domain that the PDE simulator requires as input. To properly design a grid, the gridder must not only consider the characteristics of the spatial domain, but also the physics of the situation and the peculiarities of the numerical simulator. This article describes an intelligent gridder that is capable of analyzing the topology of the spatial domain and of predicting approximate physical behaviors based on the geometry of the spatial domain to automatically generate grids for computational fluid dynamics simulators. Typically, gridding programs are given a partitioning of the spatial domain to assist the gridder. Our gridder is capable of performing this partitioning. This enables the gridder to automatically grid spatial domains with a wide range of configurations.

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Section: Future Workmentioning

confidence: 99%

Intelligent automated grid generation for numerical simulations

Yao¹,

Gelsey²

1996

AIEDAM

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“…It is evident from the literature that there has been much research on complex application domains that do not use CBR, particularly under the headings of model based reasoning and qualitative reasoning (see for instance [13][14][15]). This research emphasises representation but it is evident that the reasoning process in mind is one of planning, with search involving backtracking through a solution space.…”

Section: System Issuesmentioning

confidence: 99%

Knowledge engineering requirements in derivational analogy

Cunningham

Finn

Slattery

1994

Topics in Case-Based Reasoning

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Abstract. A major advantage in using a case-based approach to developing knowledge-based systems is that it can be applied to problems where a strong domain theory may be difficult to determine. However the development of case-based reasoning (CBR) systems that set out to support a sophisticated case adaptation process does require a strong domain model. The Derivational Analogy (DA) approach to CBR is a case in point. In DA the case representation contains a trace of the reasoning process involved in producing the solution for that case. In the adaptation process this reasoning trace is reinstantiated in the context of the new target case; this requires a strong domain model and the encoding of problem solving knowledge. In this paper we analyse this issue using as an example a CBR system called CoBRA that assists with the modelling tasks in numerical simulation.

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“…The motivation of this work is in certain ways analogous to the graph of models in so far as focus is placed on the use of different idealized models as the basis for finite element analysis. Ling and Steinberg (1993) describe an automated system for generating sets of governing equations that can be used in the analysis and design of thermal systems. These equations can exist at different levels of abstraction and include; algebraic equations; ordinary differential equations; and partial differential equations.…”

Section: Qualitative Physicsmentioning

confidence: 99%

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

Finn¹

1993

AIEDAM

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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.

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MSG: A computer system for automated modeling of heat transfer

Cited by 5 publications

References 7 publications

Intelligent automated grid generation for numerical simulations

Intelligent automated grid generation for numerical simulations

Knowledge engineering requirements in derivational analogy

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

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