Information modeling for intent-based retrieval of parametric finite element analysis models.

摘要

Adaptive reuse of parametric finite element analysis (FEA) models is a common form of reuse that involves integrating new information into an archived FEA model to apply it towards a new similar physical problem. Adaptive reuse of archived FEA models is often motivated by the need to assess the impact of minor improvements to component-based designs such as addition of new structural components, or the need to assess new failure modes that arise when a device is redesigned for new operating environments or loading conditions. Successful adaptive reuse of FEA models involves reference to supporting documents that capture the formulation of the model to determine what new information can be integrated and how. However, FEA models and supporting documents are not stored in formats that are semantically rich enough to support automated inference of their relevance to a modeler's needs. The modeler's inability to precisely describe information needs and execute queries based on such requirements results in inefficient queries and time spent manually assessing irrelevant models. The central research question in this research is thus "how do we incorporate a modeler's intent into automated retrieval of FEA models for adaptive reuse?";An automated retrieval method to support adaptive reuse of parametric FEA models has been developed in the research documented in this thesis. The method consists of a classification-based retrieval method based on ALE subsumption hierarchies that classify models using semantically rich description logic representations of physical problem structure and a reusability-based ranking method. Conceptual data models have been developed for the representations that support both retrieval and ranking of archived FEA models. The method is validated using representations of FEA models of several classes of electronic chip packages. Experimental results indicate that the properties of the representation methods support effective automation of retrieval functions for FEA models of component-based designs.