Geometric versus finite element modeling current and future trends at Northrop

Engineering Automation at Northrop encompasses the various design and analytical phases of air vehicle development. Design systems addresses automation of engineering/tooling design and computer aided manufacturing processes. The analysis systems automate aeroelastic modeling and postprocessing analysis results. These systems interface with aircraft loft and geometric entities thru localized transfer techniques. However, total integration effort based on a geometric database nucleus with peripheral design, analytical and manufacturing systems is well underway. An outline of the present and future trends is presented to help channel the RPI effort in this direction

Automatic mesh generation

The objective of this thesis project is a study of Pre-Processors and development of an Automatic Mesh Generator for Finite Element Analysis. The Mesh Generator developed in this thesis project can create triangular finite elements from the geometric database of Macintosh Applications. The user is required to give the density parameter to the program for mesh generation. The research is limited to Mesh Generators of planar surfaces. Delauny Triangulation method maximizes the minimum angles of a triangle. Watson\u27s Delauny Triangulation method can mesh only the \u27convex hull\u27 of a set of nodes. This algorithm has been modified to create triangular elements in convex and non-convex surfaces. The surfaces can have holes also. A node generation algorithm to place nodes on and inside a geometry has been developed in this thesis project. The mesh generation is very efficient and flexible. Geometric modeling methods have been studied to understand and integrate the Geometric Modeler with the Finite Element Mesh Generator. Expert Systems can be integrated with Finite Element Analysis. This will make Finite Element Method fully automatic. In this thesis project, Expert Systems in Finite Element Analysis are reviewed. Proposals are made for future approach for the integration of the two fields

SINGLE BUILDING POINT CLOUD SEGMENTATION: TOWARDS URBAN DATA MODELING AND MANAGEMENT

To manage urban areas, a key step is the development of a geometric survey and its subsequent analysis and processing in order to provide useful information, and to become a good basis for urban modeling. Surveys of urban areas can be developed with various technologies, such as Aerial Laser Scanning, Unmanned Aerial Systems photogrammetry, and Mobile Mapping Systems. To make the resulting point clouds useful for subsequent steps, it is necessary to segment them into classes representing urban elements. On the other hand, there are 2D land representations that provide a variety of information related to the elements in the urban environment, which are linked to databases that have information content related to them. In this context, the element identified as interesting for urban management of the built heritage is the individual building unit. This paper presents an automated method for using map datasets to segment individual building units on a point cloud of an urban area. A unique number is then assigned to the segmented points, linking them directly to the corresponding element in the map database. The resulting point cloud thus becomes a container of the information in the map database, and a basis for possible city modeling. The method was successfully tested on the historic city of Sabbioneta (northern Italy), using two point clouds, one obtained through the use of a Mobile Mapping System and one obtained with Unmanned Aerial System photogrammetry. Two cartographic databases were used, one opensource (OpenStreetMap) and one provided by the regional authorities (regional cartographic database)

Predictive models for multibiometric systems

Recognizing a subject given a set of biometrics is a fundamental pattern recognition problem. This paper builds novel statistical models for multibiometric systems using geometric and multinomial distributions. These models are generic as they are only based on the similarity scores produced by a recognition system. They predict the bounds on the range of indices within which a test subject is likely to be present in a sorted set of similarity scores. These bounds are then used in the multibiometric recognition system to predict a smaller subset of subjects from the database as probable candidates for a given test subject. Experimental results show that the proposed models enhance the recognition rate beyond the underlying matching algorithms for multiple face views, fingerprints, palm prints, irises and their combinations

A semantic-based platform for the digital analysis of architectural heritage

This essay focuses on the fields of architectural documentation and digital representation. We present a research paper concerning the development of an information system at the scale of architecture, taking into account the relationships that can be established between the representation of buildings (shape, dimension, state of conservation, hypothetical restitution) and heterogeneous information about various fields (such as the technical, the documentary or still the historical one). The proposed approach aims to organize multiple representations (and associated information) around a semantic description model with the goal of defining a system for the multi-field analysis of buildings