In a discussion I had today around ways to advance a scientific problem I was reminded of Jim Gray and Gerd Heber’s trilogy – Supporting Finite Element Analysis with a Relational Database Backend. The three papers are really a good resource for understanding how databases can be used in scientific challenges.
We show how to use a Relational Database Management System in support of Finite Element Analysis. We believe it is a new way of thinking about data management in well-understood applications to prepare them for two major challenges, – size and integration (globalization). Neither extreme size nor integration (with other applications over the Web) was a design concern 30 years ago when the paradigm for FEA implementation first was formed. On the other hand, database technology has come a long way since its inception and it is past time to highlight its usefulness to the field of scientific computing and computer based engineering. This series aims to widen the list of applications for database designers and for FEA users and application developers to reap some of the benefits of database development.
This is Part II of a three articles on using databases for Finite Element Analysis (FEA). It discusses (1) db design, (2) data loading, (3) typical use cases during grid building, (4) typical use cases during simulation (get and put), (5) typical use cases during analysis (also done in Part III) and some performance measures of these cases. It argues that using a database is simpler to implement than custom data schemas, has better performance because it can use data parallelism, and better supports FEA modularity and tool evolution because database schema evolution, data independence, and self-defining data.
In this report, we show a unified visualization and data analysis approach to Finite Element Analysis. The example application is visualization of 3D models of (metallic) polycrystals. Our solution combines a mature, general purpose, rapid-prototyping visualization tool, OpenDX (formerly known as IBM Visualization Data Explorer) [1,2], with an enterprise-class relational database management system, Microsoft SQL Server . Substantial progress can be made with established off-the-shelf technologies. This approach certainly has its limits and we point out some of the shortcomings which require more innovative products for visualization, data-, and knowledge management. But, overall, the approach is a substantial improvement in the FEA lifecycle, and probably will work for other data-intensive sciences wanting to visualize and analyze massive simulation or measurement datasets.