Velo: riding the knowledge management wave for simulation and modeling

  • Authors:

  • Ian Gorton;Chandrika Sivaramakrishnan;Gary Black;Signe White;Sumit Purohit;Michael Madison;Karen Schuchardt

  • Affiliations:

  • Pacific Northwest National Laboratory, Richland, WA, USA;Pacific Northwest National Laboratory, Richland, WA, USA;Pacific Northwest National Laboratory, Richland, WA, USA;Pacific Northwest National Laboratory, Richland, WA, USA;Pacific Northwest National Laboratory, Richland, WA, USA;Pacific Northwest National Laboratory, Richland, WA, USA;Pacific Northwest National Laboratory, Richland, WA, USA

  • Venue:
  • Proceedings of the 4th International Workshop on Software Engineering for Computational Science and Engineering
  • Year:
  • 2011

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Abstract

Modern scientific enterprises are inherently knowledge-intensive. In general, scientific studies in domains such as geosciences, climate, and biology require the acquisition and manipulation of large amounts of experimental and field data in order to create inputs for large-scale computational simulations. The results of these simulations must then be analyzed, leading to refinements of inputs and models and additional simulations. Further, these results must be managed and archived to provide justifications for regulatory decisions and publications that are based on these models. In this paper we introduce our Velo framework that is designed as a reusable, domain independent knowledge management infrastructure for modeling and simulation. Velo leverages, integrates, and extends open source collaborative and content management technologies to create a scalable and flexible core platform that can be tailored to specific scientific domains. We describe the architecture of Velo for managing and associating the various types of data that are used and created in modeling and simulation projects, as well as the framework for integrating domain-specific tools. To demonstrate a realization of Velo, we describe the Geologic Sequestration Software Suite (GS3) that has been developed to support geologic sequestration modeling. This provides a concrete example of the inherent extensibility and utility of our approach.