SBIR Phase II: Visualization of Massive Multivariate Adaptive Mesh Refinement (AMR) Data

SBIR 第二阶段：海量多元自适应网格细化 (AMR) 数据的可视化

• 批准号: 0548729
• 负责人: Lisa Avila
• 金额: --
• 依托单位: KITWARE INC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-01 至 2008-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0548729&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Visualization; Massive

This Small Business Innovation Research (SBIR) Phase II project addresses the lack of visualization technology for hierarchical structured grids created through an advanced simulation process known as Adaptive Mesh Refinement (AMR). Although the AMR structure makes possible simulations that are too computationally expensive using a uniform grid approach, it leaves the scientist with a lack of visualization tools to properly render the resulting volumetric data. With the successful completion of this Phase II effort, Kitware will meet this need by developing visualization tools that are focused on efficiently and effectively rendering the large, multivariate, time-varying data produced using the AMR technique. The primary technical accomplishment of the Phase II effort will be the development of a high performance volume rendering strategy for AMR data that runs across a variety of platforms from a standard desktop system to a large cluster of high-end workstations. Advanced transfer function techniques will aid scientific discovery by allowing scientists to visualize relationships in their data. Packaging these visualization tools into a user-friendly application will make this complex technology accessible to researchers. In addition, Kitware will adapt this technology to the clinical medical visualization market, where large, multivariate, hierarchical data will become commonplace in the near future.The state-of-the-art AMR visualization technology developed during this Phase II project will be donated to the scientific community as part of two open-source packages. This technology will be available to software developers through the Visualization Toolkit (VTK), a C++ class library of visualization, graphics, and image processing algorithms. This technology will also be incorporated into the end-user scientific visualization application ParaView, which can run on a desktop computer or across a high performance cluster. Through the use of extreme programming principles, these open source packages are developed, tested, and released daily, allowing Kitware to deliver the latest technology for immediate use by the scientific community. In return, this provides Kitware with continual feedback from users and developers that will help the firm to improve not only the open source software, also the firm's commercial products that are built on top of this code base. Kitware intends to leverage the Research Opportunities for Undergraduates (REU) and Research Opportunities for Teachers (RET) programs to build a team of students and teachers who will generate educational material from the software including lesson plans, presentation materials, animations, and suggested projects. This material will be distributed to educators at the high school and undergraduate levels.

这个小型企业创新研究（SBIR）第二阶段项目解决了缺乏可视化技术的分层结构网格创建通过一个先进的模拟过程称为自适应网格细化（AMR）。 虽然AMR结构使得使用统一网格方法进行计算成本太高的模拟成为可能，但它使科学家缺乏可视化工具来正确渲染所产生的体积数据。 随着第二阶段工作的成功完成，Kitware将通过开发可视化工具来满足这一需求，这些工具专注于高效地渲染使用AMR技术产生的大型、多变量、时变数据。 第二阶段工作的主要技术成就将是为AMR数据开发一种高性能体绘制策略，该策略可在从标准桌面系统到大型高端工作站集群的各种平台上运行。 先进的传递函数技术将通过允许科学家可视化数据中的关系来帮助科学发现。 将这些可视化工具打包成一个用户友好的应用程序将使研究人员能够使用这种复杂的技术。 此外，Kitware还将使这项技术适应临床医学可视化市场，在不久的将来，大型、多变量、分层数据将变得司空见惯。在这个第二阶段项目中开发的最先进的AMR可视化技术将作为两个开源软件包的一部分捐赠给科学界。 该技术将通过可视化工具包（VTK）提供给软件开发人员，这是一个可视化、图形和图像处理算法的C++类库。 该技术还将被纳入最终用户科学可视化应用程序ParaView，该应用程序可以在台式计算机上运行，也可以跨高性能集群运行。 通过使用极端编程原则，这些开源软件包每天都在开发、测试和发布，使Kitware能够提供最新的技术供科学界立即使用。 作为回报，这为Kitware提供了来自用户和开发人员的持续反馈，这将有助于该公司不仅改进开源软件，而且改进该公司基于此代码库构建的商业产品。 Kitware打算利用本科生研究机会（REU）和教师研究机会（RET）计划来建立一个学生和教师团队，他们将从软件中生成教育材料，包括课程计划，演示材料，动画和建议的项目。 这份材料将分发给高中和大学教育工作者。