SI2-SSE: Software Elements to Enable Immersive Simulation

SI2-SSE：实现沉浸式仿真的软件元素

• 批准号: 1740330
• 负责人: Kenneth Jansen
• 金额: $ 50万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1740330&HistoricalAwards=false
• 关键词: SI2; SSE; Software; Elements; Enable

Parallel computers have grown so powerful that they are now able to solve extremely complex fluid flow or structures problems in seconds. Unfortunately, it may take a researcher many hours or even days to set up a complex problem before it can be solved. Furthermore it may take hours or often weeks to extract insight from the volume of data the simulation produces, if using standard techniques. For discovery and design questions, where the next variant of the problem requires a change to the problem definition, these delays disrupt the flow of experimentation and the associated intuition and learning about how the change in the problem definition relates to a change in the solution. To address this issue, a paradigm shift, referred to here as "immersive simulation", is planned to enable new approaches to problem definition editing that allow practitioners to interact with the simulations (visual model iteration) in a manner where they can dynamically experience the influence of parameter variations from a single, live, and ongoing simulation. Examples include a surgeon virtually altering the shape of a bypass graft on one computer monitor and then virtually observing the change in the blood flow patterns not only within the bypass but throughout the vascular system. Likewise, an engineer altering the shape of a virtual car to see if the flow pattern improves or worsens. These applied research examples have parallels in fundamental research where live insight into the flow physics of unsteady, turbulent flows and their sensitivity to live parameter changes will be made available to researchers for the first time. Visually connecting the solution change to the visually iterated geometry and/or parameter change will enable a new age of intuition-driven discovery and design. This paradigm shift will also be incorporated into foundational undergraduate and graduate courses to enable deeper, experiential-based learning. The central goal of this project is to advance state-of-the-art tools into generic components that, when integrated, will make the following capabilities available to any partial differential equation solver: 1) live, reconfigurable visualization of ongoing simulations, 2) live, reconfigurable problem definition to allow the dynamic solution insight to guide the choice of key problem parameters, 3) real-time parameter sensitivity feedback, 4) adaptive simulation control to account for discretization errors and geometry changes, and 5) integration and demonstration of reliable, immersive simulation. The first communities that these software components will be developed with include cardiovascular flow and aerodynamic flow control. They have already articulated a need for software to more rapidly explore the performance of their systems under a broad parameter space with intuitive and quantitative parameter sensitivity. This software will enable not only design (applied research e.g., exploring bypass vs. stent type and placement for a particular patient's diseased vasculature or flow control actuator placement), but also discovery (fundamental research e.g., explore physics of flow response to discover completely new surgical procedures and flow control processes and devices). This twofold and complementary software application will have a similar impact on education, where foundational courses will use the integrated software modules to create immersive simulations that build intuition about flow physics, and then reinforce that learning in an applied nature in capstone design courses. While the ideas will be prototyped and proven within the field of fluid dynamics, they will be developed generally, with sustainable software engineering, for easy adoption by other fields that make use of simulation. The successful development, integration, and demonstration of these tools at scale will transform massively parallel simulation from a series of I/O-intensive steps to live, reconfigurable discovery using carefully designed interfaces that blaze the trail for all simulation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

并行计算机已经变得如此强大，以至于它们现在能够在几秒钟内解决极其复杂的流体流动或结构问题。 不幸的是，研究人员可能需要几个小时甚至几天才能解决一个复杂的问题。 此外，如果使用标准技术，则可能需要数小时或通常数周才能从模拟产生的大量数据中提取见解。对于发现和设计问题，其中问题的下一个变体需要改变问题定义，这些延迟中断了实验流程和相关的直觉，以及学习问题定义的变化如何与解决方案的变化相关。为了解决这个问题，一个范式的转变，这里称为“沉浸式仿真”，计划启用新的方法来编辑问题定义，允许从业者与模拟（可视化模型迭代）的方式，他们可以动态地体验参数变化的影响，从一个单一的，活的，和正在进行的模拟。 例子包括外科医生在一个计算机显示器上虚拟地改变旁路移植物的形状，然后虚拟地观察不仅在旁路内而且在整个血管系统中的血流模式的变化。同样，一个工程师改变了一辆虚拟汽车的形状，看看它的流动模式是改善了还是改变了。这些应用研究的例子在基础研究中有相似之处，其中对不稳定湍流的流动物理学及其对实时参数变化的敏感性的实时洞察将首次提供给研究人员。将解决方案的变化与可视化迭代的几何形状和/或参数变化可视化地联系起来，将开启一个直觉驱动的发现和设计的新时代。这种范式转变也将被纳入基础本科和研究生课程，以实现更深入的体验式学习。 该项目的中心目标是将最先进的工具推进到通用组件中，这些组件在集成后将使任何偏微分方程求解器都可以使用以下功能：1）正在进行的模拟的实时、可重构的可视化，2）实时、可重构的问题定义，以允许动态解决方案洞察力来指导关键问题参数的选择，3）实时参数灵敏度反馈，4）自适应模拟控制以考虑离散化误差和几何变化，以及5）可靠的、沉浸式模拟的集成和演示。这些软件组件将开发的第一个社区包括心血管流量和空气动力学流量控制。他们已经明确表示，需要软件在广泛的参数空间下更快速地探索其系统的性能，并具有直观和定量的参数灵敏度。该软件不仅可以进行设计（应用研究，例如，探索旁路与支架类型和用于特定患者的患病脉管系统的放置或流动控制致动器放置），而且还发现（基础研究例如，探索流动响应的物理学，以发现全新的外科手术和流动控制过程和设备）。这种双重和互补的软件应用程序将对教育产生类似的影响，基础课程将使用集成的软件模块来创建沉浸式模拟，建立对流动物理的直觉，然后在顶点设计课程中加强应用性质的学习。 虽然这些想法将在流体动力学领域内进行原型设计和验证，但它们将通过可持续的软件工程进行普遍开发，以便易于被其他使用模拟的领域采用。这些工具的成功开发、集成和大规模演示将把大规模并行模拟从一系列I/O密集型步骤转变为使用精心设计的接口的实时可重构发现，为所有模拟开辟道路。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Interface Tracking Investigation of Geometric Effects on the Bubbly Flow in PWR Subchannels

• DOI: 10.1080/00295639.2018.1499280
• 发表时间: 2018-08
• 期刊: Nuclear Science and Engineering
• 影响因子: 1.2
• 作者: J. Fang;J. Cambareri;M. Rasquin;A. Gouws;R. Balakrishnan;K. Jansen;I. Bolotnov

Bi-fidelity reduced polynomial chaos expansion for uncertainty quantification

用于不确定性量化的双保真减少多项式混沌展开

• DOI: 10.1007/s00466-021-02096-0
• 发表时间: 2022
• 期刊: Computational Mechanics
• 影响因子: 4.1
• 作者: Newberry, Felix;Hampton, Jerrad;Jansen, Kenneth;Doostan, Alireza
• 通讯作者: Doostan, Alireza