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，研究人员可能需要花费数小时甚至数天的时间来设置一个复杂的问题才能得到解决。 此外，如果使用标准技术，可能需要数小时甚至数周才能从模拟产生的大量数据中提取洞察。对于发现和设计问题，问题的下一个变体需要更改问题定义，这些延迟会扰乱实验流程以及相关的直觉以及了解问题定义的更改如何与解决方案的更改相关的知识。为了解决这个问题，计划进行范式转变（这里称为“沉浸式模拟”），以实现问题定义编辑的新方法，使从业者能够与模拟（视觉模型迭代）进行交互，从而动态地体验来自单个实时和持续模拟的参数变化的影响。 例如，外科医生在一台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血流模式的变化。同样，工程师改变虚拟汽车的形状，看看流动模式是改善还是恶化。这些应用研究实例与基础研究有相似之处，研究人员将首次获得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。将解决方案更改可视化地连接到可视化迭代的几何图形和/或参数更改将开启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本科生和研究生的基础课程中，以实现更深入、基于体验的学习。 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为任何偏微分方程求解器提供以下功能：1）实时、可重新配置的持续仿真可视化；2）实时、可重新配置的问题定义，以允许动态解决方案洞察力指导关键问题参数的选择；3）实时参数灵敏度反馈；4）自适应仿真控制 离散化误差和几何变化，以及 5) 可靠、沉浸式仿真的集成和演示。这些软件组件将首先开发的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能够在广泛的参数空间下以直观和定量的参数灵敏度更快地探索其系统的性能。该软件不仅可以实现设计（应用研究，例如，探索旁路与支架类型以及针对特定患者患病脉管系统的放置或流量控制执行器的放置），还可以实现发现（基础研究，例如，探索流量响应的物理学，以发现全新的外科手术和流量控制过程和设备）。这种双重且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，建立有关流动物理学的直觉，然后在顶点设计课程中强化应用性的学习。 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总体开发，以便于其他利用仿真的领域轻松采用。这些工具的大规模成功开发、集成和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界面进行实时、可重新配置的发现，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优点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