DDDAS-TMRP: Planet-in-a-Bottle: A Numerical Fluid-Laboratory System

DDDAS-TMRP：瓶中行星：数值流体实验室系统

• 批准号: 0540248
• 负责人: Charles Leiserson
• 金额: $ 60万
• 依托单位: Massachusetts Institute of Technology
• 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: 2006-01-01 至 2009-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0540248&HistoricalAwards=false
• 关键词: DDDAS; TMRP; Planet; Bottle; Numerical

This project will create a "Numerical Fluid-Laboratory System" to enable enhancing the understanding of the Earth's weather and climate, which depend critically on accurate forecasting and state-estimation technology. This research effort aims to design and build a laboratory-scale DDDAS, called Planet-in-a-Bottle, as a practical and inexpensive step toward a planet-scale DDDAS. The Planet-in-a-Bottle DDDAS will emulate many of the large-scale challenges of meteorological and oceanographic state-estimation and forecasting but provide a controlled setting to allow systematic engineering strategies to be employed to devise more efficient and accurate techniques. The Planet-in-a-Bottle DDDAS will consist of two interacting parts: a fluid lab experiment and a numerical simulator. The system will employ data assimilation in which actual observations are fed into the simulator to keep the models on track with reality, and will employ sensitivity-driven observations and mesh refinement in which the simulator targets the real-time deployment of sensors to particular geographical regions and times for maximal effect, and refines the mesh to better predict the future course of the fluid experiment. In addition, the feedback loop between targeting of both the observational system and mesh refinement will be mediated, if desired, by human control.The project will investigate adjoint methods to determine how and where to deploy observations, as well as how and where to refine the simulation. The laboratory will provide insights into nonlinear fluid dynamics by visualizing and analyzing the three-dimensional behavior of a natural fluid noninvasively. Fundamental questions regarding the predictability of chaotic systems and the impact of adapting models and observations dynamically on the predictive capabilities will be explored. To design an effective Planet-in-a-Bottle DDDAS requires a substantial investment in advanced computing technology, because a naive simulation on regular meshes take far longer than the real-time evolution of the fluid. To enhance performance, the research project will devise novel algorithms and software that exploit low-cost clusters of commodity processors. Specifically, the researchers will investigate memory layout strategies for irregular meshes based on the theory of decomposition trees, which will lead to algorithms that can effectively exploit both memory hierarchy and parallelism. To investigate how the programming of such complex algorithms can be simplified, they shall investigate and develop a distributed transactional memory library to integrate sharing and synchronization in a cluster programming environment. Developing these computer-science technologies will require substantial technical expertise in the areas of algorithms and computer systems.

该项目将建立一个“数值流体实验室系统”，以加强对地球天气和气候的了解，这在很大程度上取决于准确的预报和状态估计技术。这项研究旨在设计和建造一个实验室规模的DDDAS，称为“瓶中行星”，作为迈向行星规模DDDAS的实用而廉价的一步。瓶中行星DDDAS将模拟气象和海洋学状态估计和预测的许多大规模挑战，但提供一个可控的设置，允许采用系统工程策略来设计更有效和准确的技术。瓶中行星DDDAS将由两个相互作用的部分组成：一个流体实验室实验和一个数值模拟器。该系统将采用数据同化，将实际观测数据馈送到模拟器中，使模型与现实保持一致；采用灵敏度驱动观测和网格细化，模拟器将传感器实时部署到特定地理区域和时间，以获得最大效果，并细化网格，以更好地预测流体实验的未来进程。此外，如果需要，观测系统的目标和网格细化之间的反馈回路将由人为控制来调节。该项目将研究伴随的方法，以确定如何和在哪里部署观测，以及如何和在哪里改进模拟。该实验室将通过可视化和无创分析天然流体的三维行为，为非线性流体动力学提供见解。关于混沌系统的可预测性和动态适应模型和观测对预测能力的影响的基本问题将被探讨。为了设计一个有效的瓶中行星DDDAS，需要在先进的计算技术上进行大量投资，因为在规则网格上进行简单的模拟比流体的实时演化要长得多。为了提高性能，该研究项目将设计新的算法和软件，利用低成本的商品处理器集群。具体来说，研究人员将研究基于分解树理论的不规则网格的内存布局策略，这将导致能够有效利用内存层次和并行性的算法。为了研究如何简化这种复杂算法的编程，他们应该研究和开发一个分布式事务性内存库，在集群编程环境中集成共享和同步。发展这些计算机科学技术将需要在算法和计算机系统领域的大量技术专长。