CDS&E: Collaborative Research: Data-Driven Predictive Modeling of Flows Containing Aggregating Particles

CDS

• 批准号: 1404826
• 负责人: Talid Sinno
• 金额: $ 57.5万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1404826&HistoricalAwards=false
• 关键词: CDS& Collaborative; Research; Data; Driven

CBET-1404826/1404832Sinno/KevrikidisFluid flows containing complex particles that interact with each other and with vessel walls are a central feature of an enormous range of biological, chemical, and physical processes, and the potential scientific and technological impact of having access to predictive computer models is difficult to overstate. Consequently, improvements in computer simulations for aggregating particulate flows have been actively sought for many years, and to date have been driven largely by increased availability of computer power coupled with advances in mathematical algorithms and techniques. As this trend continues, computational modeling is increasingly blessed (and cursed) by the "big data" streams generated by high resolution experimental measurements and/or by detailed computational simulations. In particular, the meaningful comparison of computational outputs and experimental measurements, both of which are large, complex, and statistically noisy, has emerged as a key challenge. As a result, models often capture many qualitative phenomena correctly but their predictive ability, and hence their usefulness to industry and manufacturing, becomes increasingly hard to establish and exploit. The proposed work seeks to close this gap by implementing, extending and exploiting a broad (and evolving) set of novel data mining techniques that enable new ways of linking tailored experiments to smartly designed simulations and back to model building. A multifaceted approach will be pursued to interrogate and use data jointly from a multiscale/multi-element model and two particulate-flow experimental systems. The experimental systems include a "target" system (platelets in blood), whose predictive description is ultimately sought, and a "model" system (DNA-functionalized colloids in water), which will be used to develop methods and help interpret the more complicated target. Both systems are defined by "complex" particles that exhibit time-dependent adhesivity leading to transiently evolving aggregates at a specified location on the vessel surface. Modern data mining techniques will be exploited and extended to process the native, high-dimensional data generated by these three sources to discover low-dimensional statistical measures that enable meaningful merging/comparisons of data streams from different sources and runs. Ultimately, the project deliverables are (i) a better understanding of the physical, chemical and biological mechanisms operating in these complex systems, (ii) data-enhanced and data-validated engineering models, and (iii) experimental design rules for complex, multi-parameter systems.

CBET-1404826/1404832 Sinno/Kevin rikidisFluid Flow包含相互作用和与管壁相互作用的复杂颗粒，是一系列生物、化学和物理过程的中心特征，获得预测计算机模型的潜在科学和技术影响怎么强调都不为过。因此，多年来一直在积极寻求改善聚集颗粒流的计算机模拟，到目前为止，主要是由于计算机能力的增加以及数学算法和技术的进步。随着这一趋势的继续，计算建模越来越受到由高分辨率实验测量和/或详细计算模拟产生的“大数据”流的祝福(和诅咒)。特别是，对计算输出和实验测量进行有意义的比较，这两者都很大、很复杂，而且在统计上有噪音，已经成为一个关键的挑战。因此，模型往往正确地捕捉到许多定性现象，但它们的预测能力，以及它们对工业和制造业的实用性，变得越来越难以建立和利用。这项拟议的工作试图通过实施、扩展和利用一套广泛的(和不断发展的)新的数据挖掘技术来缩小这一差距，这些技术能够以新的方式将量身定制的实验与设计巧妙的模拟联系起来，并返回到模型建立。将采取多方面的办法，联合询问和使用来自多尺度/多元素模型和两个颗粒流实验系统的数据。这些实验系统包括一个“靶标”系统(血液中的血小板)和一个“模型”系统(水中的DNA功能化胶体)，前者最终寻求预测性描述，后者将用于开发方法并帮助解释更复杂的靶标。这两个系统都是由“复杂”颗粒定义的，这些颗粒表现出随时间变化的粘附性，导致在血管表面的特定位置瞬时演化聚集体。现代数据挖掘技术将被利用和扩展，以处理由这三个来源产生的本地高维数据，以发现能够对来自不同来源和运行的数据流进行有意义的合并/比较的低维统计度量。归根结底，项目成果是(I)更好地了解这些复杂系统中运行的物理、化学和生物机制，(Ii)数据增强和数据验证的工程模型，以及(Iii)复杂、多参数系统的实验设计规则。