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

CDS

• 批准号: 1404832
• 负责人: Yannis Kevrekidis
• 金额: $ 17.5万
• 依托单位: Princeton University
• 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 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1404832&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-1404826/1404832Sinno/Kevrikidis 含有相互相互作用以及与容器壁相互作用的复杂颗粒的流体是众多生物、化学和物理过程的核心特征，也是使用预测计算机的潜在科学和技术影响模型的重要性怎么强调都不为过。 因此，多年来人们一直在积极寻求对聚集颗粒流的计算机模拟的改进，并且迄今为止，这在很大程度上是由计算机能力的增强以及数学算法和技术的进步所推动的。 随着这种趋势的继续，计算建模越来越受到高分辨率实验测量和/或详细计算模拟生成的“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，这两者都是庞大、复杂且统计噪声的，已成为一个关键挑战。 因此，模型通常能够正确地捕捉许多定性现象，但它们的预测能力以及它们对工业和制造业的有用性却变得越来越难以建立和利用。 拟议的工作旨在通过实施、扩展和利用一系列广泛（且不断发展）的新颖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定制实验与智能设计的模拟联系起来并返回到模型构建。将采用多方面的方法来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。 实验系统包括最终寻求预测性描述的“目标”系统（血液中的血小板）和“模型”系统（水中的 DNA 功能化胶体），该系统将用于开发方法并帮助解释更复杂的情况。目标。 这两个系统均由“复杂”颗粒定义，这些颗粒表现出随时间变化的粘附性，导致在容器表面的指定位置瞬时形成聚集体。 将利用和扩展现代数据挖掘技术来处理这三个来源生成的本机高维数据，以发现低维统计度量，从而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。 最终，项目可交付成果是（i）更好地理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、多系统的实验设计规则-参数系统。