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/KEVRIKIDISFLUID流动，含有彼此相互作用的复杂颗粒，与容器壁相互作用是巨大的生物学，化学和物理过程的核心特征，并且具有对预测计算机模型的潜在科学和技术影响很难跨越夸大其有。 因此，多年来一直在积极寻求计算机仿真的改进，并且迄今为止一直在很大程度上取决于计算机功率的可用性以及数学算法和技术的进步。 随着这种趋势的继续，通过高分辨率实验测量和/或通过详细的计算模拟产生的“大数据”流越来越有福（和诅咒）。 特别是，对计算输出和实验测量的有意义的比较，它们都是大型，复杂和统计上的嘈杂的，已成为一个关键挑战。 结果，模型通常正确地捕获了许多定性现象，但是它们的预测能力及其对行业和制造业的有用性变得越来越难以建立和利用。 拟议的工作旨在通过实施，扩展和利用一组广泛（且不断发展的新型数据挖掘技术）来缩小这一差距，从而使新的方法可以将量身定制的实验与智能设计的模拟并返回模型构建。将采用一种多方面的方法来询问和使用多尺度/多元素模型和两个颗粒流实验系统共同使用数据。 实验系统包括最终寻求预测性描述的“目标”系统（血小板），以及“模型”系统（水中的DNA官能化胶体），该系统将用于开发方法并帮助解释更复杂的目标。 两种系统都由“复杂”颗粒定义，这些颗粒表现出时间依赖性的粘合剂，从而导致在血管表面指定位置的瞬时演变骨料。 现代数据挖掘技术将被利用并扩展，以处理由这三个来源生成的天然高维数据，以发现低维统计量度，以实现来自不同来源和运行的数据流的有意义的合并/比较。 最终，项目可交付成果（i）更好地理解这些复杂系统中运行的物理，化学和生物学机制，（ii）数据增强和数据验证的工程模型以及（iii）复杂的多参数系统的实验设计规则。