Collaborative Research: Measurement of Particle Aggregation in Laboratory-scale Flows for Improved Models of Volcanic Ash Fallout and Entrainment

合作研究：测量实验室规模流动中的颗粒聚集，以改进火山灰沉降和夹带模型

• 批准号: 1756259
• 负责人: Raul Cal
• 金额: $ 21.1万
• 依托单位: Portland State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-15 至 2021-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1756259&HistoricalAwards=false
• 关键词: Collaborative; Research; Measurement; Particle; Aggregation

Volcanic eruptions produce clouds of ash, which are hazardous to air travel and infrastructure. In emergencies, forecasters predict the transport of ash using numerical models, but they typically assume that particles fall as individuals to simplify the analysis. In reality, fine ash particles combine together into larger clusters, accelerating their rate of fall. This allows the ash to fall out of the cloud. Rates of aggregation depend on many factors, including moisture, electric charge, and turbulence. There is limited existing data on the effects of these properties, particularly for turbulent flows, even though aggregation affects up to 95% of fallout in some eruptions. Hence, there is a need for better experimental measurements of aggregation processes, along with more advanced models that include the phenomenon.This study aims to improve forecasting of volcanic hazards through both high-accuracy experiments and upgraded models. Ash aggregation will be examined in two different, relevant flow conditions: homogeneous turbulence and high-speed jets. High-resolution optical techniques will be used to study the flow, turbulence, and particle clustering. The results will be integrated into state-of-the-art analytical models, including some used for forecasting. Further, this research will be presented to the broader public through a series of hands-on demonstrations at the Oregon Museum of Science and Industry (OMSI).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.

火山喷发产生火山灰云，对航空旅行和基础设施造成危害。 在紧急情况下，预报员使用数值模型预测火山灰的输送，但他们通常假设颗粒作为个体掉落以简化分析。 事实上，细小的灰烬颗粒结合在一起形成更大的团簇，加速了它们的下落速度。 这使得火山灰从云中掉下来。 聚集速率取决于许多因素，包括湿度、电荷和湍流。 尽管聚集影响了某些火山喷发中高达 95% 的沉降物，但关于这些特性的影响（尤其是湍流）的现有数据有限。 因此，需要对聚集过程进行更好的实验测量，以及包含该现象的更先进的模型。本研究旨在通过高精度实验和升级模型来改进对火山灾害的预测。 将在两种不同的相关流动条件下检查灰分聚集：均匀湍流和高速射流。 高分辨率光学技术将用于研究流动、湍流和粒子聚集。 结果将被整合到最先进的分析模型中，包括一些用于预测的模型。 此外，这项研究将通过在俄勒冈州科学与工业博物馆 (OMSI) 进行的一系列实践演示向更广泛的公众展示。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力评估进行评估，被认为值得支持。优点和更广泛的影响审查标准。