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: 1756267
• 负责人: Stephen Solovitz
• 金额: $ 13.62万
• 依托单位: Washington 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 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1756267&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.

火山爆发会产生火山灰云，这对航空旅行和基础设施都是危险的。在紧急情况下，预报员使用数值模型预测火山灰的移动，但为了简化分析，他们通常假设颗粒单个落下。在现实中，细小的灰颗粒结合在一起形成更大的簇，加速了它们下落的速度。这使得火山灰从云层中脱落。聚集速率取决于许多因素，包括湿度、电荷和湍流。关于这些特性的影响，特别是湍流的影响，现有的数据有限，尽管在一些喷发中聚集影响了高达95%的沉降物。因此，有必要对聚集过程进行更好的实验测量，以及包括这种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提高火山灾害的预测能力。火山灰聚集将在两种不同的相关流动条件下进行研究：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。研究结果将被纳入最先进的分析模型，包括一些用于预测的模型。此外，这项研究将通过俄勒冈科学与工业博物馆（OMSI）的一系列实践演示向更广泛的公众展示。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Dynamics of the December 2020 Ash‐Poor Plume Formed by Lava‐Water Interaction at the Summit of Kīlauea Volcano, Hawaiʻi

2020 年 12 月夏威夷卡劳厄火山顶峰熔岩与水相互作用形成的火山灰与贫羽羽流的动态

• DOI: 10.1029/2022gc010718
• 发表时间: 2023
• 期刊: Geosystems
• 作者: Cahalan, Ryan C.;Mastin, Larry G.;Van Eaton, Alexa R.;Hurwitz, Shaul;Smith, Adam B.;Dufek, Josef;Solovitz, Stephen A.;Patrick, Matt;Schmith, Johanne;Parcheta, Carolyn
• 通讯作者: Parcheta, Carolyn

Lagrangian diffusion properties of a free shear turbulent jet

• DOI: 10.1017/jfm.2021.325
• 发表时间: 2021-02
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: B. Viggiano;T. Basset;S. Solovitz;T. Barois;M. Gibert;N. Mordant;L. Chevillard;R. Volk;M. Bourgoin;R. B. Cal

A One‐Dimensional Volcanic Plume Model for Predicting Ash Aggregation

用于预测火山灰聚集的一维火山羽流模型

• DOI: 10.1029/2023jb027002
• 发表时间: 2023
• 期刊: Journal of Geophysical Research: Solid Earth
• 作者: Hoffman, D. W.;Mastin, L. G.;Van Eaton, A. R.;Solovitz, S. A.;Cal, R. B.;Eaton, J. K.
• 通讯作者: Eaton, J. K.

Flow Development and Entrainment in Turbulent Particle‐Laden Jets

湍流粒子的流动发展和夹带——满载射流

• DOI: 10.1029/2022jd038108
• 发表时间: 2023
• 期刊: Journal of Geophysical Research: Atmospheres
• 作者: Shannon, Laura K.;Viggiano, Bianca;Cal, Raúl B.;Mastin, Larry G.;Van Eaton, Alexa R.;Solovitz, Stephen A.
• 通讯作者: Solovitz, Stephen A.