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.

火山喷发会产生灰烬云，对空中旅行和基础设施有危险。 在紧急情况下，预报员使用数值模型预测了灰分的运输，但他们通常认为粒子是个体以简化分析的掉落。 实际上，细灰颗粒将组合成较大的簇，从而加速了它们的跌落速率。 这使灰烬可以从云层中掉出来。 聚集率取决于许多因素，包括水分，电荷和湍流。 关于这些特性的影响的现有数据有限，尤其是对于湍流，即使聚集在某些喷发中影响了95％的辐射。 因此，需要更好地测量聚合过程的实验测量，以及包括现象的更先进的模型。这项研究旨在通过高准确性实验和升级模型来改善火山危害的预测。 灰分聚集将在两个不同的相关流条件下进行检查：均匀的湍流和高速喷射。 高分辨率光学技术将用于研究流，湍流和粒子聚类。 结果将集成到最新的分析模型中，包括用于预测的一些模型。 此外，这项研究将通过俄勒冈州科学与工业博物馆（OMSI）的一系列动手示威（OMSI）向更广泛的公众提出。该奖项反映了NSF的法定任务，并认为值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来获得支持。

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

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.

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