Compressible Flow Phenomena at Inception of Lateral Density Currents Fed by Collapsing Gas‐Particle Mixtures

Valentine, Greg A.; Sweeney, Matthew

doi:10.1002/2017jb015129

Cited by 38 publications

(46 citation statements)

References 43 publications

(60 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The piles are a simplified representation of the inception of PDCs at the impact location of a collapsing eruption column; this simplification allows us to use the relatively fast‐running TITAN2D model instead of a computationally more intensive multiphase model of collapsing columns. Recent modeling of the dynamics of impacting gas‐particle flows indicates that coarse particles concentrate immediately upon impact and move outward as concentrated granular flows if the collapsing mixture is relatively coarse‐grained (Sweeney & Valentine, ; Valentine & Sweeney, ). Our model setup (assumptions 2 and 3 above) is crudely consistent with those results, noting though that in reality the presence of a large proportion of fine particles would complicate this picture.…”

Section: Methodsmentioning

confidence: 99%

“…Explosive volcanoes are multihazard systems where diverse hazardous processes can occur in succession (for instance, lahars triggered by rainfall after a certain volume of pyroclastic material has been deposited around the volcano; e.g., Pierson & Major, 2014) or simultaneously (for instance, contemporaneous tephra fallout and PDC propagation, Valentine & Giannetti, 1995). These hazard interactions can be very complex and our probabilistic hazard assessment of dense PDCs represents an important step toward quantitative multihazard assessments that explicitly account for them.…”

Section: Implications For Quantitative Volcanic Multihazard Assessmentsmentioning

confidence: 99%

See 1 more Smart Citation

Towards Quantitative Volcanic Risk of Pyroclastic Density Currents: Probabilistic Hazard Curves and Maps Around Somma‐Vesuvius (Italy)

et al. 2018

Self Cite

View full text Add to dashboard Cite

Pyroclastic density currents (PDCs) are hot flowing mixtures of gas and pyroclasts that can cause widespread loss of life and structural damage around the erupting volcano. Hazard assessments that include quantification of aleatory and epistemic uncertainty are a necessary step toward calculating volcanic risk of PDCs in an accurate and complete manner. We develop a three‐stage procedure to quantify such uncertainties for dense PDCs. First, the TITAN2D model is parameterized to simulate the PDC phenomenology at the target volcano. Second, TITAN2D is coupled with Polynomial Chaos Quadrature to propagate aleatory uncertainty from model parameters to hazard intensity measures (flow depth and speed). Third, the TITAN2D‐PCQ analysis is merged with the Bayesian Event Tree for Volcanic Hazard to include other volcano‐specific aleatory uncertainty and estimates of epistemic uncertainty. A comprehensive collection of probabilistic hazard curves and maps for flow depth and speed around the volcano is obtained through this methodology and its application is illustrated at Somma‐Vesuvius (Italy). Our results indicate that, given an eruption from the current central crater, exceedance probabilities are around 30% (aleatory uncertainty only) and between 10% and 60% (aleatory and epistemic uncertainty), for flow depth = 1 m and flow speed = 2 m/s, over the first 2–3 km around the vent. Dense PDCs faster than 30 m/s may cover areas about 50 km2 around the vent, on average, 1 every 10 eruptions. This type of probabilistic hazard assessment represents a crucial step toward quantitative volcanic risk of dense PDCs at Somma‐Vesuvius and worldwide.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Implications For Quantitative Volcanic Multihazard Assessmentsmentioning

confidence: 99%

Towards Quantitative Volcanic Risk of Pyroclastic Density Currents: Probabilistic Hazard Curves and Maps Around Somma‐Vesuvius (Italy)

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…Note that we are not endorsing gravity-driven, shallow water like flow models such as TITAN2D as "good" models for flow events resulting from column collapse, but our approach is agnostic to physical/computational models. That is, further analysis could be done with a different model specifically describing large mass flows from column collapse events, including multiphase and 3-D effects (e.g., Iverson & George, 2014;Neri et al, 2003;Pitman & Le, 2005;Valentine & Wohletz, 1989;Valentine & Sweeney, 2018).…”

Section: Emulating Hazard Contours and Calculating Hazard Probabilitiesmentioning

confidence: 99%

Dynamic Probabilistic Hazard Mapping in the Long Valley Volcanic Region CA: Integrating Vent Opening Maps and Statistical Surrogates of Physical Models of Pyroclastic Density Currents

et al. 2019

View full text Add to dashboard Cite

Ideally, probabilistic hazard assessments combine available knowledge about physical mechanisms of the hazard, data on past hazards, and any precursor information. Systematically assessing the probability of rare, yet catastrophic hazards adds a layer of difficulty due to limited observation data. Via computer models, one can exercise potentially dangerous scenarios that may not have happened in the past but are probabilistically consistent with the aleatoric nature of previous volcanic behavior in the record. Traditional Monte Carlo‐based methods to calculate such hazard probabilities suffer from two issues: they are computationally expensive, and they are static. In light of new information, newly available data, signs of unrest, and new probabilistic analysis describing uncertainty about scenarios the Monte Carlo calculation would need to be redone under the same computational constraints. Here we present an alternative approach utilizing statistical emulators that provide an efficient way to overcome the computational bottleneck of typical Monte Carlo approaches. Moreover, this approach is independent of an aleatoric scenario model and yet can be applied rapidly to any scenario model making it dynamic. We present and apply this emulator‐based approach to create multiple probabilistic hazard maps for inundation of pyroclastic density currents in the Long Valley Volcanic Region. Further, we illustrate how this approach enables an exploration of the impact of epistemic uncertainties on these probabilistic hazard forecasts. Particularly, we focus on the uncertainty of vent opening models and how that uncertainty both aleatoric and epistemic impacts the resulting probabilistic hazard maps of pyroclastic density current inundation.

show abstract

“…For instance, there is evidence that mobile pyroclastic flows have no continuous gas supply and that the grain size must have a large control on pore pressure decay, which will affect the mobility of pyroclastic flows (Calder et al, 1999). Generation of high pore pressure in pyroclastic flows has been attributed to upward fluidization by particle degassing (Sparks, 1976) and to hindered settling of a collapsing column (Valentine & Sweeney, 2018) or through rapid sedimentation of mesoscale clusters at the base of PCs (Breard et al, 2017). From the point where no continuous gas source exists, the intergranular pore-fluid pressure in pyroclastic flows wanes over time (Druitt et al, 2007).…”

Section: Defluidization 321 Experimental Investigationmentioning

confidence: 99%

The Permeability of Volcanic Mixtures—Implications for Pyroclastic Currents

et al. 2019

View full text Add to dashboard Cite

Geophysical fluid‐granular flows, such as pyroclastic currents and debris flows, owe much of their runout and hazard behavior to the occurrence and time‐variant decay of a flow‐internal fluid pore pressure. However, modeling the effects of fluid pore pressure to forecast hazards is challenging because a unified method in Earth Sciences to quantitatively determine the permeability of these natural mixtures is currently missing. Here we combine experiments on fluidization and defluidization of pyroclastic materials, eolian sediments, and glass beads mixtures with numerical multiphase simulations to compare previous attempts to compute the permeability of complex natural particle‐fluid mixtures. In analogy to particle‐engineering studies on simple gas‐particle mixtures, we demonstrate that the effective length‐scale in the characterization of the fluid‐particle interaction of complex natural mixtures is the product of the Sauter mean diameter and the particle sphericity. Its use in the Kozeny‐Carman equation allows accurate prediction of mixture permeability, and we suggest the routine calculation of the Sauter mean from grain size distributions of the deposits of geophysical mass flows in Earth Sciences. We also show, through defluidization experiments, that the duration of gas retention in natural mixtures is well described when using the Sauter mean as the effective particle size. Further, we show through multiphase simulations that initial bed expansion extends the pore pressure diffusion timescale up to nine times. These results can be applied to small‐to‐large volume dense pyroclastic currents where the ranges of Sauter mean diameter predict gas retention for long duration and to debris flows and snow avalanches.

show abstract

Compressible Flow Phenomena at Inception of Lateral Density Currents Fed by Collapsing Gas‐Particle Mixtures

Cited by 38 publications

References 43 publications

Towards Quantitative Volcanic Risk of Pyroclastic Density Currents: Probabilistic Hazard Curves and Maps Around Somma‐Vesuvius (Italy)

Towards Quantitative Volcanic Risk of Pyroclastic Density Currents: Probabilistic Hazard Curves and Maps Around Somma‐Vesuvius (Italy)

Dynamic Probabilistic Hazard Mapping in the Long Valley Volcanic Region CA: Integrating Vent Opening Maps and Statistical Surrogates of Physical Models of Pyroclastic Density Currents

The Permeability of Volcanic Mixtures—Implications for Pyroclastic Currents

Contact Info

Product

Resources

About