total grain size distribution
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Solid Earth ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 119-139
Author(s):  
Alessandro Tadini ◽  
Andrea Bevilacqua ◽  
Augusto Neri ◽  
Raffaello Cioni ◽  
Giovanni Biagioli ◽  
...  

Abstract. We use PyBox, a new numerical implementation of the box-model approach, to reproduce pyroclastic density current (PDC) deposits from the Somma–Vesuvius volcano (Italy). Our simplified model assumes inertial flow front dynamics and mass deposition equations and axisymmetric conditions inside circular sectors. Tephra volume and density and total grain size distribution of EU3pf and EU4b/c, two well-studied PDC units from different phases of the 79 CE Pompeii eruption, are used as input parameters. Such units correspond to the deposits from variably dilute, turbulent PDCs. We perform a quantitative comparison and uncertainty quantification of numerical model outputs with respect to the observed data of unit thickness, inundation areas and grain size distribution as a function of the radial distance to the source. The simulations consider (i) polydisperse conditions, given by the total grain size distribution of the deposit, or monodisperse conditions, given by the mean Sauter diameter of the deposit; (ii) axisymmetric collapses either covering the whole 360∘ (round angle) or divided into two circular sectors. We obtain a range of plausible initial volume concentrations of solid particles from 2.5 % to 6 %, depending on the unit and the circular sector. Optimal modelling results of flow extent and deposit thickness are reached on the EU4b/c unit in a polydisperse and sectorialized situation, indicating that using total grain size distribution and particle densities as close as possible to the real conditions significantly improves the performance of the PyBox code. The study findings suggest that the simplified box-model approach has promise for applications in constraining the plausible range of the input parameters of more computationally expensive models. This could be done due to the relatively fast computational time of the PyBox code, which allows the exploration of the physical space of the input parameters.


2020 ◽  
Author(s):  
Alessandro Tadini ◽  
Andrea Bevilacqua ◽  
Augusto Neri ◽  
Raffaello Cioni ◽  
Giovanni Biagioli ◽  
...  

Abstract. In this study we use PyBox, a new numerical implementation of the box-model approach, to reproduce pyroclastic density current (PDC) deposits from the Somma-Vesuvius volcano (Italy). Our simplified model assumes inertial flow front dynamics and mass deposition equations, and axisymmetric conditions inside circular sectors. Tephra volume and density, and Total Grain Size Distribution of EU3pf and EU4b/c, two well-studied PDC units from different phases of the AD 79 Pompeii eruption of Somma-Vesuvius (Italy) are used as input parameters. Such units correspond to the deposits from variably dilute, turbulent PDCs. We perform a quantitative comparison and uncertainty quantification of numerical model outputs with respect to the observed data of unit thickness, inundation areas, and grain size distribution as a function of the radial distance to the source. The simulations that we performed with PyBox were done considering: (i) polydisperse conditions, given by the total grain size distribution of the deposit, or monodisperse conditions, given by the mean Sauter diameter of the deposit; (ii) round-angle axisymmetrical collapses or divided in two circular sectors. We obtain a range of plausible initial volume concentrations of solid particles from 2.5 % to 6 %, depending on the unit and the circular sector. Optimal modelling results of flow extent and deposit thickness are reached on the EU4b/c unit in a polydisperse and sectorialized situation, indicating that using total grain size distribution and particle densities as close as possible to the real conditions significantly improve the performance of the PyBox code. The study findings suggest that the box model simplified approaches adopted have promising applications in constraining the plausible range of the input parameters of more computationally expensive models.


2019 ◽  
Vol 81 (7) ◽  
Author(s):  
Sebastian B Mueller ◽  
Bruce F Houghton ◽  
Donald A Swanson ◽  
Matthieu Poret ◽  
Sarah A Fagents

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