Benchmarking computational fluid dynamics models of lava flow simulation for hazard assessment, forecasting, and risk management

Abstract

Numerical simulations of lava flow emplacement are valuable for assessing lava flow hazards, forecasting active flows, designing flow mitigation measures, interpreting past eruptions, and understanding the controls on lava flow behavior. Existing lava flow models vary in simplifying assumptions, physics, dimensionality, and the degree to which they have been validated against analytical solutions, experiments, and natural observations. In order to assess existing models and guide the development of new codes, we conduct a benchmarking study of computational fluid dynamics (CFD) models for lava flow emplacement, including VolcFlow, OpenFOAM, FLOW-3D, COMSOL, and MOLASSES. We model viscous, cooling, and solidifying flows over horizontal planes, sloping surfaces, and into topographic obstacles. We compare model results to physical observations made during well-controlled analogue and molten basalt experiments, and to analytical theory when available. Overall, the models accurately simulate viscous flow with some variability in flow thickness where flows intersect obstacles. OpenFOAM, COMSOL, and FLOW-3D can each reproduce experimental measurements of cooling viscous flows, and OpenFOAM and FLOW-3D simulations with temperature-dependent rheology match results from molten basalt experiments. We assess the goodness-of-fit of the simulation results and the computational cost. Our results guide the selection of numerical simulation codes for different applications, including inferring emplacement conditions of past lava flows, modeling the temporal evolution of ongoing flows during eruption, and probabilistic assessment of lava flow hazard prior to eruption. Finally, we outline potential experiments and desired key observational data from future flows that would extend existing benchmarking data sets.

Lava flow hazard prediction and monitoring with UAS: a case study from the 2014–2015 Pāhoa lava flow crisis, Hawai‘i

“…However, more sophisticated lava flow modelling efforts, including stochastic slope-controlled models (Harris and Rowland 2001;Favalli et al 2005), cellular automata models (Crisci et al 2004;Del Negro et al 2005;Vicari et al 2007), and other numerical simulations (Dietterich et al 2015), also rely on high quality DEMs input layers to produce successful results. UAS provide a means of effectively generating these needed DEMs, regardless of the modeling method.…”

Analog experiments of lava flow emplacement

“…Data collected by cameras can be analyzed both manually and automatically using computer vision tools, to extract information about the evolution of each flow over the course of the experiments. Analysis can include tracking the flow front position and the flow width or thickness over time [e.g., Blake, 1990;Balm− forth et al, 2000], as these are observables that are di− rectly comparable with predictions from analytical or numerical models [Cordonnier et al, 2015;Dietterich et al, 2017]. A more complete dataset on the velocity dis− tribution of the flow surface can be obtained using techniques such as Particle Image Velocimetry (PIV) [e.g., Applegarth et al, 2010] or Optical Flow [e.g., Horn and Schunck, 1981;Lucas and Kanade, 1981;Lev et al, 2012].…”

“…The experiments reported by Diet− terich et al [2015] were performed using sugar syrup (a Newtonian, isoviscous fluid) and molten basalt. The re− sults from the experiments have already been used as a benchmark test to numerical flow models, as sum− marized by Dietterich et al [2017]. Flow inflation be− hind an obstacle appeared to be a challenge for most codes tested, yet its importance cannot be overstated, especially in the context of flow hazard mitigation and flow diversion.…”

A new approach to probabilistic lava flow hazard assessments, applied to the Idaho National Laboratory, eastern Snake River Plain, Idaho, USA

“…MOLASSES (MOdular LAva Simulation Software for Earth Science), a lava flow simulator modified from the LavaPL algorithm of Connor et al (2012), distributes lava between cells based on rules that govern flow behavior (Kubanek et al, 2015). MOLASSES has been successfully benchmarked (Dietterich et al, 2017), performs well at recreating flow geometries similar to those found on the ESRP (Fig. DR2), and is sensitive to the geometries of lava flows, their thickness, area, and the underlying topography, rather than to the mechanics of lava flow emplacement.…”