MagmaFOAM-1.0: a modular framework for the simulation of magmatic systems

Abstract

Abstract. Numerical simulations of volcanic processes play a fundamental role in understanding the dynamics of magma storage, ascent and eruption. The recent extraordinary progress in computer performance and improvements in numerical modeling techniques allow simulating multiphase systems in mechanical and thermodynamical disequilibrium. Nonetheless, the growing complexity of these simulations requires the development of flexible computational tools that can easily switch between sub-models and solution techniques. In this work we present MagmaFOAM, a library based on the open source computational fluid dynamics software OpenFOAM, that incorporates models for solving the dynamics of multiphase, multicomponent magmatic systems. Retaining the modular structure of OpenFOAM, MagmaFOAM allows run-time selection of the solution technique depending on the physics of the specific process, and sets a solid framework for in-house and community model development, testing and comparison. MagmaFOAM models thermo-mechanical non-equilibrium phase coupling and phase change, and implements state-of-the-art multiple volatile saturation models and constitutive equations with composition-dependent and space-time local computation of thermodynamic and transport properties. Code testing is performed using different multiphase modeling approches for processes relevant to magmatic systems: Rayleigh-Taylor instability, for buyoancy-driven magmatic processes; multiphase shock tube simulations, propedeutical to conduit dynamics studies; bubble growth and breakage in basaltic melts. Benchmark simulations illustrate the capabilities and potential of MagmaFOAM to account for the variety of non-linear physical and thermodynamical processes characterizing the dynamics of volcanic systems.

Machine Learning in Petrology: State-of-the-Art and Future Perspectives

“…However, as reported by Angione et al (2022) term 'emulator' should be specifically used for methods that provide full probabilistic predictions of simulation behavior, not only approximate results. Natural use of emulators and surrogate is in computational flow dynamic simulations, a scientific tool that found large applications in petrology (Katz, 2008;Gutiérrez and Parada, 2010;Petrelli et al, 2011Parmigiani et al, 2014Parmigiani et al, , 2016Parmigiani et al, , 2017Robertson et al, 2015;Keller and Katz, 2016;Brogi et al, 2022;Longo et al, 2023). As an example, using the output of costly computational flow dynamic simulations, we can train deep ML surrogates or emulators that can predict the outcomes of the modeling.…”