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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Molecular Insights into Geochemical Reactions of Iron-Bearing Minerals: Implications for Hydrogen Geo-Storage

Here, this study investigates the reaction of hydrogen (H 2 ) with pyrite (FeS 2 ), focusing on how temperature and the presence of water influence the reaction pathways and kinetics. Utilizing computational molecular simulations and kinetic analyses, we explore the impact of these factors on the formation of hydrogen sulfide (H 2 S) and related species. First, grand canonical Monte Carlo/molecular dynamics (GCMC/MD) simulations reveal that physical H 2 adsorption occurs in distinct layers on the pyrite surface. In addition, increased temperatures reduce the absolute adsorption capacities. Reactive MD simulations demonstrate that H 2 interacts differently with pyrite under varying conditions. At 298 K, H 2 reacts with pyrite to form HS – , leading to the formation of HS – through covalent bonding with sulfur of pyrite. However, no H 2 S is produced at this temperature, suggesting that a kinetic barrier (i.e., activation energy) may prevent this reaction. At higher temperatures, H 2 S production significantly increases. The presence of water introduces additional complexity to the reaction mechanism. Unlike dry conditions, water enhances H 2 S generation, even at low temperatures. Water also facilitates the formation of additional products, such as SOH, indicating a more intricate chemical environment on the pyrite surface. Our findings identify the association of HS – ions to form H 2 S as the rate-limiting step, with temperature influencing this process. This finding suggests that while the presence of water can create a more dynamic reaction environment, the overall mechanisms leading to H 2 S formation remain consistent. These outcomes suggest the need for developing targeted strategies to manage and control H 2 S emissions within the context of underground hydrogen storage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Complexity of Fault Rupture and Fluid Leakage in Shale: Insights From a Controlled Fault Activation Experiment

Abstract We observed rupture growth caused by controlled fluid injections at 340‐m depth within a fault zone in the low‐permeability Opalinus Clay in the Mont Terri Underground Research Laboratory (Switzerland). The rupture mechanisms were evaluated using measurements of the three‐component borehole wall displacements and fluid pressure in two sections of the fault zone and located horizontally 3 m apart from each other. One section was set across a secondary segment of the fault and used for stepwise fluid injection intended to trigger rupture growth. The other section was set across the principal shear zone of the fault for monitoring. After stepwise pressure increase up to 5.95 MPa at injection, rupture initiated as slip activation, followed by an overall opening of the fault planes connected to the injection. After 19 s of continued injection, displacements arrived at the monitoring point on the principal shear zone. These displacements are about 2.4 times larger than in the secondary fault segment. Overall, the displacements corresponded to a normal fault activation. About 9 s after the displacement front arrived, a strong pressure increase of 4.17 MPa was measured at the monitoring point, indicating a hydraulic connection had formed along the initially very low permeability fault planes between the injection and the monitoring points. Our analyses highlight that the fault activation is consistent with the state of stress but that injection pressure must be close to the normal stress acting on the fault for permeability to be generated and for fluid leakage to occur.

Guglielmi, Yves↗
DOE OSTI2020

Microbial Community Composition in Deep‐Subsurface Reservoir Fluids Reveals Natural Interwell Connectivity

Abstract The identification of natural fractures and the wells they connect is crucial for the development of geological reservoirs because it may have an important impact on reservoir model construction and hydraulic fracture propagation. In this study we investigated the use of a novel data source, the microbial community composition in the reservoir formation fluids, for identification of interwell connectivity caused by natural fractures. We verified this concept at a newly developed mesoscale enhanced geothermal system (EGS) field testbed located 4,850 ft (1,478 m) beneath ground surface at the Sanford Underground Research Facility in Lead, SD. Fluids produced at or near the EGS testbed were sampled and subjected to high‐throughput 16S rRNA gene amplicon sequencing to analyze the microbial community profile therein. Despite the typically substantial heterogeneity across the community profiles of samples spatially distributed (10 m to 1.9 km apart) throughout the site, samples from two wells at the EGS testbed showed highly similar microbial community composition, suggesting the two wells intersected the same natural fracture. This evidence of natural connectivity between the two wells at the EGS testbed was later corroborated by core log analysis and sewer camera surveys into the boreholes. Besides the field case described in this study, microbial community composition as a reservoir diagnostic tool would be applicable in a much broader context such as unconventional hydrocarbon exploitation, groundwater reservoir characterization, and environmental remediation, adding valuable “hard” data capable of pinpointing the origins of fluids unambiguously.

Zhang, Yuran↗
DOE OSTI2020

Creation of a Mixed-Mode Fracture Network at Mesoscale Through Hydraulic Fracturing and Shear Stimulation

Enhanced Geothermal Systems could provide a substantial contribution to the global energy demand if their implementation could overcome inherent challenges. Examples are insufficient created permeability, early thermal breakthrough, and unacceptable induced seismicity. In this study we report on the seismic response of a mesoscale hydraulic fracturing experiment performed at 1.5-km depth at the Sanford Underground Research Facility. We have measured the seismic activity by utilizing a 100-kHz, continuous seismic monitoring system deployed in six 60-m length monitoring boreholes surrounding the experimental domain in 3-D. The achieved location uncertainty was on the order of 1 m and limited by the signal-to-noise ratio of detected events. These uncertainties were corroborated by detections of fracture intersections at the monitoring boreholes. Three intervals of the dedicated injection borehole were hydraulically stimulated by water injection at pressures up to 33 MPa and flow rates up to 5 L/min. We located 1,933 seismic events during several injection periods. The recorded seismicity delineates a complex fracture network comprised of multistrand hydraulic fractures and shear-reactivated, preexisting planes of weakness that grew unilaterally from the point of initiation. We find that heterogeneity of stress dictates the seismic outcome of hydraulic stimulations, even when relying on theoretically well-behaved hydraulic fractures. Once hydraulic fractures intersected boreholes, the boreholes acted as a pressure relief and fracture propagation ceased. In order to create an efficient subsurface heat exchanger, production boreholes should not be drilled before the end of hydraulic stimulations.

58 GEOSCIENCES↗
DOE OSTI2021

Close Observation of Hydraulic Fracturing at EGS Collab Experiment 1: Fracture Trajectory, Microseismic Interpretations, and the Role of Natural Fractures

Abstract Despite the wide application of hydraulic fracturing in enhanced geothermal system (EGS) development and unconventional hydrocarbon production, our understanding of the many factors affecting the propagation of hydraulic fractures has relied on circumstantial evidence. This is partly due to the scarcity of direct observations in the subsurface. The EGS Collab project attempts to address these issues in the context of EGS research by performing intermediate‐scale (∼10 m) hydraulic stimulation experiments in a thoroughly characterized and heavily instrumented underground testbed. This paper analyzes the data collected from the first suite of hydraulic fracturing tests in this testbed, consisting of seven stimulation episodes. High‐quality microseismic data delineate five planar features very clearly. Combining fracture‐wellbore intersection observations from distributed temperature sensing (DTS) and visual observations from an open‐hole well, as well as prior in‐situ stress measurements, we conclude with high certainty that the four larger planes were hydraulic fractures. The growth of the hydraulic fractures was temporarily halted by a prominent, open natural fracture in the testbed but they eventually crossed and slightly reoriented under continued stimulation. Mineral‐filled (i.e., healed) natural fractures, though prevalent in the testbed, did not have perceivable effects on hydraulic fracture propagation. The high‐quality, mutually corroborating data sets allowed conclusions to be drawn with high confidence and attests to the advantage of intermediate‐scale experiments in subsurface research.

58 GEOSCIENCES↗
DOE OSTI2021

Potential Chemical Impacts of Subsurface CO 2 : An Integrated Experimental and Numerical Assessment for a Case Study of the Ogallala Aquifer

Leakage from geologic CO 2 sequestration (GCS) reservoirs to overlying underground sources of drinking water (USDW) is a tangible risk. This study is an integrated assessment that combines column experiments and reactive transport simulations of sediments sampled from the Ogallala aquifer above an active commercial-scale GCS site (the Farnsworth Unit in northern Texas). Experimental and simulation results suggest that carbonate mineral (calcite and dolomite) dissolution is the most significant reaction following CO 2 intrusion, and is also the dominant source of trace metal release. Cation exchange is another key mechanism controlling trace metal release by cation interference. Most of the trace metals, including Ba, Sr, As, Pb, and Zn, show a short-term release and quickly drop to the baseline values, suggesting low risk to the overlying USDW quality. Other trace metals, such as Mn and U, exhibit a tangible increase of their concentrations in the beginning, and drop to a higher level compared to the baseline, which may become a potential concern of long-term USDW quality change with CO2introduction. This study provides a comprehensive example of combining laboratory experiments and simulations for assessment of CO 2 -sediment interactions with combined release mechanisms in shallow groundwater aquifers. Data presented here provides useful insights for quantitative risk assessment and effective public education regarding CO 2 geological sequestration, and trace metal reactive transport studies in shallow groundwater aquifers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Hydro-Mechanical Measurements of Sheared Crystalline Rock Fractures With Applications for EGS Collab Experiments 1 and 2

We present hydro-mechanical measurements that characterize shear on natural fractures in schist, amphibolite, and rhyolite specimens from the enhanced geothermal system (EGS) Collab Project's Experiment 1 and 2 sites (E1 and E2) at the Sanford Underground Research Facility. We employed a triaxial direct shear method augmented with X-ray imaging to perform hydroshearing (injection-induced shearing) and mechanical shearing on naturally fractured specimens at in situ stress conditions. Measurements included fracture permeability, strength, stress-dependent aperture, shear dilation, and frictional strength. Results reveal that in situ natural fractures must be permeable, weak, and shear-oriented to be hydrosheared, and only a subset of the observable in situ fractures were suitable. When sheared, the fracture permeability typically increased by a factor of 10 or more and this increase was retained over time. However, shear slip did not always result in permeability increase. High phyllosilicate content associated with exceptionally weak fractures exhibited poor or even decreased permeability after stimulation. These measurements in combination with site data were used to conduct a slip-tendency analysis for different fracture sets, and we selected the top candidate natural fractures for hydroshearing at the EGS Collab sites. We also found that the lower in situ shear stress and stronger fractures at the E2 site make hydroshearing more challenging than at the E1 site. Overall, the methods and analysis used in our work can be applied to any geothermal project to identify in situ joint sets that are best suited for hydroshearing, which in turn can help to optimize well placement and energy production.

58 GEOSCIENCES↗
DOE OSTI2022

Multi‐Layered Systems for Permanent Geologic Storage of CO 2 at the Gigatonne Scale

Abstract The effectiveness of Carbon Capture and Storage (CCS) as an imperative decarbonization technology relies on the sealing capacity of a fine‐grained caprock to permanently store CO 2 deep underground. Uncertainties in assessing the caprock sealing capacity increase with the spatial and temporal scales and may delay CCS deployment at the gigatonne scale. We have developed a computationally efficient transport model to capture the dynamics of basin‐wide upward CO 2 migration in a multi‐layered setting over geological time scales. We find that massive capillary breakthrough and viscous flow of CO 2 , even through pervasively fractured caprocks, are unlikely to occur and compromise the storage security. Potential leakage from the injection reservoir is hampered by repetitive layering of overlying caprocks. This finding agrees with geologic intuition and should be understandable by the public, contributing to the development of climate policies around this technology with increased confidence that CO 2 will be indefinitely contained in the subsurface.

58 GEOSCIENCES↗
DOE OSTI2023

Global Nuclear Explosion Discrimination Using a Convolutional Neural Network

Using P-wave seismograms, we trained a seismic source classifier using a Convolutional Neural Network. We trained for three classes: earthquake P-wave, underground nuclear explosion (UNE) P-wave, and noise. With the current absence of nuclear testing by countries that have signed the Comprehensive Test Ban Treaty, high quality seismic data from UNEs is limited. Even with limited training data, our model can accurately characterize most events recorded at regional and teleseismic distances, finding over 95% signals in the validation set. We applied the model on holdout datasets of the North Korean test explosions to evaluate the performance on unique region and station-source pairs, with promising results. Additionally, we tested on the Source Physics Experiment events to investigate the potential for chemical explosions to act as a surrogate for nuclear explosions. We anticipate that machine-learning models like our classifier system can have broad application for other seismic signals including volcanic and non-volcanic tremor, anomalous earthquakes, ice-quakes or landslide-quakes.

58 GEOSCIENCES↗
DOE OSTI2024

Sensitivity Analysis in the Presence of Intrinsic Stochasticity for Discrete Fracture Network Simulations

Abstract Large‐scale discrete fracture network (DFN) simulators are standard fare for studies involving the sub‐surface transport of particles since direct observation of real world underground fracture networks is generally infeasible. While these simulators have successfully been used in several engineering applications, estimates of output quantities of interest (QoI) — such as breakthrough time of particles reaching the edge of the system — suffer from two distinct types of uncertainty. A run of a DFN simulator requires several parameters to be set that dictate the placement and size of fractures, the density of fractures, and the overall permeability of the system; uncertainty on the proper parameters will lead to uncertainty in the QoI, called epistemic uncertainty. Furthermore, since these input settings to DFN simulators control the stochastic processes which place fractures and govern flow, understanding how this randomness affects the QoI requires several runs of the simulator at distinct random seeds. The uncertainty in the QoI attributed to different realizations (i.e., different seeds) of the same random process (i.e., identical input parameters) leads to a second type of uncertainty, called aleatoric uncertainty. In this paper, we perform a Sensitivity Analysis, which directly attributes the uncertainty observed in the QoI to the epistemic uncertainty from each input parameter and to the aleatoric uncertainty. Beyond the specific takeaways on which input variables influence uncertainty in the QoI the most, a major contribution of this paper is the introduction of a statistically rigorous workflow for characterizing the uncertainty in DFN flow simulations that exhibit heteroskedasticity.

58 GEOSCIENCES↗
DOE OSTI2024

Accelerating Multiphase Simulations With Denoising Diffusion Model Driven Initializations

This study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics‐based simulations, aiming at reducing the computational costs of flow simulations while still honoring all the physical properties of interest. Pore‐scale simulations enhance our understanding of applications such as assessing hydrogen and storage efficiency in underground reservoirs. Nevertheless, they are computationally expensive and the presence of non‐unique solutions can require multiple simulations within a single geometry. To overcome the computational cost hurdle, we propose a method that couples generative diffusion models and physics‐based simulations. While training the data‐driven model, we simultaneously generate initial conditions and perform physics‐based simulations using these. This integrated approach enables us to receive real‐time feedback on a single compute node equipped with both CPUs and GPUs. By efficiently managing these processes within a single compute node, we can continuously monitor performance and halt training once the model meets the specified criteria. To test our model, we generate realizations in a real Berea sandstone fracture which shows that our technique is up to 4.4 times faster than commonly used flow simulation initializations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Complex Dependence of Calcite Crack Kinetics on Salinity: The Role of DLVO and Hydration Forces

Abstract Subcritical crack growth (SCG) plays an important role in many geological processes such as delayed earth rupture and rock weathering. The complex dependency of SCG on the in‐crack fluid chemistry, however, is still poorly understood. In this study, we utilize the newly developed surface force‐based fracture theory (SFFT) to elucidate the relative contributions of surface forces and solute transport to the crack growth kinetics of calcite in NaCl solutions. Expanding on Barenblatt's cohesive crack model, SFFT introduces an effective stress intensity at the crack tip that encompasses all the relevant intermolecular forces across the crack in addition to the external far‐field stresses. The nonlinear system of equations portraying the crack opening profile, the solute distribution in a propagating crack, and the crack growth velocity are numerically solved via an implicit scheme. After carefully calibrating the model for calcite‐water systems, the SFFT is used to predict the SCG response of calcite at different NaCl concentrations, based on various hypotheses. These predictions are then compared to existing SCG data from the literature. We demonstrate that the experimentally observed variation of SCG rate with NaCl concentration cannot be explained solely by DLVO forces (electrostatic and Van der Waals interactions). This can be remediated by introducing an exponentially decaying hydration force with a nonlinear, nonmonotonic dependence on NaCl concentration. Furthermore, we demonstrate that accounting for both diffusive and advective transport of ions is important in explaining the absence of a stage‐II SCG response for calcite in electrolyte solutions. Plain Language Summary Subcritical crack growth (SCG) refers to the slow propagation of cracks in materials under a stress below the threshold for catastrophic failure. SCG is a key process in many geological events, for example, delayed earth ruptures and rock weathering. New initiatives such as underground CO 2 and H 2 storage in carbonate reservoirs further call for better understanding of SCG in carbonate minerals subjected to varying fluid chemistry. This study examines the SCG of calcite, a key mineral found in carbonate rocks, intergranular cement in sandstones, and filling material in mineral veins and faults, determining their deformation and strength. A mathematical model is developed to describe how the crack opens and propagates, how solutes (like salts) distribute within the crack, and how the crack surfaces interact with each other. We used the model to predict calcite SCG in water at different salt concentrations and compared it with experimental data. Our results revealed that the hydration force is the dominating factor in determining the complex, non‐linear dependency of SCG on salinity. We also found that both the movement of ions by diffusion and by bulk water flow are crucial for explaining the SCG rates, especially when the cracks grow quickly. Key Points Surface Force‐Based Fracture Theory predicts the complex subcritical crack growth patterns of calcite crystals immersed in NaCl solutions Results highlight the dominant role of hydration forces in altering the fracture behavior of calcite compared to VdW and electric double‐layer forces Advective solute transport explains the absence of stages‐II and ‐III subcritical crack growth responses in solid‐liquid systems

DLVO↗
DOE OSTI2025

Monitoring Fracture Hydromechanical Evolution in the Lab and Field Using Unsupervised Metric Learning

Fractures evolve in time through thermal‐hydraulic‐mechanical‐chemical (THMC) processes that alter their long‐range hydraulic transport properties and modify subsurface behavior and activities. The location of subsurface fractures makes it necessary to use remote sensing techniques such as passive or active seismic monitoring for fracture characterization. In this paper, we develop a machine learning approach to monitor the evolution of fracture properties using passive seismic sources in a laboratory setting and using active seismic monitoring from the Sanford Underground Research Facility in Lead, South Dakota, at a depth of 1.25 km in amphibolite rock during stimulation of natural fractures as well as during induced fracturing. The unsupervised metric learning technique applies tandem neural networks (twin (Siamese) or triplet) with contrastive loss and adaptive margins to track slowly varying systems for which class or similarity labels are not available. The approach adopts locality‐sensitive hashing to divide time‐ordered contiguous data into an arbitrary number of pseudo‐classes. Contrastive‐loss training with many hash bins generates an evolving latent‐space trajectory. This approach enables unsupervised metric learning for seismic data stacks under the condition of contiguous state sampling and slowly varying fracture properties. The displacement discontinuity theory provides a mechanistic foundation for the fracture‐dependent trajectories that are related to relaxation of fractures with time‐dependent specific stiffness responding to changes in stress or fluid saturation.

02 PETROLEUM↗
DOE OSTI2022

Transport of polymer-coated metal–organic framework nanoparticles in porous media

Abstract Injecting fluids into deep underground geologic structures is a critical component to development of long-term strategies for managing greenhouse gas emissions and facilitating energy extraction operations. Recently, we reported that metal–organic frameworks are low-frequency, absorptive-acoustic metamaterial that may be injected into the subsurface to enhance geophysical monitoring tools used to track fluids and map complex structures. A key requirement for this nanotechnology deployment is transportability through porous geologic media without being retained by mineral-fluid interfaces. We used flow-through column studies to estimate transport and retention properties of five different polymer-coated MIL-101(Cr) nanoparticles (NP) in siliceous porous media. When negatively charged polystyrene sulfonate coated nanoparticles (NP-PSS-70K) were transported in 1 M NaCl, only about 8.4% of nanoparticles were retained in the column. Nanoparticles coated with polyethylenimine (NP-PD1) exhibited significant retention (> 50%), emphasizing the importance of complex nanoparticle-fluid-rock interactions for successful use of nanofluid technologies in the subsurface. Nanoparticle transport experiments revealed that nanoparticle surface characteristics play a critical role in nanoparticle colloidal stability and as well the transport.

58 GEOSCIENCES↗
DOE OSTI2023

Physics-embedded inverse analysis with algorithmic differentiation for the earth’s subsurface

Abstract Inverse analysis has been utilized to understand unknown underground geological properties by matching the observational data with simulators. To overcome the underconstrained nature of inverse problems and achieve good performance, an approach is presented with embedded physics and a technique known as algorithmic differentiation. We use a physics-embedded generative model, which takes statistically simple parameters as input and outputs subsurface properties (e.g., permeability or P-wave velocity), that embeds physical knowledge of the subsurface properties into inverse analysis and improves its performance. We tested the application of this approach on four geologic problems: two heterogeneous hydraulic conductivity fields, a hydraulic fracture network, and a seismic inversion for P-wave velocity. This physics-embedded inverse analysis approach consistently characterizes these geological problems accurately. Furthermore, the excellent performance in matching the observational data demonstrates the reliability of the proposed method. Moreover, the application of algorithmic differentiation makes this an easy and fast approach to inverse analysis when dealing with complicated geological structures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Machine learning surrogates for surface complexation model of uranium sorption to oxides

Abstract The safety assessments of the geological storage of spent nuclear fuel require understanding the underground radionuclide mobility in case of a leakage from multi-barrier canisters. Uranium, the most common radionuclide in non-reprocessed spent nuclear fuels, is immobile in reduced form (U(IV) and highly mobile in an oxidized state (U(VI)). The latter form is considered one of the most dangerous environmental threats in the safety assessments of spent nuclear fuel repositories. The sorption of uranium to mineral surfaces surrounding the repository limits their mobility. We quantify uranium sorption using surface complexation models (SCMs). Unfortunately, numerical SCM solvers often encounter convergence problems due to the complex nature of convoluted equations and correlations between model parameters. This study explored two machine learning surrogates for the 2-pK Triple Layer Model of uranium retention by oxide surfaces if released as U(IV) in the oxidizing conditions: random forest regressor and deep neural networks. Our surrogate models, particularly DNN, accurately reproduce SCM model predictions at a fraction of the computational cost without any convergence issues. The safety assessment of spent fuel repositories, specifically the migration of leaked radioactive waste, will benefit from having ultrafast AI/ML surrogates for the computationally expensive sorption models that can be easily incorporated into larger-scale contaminant migration models. One such model is presented here.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

The influence of exploration activities of a potential lithium mine to the environment in Western Serbia

The proposed exploitation of the Jadar Valley lithium/borate deposit in Serbia, by the Rio Tinto Corporation, indicates that it would become large-scale processing of boron- and lithium-containing ore. It would be one of the world’s very first lithium mines in populated and agricultural area. The company claims that the envisioned mining will be in accordance with environmental protection requirements. The Jadar Valley deposits have been claimed to cover 90% of Europe’s current lithium needs. Yet, local opposition to the mining has arisen due to potential devastating impacts on groundwater, soil, water usage, biodiversity loss, and waste accumulation. Research drilling by the mining company has already produced environmental damage, with mine water containing high levels of boron leaking from exploratory wells and causing crops to dry out. Furthermore, our investigations reveal substantially elevated downstream concentrations of boron, arsenic, and lithium in nearby rivers as compared to upstream regions. Additionally, here we show that soil samples exhibit repeated breaches of remediation limit values with environmental consequences on both surface and underground waters. With the opening of the mine, problems will be multiplied by the tailings pond, mine wastewater, noise, air pollution, and light pollution, endangering the lives of numerous local communities and destroying their freshwater sources, agricultural land, livestock, and assets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Modeling the formation of Sedan Crater using the FLAG and HOSS codes

Numerical modeling of explosion crater formation requires accounting for complex physical processes. Numerical validation of explosion cratering is an important step in modeling and requires experimental data for comparison. Models using discrete elements and continuum models have both benefits and drawbacks to their approaches. In this work, we consider both an arbitrary Lagrangian–Eulerian (ALE) hydrocode and a finite discrete element method (FDEM) approach to modeling the formation of the Sedan crater, the largest human-made crater in the United States. The Sedan crater formed from an underground nuclear detonation in the Nevada desert as part of Project Plowshare. Our models show that the continuum approach of the hydrocode matched well compared to early test time prior to the mound rupture and subsequent fireball venting, when most of the alluvium exhibited fluid behavior. Our FDEM approach matched the final crater dimensions well, after material had settled back into the crater, when material strength and solid mechanics play key roles. Our work shows how leveraging the benefits of multiple numerical approaches can lead to better understanding of complex physical problems, especially problems with limited experimental data. By using a continuum approach to early-time hydrodynamics and an FDEM approach to later-time solid mechanics, we can better understand the different physical regimes of explosion crater formation.

36 MATERIALS SCIENCE↗