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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Behavioral simulations and experimental evaluations of stress induced spatial nonuniformity of dynamic bulk modulus in additive friction stir deposited AA 6061

Additive friction stir deposition (AFSD) is a rapidly developing solid-state additive manufacturing (AM) technology that enables the fabrication of customized products on a relatively large scale. The unique forge-liked process offers high quality products with refined equiaxed microstructure and exceptional mechanical performance. However, in AFSD, the spatially distributed mechanical properties within a deposited layer have not been established so far, due to the lack of a suitable large-scale mapping method. In the present work, an ultrasonic dynamic-elasticity mapping method was employed to map the distribution of physical properties in a AFSD 6061 Al. Strong spatial asymmetries in elastographies were mainly due to the residual stresses. The origin of the asymmetries was investigated through thermo-mechanical simulations of the AFSD process. It was observed that majority of the asymmetries were associated with the mechanical residual stresses instead of the symmetric thermal residual stresses during linear deposition of the AFSD process. However, the thermal residual stresses can be manipulated to become asymmetric with a 90° turn in the linear deposition path, due to the difference between the levels of cooling along the edges of the inner and outer corners. Hence, the mechanical and thermo-mechanical residual stresses can be additive or subtractive depending on the direction of the turns. Here, such numerically predicted behaviors were also experimentally observed on the AFSD fabricated samples by ultrasonic elastography. Furthermore, additional preliminary studies on multi-layer AFSD fabrications show the asymmetric mechanical stresses generated during the deposition process on different layers generate constructive or destructive interference which results to spatially variable dynamic bulk modulus depending on the deposition orientations of layers.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Amphiphile conformation impacts aggregate morphology and solution structure across multiple lengthscales

Although the self-assembly of amphiphiles is well-studied in aqueous solutions, much less is understood about the fundamental driving forces and structure property relationships in non-polar media. In recent work [Journal of Physical Chemistry B, 2020, 124, 10822.] the authors have studied a series of malonomide-based amphiphiles that are relevant to liquid-liquid extraction. That work demonstrated that aggregation is largely driven by local dipole-dipole interactions between molecules. Here, we build upon this observation to develop a more detailed understanding of how the balance of dipole-dipole interactions (controlled by conformation) and molecular architecture influences the morphology of the aggregates across lengthscales. Using constrained molecular dynamics about key degrees of freedom, we demonstrate that the conformation of N,N-dimethyl,N,N-dioctylhexylethoxy malonamide (DMDOHEMA) and N,N-dimethyl,N,N-dibutyltetradecyl malonamide (DMDBTDMA) has a significant impact upon self-association - where appropriate conformational sampling is essential. To quantify the aggregate morphology, several graph theoretic and persistent homology based properties are determined. The former examines the patterns of intermolecular interactions within clusters, while the latter examines the 3-dimensional spatial distribution. Based upon these analyses, we find that the morphology of aggregates, particularly at higher malonamide concentration, depends on a balance of dipole alignment and alkyl tail sterics. Dipole alignment encourages linear patterns of the intermolecular interactions within aggregates, while the alkyl tail steric interactions between the malonamide result in noticeably less linear aggregates for DMDOHEMA than DMDBTDMA. This is reflected in the spatial distribution, where more holes or voids exist between extractants within the DMDOHEMA that distribute within the solution in more of a "swiss cheese" arrangement as opposed to the more filamentous distribution of DMBDTDMA. This study links conformation and molecular structure to the morphology of amphiphile assemblies, and serves as a basis for ongoing study of multicomponent amphiphile solutions with polar and other solutes, and how these impact aggregation phenomena.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

The Future Electron-Ion Collider

Generalized Parton Distributions include rich information and became a powerful tool for studying hadron structure. Deeply Virtual Compton Scattering is the golden channel to access GPDs. The development of high luminosity and high-acceptance detectors (Electron-Ion Collider) allows physicists to overcome the difficulty of DVCS measurements. The outstanding performance of the accelerator performs the eA collisions at the center of mass energy from 20 to 140 GeV, while the luminosity at the scale of 1034 cm?2s?1. The new type of high-density crystal, PWO-II, produced by CRYTUR in 2×2×20 cm3 will be utilized in the calorimeter. The transparency of PWO-II crystals is > 70% at 620 nm, > 60% at 420 nm, and >35% at 360 nm. The crystals also show good radiation hardness under 30 Gy radiation exposure. Ten PWO-II crystals have a uniform light yield at 30 p.e./MeV, providing sufficient light yield to reduce the fluctuation of the energy measurements. Near 3000 crystals were constructed inside the 12-sided polygon supporting structure in the simulation. The island clustering algorithm was used for reconstructed the energy. The energy resolution study by the particle gun shows the stochastic term and constant term are 1.8% and 1.2%, respectively. The spatial resolution of NEEMC varies from 5% to 15% of the crystal?s width depending on the particle?s incident angle. The pion rejection of NEEMC can reach 103 with electron efficiency > 85% when the particle?s energy is larger than 1GeV. The Pi0- identified efficiency study can be interpreted as finding the local maxima in the single cluster caused by two high energy close photons. The study results show that efficiency is nearly 100% for pi0 energy is smaller than 10 GeV, and efficiency drops to 30% with 20 GeV pi0. The new type of 3x3 pixelated AC-LGAD were wire bonded to ALTIROC for performance test. The cross-talk between the adjacent channels is about 20% for VPA and 10% for TZ. A series of the TDC characteristic measurements show that the jitter for both preamplifiers is about 20 ps, and the time-walk effect is mild for inject charge > 12 pF. Furthermore, the beta source radiation results quantify the sharing scale of the 3x3 pixels AC-LGAD (? 20%). The electronics simulation study results show no significant changes in the spatial resolution, whether ADC resolution is 8, 10, or 12 bits. The 8-bit ADC is decided to use in the EICROC as it has a smaller size and power consumption than the 10-bit and 12-bit ADC. The ECCE is one of the full detector proposals for EIC. The new type of DVCS generator, called TOPEG, is used to generate the high acceptance beam configurations of 18×110 GeV2 electron and 4He. The acceptance study shows a -1.8 < ? < -1.4 gap between the BEMC and EEMC. The 10?x,y geometry cut is applied on the Roman Pots, so the acceptance of Roman Pots quickly drops to 0% when the polar angle of the recoiled 4He < 2 mrad. This primary ECCE simulation study suggests extending the acceptance of BEMC longitudinally as the structure limits the radial size of EEMC. The acceptance of the Roman Pots is still challenging. In 2023, The overall design of ePIC was finalized by merging two full detector proposals (ECCE and ATHENA) after a series of intensive simulation studies.

Wang, Pu-Kai↗
DOE OSTI2024

Simulations of divertor designs that spatially separate power and particle exhaust using mid-leg divertor particle pumping

Predictive design modeling of a Dissipation-Focused Divertor for future operation in DIII-D reveals that increasing the poloidal distance of the pump duct entrance from the target surface along the low-field side divertor baffle increases neutral compression and modifies the spatial distribution of power dissipation. With a divertor pump located mid-leg between the target and the X-point, SOLPS-ITER boundary plasma simulations without drifts predict the formation of a dense neutral cloud near the target with > 30x higher neutral compression in detachment, a more stable detachment front located further from the target, and ~25% lower outer midplane separatrix density required for detachment onset, compared to a pump located in the scrape-off layer at the target surface. Up to 19 MW of power flowing into the divertors is modeled using the following two numerical implementations for particle pumping: a specified fraction of particles incident on variable wall sections of the plasma grid is removed from the computational domain (so-called albedo pumping), and a pump duct is modeled which includes dynamics of kinetic neutrals in the duct. The simulations show that the detachment front is located between the divertor target and the X-point and is relatively stable near the pump entrance, without a strong dependence on gas puff rate or injected power. The mid-leg pump design spatially separates the two primary functions of a divertor (power handling and particle exhaust), with the majority of power dissipation occurring near the target plate and particle exhaust taking place further upstream. The benefit of enhanced dissipation using mid-leg pumping comes at the cost of a higher outer midplane separatrix density for a given amount of particle injection.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Methane and Carbon Dioxide Fluxes in a Temperate Tidal Salt Marsh: Comparisons Between Plot and Ecosystem Measurements

Tidal wetlands are comprised of complex interdependent pathways where measurements of carbon exchange are often scale dependent. Common data collection methods (i.e., chambers and eddy covariance) are inherently constrained to different spatial and temporal scales which could generate biased information for applications of carbon accounting, identifying functional relationships and predicting future responses to climate change. Consequently, it is needed to systematically evaluate measurements derived from multiple approaches to identify differences and how techniques complement each other to reconcile interpretations. For our study, to accomplish this, we tested ecosystem-scale eddy covariance with plot-scale chamber measurements within a temperate salt marsh. We found good agreement (R 2 = 0.71–0.95) when comparing measurements of CH 4 emissions and CO 2 exchange but this agreement was dependent upon canopy phenology with discrepancies mainly arising during senescence and dormancy phenophases. The environmental drivers for CH 4 and CO 2 fluxes were mostly preserved across different measurement techniques, but the number of drivers increases while their individual strength decreases at the ecosystem scale. Empirical upscaling models parameterized with chamber measurements overestimated annual net ecosystem exchange (NEE; 108%) and gross primary production (GPP; 12%) while underestimating ecosystem respiration (Reco; 14%) and CH 4 emissions (69%) compared to eddy covariance measurements. Our results suggest that the environmental complexity of CH 4 and CO 2 fluxes in salt marshes may be underestimated by chamber-based measurements, and highlights how different techniques are complementary while considering limitations at each level of measurement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Super-Resolution Photothermal Patterning in Conductive Polymers Enabled by Thermally Activated Solubility

Doping induced solubility control (DISC) patterning is a recently developed technique which uses the change in polymer solubility upon doping, along with an optical dedoping process, to achieve high resolution optical patterning. DISC patterning is remarkable because the process resolution exceeds the linear Abbe’s diffraction limit, however, no mechanism has been proposed to explain such high resolution. Here, we use diffraction to spatially modulate the light intensity and determine the dissolution rate, revealing a superlinear dependence on light intensity, which yields features that are sharper than the profile of the writing photomask. This rate law is independent of wavelength, indicating that patterning resolution is not dominated by an optical dedoping reaction, as was previously proposed. Instead we show here that the optical patterning mechanism is primarily controlled by the thermal profile generated by the laser. To quantify this effect, the thermal profile and dissolution rate are modeled using a finite-element model and compared against patterned line cross sections as a function of wavelength, laser intensity, and dwell time. Our model reveals that although the laser-generated thermal profile is broadened considerably beyond the lasers resolution, the highly temperature dependent dissolution rate results in selective dissolution near the peak of the thermal profile. Therefore, the key factor in achieving super-resolution patterning is a strongly temperature dependent dissolution rate|a common feature of many polymers. In addition to suggesting several routes to improved resolution, our model also demonstrates that doping is not required for optical patterning of conjugated polymers, as was previously believed. Instead, we demonstrate that superlinear resolution optical patterning is attainable in any conjugated polymer simply by tuning the solvent quality during patterning, thus extending the applicability of our method to a wide class of materials. Here, we demonstrate the generality of photothermal patterning by writing sub-400 nm features into undoped Pff-BT4T-2OD.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Rapid wavefield forecasting for earthquake early warning via deep sequence to sequence learning

We propose a deep learning model, WaveCastNet, to forecast high-dimensional wavefields. WaveCastNet integrates a convolutional long expressive memory architecture into a sequence-to-sequence forecasting framework, enabling it to model long-term dependencies and multiscale patterns in both space and time. By sharing weights across spatial and temporal dimensions, WaveCastNet requires significantly fewer parameters than more resource-intensive models such as transformers, resulting in faster inference times. Crucially, WaveCastNet also generalizes better than transformers to rare and critical seismic scenarios, such as high-magnitude earthquakes. Here, we show the ability of the model to predict the intensity and timing of destructive ground motions in real time, using simulated data from the San Francisco Bay Area. Furthermore, we demonstrate its zero-shot capabilities by evaluating WaveCastNet on real earthquake data. Our approach does not require estimating earthquake magnitudes and epicenters, steps that are prone to error in conventional methods, nor does it rely on empirical ground-motion models, which often fail to capture strongly heterogeneous wave propagation effects.

Geophysics↗
DOE OSTI2024

Vortex splitting in two-dimensional fluids and non-neutral electron plasmas with smooth vorticity profiles

Initially, elliptical, quasi-two-dimensional (2D) fluid vortices can split into multiple pieces if the aspect ratio is sufficiently large due to the growth and saturation of perturbations known as Love modes on the vortex edge. Presented here are experiments and numerical simulations, showing that the aspect ratio threshold for vortex splitting is significantly higher for vortices with realistic, smooth edges than that predicted by a simple “vortex patch” model, where the vorticity is treated as piecewise constant inside a deformable boundary. The experiments are conducted by exploiting the E × B drift dynamics of collisionless, pure electron plasmas in a Penning–Malmberg trap, which closely model 2D vortex dynamics due to an isomorphism between the Drift–Poisson equations describing the plasmas and the Euler equations describing ideal fluids. The simulations use a particle-in-cell method to model the evolution of a set of point vortices. The aspect ratio splitting threshold ranges up to about twice as large as the vortex patch prediction and depends on the edge vorticity gradient. This is thought to be due to spatial Landau damping, which decreases the vortex aspect ratio over time and, thus, stabilizes the Love modes. Near the threshold, asymmetric splitting events are observed in which one of the split products contains much less circulation than the other. These results are relevant to a wide range of quasi-2D fluid systems, including geophysical fluids, astrophysical disks, and drift-wave eddies in tokamak plasmas.

Physics↗
DOE OSTI2024

Impact of penetrating collisions of plasma ions on spectral line shapes

Spectral-line-broadening models have been moving towards including full Coulomb interactions between the atom and plasma, replacing the commonly used dipole approximation. The effects of the full Coulomb interaction have been thoroughly explored for plasma electrons, resulting in redshifts of spectral lines in high-energy-density plasmas. We explore the impact of a full Coulomb treatment on ion broadening. Penetrating collisions due to ions do not significantly impact the linewidths. The most significant aspect is the appearance of quasimolecular resonances in the far line wings, such as those previously observed in white-dwarf spectra. We identify several problems with the existing models. The most direct implementation causes the quasimolecular features to appear at the wrong photon energies. It is possible to include the detailed molecular structure, but this currently requires the Anderson-Talman approximation, which ignores the N-body properties of the plasma. We find that N-body effects can substantially broaden these quasimolecular features and even shift them, depending on the plasma conditions. We conclude that penetration of ions into the spatial extent of the radiator wave function does not strongly affect the usual diagnostics and may have a moderate to weak effect on opacity. Furthermore, the Rosseland mean opacity is weighted towards low-opacity regions of a spectrum, where these quasimolecular features are found.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Signal-preserving CMB component separation with machine learning

Analysis of microwave sky signals, such as the cosmic microwave background, often requires component separation using multifrequency methods, whereby different signals are isolated according to their different frequency behaviors. Many so-called blind methods, such as the internal linear combination (ILC), make minimal assumptions about the spatial distribution of the signal or contaminants, and only assume knowledge of the frequency dependence of the signal. The ILC produces a minimum-variance linear combination of the measured frequency maps. In the case of Gaussian, statistically isotropic fields, this is the optimal linear combination, as the variance is the only statistic of interest. However, in many cases the signal we wish to isolate, or the foregrounds we wish to remove, are non-Gaussian and/or statistically anisotropic (in particular for the case of Galactic foregrounds). In such cases, it is possible that machine learning (ML) techniques can be used to exploit the non-Gaussian features of the foregrounds and thereby improve component separation. However, many ML techniques require the use of complex, difficult-to-interpret operations on the data. We propose a hybrid method whereby we train an ML model using only combinations of the data that , and combine the resulting ML-predicted foreground estimate with the ILC solution to reduce the error from the ILC. We demonstrate our methods on simulations of extragalactic temperature and Galactic polarization foregrounds and show that our ML model can exploit non-Gaussian features, such as point sources and spatially varying spectral indices, to produce lower-variance maps than ILC—e.g., reducing the variance of the B-mode residual by factors of up to 5—while preserving the signal of interest in an unbiased manner. Moreover, we often find improved performance even when applying our ML technique to foreground models on which it was not trained. Published by the American Physical Society 2025

McCarthy, Fiona (ORCID:0000000253893565)↗
DOE OSTI2021

Scanning X-Ray Diffraction Microscopy for Diamond Quantum Sensing

An understanding of nano- and microscale crystal strain in chemical-vapor-deposition diamond is crucial to the advancement of diamond quantum technologies. In particular, the presence of such strain and its characterization presents a challenge to diamond-based quantum sensing and information applications-as well as for future dark-matter detectors, where the directional information about incoming particles is encoded in crystal strain. Here, we exploit nanofocused scanning x-ray diffraction microscopy to quantitatively measure crystal deformation from defects in diamond with high spatial and strain resolution. The combination of information from multiple Bragg angles allows stereoscopic three-dimensional modeling of strain-feature geometry; the diffraction results are validated via comparison to optical measurements of the strain tensor based on spin-state-dependent spectroscopy of ensembles of nitrogen-vacancy centers in the diamond. Finally, our results demonstrate both strain and spatial resolution sufficient for directional detection of dark matter via x-ray measurement of crystal strain and provide a promising tool for diamond growth analysis and improvement of defect-based sensing.

42 ENGINEERING↗
DOE OSTI2021

In Situ Inference for Earth System Predictability

An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

In Situ Inference for Earth System Predictability

Focal Area: Focal Area 3: Insight gleaned from complex simulated data using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Microstructure-Dependent Rate Theory Model of Radiation-Induced Segregation in Binary Alloys

Conventional rate theory often uses the mean field concept to describe the effect of inhomogeneous microstructures on the evolution of radiation induced defect and solute/fission product segregation. However, the spatial and temporal evolution of defects and solutes determines the formation and spatial distribution of radiation-induced second phase such as precipitates and gas bubbles/voids, especially in materials with complicated microstructures and subject to high dose radiation. In this work, a microstructure-dependent model of radiation-induced segregation (RIS) has been developed to investigate the effect of inhomogeneous thermodynamic and kinetics properties of defects on diffusion and accumulations of solute A in AB binary alloys. Four independent concentrations: atom A, interstitial A, interstitial B, and vacancy on [A, B] sublattice are used as field variables to describe temporal and spatial distribution and evolution of defects and solute A. The independent concentrations of interstitial A and interstitial B allow to describe their different generation rates, thermodynamic and kinetic properties, and release the assumptions of interstitial generation and sink strength used in the conventional rate theory. Microstructure and concentration dependent chemical potentials of defects are used to calculate the driving forces of defect diffusions. With the model, the effects of defect chemical potentials and mobilities on the RIS in polycrystalline AB model alloys have been simulated. The results demonstrated the model capability in predicting defect evolution in materials with inhomogeneous thermodynamic and kinetic properties of defects. The model can be extended to materials with complicated microstructures such as a wide range of grain size distribution, coating structure and multiphases as well as radiation-induced precipitation subject to severe radiation damage.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Space-Time Finite Element Tensor Network Approach for the Time-Dependent Convection–Diffusion–Reaction Equation with Variable Coefficients

In this paper, we present a new space-time Galerkin-like method, where we treat the discretization of spatial and temporal domains simultaneously. This method utilizes a mixed formulation of the tensor-train (TT) and quantized tensor-train (QTT) (please see Section Tensor-Train Decomposition), designed for the finite element discretization (Q1-FEM) of the time-dependent convection–diffusion–reaction (CDR) equation. We reformulate the assembly process of the finite element discretized CDR to enhance its compatibility with tensor operations and introduce a low-rank tensor structure for the finite element operators. Recognizing the banded structure inherent in the finite element framework’s discrete operators, we further exploit the QTT format of the CDR to achieve greater speed and compression. Additionally, we present a comprehensive approach for integrating variable coefficients of CDR into the global discrete operators within the TT/QTT framework. The effectiveness of the proposed method, in terms of memory efficiency and computational complexity, is demonstrated through a series of numerical experiments, including a semi-linear example.

convection–diffusion–reaction equation↗
DOE OSTI2025

Floquet engineering with spatially nonuniform driving fields

In Floquet engineering, we apply a time-periodic modulation to change the effective behavior of a wave system. In this work, we generalize Floquet engineering to more fully exploit spatial degrees of freedom, expanding the scope of effective behaviors we can access. We develop a perturbative procedure to engineer space-time dependent driving forces that effectively transform broad classes of tight-binding systems into one another. We demonstrate several applications, including removing disorder, undoing Anderson localization, and enhancing localization to an extreme in spatially modulated waveguides. This procedure straightforwardly extends to other types of physical systems and different Floquet driving field implementations.

42 ENGINEERING↗
DOE OSTI2021

Type Ia supernova magnitude step from the local dark matter environment

ABSTRACT Residuals in the Hubble diagram at optical wavelengths and host galaxy stellar mass are observed to correlate in type Ia supernovae (SNe Ia) (‘magnitude step’). Among possible progenitor channels for the associated explosions, those based on dark matter (DM) have attracted significant attention, including our recent proposal that ‘normal’ SNe Ia from bare detonations in sub-Chandrasekhar white dwarf stars are triggered by the passage of asteroid-mass primordial black holes (PBHs): the magnitude step could then originate from a brightness dependence on stellar properties, on DM properties, or both. Here, we present a method to estimate the local DM density and velocity dispersion of the environment of SN Ia progenitors. We find a luminosity step of $0.52\pm 0.11\,$ mag corresponding to bins of high versus low DM density in a sample of 222 low-redshift events from The Open Supernova Catalog. We investigate whether the magnitude step can be attributed to local DM properties alone, assuming asteroid-mass PBHs. Given the inverse correlation between SN Ia brightness and PBH mass, an intriguing explanation is a spatially inhomogeneous PBH mass function. If so, a strong mass segregation in the DM density-dependent PBH mass scale is needed to explain the magnitude step. While mass segregation is observed in dense clusters, it is unlikely to be realized on galactic scales. Therefore, if DM consists of asteroid-mass PBHs, the magnitude step is more likely to exist, and dominantly to be attributed to local stellar properties.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Simulation of dilatancy-controlled gas migration processes in saturated bentonite using a coupled multiphase flow and elastoplastic H 2 M model

Dilatancy-controlled gas flow in preferential pathways plays a key role in the safety analysis of radioactive waste repositories. This is particularly the case for bentonite, an often-preferred barrier material. Gas flow in preferential pathways is characterized by localization and spontaneous behavior, which is challenging to simulate in numerical models due to strong hydro-mechanical coupling. To analyze a laboratory experiment in the framework of the DECOVALEX-2023 project, this study introduced a new approach of combining continuous modelling methods with spatial material properties derived from material heterogeneities and experimental observations. The proposed model utilized hydro-mechanical spatial distributions, namely Young's modulus and gas entry pressure, and elastoplasticity combined with a linear swelling model. A conceptual strain-dependent permeability approach simulated dilatancy-controlled gas flow based on hydro-mechanical coupling. To test the effectiveness of the presented approach, a gas injection test in a compacted, saturated bentonite sample was simulated using the open-source code OpenGeoSys 5.8 and compared with experimental observations. The presented methodology is capable of simulating localized gas flow in preferential pathways. The spatial distributions of Young's modulus and gas entry pressure affect the swelling pressure, relative permeability and, in combination with the strain-dependent permeability model, also the intrinsic permeability.

42 ENGINEERING↗