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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Response of CR-39 nuclear track detectors to protons with non-normal incidence

This paper presents data from experiments with protons at non-normal incidence to CR-39 nuclear track detectors, analyzing the properties of detection efficiency, proton track diameter, track contrast, and track eccentricity. Understanding the CR-39 response to protons incident at an angle is important for designing charged particle detectors for inertial confinement fusion (ICF) applications. This study considers protons with incident energies less than 3 MeV. In this regime, an incident angle of 10° has no effect on CR-39 detection efficiency, and >85% detection efficiency is preserved up through 25° in the range of 1.0 MeV–2.1 MeV. For ICF applications, incident angles above 30° are deemed impractical for detector design due to significant drops in proton detection at all energies. We observe significant reductions in detection efficiency compared to theoretical predictions, particularly at low energies where proton tracks are etched away. The proton track diameter measured by the scan system is observed to decrease with higher incident angles. The track diameters are analyzed with two fitting models, and it is shown that the diameter–energy relation can be fit with the existing models at angles up to 30°. The optical contrast of the tracks tends to increase with the angle, meaning that the tracks are fainter, and a larger increase is observed for higher energies. Eccentricity, a measure of how elongated proton tracks are, increases with the incident angle and drops after the critical angle. The lowest energy tracks remain nearly circular even at higher angles.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Missing components in ΛCDM from DESI Y1 baryonic acoustic oscillation measurements: Insights from redshift remapping

We explore transformations of the Friedman-Lemaître-Robertson-Walker (FLRW) metric and cosmological parameters that align with observational data while aiming to gain insights into potential extensions of standard cosmological models. We modified the FLRW metric by introducing a scaling factor, e 2Θ(a) –the cosmological scaling function (CSF), which alters the standard relationship between cosmological redshift and the cosmic scale factor without affecting angular measurements or cosmic microwave background (CMB) anisotropies. Using data from DESI Year 1, Pantheon+ supernovae, and the Planck CMB temperature power spectrum, we constrained both the CSF and cosmological parameters through a Markov chain Monte Carlo approach. Our results indicate that the CSF model fits observational data with a lower Hubble constant (although it is compatible with the value given by Planck 2018 within 1σ) and is predominantly dark matter dominated. Additionally, the CSF model produces temperature and lensing power spectra similar to those predicted by the standard model, though with lower values in the CSF model at large scales. We also checked that when fitting a CSF model without dark energy to the data, we obtain a more negative conformal function. This suggests that the CSF model may offer hints about missing elements and opens up a new avenue for exploring physical interpretations of cosmic acceleration.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Validation of sea ice models using an uncertainty-based distance metric for multiple model variables: NEW METRIC FOR SEA ICE MODEL VALIDATION

Here, we implement a variance-based distance metric (D n ) to objectively assess skill of sea ice models when multiple output variables or uncertainties in both model predictions and observations need to be considered. The metric compares observations and model data pairs on common spatial and temporal grids improving upon highly aggregated metrics (e.g., total sea ice extent or volume) by capturing the spatial character of model skill. The D n metric is a gamma-distributed statistic that is more general than the χ 2 statistic commonly used to assess model fit, which requires the assumption that the model is unbiased and can only incorporate observational error in the analysis. The D n statistic does not assume that the model is unbiased, and allows the incorporation of multiple observational data sets for the same variable and simultaneously for different variables, along with different types of variances that can characterize uncertainties in both observations and the model. This approach represents a step to establish a systematic framework for probabilistic validation of sea ice models. The methodology is also useful for model tuning by using the D n metric as a cost function and incorporating model parametric uncertainty as part of a scheme to optimize model functionality. We apply this approach to evaluate different configurations of the standalone Los Alamos sea ice model (CICE) encompassing the parametric uncertainty in the model, and to find new sets of model configurations that produce better agreement than previous configurations between model and observational estimates of sea ice concentration and thickness.

58 GEOSCIENCES↗
DOE OSTI2019

High Removal Capacity of Arsenic from Drinking Water Using Modified Magnetic Polyurethane Foam Nanocomposites

In this study, a novel polyurethane foam (PU) nanocomposite adsorbent based on silane-modified magnetic iron-oxide nanoparticles (Fe{sub 3}O{sub 4}@APTES) is synthesized via a low cost and simple in situ polymerization method for the removal of arsenic ions from aqueous solutions. The chemical structure and surface morphology of the prepared nanoparticles and adsorbent were characterized using Fourier transform infrared spectroscopy, attenuated total reflection, scanning electron microscopy, transmission electron microscopy, and energy-dispersive X-ray spectroscopy. Inductively coupled plasma mass spectrometry was used to measure the arsenic concentration of the treated solutions. Sorption isotherms models were applied to determine the adsorption mechanism and modeling parameters. The removal capacity of the modified PU foam was at its highest during a contact time of four hours which resulted in a removal capacity of 95%. Kinetic studies were conducted to determine the adsorption capacity and the uptake rate of arsenic. A Pseudo-order model was found to be the best fit model for adsorption. The prepared adsorbent can be separated from the solution by using an external magnet field.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Similarity Metric for Data Optimization and Efficient Training of Reactive Machine Learning Force Fields for Hydrocarbon Radiolysis

Radiolysis is a common approach to sterilize polymers, chemically modify them for upcycling, and accelerate their decomposition for recycling purposes. Reactive molecular dynamics (MD) simulations provide a powerful tool to generate atomic-level trajectories of the reactive processes and quantify radiolytic chemical degradation pathways. For this, machine learning (ML) surrogate models for reactive force fields with quantum mechanical accuracy are now widely used, which require ML training data sets that can provide information on atomic environments for target chemical systems. However, radiolysis chemistry can be highly complex and diverse, which poses significant challenges for generating training data to parametrize ML models. In this regard, we developed a method for optimizing the training data set using a cosine similarity metric to help guide training set selection for radiolysis of polyethylene, a model hydrocarbon polymer, as well as to enhance the transferability of our reactive ML force field (MLFF) to a variety of molecular and polymeric systems. Our approach performs atom-by-atom comparisons between local atomic environments to pinpoint important data points associated with rare and localized events, such as radiolysis damage within structures. We apply this approach to train the Chebyshev Interaction Model for Efficient Simulation (ChIMES) MLFF model, which expresses the atomic interaction potentials in terms of linear combinations of many-body Chebyshev polynomials. We first show that our method can reduce our training set size by ∼70% while improving overall accuracy compared to more standard MD model fitting approaches. We then validate our optimum model against diverse hydrocarbon simulation data, including simple alkanes and systems with unsaturated carbon bonds, over a wide range of thermodynamic conditions. Finally, we use our ChIMES model to perform MD simulations of radiolytic damage with large-scale systems that help avoid system size effects. Overall, our approach yields an MD force field that retains most of the accuracy of the underlying quantum method while yielding many orders of improvement in computational efficiency. In conclusion, our efforts will have impact on future hydrocarbon polymer radiolysis studies, where the chemical details of the polymer–radiation interactions can have a strong effect on the resulting products observed in experiments.

Hydrocarbons↗
DOE OSTI2013

WINDS, CLUMPS, AND INTERACTING COSMIC RAYS IN M82

We construct a family of models for the evolution of energetic particles in the starburst galaxy M82 and compare them to observations to test the calorimeter assumption that all cosmic ray energy is radiated in the starburst region. Assuming constant cosmic ray acceleration efficiency with Milky Way parameters, we calculate the cosmic-ray proton and primary and secondary electron/positron populations as a function of energy. Cosmic rays are injected with Galactic energy distributions and electron-to-proton ratio via Type II supernovae at the observed rate of 0.07 yr{sup -1}. From the cosmic ray spectra, we predict the radio synchrotron and {gamma}-ray spectra. To more accurately model the radio spectrum, we incorporate a multiphase interstellar medium in the starburst region of M82. Our model interstellar medium is highly fragmented with compact dense molecular clouds and dense photoionized gas, both embedded in a hot, low density medium in overall pressure equilibrium. The spectra predicted by this one-zone model are compared to the observed radio and {gamma}-ray spectra of M82. {chi}{sup 2} tests are used with radio and {gamma}-ray observations and a range of model predictions to find the best-fit parameters. The best-fit model yields constraints on key parameters in the starburst zone of M82, including a magnetic field strength of {approx}250 {mu}G and a wind advection speed in the range of 300-700 km s{sup -1}. We find that M82 is a good electron calorimeter but not an ideal cosmic-ray proton calorimeter and discuss the implications of our results for the astrophysics of the far-infrared-radio correlation in starburst galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

A broadband X-ray study of the Geminga pulsar with NuSTAR And XMM-Newton

We report on the first hard X-ray detection of the Geminga pulsar above 10 keV using a 150 ks observation with the Nuclear Spectroscopic Telescope Array (NuSTAR) observatory. The double-peaked pulse profile of non-thermal emission seen in the soft X-ray band persists at higher energies. Broadband phase-integrated spectra over the 0.2-20 keV band with NuSTAR and archival XMM-Newton data do not fit to a conventional two-component model of a blackbody plus power law, but instead exhibit spectral hardening above ∼5 keV. We find that two spectral models fit the data well: (1) a blackbody (kT {sub 1} ∼ 42 eV) with a broken power law (Γ{sub 1} ∼ 2.0, Γ{sub 2} ∼ 1.4 and E {sub break} ∼ 3.4 keV) and (2) two blackbody components (kT {sub 1} ∼ 44 eV and kT {sub 2} ∼ 195 eV) with a power-law component (Γ ∼ 1.7). In both cases, the extrapolation of the Rayleigh-Jeans tail of the thermal component is consistent with the UV data, while the non-thermal component overpredicts the near-infrared data, requiring a spectral flattening at E ∼ 0.05-0.5 keV. While strong phase variation of the power-law index is present below ∼5 keV, our phase-resolved spectroscopy with NuSTAR indicates that another hard non-thermal component with Γ ∼ 1.3 emerges above ∼5 keV. The spectral hardening in non-thermal X-ray emission as well as spectral flattening between the optical and X-ray bands argue against the conjecture that a single power law may account for multi-wavelength non-thermal spectra of middle-aged pulsars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Constraining the thermally pulsing asymptotic giant branch phase with resolved stellar populations in the Small Magellanic Cloud

The thermally-pulsing asymptotic giant branch (TP-AGB) experienced by low- andintermediate-mass stars is one of the most uncertain phases of stellar evolution and themodels need to be calibrated with the aid of observations. To this purpose, we couplehigh-quality observations of resolved stars in the Small Magellanic Cloud (SMC) withdetailed stellar population synthesis simulations computed with theTRILEGALcode.The strength of our approach relies on the detailed spatially-resolved star formationhistory of the SMC, derived from the deep near-infrared photometry of the VISTAsurvey of the Magellanic Clouds, as well as on the capability to quickly and accuratelyexplore a wide variety of parameters and effects with theCOLIBRIcode for the TP-AGB evolution. Adopting a well-characterized set of observations – star counts andluminosity functions – we set up a calibration cycle along which we iteratively changea few key parameters of the TP-AGB models until we eventually reach a good fit tothe observations. Our work leads to identify two best-fitting models that mainly differin the efficiencies of the third dredge-up and mass loss in TP-AGB stars with initialmasses larger than about 3 M. On the basis of these calibrated models we providea full characterization of the TP-AGB stellar population in the SMC in terms of stel-lar parameters (initial masses, C/O ratios, carbon excess, mass-loss rates). Extensivetables of isochrones including these improved models are publicly available

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Verification Testing of OLI Systems Mixed Solvent Electrolyte Model for the Na-K-Mg-Ca-H-Cl-SO 4 -OH-HCO 3 -CO 3 -CO 2 -H 2 ) System to High Ionic Strength at 25°C

This technical report summarizes model verification results and summary statistics for 41 evaporite mineral solubility cases evaluated by Savannah River National Laboratory using OLI Systems’ aqueous electrolyte thermodynamic modeling software. The 41 verification cases containing a total of 60 solubility curves comprise mineral solubility data from low to high ionic strength at 25°C for the eight-component system Na-K-Mg-Ca-H-Cl-SO 4 -OH-HCO 3 -CO 3 -CO 2 -H 2 O as reported by Harvie et al. (1984). Thermodynamic calculations were executed using OLI Systems’ Stream Analyzer computation module within the OLI Studio software platform (Ver. 11.0, Rev. 11.0.1.9). The Mixed Solvent Electrolyte (MSE) thermodynamic framework was chosen for this investigation because of its superiority in modeling high ionic-strength inorganic salt solutions and actinide redox chemistry and solubility, both of which are relevant to the geological repository conditions at the Waste Isolation Pilot Plant in Carlsbad, New Mexico. Mineral solubility data in various inorganic salt solutions were digitized and extracted from figures generated by Harvie et al. (1984). For each of the 60 solubility curves, a case-specific chemistry model and input file were generated in OLI Studio using OLI Stream Analyzer and the MSE (H 3 O + ion) public databank provided by OLI Systems. Model simulation results were exported to Microsoft Excel to calculate summary statistics and to generate graphs comparing the OLI model predictions to the solubility data. Summary statistics include residuals (model – data) and concordance (accuracy × precision, where precision is indicated by the Pearson correlation coefficient and accuracy accounts for bias and scale differential). Private databanks were not developed, and activity coefficient model regressions were not performed to improve OLI model fits to the data. Of the 41 model verification plots, 83% have a mean of the percent residuals less than or equal to 25%. Similarly, 75% display a concordance greater than or equal to 0.75. Only seven of the 41 verification plots fail to show good agreement between the model and data. Of these seven, three are relevant to the WIPP repository because they involve the Mg-OH-Cl-SO 4 -CO 3 aqueous system. The remaining four address salt solubilities at the pH extremes (strong acid and strong base). It should be noted that in two of the three Mg-OH-Cl-SO 4 -CO 3 system cases, the regressed Harvie et al. (1984) solubility curve also deviated from the data. Lack of agreement between the OLI model-predicted solubility curves and the data is attributable to one or more of the following: specific solid species are not included in the OLI MSE databank; there is significant variation among the different solubility datasets chosen by Harvie et al. (1984); the OLI MSE model’s thermodynamic parameters were determined using different solubility datasets; and the activity coefficient parameters for certain relevant ion-ion and ion-molecule pairs have not been optimized via data regression. Two recommendations for future work are to (1) evaluate solubility data for the Mg-OH-Cl-SO 4 -CO 3 system at high ionic strength and, if necessary, develop a private OLI MSE database that includes missing species and, where necessary, regressed standard state properties and interaction parameters; (2) perform similar verification testing of the OLI model for actinide solubility data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Glass Property-Composition Models Update for use in Direct Feed High-Level Waste Flowsheet Development

A set of preliminary glass property models and constraints were developed and augmented by models from literature for use in design of direct-feed high-level waste (DFHLW) glasses for flowsheet evaluation, testing, and design of the Tank Waste Treatment and Immobilization Plant (WTP) high-level waste (HLW) Facility. These models and constraints are meant to be used as a place-holder while glass property-composition data gaps are filled and final plant operating models are developed. This report describes the motivation and intended use of the models, the compilation of data, model fitting and selection, methods to apply the models and constraints in glass design and offers example calculations demonstrating their intended use.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Glass Property-Composition Models Update for use in Direct Feed High-Level Waste Flowsheet Development: EWG2.6

A set of preliminary glass property models and constraints were developed and augmented by models from literature for use in design of direct-feed high-level waste (DFHLW) glasses for flowsheet evaluation, testing, and design of the Waste Treatment and Immobilization Plant (WTP) high-level waste (HLW) Facility. These models and constraints are meant to be used as a place-holder while glass property-composition data gaps are filled and final plant operating models are developed. This report describes the motivation and intended use of the models, the compilation of data, model fitting and selection, methods to apply the models and constraints in glass design and offers example calculations demonstrating their intended use.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Multiscale Investigation of Thermal Transport in UO{sub 2} Fuel using Modeling and Simulation

The thermal conductivity of the UO{sub 2} fuel in light water reactors (LWRs) defines how efficiently heat is transported from the fuel to the coolant, and thus directly influences the reactor efficiency. Unfortunately, the same fission events that generate heat also cause radiation damage and fission product formation that decreases the thermal conductivity. This reduction in thermal conductivity reduces the safety of the reactor in addition to its efficiency, because excess heat generated during an accident cannot be transported from the fuel, resulting in high temperatures that can result in melting. Because thermal conductivity plays such an important role in reactor efficiency and safety, many experiments have been conducted to measure the thermal conductivity of UO{sub 2} and how it changes during reactor operation. Experiments on unirradiated fuel have resulted in a large set of data indicating how the bulk thermal conductivity changes as a function of temperature, which was used to develop a semiempirical model. Additional experiments have been conducted to determine how the thermal conductivity of UO{sub 2} changes as a function of burnup, leading to burnup dependent models fitted to the data. While these empirical models perform well within the conditions at which the data was collected, they cannot be extrapolated outside of those conditions. In addition, while burnup is a measure of the amount of fissioning that has occurred, it cannot account for the impact of the operating conditions on the thermal conductivity. While experimental data provides an empirical view on the value of the thermal conductivity in the fuel during reactor operation, a more mechanistic understanding of how temperature and burnup impact the transport of heat through the fuel would be extremely valuable. Such an understanding would allow for a more accurate prediction of fuel performance, but it could also be used to direct modifications to the fuel that would raise the thermal conductivity or reduce the impact of burnup. In this work we summarize recent efforts to use modeling and simulation at the atomistic and mesoscales to obtain a mechanistic understanding of thermal transport in UO{sub 2}. The thermal conductivity of LWR fuel is one of the most critical material parameters impacting the efficiency and safety of the reactor. Thus, a significant effort is being made to develop a mechanistic understanding of the impact of temperature and burnup on it. In this work, we have provided a short summary of modeling and simulation efforts being carried out at the atomistic and mesoscale to understand this critical property. These research efforts are providing a fundamental understanding of the thermal transport in unirradiated UO{sub 2}, as well as the impact of radiation damage and fission products on thermal transport. These efforts have been focused on UO{sub 2}, but they also are laying the framework for conducted similar simulations to investigate new accident tolerant fuel concepts. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Material Testing for Shear-Dominated Ductile Failure

An initial foray into the design of specimens that can be used to provide data about the quasistatic ductile failure of metals when subjected to shear-dominated (low triaxiality) states of stress was undertaken. Four specimen geometries made from two materials with different ductility (Al 7075, lower ductility and steel A286, higher ductility) were considered as candidates. Based on results from analysis and experimentation, it seems that two show promise for further consideration. Whereas preliminary results indicate that the Johnson-Cook model fit the failure data for Al 7075 well, it did not fit the data for steel A286. Further work is needed to consolidate the results and evaluate other failure models that may fit the steel data better, as well as to extend the results of this work to the dynamic loading regime.

36 MATERIALS SCIENCE↗
DOE OSTI2015

What Does The PAMELA Antiproton Spectrum Tell Us About Dark Matter?

Measurements of the cosmic ray antiproton spectrum can be used to search for contributions from annihilating dark matter and to constrain the dark matter annihilation cross section. Depending on the assumptions made regarding cosmic ray propagation in the Galaxy, such constraints can be quite stringent. We revisit this topic, utilizing a set of propagation models fit to the cosmic ray boron, carbon, oxygen and beryllium data. We derive upper limits on the dark matter annihilation cross section and find that when the cosmic ray propagation parameters are treated as nuisance parameters (as we argue is appropriate), the resulting limits are significantly less stringent than have been previously reported. We also note (as have several previous groups) that simple GALPROP-like diffusion-reacceleration models predict a spectrum of cosmic ray antiprotons that is in good agreement with PAMELA's observations above ~ 5 GeV, but that significantly underpredict the flux at lower energies. Although the complexity of modeling cosmic ray propagation at GeV-scale energies makes it difficult to determine the origin of this discrepancy, we consider the possibility that the excess antiprotons are the result of annihilating dark matter. Suggestively, we find that this excess is best fit for m(DM) ~ 35 GeV and σ v ~ 10(−)(26) cm(3)/s (to bbar b), in good agreement with the mass and cross section previously shown to be required to generate the gamma-ray excess observed from the Galactic Center.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

Soil Dust Aerosols and Wind as Predictors of Seasonal Meningitis Incidence in Niger

Epidemics of meningococcal meningitis are concentrated in sub-Saharan Africa during the dry season, a period when the region is affected by the Harmattan, a dry and dusty northeasterly trade wind blowing from the Sahara into the Gulf of Guinea. We examined the potential of climate-based statistical forecasting models to predict seasonal incidence of meningitis in Niger at both the national and district levels. We used time series of meningitis incidence from 1986 through 2006 for 38 districts in Niger. We tested models based on data that would be readily available in an operational framework, such as climate and dust, population, and the incidence of early cases before the onset of the meningitis season in January–May. Incidence was used as a proxy for immunological state, susceptibility, and carriage in the population. We compared a range of negative binomial generalized linear models fitted to the meningitis data. At the national level, a model using early incidence in December and averaged November–December zonal wind provided the best fit (pseudo-R2 = 0.57), with zonal wind having the greatest impact. A model with surface dust concentration as a predictive variable performed indistinguishably well. At the district level, the best spatiotemporal model included zonal wind, dust concentration, early incidence in December, and population density (pseudo-R2 = 0.41). We showed that wind and dust information and incidence in the early dry season predict part of the year-to-year variability of the seasonal incidence of meningitis at both national and district levels in Niger. Models of this form could provide an early-season alert that wind, dust, and other conditions are potentially conducive to an epidemic.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Hydrothermal but Not Mechanical Pretreatment of Wastewater Algae Enhanced Anaerobic Digestion Energy Balance due to Improved Biomass Disintegration and Methane Production Kinetics

This study used pilot-scale high-rate algae ponds to assess algal–bacteria biomass productivity and wastewater nutrient removal as well as the impact of mechanical and hydrothermal pretreatments on biomass disintegration, methane production kinetics, and anaerobic digestion (AD) energy balance. Mechanical pretreatment had a minor effect on biomass disintegration and methane production. By contrast, hydrothermal pretreatment significantly reduced particle size and increased the solubilized organic matter content by 3.5 times. The methane yield and production rate increased by 20–55% and 20–85%, respectively, with the highest values achieved after pretreatment at 121 °C for 60 min. While the 1st-order and pseudo-1st-order reaction equation models fitted methane production from untreated biomass best (R2 > 0.993), the modified Gompertz sigmoidal-type model provided a superior fit for hydrothermally pretreated algae (R2 ≥ 0.99). The AD energy balance revealed that hydrothermal pretreatment improved the total energy output by 25–40%, with the highest values for volume-specific and mass-specific total energy outputs reaching 0.23 kW per digester m3 and 2.3 MW per ton of biomass volatile solids. Additionally, net energy recovery (energy output per biomass HHV) increased from 20% for untreated algae to 32–34% for hydrothermally pretreated algae, resulting in net energy ratio and net energy efficiency of 2.14 and 68%, respectively.

09 BIOMASS FUELS↗
DOE OSTI2015

USING MULTIPLE-VIEWPOINT OBSERVATIONS TO DETERMINE THE INTERACTION OF THREE CORONAL MASS EJECTIONS OBSERVED ON 2012 MARCH 5

We examine the interaction of three coronal mass ejections (CMEs) that took place on 2012 March 5 at heights less than 20 R{sub ⊙} in the corona. We used a forward fitting model to reconstruct the three-dimensional trajectories and kinematics of the CMEs and determine their interaction in the observations from three viewpoints: Solar and Heliospheric Observatory (SOHO), STEREO-A, and STEREO-B. The first CME (CME-1), a slow-rising eruption near disk center, is already in progress at 02:45 UT when the second CME (CME-2) erupts from AR 11429 on the east limb. These two CMEs are present in the corona not interacting when a third CME (CME-3) erupts from AR 11429 at 03:34 UT. CME-3 has a constant velocity of 1456[±31] km s{sup −1} and drives a shock that is observed as a halo from all viewpoints. We find that the shock driven by CME-3 passed through CME-1 with no observable change in the geometry, trajectory, or velocity of CME-1. However, the elevated temperatures detected in situ when CME-1 reached Earth indicate that the plasma inside CME-1 may have been heated by the passage of the shock. CME-2 is accelerated by CME-3 to more than twice its initial velocity and remains a separate structure ahead of the CME-3 front. CME-2 is deflected 24° northward by CME-3 for a total deflection of 40° from its source region. These results suggest that the collision of CME-2 and CME-3 is superelastic. This work demonstrates the capability and utility of fitting forward models to complex and interacting CMEs observed in the corona from multiple viewpoints.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

A Nonempirical Relative Permeability Model for Hydrate-Bearing Sediments

There are currently two types of relative permeability models that are used to model gas production from hydrate-bearing sediments: fully empirical parameter-fitting models [such as the University of Tokyo model (Masuda et al. 1997) and the Brooks and Corey model (Brooks and Corey 1964)] and partially empirical models [such as the Kozeny and Carman model (Wyllie and Gardner 1958) and capillary-tube-based models that assume only a single phase]. This study proposes an analytical model to estimate relative permeability of gas and water in a hydrate-bearing porous medium without curve fitting or use of any empirical parameters. The model is derived by conserving the momentum balance with the steady-state form of the Navier-Stokes equation for gas/water flow in a hydrate-bearing porous medium. The model is validated against a number of laboratory studies and is shown to perform better than most empirical models over a full range of experimental data. The proposed model is an analytical function of rock properties (average pore size and shape, porosity, irreducible water saturation, and saturation of hydrate), fluid properties (gas/water saturations and viscosities), and the hydrate-growth pattern [pore filling (PF), wall coating (WC), and a combination of PF and WC]. Here, the benefits of the proposed model include sensitivity analysis of relevant physical parameters on relative permeability and estimation of rock parameters (such as porosity, pore size, and residual water saturation) using inverse modeling. The model can also be used to estimate two-phase permeability in a permeable medium without hydrates.

58 GEOSCIENCES↗