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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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Coupled fission fragment angular momenta

Here, nuclear fission produces fragments whose spins are coupled to the relative angular motion via angular momentum conservation. It is shown how ensembles of such spins can readily be obtained by either direct microcanonical sampling or by sampling of the associated normal modes of rotation. The resulting distribution of the spin-spin opening angle is illustrated in various three- and two-dimensional scenarios and it is demonstrated how recent mutually conflicting model calculations can be well reproduced with different assumptions about the scission geometry.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

TU-FG-BRB-03: Basis Vector Model Based Method for Proton Stopping Power Estimation From Experimental Dual Energy CT Data

Purpose: This work aims at reducing the uncertainty in proton stopping power (SP) estimation by a novel combination of a linear, separable basis vector model (BVM) for stopping power calculation (Med Phys 43:600) and a statistical, model-based dual-energy CT (DECT) image reconstruction algorithm (TMI 35:685). The method was applied to experimental data. Methods: BVM assumes the photon attenuation coefficients, electron densities, and mean excitation energies (I-values) of unknown materials can be approximated by a combination of the corresponding quantities of two reference materials. The DECT projection data for a phantom with 5 different known materials was collected on a Philips Brilliance scanner using two scans at 90 kVp and 140 kVp. The line integral alternating minimization (LIAM) algorithm was used to recover the two BVM coefficient images using the measured source spectra. The proton stopping powers are then estimated from the Bethe-Bloch equation using electron densities and I-values derived from the BVM coefficients. The proton stopping powers and proton ranges for the phantom materials estimated via our BVM based DECT method are compared to ICRU reference values and a post-processing DECT analysis (Yang PMB 55:1343) applied to vendorreconstructed images using the Torikoshi parametric fit model (tPFM). Results: For the phantom materials, the average stopping power estimations for 175 MeV protons derived from our method are within 1% of the ICRU reference values (except for Teflon with a 1.48% error), with an average standard deviation of 0.46% over pixels. The resultant proton ranges agree with the reference values within 2 mm. Conclusion: Our principled DECT iterative reconstruction algorithm, incorporating optimal beam hardening and scatter corrections, in conjunction with a simple linear BVM model, achieves more accurate and robust proton stopping power maps than the post-processing, nonlinear tPFM based DECT analysis applied to conventional reconstructions of low and high energy scans. Funding Support: NIH R01CA 75371; NCI grant R01 CA 149305.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Empirical Acoustic Source Model for Chemical Explosions in Air

Chemical explosions generate pressure disturbances in air that radiate as nonlinear shock waves near the source and transition into acoustic waves with distance. Because low-frequency acoustic waves generally travel large distances without significant loss of energy, they are often used for explosion monitoring and yield estimation. However, quantitative relationships between acoustic energy and explosion yields are required for accurate yield estimation. Here, we develop an empirical acoustic source model for chemical explosions from experimental data. The empirical model returns the acoustic pressure waveform for the detonation of 1 kg of trinitrotoluene, which is conventionally used to represent the explosive release of 4.184 MJ of explosion energy. The full-waveform model can be used to predict acoustic signals for an arbitrary yield of a high-explosive detonation based on the standard scaling law and to estimate acoustic energies in a specific frequency range. We evaluate the accuracy of the acoustic source model independently by estimating the yield of other explosive events that are not included in the model development. Finally, statistical characteristics of the model and their implications for the uncertainty quantification of estimated yields are discussed.

58 GEOSCIENCES↗
DOE OSTI2015

Bayesian Uncertainty Quantification in Predictions of Flows in Highly Heterogeneous Media and Its Applications to the CO 2 Sequestration

In this proposal, we have worked on Bayesian uncertainty quantification for predictions of fows in highly heterogeneous media. The research in this proposal is broad and includes: prior modeling for heterogeneous permeability fields; effective parametrization of heterogeneous spatial priors; efficient ensemble- level solution techniques; efficient multiscale approximation techniques; study of the regularity of complex posterior distribution and the error estimates due to parameter reduction; efficient sampling techniques; applications to multi-phase ow and transport. We list our publications below and describe some of our main research activities. Our multi-disciplinary team includes experts from the areas of multiscale modeling, multilevel solvers, Bayesian statistics, spatial permeability modeling, and the application domain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Predictive Battery Lifetime Modeling at NREL [Slides]

Battery lifetime models are used to extrapolate data from accelerated aging tests to simulate degradation in real-world applications such as electric vehicles and battery energy storage systems. Methods developed at NREL utilize both expert domain-knowledge and machine-learning to identify models, using statistical methods such as cross-validation and bootstrap resampling to interrogate model performance and quantify uncertainty. These models can be utilized in systems level simulations to predict battery performance or technoeconomic models to estimate the lifetime cost of battery systems.

25 ENERGY STORAGE↗
NASA NTRS2004

Multivariate Error Covariance Estimates by Monte-Carlo Simulation for Assimilation Studies in the Pacific Ocean

One of the most difficult aspects of ocean state estimation is the prescription of the model forecast error covariances. The paucity of ocean observations limits our ability to estimate the covariance structures from model-observation differences. In most practical applications, simple covariances are usually prescribed. Rarely are cross-covariances between different model variables used. Here a comparison is made between a univariate Optimal Interpolation (UOI) scheme and a multivariate OI algorithm (MvOI) in the assimilation of ocean temperature. In the UOI case only temperature is updated using a Gaussian covariance function and in the MvOI salinity, zonal and meridional velocities as well as temperature, are updated using an empirically estimated multivariate covariance matrix. Earlier studies have shown that a univariate OI has a detrimental effect on the salinity and velocity fields of the model. Apparently, in a sequential framework it is important to analyze temperature and salinity together. For the MvOI an estimation of the model error statistics is made by Monte-Carlo techniques from an ensemble of model integrations. An important advantage of using an ensemble of ocean states is that it provides a natural way to estimate cross-covariances between the fields of different physical variables constituting the model state vector, at the same time incorporating the model's dynamical and thermodynamical constraints as well as the effects of physical boundaries. Only temperature observations from the Tropical Atmosphere-Ocean array have been assimilated in this study. In order to investigate the efficacy of the multivariate scheme two data assimilation experiments are validated with a large independent set of recently published subsurface observations of salinity, zonal velocity and temperature. For reference, a third control run with no data assimilation is used to check how the data assimilation affects systematic model errors. While the performance of the UOI and MvOI is similar with respect to the temperature field, the salinity and velocity fields are greatly improved when multivariate correction is used, as evident from the analyses of the rms differences of these fields and independent observations. The MvOI assimilation is found to improve upon the control run in generating the water masses with properties close to the observed, while the UOI failed to maintain the temperature and salinity structure.

Borovikov, Anna↗
NASA NTRS2003

Ising model of cardiac thin filament activation with nearest-neighbor cooperative interactions

We have developed a model of cardiac thin filament activation using an Ising model approach from equilibrium statistical physics. This model explicitly represents nearest-neighbor interactions between 26 troponin/tropomyosin units along a one-dimensional array that represents the cardiac thin filament. With transition rates chosen to match experimental data, the results show that the resulting force-pCa (F-pCa) relations are similar to Hill functions with asymmetries, as seen in experimental data. Specifically, Hill plots showing (log(F/(1-F)) vs. log [Ca]) reveal a steeper slope below the half activation point (Ca(50)) compared with above. Parameter variation studies show interplay of parameters that affect the apparent cooperativity and asymmetry in the F-pCa relations. The model also predicts that Ca binding is uncooperative for low [Ca], becomes steeper near Ca(50), and becomes uncooperative again at higher [Ca]. The steepness near Ca(50) mirrors the steep F-pCa as a result of thermodynamic considerations. The model also predicts that the correlation between troponin/tropomyosin units along the one-dimensional array quickly decays at high and low [Ca], but near Ca(50), high correlation occurs across the whole array. This work provides a simple model that can account for the steepness and shape of F-pCa relations that other models fail to reproduce.

Evaluation Studies↗
NASA NTRS1999

Mathematical model to estimate risk of calcium-containing renal stones

BACKGROUND/AIMS: Astronauts exposed to microgravity during the course of spaceflight undergo physiologic changes that alter the urinary environment so as to increase the risk of renal stone formation. This study was undertaken to identify a simple method with which to evaluate the potential risk of renal stone development during spaceflight. METHOD: We used a large database of urinary risk factors obtained from 323 astronauts before and after spaceflight to generate a mathematical model with which to predict the urinary supersaturation of calcium stone forming salts. RESULT: This model, which involves the fewest possible analytical variables (urinary calcium, citrate, oxalate, phosphorus, and total volume), reliably and accurately predicted the urinary supersaturation of the calcium stone forming salts when compared to results obtained from a group of 6 astronauts who collected urine during flight. CONCLUSIONS: The use of this model will simplify both routine medical monitoring during spaceflight as well as the evaluation of countermeasures designed to minimize renal stone development. This model also can be used for Earth-based applications in which access to analytical resources is limited.

NASA Center JSC↗
NASA NTRS2017

The Substorm Cycle as Reproduced by Global MHD Models

Recently, Gordeev et al. (2015) suggested a method to test global MHD models against statistical empirical data. They showed that four community-available global MHD models supported by the Community Coordinated Modeling Center (CCMC) produce a reasonable agreement with reality for those key parameters (the magnetospheric size, magnetic field, and pressure) that are directly related to the large-scale equilibria in the outer magnetosphere. Based on the same set of simulation runs, here we investigate how the models reproduce the global loading-unloading cycle. We found that in terms of global magnetic flux transport, three examined CCMC models display systematically different response to idealized2 h north then 2 h south interplanetary magnetic field (IMF) Bz variation. The LFM model shows a depressed return convection and high loading rate during the growth phase as well as enhanced return convection and high unloading rate during the expansion phase, with the amount of loaded unloaded magnetotail flux and the growth phase duration being the closest to their observed empirical values during isolated substorms. Two other models exhibit drastically different behavior. In the BATS-R-US model the plasma sheet convection shows a smooth transition to the steady convection regime after the IMF southward turning. In the Open GGCM a weak plasma sheet convection has comparable intensities during both the growth phase and the following slow unloading phase. We also demonstrate potential technical problem in the publicly available simulations which is related to post processing interpolation and could affect the accuracy of magnetic field tracing and of other related procedures.

Gordeev, E.↗
NASA NTRSDate not supplied

Uncertainty in the response of sudden stratospheric warmings and stratosphere- troposphere coupling to quadrupled CO2 concentrations in CMIP6 models

Major sudden stratospheric warmings (SSWs), vortex formation and final breakdown dates are key highlight points of the stratospheric polar vortex. These phenomena are relevant for stratosphere-troposphere coupling, which explains the interest in understanding their future changes. However, up to now, there is not a clear consensus on which projected changes to the polar vortex are robust, particularly in the Northern Hemisphere, possibly due to short data record or relatively moderate CO2 forcing. The new simulations performed under the Coupled Model Intercomparison Project, Phase 6, together with the long daily data requirements of the DynVarMIP project in preindustrial and quadrupled CO2 (4xCO2 ) forcing simulations provide a new opportunity to revisit this topic by overcoming the limitations mentioned above. In this study, we analyze this new model output to document the change, if any, in the frequency of SSWs under 4xCO2 forcing. Our analysis reveals a large disagreement across the models as to the sign of this change, even though most models show a statistically significant change. The models, however, are in good agreement as to the impact of SSWs over the North Atlantic: there is no indication of a change under 4xCO2 forcing. Over the Pacific, however, the change is more uncertain. Finally, the models show robust changes to the seasonal cycle in the stratosphere. Specifically, we find a longer duration of the stratospheric polar vortex, and thus a longer season of stratosphere-troposphere coupling.

sudden stratospheric warmings (SSWs)↗
DOE OSTI2015

Integrated Geologic and Geophysical Approach for Establishing Geothermal Play Fairways and Discovering Blind Geothermal Systems in the Great Basin Region, Western USA: A Progress Report

We have undertaken an integrated geologic, geochemical, and geophysical study of a broad 240-km-wide, 400-km-long transect stretching from west-central to eastern Nevada in the Great Basin region of the western USA. The main goal of this study is to produce a comprehensive geothermal potential map that incorporates up to 11 parameters and identifies geothermal play fairways that represent potential blind or hidden geothermal systems. Our new geothermal potential map incorporates: 1) heat flow; 2) geochemistry from springs and wells; 3) structural setting; 4) recency of faulting; 5) slip rates on Quaternary faults; 6) regional strain rate; 7) slip and dilation tendency on Quaternary faults; 8) seismologic data; 9) gravity data; 10) magnetotelluric data (where available); and 11) seismic reflection data (primarily from the Carson Sink and Steptoe basins). The transect is respectively anchored on its western and eastern ends by regional 3D modeling of the Carson Sink and Steptoe basins, which will provide more detailed geothermal potential maps of these two promising areas. To date, geological, geochemical, and geophysical data sets have been assembled into an ArcGIS platform and combined into a preliminary predictive geothermal play fairway model using various statistical techniques. The fairway model consists of the following components, each of which are represented in grid-cell format in ArcGIS and combined using specified weights and mathematical operators: 1) structural component of permeability; 2) regional-scale component of permeability; 3) combined permeability, and 4) heat source model. The preliminary model demonstrates that the multiple data sets can be successfully combined into a comprehensive favorability map. An initial evaluation using known geothermal systems as benchmarks to test interpretations indicates that the preliminary modeling has done a good job assigning relative ranks of geothermal potential. However, a major challenge is defining logical relative rankings of each parameter and how best to combine the multiple data sets into the geothermal potential/ permeability map. Ongoing feedback and data analysis are in use to revise the grouping and weighting of some parameters in order to develop a more robust, optimized, final model. The final product will incorporate more parameters into a geothermal potential map than any previous effort in the region and may serve as a prototype to develop comprehensive geothermal potential maps for other regions.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Integrated Geologic and Geophysical Approach for Establishing Geothermal Play Fairways and Discovering Blind Geothermal Systems in the Great Basin Region, Western USA: A Progress Report

We have undertaken an integrated geologic, geochemical, and geophysical study of a broad 240-km-wide, 400-kmlong transect stretching from west-central to eastern Nevada in the Great Basin region of the western USA. The main goal of this study is to produce a comprehensive geothermal potential map that incorporates up to 11 parameters and identifies geothermal play fairways that represent potential blind or hidden geothermal systems. Our new geothermal potential map incorporates: 1) heat flow; 2) geochemistry from springs and wells; 3) structural setting; 4) recency of faulting; 5) slip rates on Quaternary faults; 6) regional strain rate; 7) slip and dilation tendency on Quaternary faults; 8) seismologic data; 9) gravity data; 10) magnetotelluric data (where available); and 11) seismic reflection data (primarily from the Carson Sink and Steptoe basins). The transect is respectively anchored on its western and eastern ends by regional 3D modeling of the Carson Sink and Steptoe basins, which will provide more detailed geothermal potential maps of these two promising areas. To date, geological, geochemical, and geophysical data sets have been assembled into an ArcGIS platform and combined into a preliminary predictive geothermal play fairway model using various statistical techniques. The fairway model consists of the following components, each of which are represented in grid-cell format in ArcGIS and combined using specified weights and mathematical operators: 1) structural component of permeability; 2) regional-scale component of permeability; 3) combined permeability, and 4) heat source model. The preliminary model demonstrates that the multiple data sets can be successfully combined into a comprehensive favorability map. An initial evaluation using known geothermal systems as benchmarks to test interpretations indicates that the preliminary modeling has done a good job assigning relative ranks of geothermal potential. However, a major challenge is defining logical relative rankings of each parameter and how best to combine the multiple data sets into the geothermal potential/permeability map. Ongoing feedback and data analysis are in use to revise the grouping and weighting of some parameters in order to develop a more robust, optimized, final model. The final product will incorporate more parameters into a geothermal potential map than any previous effort in the region and may serve as a prototype to develop comprehensive geothermal potential maps for other regions.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Changes in Spatiotemporal Precipitation Patterns in Changing Climate Conditions

Climate models robustly imply that some significant change in precipitation patterns will occur. Models consistently project that the intensity of individual precipitation events increases by approximately 6-7%/K, following the increase in atmospheric water content, but that total precipitation increases by a lesser amount (2-3%/K in the global average). Some other aspect of precipitation events must then change to compensate for this difference. We develop here a new methodology for identifying individual rainstorms and studying their physical characteristics - including starting location, intensity, spatial extent, duration, and trajectory - that allows identifying that compensating mechanism. We apply this technique to precipitation over the contiguous U.S. from both radar-based data products and high-resolution model runs simulating 100 years of business-as-usual warming. In model studies, we find that the dominant compensating mechanism is a reduction of storm size. In summer, rainstorms become more intense but smaller; in winter, rainstorm shrinkage still dominates, but storms also become less numerous and shorter duration. These results imply that flood impacts from climate change will be less severe than would be expected from changes in precipitation intensity alone. We show also that projected changes are smaller than model-observation biases, implying that the best means of incorporating them into impact assessments is via "data-driven simulations" that apply model-projected changes to observational data. We therefore develop a simulation algorithm that statistically describes model changes in precipitation characteristics and adjusts data accordingly, and show that, especially for summertime precipitation, it outperforms simulation approaches that do not include spatial information.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Star Formation Quenching Timescale of Central Galaxies in a Hierarchical Universe

Central galaxies make up the majority of the galaxy population, including the majority of the quiescent population at M{sub ∗}>10{sup 10}M{sub ⊙}. Thus, the mechanism(s) responsible for quenching central galaxies play a crucial role in galaxy evolution as whole. We combine a high-resolution cosmological N-body simulation with observed evolutionary trends of the “star formation main sequence,” quiescent fraction, and stellar mass function at z<1 to construct a model that statistically tracks the star formation histories and quenching of central galaxies. Comparing this model to the distribution of central galaxy star formation rates in a group catalog of the SDSS Data Release 7, we constrain the timescales over which physical processes cease star formation in central galaxies. Over the stellar mass range 10{sup 9.5}--10{sup 11}M{sub ⊙} we infer quenching e-folding times that span 1.5–0.5 Gyr with more massive central galaxies quenching faster. For M{sub ∗}=10{sup 10.5}M{sub ⊙}, this implies a total migration time of ∼4 Gyr from the star formation main sequence to quiescence. Compared to satellites, central galaxies take ∼2 Gyr longer to quench their star formation, suggesting that different mechanisms are responsible for quenching centrals versus satellites. Finally, the central galaxy quenching timescale we infer provides key constraints for proposed star formation quenching mechanisms. Our timescale is generally consistent with gas depletion timescales predicted by quenching through strangulation. However, the exact physical mechanism(s) responsible for this remain unclear.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Thermodynamics of order and randomness in dopant distributions inferred from atomically resolved imaging

Abstract Exploration of structure-property relationships as a function of dopant concentration is commonly based on mean field theories for solid solutions. However, such theories that work well for semiconductors tend to fail in materials with strong correlations, either in electronic behavior or chemical segregation. In these cases, the details of atomic arrangements are generally not explored and analyzed. The knowledge of the generative physics and chemistry of the material can obviate this problem, since defect configuration libraries as stochastic representation of atomic level structures can be generated, or parameters of mesoscopic thermodynamic models can be derived. To obtain such information for improved predictions, we use data from atomically resolved microscopic images that visualize complex structural correlations within the system and translate them into statistical mechanical models of structure formation. Given the significant uncertainties about the microscopic aspects of the material’s processing history along with the limited number of available images, we combine model optimization techniques with the principles of statistical hypothesis testing. We demonstrate the approach on data from a series of atomically-resolved scanning transmission electron microscopy images of Mo x Re 1- x S 2 at varying ratios of Mo/Re stoichiometries, for which we propose an effective interaction model that is then used to generate atomic configurations and make testable predictions at a range of concentrations and formation temperatures.

25 ENERGY STORAGE↗
DOE OSTI2022

Solvation Thermodynamics of Solutes in Water and Ionic Liquids Using the Multiscale Solvation-Layer Interface Condition Continuum Model

We report molecular assembly processes are generally driven by thermodynamic properties in solutions. Atomistic modeling can be very helpful in designing and understanding complex systems, except that bulk solvent is very inefficient to treat explicitly as discrete molecules. The SLIC/CDC multiscale model combines continuum solvent electrostatics based on the solvent layer interface condition (SLIC) with statistical thermodynamic models for hydrogen bonding and nonpolar modes: cavity formation, dispersion interactions, combinatorial mixing (CDC). The SLIC/CDC model predicts Gibbs energies of solvation for a database of 500 solutes in water with average accuracy better than 1 kcal/mol both for experimental measurements and for explicit-solvent molecular dynamics simulations. The separate SLIC/CDC energy mode values agree quantitatively with those computed from explicit-solvent molecular dynamics. The SLIC/SASA multiscale model combines the SLIC continuum electrostatic model with the solvent accessible surface area (SASA) nonpolar energy mode. The SLIC/SASA model predicts Gibbs energies of solvation with better than 1.4 kcal/mol average accuracy in aqueous systems and better than 1.6 kcal/mol average accuracy in ionic liquids. Both models predict solvation entropies, and are the first implicit-solvation models capable of predicting solvation heat capacities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Random insights into the complexity of two-dimensional tensor network calculations

Projected entangled pair states (PEPS) offer memory-efficient representations of some quantum many-body states that obey an entanglement area law and are the basis for classical simulations of ground states in two-dimensional (2d) condensed matter systems. However, rigorous results show that exactly computing observables from a 2d PEPS state is generically a computationally hard problem. Yet approximation schemes for computing properties of 2d PEPS are regularly used, and empirically seen to succeed, for a large subclass of (“not too entangled”) condensed matter ground states. Adopting the philosophy of random matrix theory, in this work, we analyze the complexity of approximately contracting a 2d random PEPS by exploiting an analytic mapping to an effective replicated statistical mechanics model that permits a controlled analysis at a large bond dimension. Through this statistical-mechanics lens, we argue that (i) although approximately sampling wave-function amplitudes of random PEPS faces a computational-complexity phase transition above a critical bond dimension, and (ii) one can generically efficiently estimate the norm and correlation functions for any finite bond dimension. Furthermore, these results are supported numerically for various bond-dimension regimes. It is an important open question whether the above results for random PEPS apply more generally also to PEPS representing physically relevant ground states.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Modeling Crack Propagation in Polycrystalline Microstructure Using Variational Multiscale Method

Crack propagation in a polycrystalline microstructure is analyzed using a novel multiscale model. The model includes an explicit microstructural representation at critical regions (stress concentrators such as notches and cracks) and a reduced order model that statistically captures the microstructure at regions far away from stress concentrations. Crack propagation is modeled in these critical regions using the variational multiscale method. In this approach, a discontinuous displacement field is added to elements that exceed the critical values of normal or tangential tractions during loading. Compared to traditional cohesive zone modeling approaches, the method does not require the use of any special interface elements in the microstructure and thus can model arbitrary crack paths. The capability of the method in predicting both intergranular and transgranular failure modes in an elastoplastic polycrystal is demonstrated under tensile and three-point bending loads.

36 MATERIALS SCIENCE↗