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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Photoelectric absorption cross section of silicon near the bandgap from room temperature to sub-Kelvin temperature

The use of cryogenic silicon as a detector medium for dark matter searches is gaining popularity. Many of these searches are highly dependent on the value of the photoelectric absorption cross section of silicon at low temperatures, particularly near the silicon bandgap energy, where the searches are most sensitive to low mass dark matter candidates. While such cross section data have been lacking from the literature, previous dark matter search experiments have attempted to estimate this parameter by extrapolating it from higher temperature data. However, discrepancies in the high temperature data have led to order-of-magnitude differences in the extrapolations. In this paper, we resolve these discrepancies by using a novel technique to make a direct, low temperature measurement of the photoelectric absorption cross section of silicon at energies near the bandgap (1.2 eV–2.8 eV).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Crystal plasticity based constitutive modeling of ZEK100 magnesium alloy combined with in-situ HEXRD experiments

In the current works, both micro- and macro-mechanical properties of a hexagonal close-packed (HCP) polycrystalline ZEK100 magnesium alloy were investigated. In the experimental perspective, in-situ high energy X-ray diffraction (HEXRD) from a synchrotron source was conducted during the uniaxial tension along the rolling direction (RD) and the transverse direction (TD) to measure the lattice strain evolutions and stress-strain behaviors. In the modeling perspective, crystal plasticity finite element (CPFE) model was developed incorporating the deformation twinning for the HCP-structured metals. The HEXRD experiments and crystal plasticity models were then coupled to characterize the constitutive behaviors of the ZEK100 alloy. The lattice strain data representing the microscopic behavior of the material and the macroscopic stress-strain behavior were then tied together as objective values to estimate the critical resolved shear stress (CRSS) and hardening parameters of available slip and twin systems of the ZEK100 alloy using the developed CPFE model. The stress-strain behavior as well as the lattice strain variation during the uniaxial tension tests are presented using the CPFE model and compared with the actual HEXRD data.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Geothermal play fairway analysis, part 1: Example from the Snake River Plain, Idaho

The Snake River Plain (SRP) volcanic province overlies the track of the Yellowstone hotspot, a thermal anomaly that extends deep into the mantle. Most of the area is underlain by a basaltic volcanic province that overlies a mid-crustal intrusive complex, which in turn provides the long-term heat flux needed to sustain geothermal systems. Previous studies have identified several known geothermal resource areas within the SRP. For the geothermal study presented herein, our goals were to: (1) adapt the methodology of Play Fairway Analysis (PFA) for geothermal exploration to create a formal basis for its application to geothermal systems, (2) assemble relevant data for the SRP from publicly available and private sources, and (3) build a geothermal PFA model for the SRP and identify the most promising plays, using GIS-based software tools that are standard in the petroleum industry. The study focused on identifying three critical resource parameters for exploitable hydrothermal systems in the SRP: heat source, reservoir and recharge permeability, and cap or seal. Data included in the compilation for heat source were heat flow, distribution and ages of volcanic vents, groundwater temperatures, thermal springs and wells, helium isotope anomalies, and reservoir temperatures estimated using geothermometry. Reservoir and recharge permeability was inferred from the analysis of stress orientations and magnitudes, post-Miocene faults, and subsurface structural lineaments based on magnetics and gravity data. Data for cap or seal included the distribution of impermeable lake sediments and clay-seal associated with hydrothermal alteration below the regional aquifer. These data were used to compile Common Risk Segment maps for heat, permeability, and seal, which were combined to create a Composite Common Risk Segment map for all southern Idaho that reflects the risk associated with geothermal resource exploration and identifies favorable resource tracks. Our regional assessment indicated that undiscovered geothermal resources may be located in several areas of the SRP. Two of these areas, the western SRP and Camas Prairie, were selected for more detailed assessment, during which heat, permeability, and seal were evaluated using newly collected field data and smaller grid parameters to refine the location of potential resources. These higher resolution assessments illustrate the flexibility of our approach over a range of scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Developing a New Model to Estimate the Costs to Decommission DOE's Active Facilities - 16368

The Active Facilities Data Collection System (AFDCS) is a major means the U.S. Department of Energy (DOE or Department) uses to estimate its out-year environmental liability for the active and surplus facilities across the Department. The previous AFDCS cost model was based on cost assumptions that date back to the mid-1990s. The model is being replaced with a model that reflects more recent facility disposition experience and costs. DOE's Office of Environmental Management's (DOE-EM) historical cost collection database provides the basis for the new AFDCS cost model. This paper describes the process that was used to update the AFDCS facility data inputs, analyze this data, and formulate historical costs and parameters. This process created a robust and reliable cost model that will provide DOE with a valid estimate of the facilities' disposition costs. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Automated correlative segmentation of large Transmission X-ray Microscopy (TXM) tomograms using deep learning

Highlights: • A unique automated segmentation approach for large 3D nanotomography datasets obtained by Transmission X-ray Microscopy (TXM) in an Al-Cu alloy. • Automated segmentation using a Convolutional Neural Network (CNN) architecture based on a deep learning approach was employed. • This extremely versatile technique is capable of emulating the manual segmentation process effectively. • Quantitative comparison between manual segmentation and CNN architecture established the accuracy of this automated technique. • Its ability to robustly process ultra-large volumes of data in relatively small time frames can exponentially accelerate tomographic data analysis. - Abstract: A unique correlative approach for automated segmentation of large 3D nanotomography datasets obtained using Transmission X-ray Microscopy (TXM) in an Al-Cu alloy has been introduced. Automated segmentation using a Convolutional Neural Network (CNN) architecture based on a deep learning approach was employed. This extremely versatile technique is capable of emulating the manual segmentation process effectively. Coupling this technique with post-scanning SEM imaging ensured precise estimation of 3D morphological parameters from nanotomography. The segmentation process as well as subsequent analysis was expedited by several orders of magnitude. Quantitative comparison between segmentation performed manually and using the CNN architecture established the accuracy of this automated technique. Its ability to robustly process ultra-large volumes of data in relatively small time frames can exponentially accelerate tomographic data analysis, possibly opening up novel avenues for performing 4D characterization experiments with finer time steps.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Effect of solutes on the lattice parameters and elastic stiffness coefficients of body-centered tetragonal Fe

In this study, we compute changes in the lattice parameters and elastic stiffness coefficients C ij of body-centered tetragonal (bct) Fe due to Al, B, C, Cu, Mn, Si, and N solutes. Solute strain misfit tensors determine changes in the lattice parameters as well as strain contributions to the changes in the C ij . We also compute chemical contributions to the changes in the C ij , and show that the sum of the strain and chemical contributions agree with more computationally expensive direct calculations that simultaneously incorporate both contributions. Octahedral interstitial solutes, with C being the most important addition in steels, must be present to stabilize the bct phase over the body-centered cubic phase. We therefore compute the effects of interactions between interstitial C solutes and substitutional solutes on the bct lattice parameters and C ij for all possible solute configurations in the dilute limit, and thermally average the results to obtain effective changes in properties due to each solute. Finally, the computed data can be used to estimate solute-induced changes in mechanical properties such as strength and ductility, and can be directly incorporated into mesoscale simulations of multiphase steels to model solute effects on the bct martensite phase.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Constraining the time evolution of dark energy, curvature and neutrino properties with cosmic chronometers

We use the latest compilation of observational Hubble parameter measurements estimated with the differential evolution of cosmic chronometers , in the redshift range 0< z <2, to place constraints on cosmological parameters. We used a Markov-Chain Monte-Carlo approach to sample the parameter space for the cosmic chronometers dataset alone and in combination with other state-of-the art cosmological measurements: CMB data from the latest Planck 2015 release, the most recent estimate of the Hubble constant H {sub 0}, a compilation of recent baryon acoustic oscillation data, and the latest type Ia cosmological supernovae sample. From late-Universe probes alone ( z <2) we find that w {sub 0} = −0.9 ± 0.18 and w {sub a} = −0.5 ± 1.7, and when combining also Planck 2015 data we obtain w {sub 0}=−0.98± 0.11 and w {sub a} =−0.30±0.4. These new constraints imply that nearly all quintessence models are disfavoured by the data; only phantom models or a pure cosmological constant are favoured. This is a remarkable finding as it imposes severe constraints on the nature of dark energy. For the curvature our constraints are Ω {sub k} = 0.003 ± 0.003, considering also CMB data. We also find that H ( z ) data from cosmic chronometers are important to constrain parameters that do no affect directly the expansion history, by breaking or reducing degeneracies with other parameters. We find that N {sub eff} = 3.17 ± 0.15, thus excluding the possibility of an extra (sterile) neutrino at more than 5 σ, and put competitive limits on the sum of neutrino masses, Σ m {sub ν}< 0.27 eV at 95% confidence level. Finally, we constrain the redshift evolution of dark energy by exploring separately the early and late-Universe, and find a dark energy equation of state evolution w ( z ) consistent with that in the ΛCDM model at the ± 0.4 level over the entire redshift range 0 < z < 2.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Measurement of absolute myocardial blood flow in humans using dynamic cardiac SPECT and 99m Tc-tetrofosmin: Method and validation

The objective of this study was to measure myocardial blood flow (MBF) in humans using 99m Tc-tetrofosmin and dynamic single-photon emission computed tomography (SPECT). Dynamic SPECT using 99m Tc-tetrofosmin and dynamic positron emission tomography (PET) was performed on a group of 16 patients. The SPECT data were reconstructed using a 4D-spatiotemporal iterative reconstruction method. The data corresponding to 9 patients were used to determine the flow-extraction curve for 99m Tc-tefrofosmin while data from the remaining 7 patients were used for method validation. The nonlinear tracer correction parameters A and B for 99m Tc-tefrofosmin were estimated for the 9 patients by fitting the flow-extraction curve K 1 = F(1–Aexp(–B/F)) for K 1 values estimated with 99m Tc-tefrofosmin using SPECT and MBF values estimated with 13 N-NH 3 using PET. These parameters were then used to calculate MBF and coronary flow reserve (CFR) in three coronary territories (LAD, RCA, and LCX) using SPECT for an independent cohort of 7 patients. The results were then compared with that estimated with 13 N-NH 3 PET. The flow-dependent permeability surface-area product (PS) for 99m Tc-tefrofosmin was also estimated. The estimated flow-extraction parameters for 99m Tc-tefrofosmin were found to be A = 0.91 ± 0.11, B = 0.34 ± 0.20 (R 2 = 0.49). The range of MBF in LAD, RCA, and LCX was 0.44-3.81 mL/min/g. The MBF between PET and SPECT in the group of independent cohort of 7 patients showed statistically significant correlation, r = 0.71 (P < .001). However, the corresponding CFR correlation was moderate r = 0.39 yet statistically significant (P = .037). The PS for 99m Tc-tefrofosmin was (0.019 ± 0.10)*MBF + (0.32 ± 0.16). Dynamic cardiac SPECT using 99m Tc-tetrofosmin and a clinical two-headed SPECT/CT scanner can be a useful tool for estimation of MBF.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Distributed database kriging for adaptive sampling (D²KAS)

We present an adaptive sampling method supplemented by a distributed database and a prediction method for multiscale simulations using the Heterogeneous Multiscale Method. A finite-volume scheme integrates the macro-scale conservation laws for elastodynamics, which are closed by momentum and energy fluxes evaluated at the micro-scale. In the original approach, molecular dynamics (MD) simulations are launched for every macro-scale volume element. Our adaptive sampling scheme replaces a large fraction of costly micro-scale MD simulations with fast table lookup and prediction. The cloud database Redis provides the plain table lookup, and with locality aware hashing we gather input data for our prediction scheme. For the latter we use kriging, which estimates an unknown value and its uncertainty (error) at a specific location in parameter space by using weighted averages of the neighboring points. We find that our adaptive scheme significantly improves simulation performance by a factor of 2.5 to 25, while retaining high accuracy for various choices of the algorithm parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2020

An a priori evaluation of a principal component and artificial neural network based combustion model in diesel engine conditions

A principal component analysis (PCA) and artificial neural network (ANN) based chemistry tabulation approach is presented. ANNs are used to map the thermochemical state onto a low-dimensional manifold consisting of five control variables that have been identified using PCA. Three canonical configurations are considered to train the PCA-ANN model: a series of homogeneous reactors, a nonpremixed flamelet, and a two-dimensional lifted flame. The performance of the model in predicting the thermochemical manifold of a spatially-developing turbulent jet flame in diesel engine thermochemical conditions is a priori evaluated using direct numerical simulation (DNS) data. The PCA-ANN approach is compared with a conventional tabulation approach (tabulation using ad hoc defined control variables and linear interpolation). The PCA-ANN model provides higher accuracy and requires several orders of magnitude less memory. Here, these observations indicate that the PCA-ANN model is superior for chemistry tabulation, especially for modelling complex chemistries that present multiple combustion modes as observed in diesel combustion. The performance of the PCA-ANN model is then compared to the optimal estimator, i.e. the conditional mean from the DNS. The results indicate that the PCA-ANN model gives high prediction accuracy, comparable to the optimal estimator, especially for major species and the thermophysical properties. Higher errors are observed for the minor species and reaction rate predictions when compared to the optimal estimator. It is shown that the prediction of minor species and reaction rates can be improved by using training data that exhibits a variation of parameters as observed in the turbulent flame. The output of the ANN is analysed to assess mass conservation. It is observed that the ANN incurs a mean absolute error of 0.05% in mass conservation. Furthermore, it is demonstrated that this error can be reduced by modifying the cost function of the ANN to penalise for deviation from mass conservation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

False vacuum energy dominated inflation with large r and the importance of κ{sub s}

We investigate to which extent and under which circumstances false vacuum energy (V{sub 0}) dominated slow-roll inflation is compatible with a large tensor-to-scalar ratio r = O(0.1), as indicated by the recent BICEP2 measurement. With V{sub 0} we refer to a constant contribution to the inflaton potential, present before a phase transition takes place and absent in the true vacuum of the theory, like e.g. in hybrid inflation. Based on model-independent considerations, we derive an upper bound on the possible amount of V{sub 0} domination and highlight the importance of higher-order runnings of the scalar spectral index (beyond α{sub s}) in order to realise scenarios of V{sub 0} dominated inflation. We study the conditions for V{sub 0} domination explicitly with an inflaton potential reconstruction around the inflaton field value 50 e-folds before the end of inflation, taking into account the present observational data. To this end, we provide the up-to-date parameter constraints within ΛCDM + r + α{sub s} + κ{sub s} using the cosmological parameter estimation code Monte Python together with the Boltzmann code CLASS.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Joint Estimation of Behind-the-Meter Solar Generation in a Community

Distribution grid planning, control, and optimization require accurate estimation of solar photovoltaic (PV) generation and electric load in the system. Most of the small residential solar PV systems are installed behind-the-meter making only the net load readings available to the utilities. This paper presents an unsupervised framework for joint disaggregation of the net load readings of a group of customers into the solar PV generation and electric load. Our algorithm synergistically combines a physical PV system performance model for individual solar PV generation estimation with a statistical model for joint load estimation. The electric loads for a group of customers are estimated jointly by a mixed hidden Markov model (MHMM) which enables modeling the general load consumption behavior present in all customers while acknowledging the individual differences. At the same time, the model can capture the change in load patterns over a time period by the hidden Markov states. The proposed algorithm is also capable of estimating the key technical parameters of the solar PV systems. Our proposed method is evaluated using the net load, electric load, and solar PV generation data gathered from residential customers located in Austin, Texas. Testing results show that our proposed method reduces the mean squared error of state-of-the-art net-load disaggregation algorithms by 67%.

behind-the-meter solar generation↗
DOE OSTI2015

A New Approach to Reduce Uncertainties in Space Radiation Cancer Risk Predictions

The prediction of space radiation induced cancer risk carries large uncertainties with two of the largest uncertainties being radiation quality and dose-rate effects. In risk models the ratio of the quality factor (QF) to the dose and dose-rate reduction effectiveness factor (DDREF) parameter is used to scale organ doses for cosmic ray proton and high charge and energy (HZE) particles to a hazard rate for γ-rays derived from human epidemiology data. In previous work, particle track structure concepts were used to formulate a space radiation QF function that is dependent on particle charge number Z, and kinetic energy per atomic mass unit, E. QF uncertainties where represented by subjective probability distribution functions (PDF) for the three QF parameters that described its maximum value and shape parameters for Z and E dependences. Here I report on an analysis of a maximum QF parameter and its uncertainty using mouse tumor induction data. Because experimental data for risks at low doses of γ-rays are highly uncertain which impacts estimates of maximum values of relative biological effectiveness (RBE max ), I developed an alternate QF model, denoted QF γAcute where QFs are defined relative to higher acute γ-ray doses (0.5 to 3 Gy). The alternate model reduces the dependence of risk projections on the DDREF, however a DDREF is still needed for risk estimates for high-energy protons and other primary or secondary sparsely ionizing space radiation components. Risk projections (upper confidence levels (CL)) for space missions show a reduction of about 40% (CL~50%) using the QF γAcute model compared the QFs based on RBE max and about 25% (CL~35%) compared to previous estimates. In addition, I discuss how a possible qualitative difference leading to increased tumor lethality for HZE particles compared to low LET radiation and background tumors remains a large uncertainty in risk estimates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

RHIC polarization for Runs 9-17

This note describes the RHIC polarization measurements for use by the collider experiments for the Run91 and later polarized proton running periods. The measurement procedure is outlined and the resulting polarization parameters are defined. The systematic uncertainties for each step of the procedure are discussed and estimated; when possible the uncertainties are evaluated using the present data. Finally the use of the provided results to determine mean polarization and uncertainty for a data set is described. The results used for this are compiled on the web pages linked at https://wiki.bnl.gov/rhicspin/Results; there, for each year the results are at the link 'Results'.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Efficient Generalizable Deep Learning

Stochastic optimization is a fundamental field of research for machine learning. Stochastic gradient descent (SGD) and related methods provide a feasible means to train complicated prediction models over large datasets. SGD, however, does not explicitly address the problem of overfitting, which can lead to predictions that perform poorly on new data. This difference between loss performance on unseen testing data verses that of training data defines the generalization gap of a model. We introduce a new computational kernel called Stochastic Hessian Projection (SHP) that uses a maximum likelihood framework to simultaneously estimate gradient noise covariance and local curvature of the loss function. Our analysis illustrates that these quantities affect the evolution of parameter uncertainty and therefore generalizability. We show how these computations allow us to predict the generalization gap without requiring holdout data. Explicitly assessing this metric for generalizability during training may improve machine learning predictions when data is scarce and understanding prediction variability is critical.

42 ENGINEERING↗
DOE OSTI2023

Assessing the nexus between groundwater and solar energy plants in a desert basin with a dual-model approach under uncertainty

Globally, many solar power plants and other types of renewable energy are being located in water-scarce regions. Many projects rely on groundwater resources whose sustainability is uncertain. In the Chuckwalla Basin in California, quantification of recharge and trans-valley underflow is needed to estimate the impacts of solar project withdrawals on the water table. However, such estimates are highly challenging due to data scarcity, heterogeneous soils and long residence times. Conventional assessment employs isolated groundwater models configured with crude and uniform estimates of recharge. Here, we employ a data-constrained surface subsurface processes model, PAWS+CLM, to provide an ensemble of recharges and underflows with perturbed parameters. Then, the Parameter Estimation (PEST) package is used to calibrate MODFLOW aquifer conductivity and filter out implausible recharges. The novel dual-model approach, potentially applicable in other arid regions, can effectively assimilate groundwater head observations, reject unrealistic parameters, and narrow the range of estimated drawdowns. Simulated recharge concentrates along alluvial fans at the mountain fronts and ephemeral washes where run-off water infiltrates. If an evenly distributed recharge was assumed, it resulted in under-estimated drawdown and larger uncertainty bounds. The withdrawals are approaching total inflow, suggesting the system will be nearing, if not exceeding, its sustainable groundwater production capacity, and a boom of such projects will not be sustainable. Especially, the cost/benefit of pumped-storage projects is called into question as the initial-fill phase depletes entire area’s recharge. Our study highlights the stress on groundwater resources of solar development, and that the speed of groundwater recovery does not indicate sustainability. Main point 1: A novel dual model approach, involving an integrated surface/subsurface model and a groundwater parameter-estimation model, was able to better constrain the model. Main point 2: The groundwater system may be nearing, if not exceeding, its sustainable groundwater production capacity and the speed of recovery is not indicative of sustainability. Main point 3: Results from using conventionally-assumed uniform recharge distort calibrated K fields and impacts assessment

14 SOLAR ENERGY↗
DOE OSTI2019

Equation Discovery Using Fast Function Extraction: a Deterministic Symbolic Regression Approach

Advances in machine learning (ML) coupled with increased computational power have enabled identification of patterns in data extracted from complex systems. ML algorithms are actively being sought in recovering physical models or mathematical equations from data. This is a highly valuable technique where models cannot be built using physical reasoning alone. In this paper, we investigate the application of fast function extraction (FFX), a fast, scalable, deterministic symbolic regression algorithm to recover partial differential equations (PDEs). FFX identifies active bases among a huge set of candidate basis functions and their corresponding coefficients from recorded snapshot data. This approach uses a sparsity-promoting technique from compressive sensing and sparse optimization called pathwise regularized learning to perform feature selection and parameter estimation. Furthermore, it recovers several models of varying complexity (number of basis terms). FFX finally filters out many identified models using non-dominated sorting and forms a Pareto front consisting of optimal models with respect to minimizing complexity and test accuracy. Numerical experiments are carried out to recover several ubiquitous PDEs such as wave and heat equations among linear PDEs and Burgers, Korteweg–de Vries (KdV), and Kawahara equations among higher-order nonlinear PDEs. Additional simulations are conducted on the same PDEs under noisy conditions to test the robustness of the proposed approach.

42 ENGINEERING↗
DOE OSTI2021

A Rate Theory Model of Radiation-Induced Swelling in an Austenitic Stainless Steel

Many rate theory models of cavity (void) swelling have been published over the past 50 years, all having the same, or similar, structures. A rigorous validation of the models has not been possible because of the dearth of information concerning the microstructures that correspond with the swelling data. Whereas the lack of microstructure information is still an issue for historical swelling data, in the past 10–20 years data have been published on the evolution of the microstructure (point defect yields from collision cascades, cavity number densities, and dislocation densities/yield strengths) allowing certain gaps in information to be filled when considering historic swelling data. With reasonable estimates of key microstructure parameters, a standard rate theory model can be applied, and the model parameter space explored, in connection with historical swelling data. By using published data on: (i) yield strength as a function of dose and temperature (to establish an empirical expression for dislocation density evolution); (ii) cavity number densities as a function of temperature; and (iii) freely migrating defect (FMD) production as a function of primary knock-on atom (PKA) spectrum, the necessary parameter and microstructure inputs that were previously unknown can be used in model development. This paper describes a rate-theory model for void swelling of 316 stainless steel irradiated in the EBR-2 reactor as a function of irradiation temperature and neutron dose.

36 MATERIALS SCIENCE↗