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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

A New Data Analysis Technique for Determining Mix Parameters from HED Experiments

In designing, analyzing, and understanding high-energy-density (HED) physics experiments, including inertial confinement fusion (ICF) experiments, we must contend with the extension of hydrodynamics to new regimes where additional physics, such as radiation transport and nuclear reactions, become important and coupled to the dynamics. Our most sophisticated models and understanding are based in classical, low-energy physics, since that is what we have the easiest access to creating and diagnosing. To assess our extrapolation of that understanding to the HED regime will require comparable precision in experiments and measurements. This is particularly the important in the case of hydrodynamic turbulence — most experiments are designed with one or two dimensions of symmetry, but at small scales hydrodynamics is generically observed to crumple into a fine structure of apparently random, three-dimensional eddies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

The Transportation Energy and Mobility Pathway Options (TEMPO) Model: Overview and Validation of V1.0 [Slides]

This presentation documents the approaches and methods used in the Transportation Energy & Mobility Pathway Options™ (TEMPO) model to evaluate passenger and freight demand for transportation and mobility services, project vehicle ownership and technology adoption decisions, and determine transport mode choices to derive scenarios of future energy use and emissions. TEMPO is an all-inclusive transportation demand model that covers the entire United States, with an implicit spatial resolution and an hourly temporal resolution that allows for generating time-resolved energy use profiles to assess multisectoral integration aspects. Key features of the TEMPO model include the ability to perform endogenous out-of-sample forecasting to extrapolate recent emerging trends and analyze impacts of disruptive technological breakthroughs and behavioral changes. TEMPO employs an innovative representation of passenger mobility demand stemming from household-level decisions that determine vehicle adoption, ownership, and use based on sociodemographics (e.g., income, household composition), technology attributes (e.g., travel cost, time), geography (e.g., urban, suburban, rural) and population-specific multiday mobility and travel requirements. This representation enables a more forward-looking perspective on the use of new mobility options and the adoption of alternative fuel vehicles, as well as a more accurate representation of their energy usage profiles than previous modeling approaches. A comparison with the U.S. Energy Information Administration’s Annual Energy Outlook showcases the ability of TEMPO to accurately replicate widely accepted projections by representing the key elements of the entire transportation sector at the appropriate level of resolution. TEMPO is intended to generate future scenarios of technology adoption, energy use, and emissions in the transportation sector to compare alternatives, inform decision makers, and assess integration with energy infrastructure and supply systems at an appropriate spatiotemporal resolution.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

A consistent, Bayesian, approach to the cross section probability distribution in the unresolved resonance region

The cross sections of neutron-induced reactions can be divided into three energy ranges: the resolved resonance region (RRR), the unresolved resonance region (URR), and the fast region. In general, the cross sections in the URR show significant fluctuations that cannot be predicted and cannot be experimentally resolved, thus, it is commonly assumed that the cross section at a specific energy is given by a probability distribution function (PDF) over a range of values that can span several orders of magnitude. The current methodology used to describe such behavior is to construct the PDF by stochastically generating resonance ladders and numerically measuring the PDF. The resonance ladders are sampled using known resonance statistical properties and average resonance widths and spacings extrapolated from the RRR. Although this is a standard and widely used technique, it is computationally very expensive, therefore, an alternative, analytical, approach would be preferable due to the considerable speed up of the computational time in real life applications. Moreover, the current methodology does not take into account existing experimental data, such for total and capture cross sections, that are available for many nuclei. Finally, this approach was developed to be used in reactor-scale applications and it is not suited for use in single-event applications. In this work we will rethink the entire approach to the PDF construction using a Bayesian mindset. This will allow us to provide a different definition of the PDF that allows a much faster calculation of the higher-temperature PDFs and a proper combination of theoretical and experimental PDFs following the probability theory. We will also show that our definition is well suited for single-event applications and we will make an explicit connection between our method and the standard approach. We do this by showing that the central limit theorem applies and our method leads to the same PDF obtained with the standard methodology, for a large number of events per history.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Resolution of Tolerance Variations

The connector/adapter assembly (CAA) is one component of a higher-level assembly. A simplified representation of the CAA is illustrated in Figure 1. The location of each CAA is required to meet a specific positional requirement. Several CAAs have been measured and found to be out of tolerance. The connector and the adapter are welded to each other such that the cylindrical axes of the two components are coaxial. The positional tolerance condition requires that all points on the outer surface of the CAA must be within a defined maximum diameter, as illustrated in Figure 1. Two different methods have been developed to measure the CAA positions from coordinate measuring machine (CMM) data. One method (RevD) extrapolates the data from two circles approximately midway from the weld region to each end. The second method (RevF) measures points that are much closer to the actual ends.

42 ENGINEERING↗
DOE OSTI2021

Update to the Finite Cloud Dose Correction Factors in MACCS

In WASH - 1400, external exposure from the finite radioactive cloud (cloudshine) is calculated by assuming that the cloud is semi-infinite, the concentration of radioactive material is uniform, and by using a correction factor to account for these approximations. This correction factor is originally based upon formulations by Healy and depends on the effective size of the plume and the distance from the plume center to the receptor. The range of the finite cloud dose correction factor table from WASH - 1400 developed using Healy formulations can be exceeded in certain situations. When the range of the table is exceeded, no extrapolation is performed; rather interpolation at the edge of the table is performed per WASH - 1400. The tabulated values of these finite cloud dose correction factors from WASH - 1400 and the interpolation at the edge of the table have been used in MACCS since its creation. An expanded table of finite cloud dose correction factors is one way to reduce the need of using interpolation at the edge of the table. The generation of an expanded finite cloud dose correction factor table for future use in MACCS is documented in this report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Characterization of CST Post-Processing AP-105 Hanford Tank Waste

The primary goal of the Tank Side Cesium Removal (TSCR) system, under development by Washington River Protection Solutions, LLC (WRPS), is to remove entrained solids and 137 Cs from the Hanford tank waste supernate to expedite production of low-activity waste. Ion exchange (IX) testing of 10.9 L of waste from Hanford tank 241-AP-105 (AP-105), performed by Pacific Northwest National Laboratory, used a lead-lag-polish column format, with a bed volume of 10 mL per column, to decontaminate tank waste supernate using crystalline silicotitanate (CST) as the IX media. The AP-105 Cs IX processing test, discussed elsewhere, resulted in a shorter transition zone (i.e., steeper load curve) than those defined by wastes from tanks 241-AP-107 and 241-AW-102.1 The shorter transition zone was indicative of a matrix effect retarding Cs capacity. Therefore, aliquots of spent CST from the lead, lag, and polish columns were subjected to a digestion protocol to quantify analytes retained by the CST and extrapolate the impact on Cs capacity. The spent CST was digested using a combination of 5 M HNO 3 and H 2 O 2 with vigorous heating and stirring. Due to the radiation dose accompanying the 137 Cs on the CST columns, a secondary Cs separation by ammonium molybdophosphate embedded in polyacrylonitrile (AMP-PAN) was performed to separate the 137 Cs from the CST so the samples could be contact-handled for analysis outside of a shielded facility.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Subcritical Californium Source Drive Noise Analysis Measurements With Unreflected Uranium (93.15) Hydride

On July 6, 1989, subcritical californium source-driven noise analysis (CSDNA) measurements were performed with bare uranium hydride cylindrical assemblies at the Los Alamos National Laboratory Critical Experiments Facility in KIVA 1. Three configurations of ~7.5 cm radius-enriched (93.15 wt. % 235 U) uranium hydride with a density of ~10 g/cm 3 cylinders were assembled with uranium hydride heights of ~11, ~14, and ~16 cm. The uranium hydride (~2 and ~3 cm high) was in thin-welded stainless steel cans. The neutron multiplication factors obtained on-line from the measured ratios of spectral densities were 0.942 ± 0.002, 0.917 ± 0.003, and 0.867 ± 0.004 for uranium hydride heights of ~16, ~14, and ~11 cm., respectively. Neutron multiplication factors for these three configurations calculated by Los Alamos National Laboratory Monte Carlo methods were 0.952 ± 0.005, 0.922 ± 0.005, and 0.860 ± 0.005, all of which are in good agreement with the measurements. The break frequency noise analysis data could be fitted to obtain the prompt neutron decay constant at all subcritical states. Some data presented in this report are from notes that are not in the logbooks. A discussion of the two different point kinetics theories of the measurements that illustrated the deficiencies of the rigorous theory at low neutron multiplication factors (k eff < 0.80) is given in an Appendix. These measurements may be the only configuration of unreflected, highly enriched uranium hydride configuration assembled. The presence of hydrogen diluted the uranium density, decreasing the reactivity, but this is offset by slowing down the neutron energy to the energy ranges of higher-fission cross sections. Extrapolations to delayed critical indicate that adding one more 3 cm can and one more 2 cm can would result in the unreflected cylindrical system being slightly above critical with a critical mass of ~37 kg, which is considerably less than the critical mass of an unreflected and unmoderated highly enriched uranium metal sphere. An unreflected, highly enriched uranium hydride sphere would have a critical mass less than 37 kg.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Reducing roughness and improving efficiency of MAPbI3 perovskite solar cells made by high-throughput photonic curing

For perovskite solar cells (PSCs) to be commercially viable, the slow and energy-insufficient thermal annealing step must be eliminated. Among the photo-irradiation methods proposed to replace thermal annealing, photonic curing is the fastest conversion method. Photonic curing delivers short (20 μs to 100 ms) but intense light pulses from a broadband (200-1500 nm) xenon flash lamp, making it the only method to convert perovskite under 20 ms. This processing time can be extrapolated to a roll-to-roll web speed of 40 m/min based on laboratory processing conditions. However, most reported PSCs made by photonic curing under 1 second have inferior performance (~10% PCE). Although SEM images show dense and pinhole-free perovskite films, AFM images indicate secondary wavy features of 500 nm-wide ridge and 80 nm-deep trenches on photonically cured perovskite films, the existence of which correlates with poor device performance. We suggest that this morphology feature is produced by volatile solvent evaporation during the fast photonic curing process. Two approaches have been made to remedy this issue: (1) adding CH2I2 as the third solvent in the conventional DMF-DMSO system and (2) applying a controlled air-blowing step before photonic curing to remove excess solvent further. Combining these two approaches produces photonically- cured perovskite films with a comparable film roughness and device performance. Alkyl halide additives have been reported to enhance PSC performance by modulated solvent-solute interactions and C-X (X = Cl, Br, and I) cleavage. Photonic curing can cleave CH2I2, producing disassociated iodide ions to replenish iodine loss induced by photonic curing, which is confirmed by EDX. As a co-solvent, the high boiling point of CH2I2 can also make the solvent less volatile, reducing surface roughness in photonically cured perovskite films. Additionally, photonically-cured perovskite films have longer PL lifetimes and a higher recombination resistance compared to thermally-annealed counterparts. As a result, we demonstrate that photonic curing is a suitable method to replace thermal annealing in high-throughput PSC fabrication.

14 SOLAR ENERGY↗
DOE OSTI2022

Control of neutral fueling and helium exhaust to NSTX-U plasmas by means of three-dimensional magnetic control fields

Resonant magnetic perturbations are used to stabilize plasma edge instabilities, so-called edge localized modes (ELMs), in high-performance (H-mode) plasmas explored for fusion energy. These ELMs cause repetitive outburst of confined energy and particles and cause cyclic loading of plasma facing components (PFCs). This endangers the integrity of the PFCs and hence limits the lifetime and hence commercial viability of fusion. It was shown that these ELMs can be stabilized by application of small amplitude resonant magnetic perturbation (RMP) fields. This removes the ELMs as edge instabilities but also induces three-dimensional (3D) effects to the plasma boundary, which was formerly toroidally axisymmetric. The impact of this 3D perturbation on the plasma boundary solution needs to be understood to extrapolate the effects to ITER and towards a fusion reactor, if ELM control by RMP fields would be utilized.

3D Boundary Plasmas↗
DOE OSTI2022

Biomaniac

Los Alamos National Laboratory researchers are developing a machine-learning based algorithm to revolutionize how biopolymers are designed and optimized. BioManIAC is a software platform that fills the gaps in data to gain insight into new potential polymer properties without making and testing them. Polymer engineers are able to use BioManIAC software to extrapolate and tune new biopolymer properties that allow the user to create small smart libraries, rather than largescale trial and error testing. This targeted design is expected to improve both time to market and R&D expenditure, as well as the resulting mechanical properties of biopolymers, enabling for more direct competition with traditional petroleum-based polymers. Los Alamos is seeking partners to further validate our software platform.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quantifying Subsurface Biogeochemical Variability in a High Altitude Watershed During Winter Isolation

Shallow subsurface microbial and geochemical processes in watersheds are dynamic and responsive to seasonal and long-term environmental change. At high elevation or latitude, such changes may occur during winter months when normal sampling is impossible, impractical, unsafe, or may actually alter the processes being studied. Yet accurate modeling of biogeochemical parameters in these environments requires sampling that can capture events and persistent cold climate trends. Gaps in current models exist where data have not been collected during extended snow- or ice-covered periods. The goals of our exploratory study were to examine the biogeochemical processes occurring in a well-studied high altitude watershed during winter when the system is largely inaccessible. We tested the hypothesis that during snow cover in the East River (ER) watershed, episodic excursions of microbial community structure and biogeochemical processes and concentrations fluctuated from values extrapolated from pre- and post-snow time periods. Through our research we identified previously unresolved subsurface-surface transport phenomena and a heretofore under-appreciated methane cycle occurring during wintertime months, thus providing valuable insight into watershed processes. Our research showed that autonomous sampling techniques, first used in marine systems, is extremely versatile and can be adapted for use in harsh continental settings to examine temporal changes in freshwater chemistry and microbial community assemblage. Further, we found that by sampling during periods when systems are hard to access, will lend insight and better accountability for global budgets for methane, an important greenhouse gas. By strengthening our knowledge of wintertime biogeochemical relationships in high altitude watersheds our research leads to more complete descriptions of cryptic processes and ultimately will inform and improve the reactive transport models that simulate processes in these systems.

54 ENVIRONMENTAL SCIENCES↗
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↗
DOE OSTI2022

Towards a combination of LHC and TeVatron W-boson mass measurements

In this note methodological and modelling considerations towards a combination of the ATLAS, CDF and D0 measurements of the W -boson mass are discussed. As they were performed at different moments in time, each measurement employed different assumptions for the modelling of W -boson production and decay, as well as different fits of the parton distribution functions of the proton (PDFs). Methods are presented to accurately evaluate the effect of PDFs and other modelling variations on existing measurements, allowing to extrapolate them to any PDF set and to evaluate the corresponding uncertainties. Based on this approach, the measurements can be corrected to a common modelling reference and to the same PDFs, and subsequently combined accounting for PDF correlations in a quantitative way.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Validation of SAS4A/SASSYS-1 for Steady-State Single-Phase Natural Circulation

The development of numerical software for engineering applications requires the validation of the code against experimental benchmark datasets. To support the validation of SAS4A/SASSYS-1 in simulating the single-phase natural circulation within nuclear reactor systems, high-precision steady-state experiments are performed to capture single-phase natural circulation phenomena on an existing scaled facility with comprehensive instrumentation. Forced convection tests are designed precisely capturing the facility’s critical thermal-hydraulic parameters required for one-dimensional modeling, and a comprehensive single-phase natural circulation dataset is obtained with well-documented experimental uncertainty and facility description. Analysis and discussion based on the finalized dataset confirm the dataset’s ability in capturing dominant phenomena and address important physical interpretation of parameters. The dataset reported in this project provides a valuable benchmark resource for the validation of system analysis codes under single-phase natural circulation inside nuclear reactors. Validation of SAS4A/SASSYS-1 is then performed against the obtained benchmark dataset to confirm the capability of its physics model in simulating the steady-state single-phase natural circulation. The experimental facility is modeled in the candidate code. Solution verification is performed using Richardson-extrapolation-based estimators to quantify and restrict numerical errors from discretization. Numerical uncertainty originating from finite maximum pseudo-transient time is also quantified and restricted. Input uncertainty provided by the benchmark dataset is forward propagated through the candidate code, directly quantifying the simulation output in a Monte Carlo approach. The composition of the output uncertainty is further determined by the estimators of Sobol’ indices through a variance-based sensitivity analysis. With uncertainty quantified for each individual condition, detailed comparison between the simulation results and experimental data is performed covering the whole dataset, which shows consistent agreement for all important quantities. The validation activity reported in this project demonstrates that SAS4A/SASSYS-1 can predict the primary parameters with satisfactorily accuracy under steady-state single-phase natural circulation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Accelerating Multiscale Materials Modeling with Machine Learning

The focus of this project is to accelerate and transform the workflow of multiscale materials modeling by developing an integrated toolchain seamlessly combining DFT, SNAP, LAMMPS, (shown in Figure 1-1) and a machine-learning (ML) model that will more efficiently extract information from a smaller set of first-principles calculations. Our ML model enables us to accelerate first-principles data generation by interpolating existing high fidelity data, and extend the simulation scale by extrapolating high fidelity data (10 2 atoms) to the mesoscale (10 4 atoms). It encodes the underlying physics of atomic interactions on the microscopic scale by adapting a variety of ML techniques such as deep neural networks (DNNs), and graph neural networks (GNNs). We developed a new surrogate model for density functional theory using deep neural networks. The developed ML surrogate is demonstrated in a workflow to generate accurate band energies, total energies, and density of the 298K and 933K Aluminum systems. Furthermore, the models can be used to predict the quantities of interest for systems with more number of atoms than the training data set. We have demonstrated that the ML model can be used to compute the quantities of interest for systems with 100,000 Al atoms. When compared with 2000 Al system the new surrogate model is as accurate as DFT, but three orders of magnitude faster. We also explored optimal experimental design techniques to choose the training data and novel Graph Neural Networks to train on smaller data sets. These are promising methods that need to be explored in the future.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Aero-Optics of Hypersonic Turbulent Boundary Layers

Aero-optics refers to optical distortions due to index-of-refraction gradients that are induced by aerodynamic density gradients. At hypersonic flow conditions, the bulk velocity is many times the speed of sound and density gradients may originate from shock waves, compressible turbulent structures, acoustic waves, thermal variations, etc. Due to the combination of these factors, aero-optic distortions are expected to differ from those common to sub-sonic and lower super-sonic speeds. This report summarizes the results from a 2019-2022 Laboratory Directed Research and Development (LDRD) project led by Sandia National Laboratories in collaboration with the University of Notre Dame, New Mexico State University, and the Georgia Institute of Technology. Efforts extended experimental and simulation methodologies for the study of turbulent hypersonic boundary layers. Notable experimental advancements include development of spectral de-aliasing techniques for highspeed wavefront measurements, a Spatially Selective Wavefront Sensor (SSWFS) technique, new experimental data at Mach 8 and 14, a Quadrature Fringe Imaging Interferometer (QFII) technique for time-resolved index-of-refraction measures, and application of QFII to shock-heated air. At the same time, model advancements include aero-optic analysis of several Direct Numerical Simulation (DNS) datasets from Mach 0.5 to 14 and development of wall-modeled Large Eddy Simulation (LES) techniques for aero-optic predictions. At Mach 8 measured and predicted root mean square Optical Path Differences agree within confidence bounds but are higher than semi-empirical trends extrapolated from lower Mach conditions. Overall, results show that aero-optic effects in the hypersonic flow regime are not simple extensions from prior knowledge at lower speeds and instead reflect the added complexity of compressible hypersonic flow physics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Inferring Heat Flow in Laser Absorption Regions using Diagnostic Magnetic Fields

This research was motivated by the desire to obtain a measurement of heat-flow in overdense regions of a laser ablated plasma for the first time using a high energy proton beam. For sufficiently high energies, the proton beam can pass through overdense regions and only be deflected by electric and magnetic fields. As the transport of magnetic fields in a plasma are intrinsically linked to the transport of thermal energy, it was posited that such a measurement could be used to infer a heat-flow. While the project did not prove the method to be infeasible, difficulties with understanding the proton radiographs without an imposed magnetic field made the final aim of this project unreached. Instead, however, a closer understanding of proton radiographs of laser-solid interactions without an imposed field has been obtained. 5 shot days on The OMEGA Laser Facility have been awarded (3 through ICF and 2 as ride-along) to further investigate some of these features. Extrapolating current results to hohlraum conditions has the magnetic energy being a significant (10%) fraction of the laser drive energy, which has the potential to explain the NIF hohlraum drive deficit.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Inferring Heat Flow in Laser Absorption Regions using Diagnostic Magnetic Fields: Abstract

This research was motivated by the desire to obtain a measurement of heat-flow in overdense regions of a laser ablated plasma for the first time using a high energy proton beam. For sufficiently high energies, the proton beam can pass through overdense regions and only be deflected by electric and magnetic fields. As the transport of magnetic fields in a plasma are intrinsically linked to the transport of thermal energy, it was posited that such a measurement could be used to infer a heat-flow. While the project did not prove the method to be infeasible, difficulties with understanding the proton radiographs without an imposed magnetic field made the final aim of this project unreached. Instead, however, a closer understanding of proton radiographs of laser-solid interactions without an imposed field has been obtained. 5 shot days on The OMEGA Laser Facility have been awarded (3 through ICF and 2 as ride-along) to further investigate some of these features. Extrapolating current results to hohlraum conditions has the magnetic energy being a significant (10%) fraction of the laser drive energy, which has the potential to explain the NIF hohlraum drive deficit.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗