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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Method to determine the electron–ion temperature relaxation rate from test particle distributions

Here a method to calculate the electron–ion energy exchange rate from the forces on and velocities of an ensemble of test particles is described. The essence of the method is that the energy exchange rate is related to the covariance between the distribution of velocities of test particles and the forces acting on them. The energy exchange rate is related to the electron–ion temperature relaxation rate in the limit of a low test particle speed. A proof of concept is conducted with first-principles molecular dynamics simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Testing the conservative character of particle simulations: I. Canonical and noncanonical guiding center model in Boozer coordinates

The guiding center (GC) Lagrangian in Boozer coordinates for toroidally confined plasmas can be cast into canonical form by eliminating terms containing the covariant component B Ψ P of the magnetic field vector with respect to the poloidal flux function Ψ P . In an unperturbed plasma, B Ψ P can be eliminated via exact coordinate transformations, but, in general, one relies on approximations, assuming that the effect of B Ψ P is small. In this work, we are interested in the question whether Hamiltonian conservation laws are still satisfied when B Ψ P is retained in the presence of fluctuations. Considering fast ions in the presence of a shear Alfvén wave field with fixed amplitude, fixed frequency, and a single toroidal mode number n, we show that simulations using the code ORBIT with and without B Ψ P yield practically the same resonant and nonresonant GC orbits. The numerical results are consistent with theoretical analyses (presented in the appendix), which show that the unabridged GC Lagrangian with B Ψ P retained yields equations of motion that possess two key properties of Hamiltonian flows: (i) phase space conservation and (ii) energy conservation. As counter-examples, we also show cases where energy conservation (ii) or both conservation laws (i) and (ii) are broken by omitting certain small terms. When testing the conservative character of the simulation code, it is found to be beneficial to apply perturbations that do not resemble normal (eigen) modes of the plasma. The deviations are enhanced and, thus, more easily spotted when one inspects wave-particle interactions using nonnormal modes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Causal interaction in high frequency turbulence at the biosphere–atmosphere interface: Structure–function coupling

At the biosphere–atmosphere interface, nonlinear interdependencies among components of an ecohydrological complex system can be inferred using multivariate high frequency time series observations. Information flow among these interacting variables allows us to represent the causal dependencies in the form of a directed acyclic graph (DAG). Here, we use high frequency multivariate data at 10 Hz from an eddy covariance instrument located at 25 m above agricultural land in the Midwestern US to quantify the evolutionary dynamics of this complex system using a sequence of DAGs by examining the structural dependency of information flow and the associated functional response. We investigate whether functional differences correspond to structural differences or if there are no functional variations despite the structural differences. We base our analysis on the hypothesis that causal dependencies are instigated through information flow, and the resulting interactions sustain the dynamics and its functionality. To test our hypothesis, we build upon causal structure analysis in the companion paper to characterize the information flow in similarly clustered DAGs from 3-min non-overlapping contiguous windows in the observational data. We characterize functionality as the nature of interactions as discerned through redundant, unique, and synergistic components of information flow. Through this analysis, we find that in turbulence at the biosphere–atmosphere interface, the variables that control the dynamic character of the atmosphere as well as the thermodynamics are driven by non-local conditions, while the scalar transport associated with CO and H 2 O is mainly driven by short-term local conditions.

58 GEOSCIENCES↗
DOE OSTI2024

A method for examining ensemble averaging forms during the transition to turbulence in HED systems for application to RANS models

This paper discusses a strategy to initialize a two-dimensional (2D) Reynolds-averaged Navier–Stokes model [LANL's Besnard–Harlow–Rauenzahn (BHR) model] in order to describe an unsteady transitional Richtmyer–Meshkov (RM)-induced flow observed in on-going high-energy-density ensemble experiments performed on the OMEGA-EP facility. The experiments consist of a nominal single-mode perturbation (initial amplitude a 0 ≈ 10 and wavelength $λ$ = 100μm) with target-to-target variations in the surface roughness subjected to the RM instability with delayed Rayleigh–Taylor in a heavy-to-light configuration. Our strategy leverages high-resolution three-dimensional (3D) implicit large eddy simulations (ILES) simulations to initialize BHR-relevant parameters and subsequently validate the 2D BHR results against the 3D ILES simulations. A suite of five 3D ILES simulations corresponding to five experimental target profiles is undertaken to generate an ensemble dataset. Using ensemble averages from the 3D simulations to initialize the turbulent kinetic energy in the BHR model ( K 0 ) demonstrates the ability of the model to predict the time evolution of the interface as well as the density-specific-volume covariance, b . To quantify the sensitivity of the BHR results to the choice of K 0 and the initial turbulent length scale, S 0 , we execute a parameter sweep spanning four orders of magnitude for both S 0 and K 0 , generating a parameter space consisting of 26 simulations. The Pearson's correlation coefficient is used as a measure of discrepancy between the 2D BHR and 3D ILES simulations and reveals that the ranges 8≲S 0 ≲20 μm and 10 9 ≲K 0 ≲10 10 cm 2 /s 2 produce predictions that agree best with the 3D ILES results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Quantitative radiography for determining density fluctuations in HED experiments

We have developed a method to extract density fluctuation measurements from x-ray radiographs of high-energy density (HED) instability growth and turbulence experiments. We use this information to calculate density fluctuation statistics for constraining the performance of turbulent mix models in HED systems. The density calculation combines image filtering, removal of systemic effects such as backlighter variation, calculation of transmission across multiple materials, and use of tracer materials to generate an approximate single-material density field. From the density map, we calculate both average density and a variance-like moment b (density-specific-volume covariance), which we compare to our models. We infer both quantities from a single image, which is significantly more information than the historic single scalar mix width measurements. We also develop a method of analyzing simulation outputs that incorporate both the density fluctuation metric from a turbulence model and the bulk material maps from the hydrodynamic code. This analysis helps address the question of how to initialize the simulations for best comparison to data from systems with large separations of scale in the mixing perturbation initial condition. We find that our data analysis method yields 1D average density and b curves with similar morphology and amplitudes as those from preliminary simulation comparisons.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Extending the Mott–Gurney law to one-dimensional nonplanar diodes using point transformations

Recent studies have applied variational calculus, conformal mapping, and point transformations to generalize the one-dimensional (1D) space-charge limited current density (SCLCD) and electron emission mechanisms to nonplanar geometries; however, these assessments have focused on extending the Child–Langmuir law (CLL) for SCLCD in vacuum. Since the charge in the diode is independent of the coordinate system (i.e., covariant), we apply bijective point transformations to extend the Mott–Gurney law (MGL) for the SCLCD in a collisional or semiconductor gap to nonplanar 1D geometries. This yields a modified MGL that replaces the Cartesian gap distance with a canonical gap distance that may be written generally in terms of geometric scale factors that are known for multiple geometries. We tabulate results for common geometries. Such an approach may be applied to any current density, including non-space-charge limited gaps and SCLCD that may fall between the CLL and MGL.

Garner, Allen L. [Purdue Univ., West Lafayette, IN↗
DOE OSTI2026

Fragmentation dynamics of CS 2 dications and trications following S 2p ionization

Here, we present the results from a detailed study of the fragmentation dynamics of CS$_2^{2+}$ and CS$_2^{3+}$, formed in intense femtosecond soft x-ray pulses above the sulfur 2p edge, primarily through single core photoionization from the S 2p site, and subsequent Auger–Meitner decay(s). By combining three-dimensional velocity map imaging with covariance analysis, we determine the relative momenta of the ions produced in each two- and three-body fragmentation channel, at significantly higher ion count rates than conventional coincidence measurements. We shed new light on the wide range of fragmentation channels observed from the CS 2 dication and trication, including channels that involve ionization-induced bond formation and fragmentations producing undetected neutral cofragments. In the latter case, a “native frames” approach is used to isolate contributions from concerted and sequential fragmentations and extract dynamical information about each step of a concerted fragmentation process. While dications often fragment sequentially, the trication is dominated by concerted fragmentation. The main trication fragmentation channel into S + + C + + S + can be well-approximated by classical Coulombic simulations of the ground-state geometry distribution, reflecting both the nature of the trication potential energy surface and the rapid multiple ionization prior to substantial structural dynamics. This study demonstrates ways in which fundamental insights into the fragmentation dynamics of polycations following x-ray ionization may be extracted, which will be beneficial to future studies that employ time-resolved x-ray Coulomb explosion imaging to study ultrafast photochemistry.

Allum, Felix [Deutsches Elektronen-Synchrotron (DE↗
DOE OSTI2022

CO 2 fertilization of terrestrial photosynthesis inferred from site to global scales

Significance The magnitude of the CO 2 fertilization effect on terrestrial photosynthesis is uncertain because it is not directly observed and is subject to confounding effects of climatic variability. We apply three well-established eco-evolutionary optimality theories of gas exchange and photosynthesis, constraining the main processes of CO 2 fertilization using measurable variables. Using this framework, we provide robust observationally inferred evidence that a strong CO 2 fertilization effect is detectable in globally distributed eddy covariance networks. Applying our method to upscale photosynthesis globally, we find that the magnitude of the CO 2 fertilization effect is comparable to its in situ counterpart but highlight the potential for substantial underestimation of this effect in tropical forests for many reflectance-based satellite photosynthesis products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Infrastructure inequality is a characteristic of urbanization

Urbanization can challenge sustainable development if it produces unequal outcomes. Infrastructure is an important urbanization dimension, providing services to support diverse urban activities. However, it can lock in unequal outcomes due to its durable nature. This paper studies inequalities in infrastructure distributions to derive insights into the structure and characteristics of unequal outcomes associated with urbanization. We analyzed infrastructure inequalities in two emerging economies in the Global South: India and South Africa. We developed and applied an inequality measure to understand the structure of inequality in infrastructure provisioning (based on census data) and infrastructure availability (based on satellite nighttime lights [NTLs] data). Consistent with differences in economic inequality, results show greater inequalities in South Africa than in India and greater urban inequalities than rural inequalities. Nevertheless, inequalities in urban infrastructure provisioning and infrastructure availability increase from finer to coarser spatial scales. NTL-based inequality measurements additionally show that inequalities are more concentrated at coarse spatial scales in India than in South Africa. Finally, results show that urban inequalities in infrastructure provisioning covary with urbanization levels conceptualized as a multidimensional phenomenon, including demographic, economic, and infrastructural dimensions. Similarly, inequalities in urban infrastructure availability increase monotonically with infrastructure development levels and urban population size. Together, these findings underscore infrastructure inequalities as a feature of urbanization and suggest that understanding urban inequalities requires applying an inequality lens to urbanization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Correlational selection and genetic architecture shape the evolution of the leaf economics spectrum in a perennial grass

The generality of the worldwide leaf economics spectrum (LES) has made it a pillar of trait-based ecological research. Yet, few studies have examined the processes shaping the evolution of the LES within species, in part, because most species occupy only a small portion of the LES. Here, to address this gap, we took advantage of the distinct leaf economics strategies present in different ecotypes of the phenotypically diverse perennial grass Panicum virgatum (switchgrass) to generate a genetic mapping population, which we planted in common gardens at three sites spanning 12 degrees of latitude in the central United States. With this genetic mapping population, we evaluated two potentially interacting causes of LES evolution: 1) genetic architecture, where multiple traits are influenced by either the same gene (pleiotropy) or by genes in close physical proximity (genetic linkage), and 2) correlational selection, where selection acts on traits in combination rather than in isolation. We found that shared genetic architecture influenced covariation between photosynthetic rate (A MASS ) and leaf nitrogen (N MASS ) and between A MASS and leaf mass per area (LMA). We also found that correlational selection favored the trait combinations predicted by the LES (e.g., high LMA with low N MASS or low LMA with high N MASS ) and disfavored other, mismatched trait combinations at two of the three sites. Together, these results demonstrate how the evolution of an integrated LES within species can arise from multiple evolutionary causes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Uncertainty Quantification of a Light Water Pulsed-Neutron Die-Away Experiment to Thermal Neutron Scattering Laws

Thermal neutron scattering laws are important nuclear data for many nuclear science and engineering applications. Validation helps to ensure that a thermal neutron scattering law has a high quality and often employs critical benchmarks as integral experiments. Recently, pulsed-neutron die-away benchmarks have been used as an experiment to validate thermal neutron scattering laws. Herein, we evidence how this alternative integral experiment has a high sensitivity to these nuclear data by performing an uncertainty quantification analysis. The analysis randomly sampled the nuclear model parameters associated with hydrogen bound in light water thermal neutron scattering law and sampled other nuclear data that influenced the experiment’s integral parameter (e.g., elastic scattering, absorption in hydrogen and oxygen) from their respective covariance matrices. The thermal neutron scattering law caused an uncertainty in the integral parameter that reached 2.67%, which exceeds by an order of magnitude the uncertainties induced in commonly used thermal solution critical benchmarks. The validation performed here, although limited due to a poor description of the historical experiment, indicated that the ENDF/B-VIII.0 thermal neutron scattering law well predicted the integral parameter. These results motivate further benchmark and validation efforts using pulsed-neutron die-away experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Design Optimization of a Criticality Experiment for the Molten Chloride Reactor Experiment Facility

Neutronics simulations of Molten Chloride Fast Reactors have quantifiable biases that arise from nuclear data, modeling choices, or numerical methods. The multiphysics nature of molten salt reactors makes it challenging to disentangle neutronics modeling biases from biases originating from other physical phenomena. In comparison to a mock-up reactor, criticality experiments can specifically assess the neutronics modeling bias while limiting multiphysics effects. The criticality experiment must be neutronically representative of the full-scale reactor to be valuable. Here, in this paper, we describe the design of a criticality experiment to validate only the neutronics of TerraPower’s Molten Chloride Reactor Experiment (MCRE) and its criticality safety upset scenarios. The proposed experiment uses different chlorine-containing materials to maximize its similarity to the MCRE. The design process uses a constrained Bayesian optimization algorithm to investigate different objective functions that use covariance information for 35 Cl nuclear data. The experiments could reduce the nuclear data–induced uncertainty in k eff of the MCRE from 2161 to 886 pcm. They would also increase the upper subcritical limit of the MCRE criticality safety upset scenario from 0.94101 to 0.94476 when using the WHISPER analysis framework.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Demonstration of TOFFEE: A Response Uncertainty Quantification Tool

A key characteristic in neutron transport is nuclear data. Cross-section uncertainty is not used in MCNP6.3 to propagate response uncertainty without external analysis. Here, the TOol For Fast Error Estimation (TOFFEE) is a Python-based code developed to automate the propagation of cross-section uncertainty for MCNP evaluations. TOFFEE implements the sandwich rule to calculate the uncertainty from cross sections with sensitivity coefficients from MCNP6.3 and ENDF/B covariance data. In this paper, TOFFEE has been tested with benchmark experiments, and it has been compared to the uncertainty quantification capabilities of Sampler and TSUNAMI, within SCALE, to verify the application’s capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Constructing a Simulation Surrogate with Partially Observed Output

Gaussian process surrogates are a popular alternative to directly using computationally expensive simulation models. When the simulation output consists of many responses, dimension-reduction techniques are often employed to construct these surrogates. However, surrogate methods with dimension reduction generally rely on complete output training data. This article proposes a new Gaussian process surrogate method that permits the use of partially observed output while remaining computationally efficient. The new method involves the imputation of missing values and the adjustment of the covariance matrix used for Gaussian process inference. The resulting surrogate represents the available responses, disregards the missing responses, and provides meaningful uncertainty quantification. In conclusion, the proposed approach is shown to offer sharper inference than alternatives in a simulation study and a case study where an energy density functional model that frequently returns incomplete output is calibrated.

42 ENGINEERING↗
DOE OSTI2024

Convolutional Non-Homogeneous Poisson Process and its Application to Wildfire Ignition Risk Quantification for Power Delivery Networks

To quantify wildfire ignition risks on power delivery networks, the current practice predominantly relies on the empirically calculated fire danger indices, which may not well capture the effects of dynamically changing environmental factors. This article proposes a spatio-temporal point process model, known as the Convolutional Non-homogeneous Poisson Process (cNHPP), and applies the model to quantify wildfire ignition risks for power delivery networks. The proposed model captures both the current (i.e., instantaneous) and cumulative (i.e., historical) effects of key environmental processes (i.e., covariates) on wildfire risks, as well as the spatio-temporal dependency among different segments of the power delivery network. The computation and interpretation of the intensity function are thoroughly investigated. We apply the proposed approach to estimate wildfire ignition risks on major transmission lines in California, using historical fire data, meteorological and vegetation data obtained from the National Oceanic and Atmospheric Administration and National Aeronautics and Space Administration. Here, a comprehensive comparison study is performed to show the applicability and predictive capability of the proposed approach.

Non-homogeneous Poisson Process↗
DOE OSTI2022

Numerical characterization of support recovery in sparse regression with correlated design

Sparse regression is employed in diverse scientific settings as a feature selection method. A pervasive aspect of scientific data is the presence of correlations between predictive features. These correlations hamper both feature selection and estimation and jeopardize conclusions drawn from estimated models. On the other hand, theoretical results on sparsity-inducing regularized regression have largely addressed conditions for selection consistency via asymptotics, and disregard the problem of model selection, whereby regularization parameters are chosen. In this numerical study, we address these issues through exhaustive characterization of the performance of several regression estimators, coupled with a range of model selection strategies. These estimators and selection criteria were examined across correlated regression problems with varying degrees of signal to noise, distributions of non-zero model coefficients, and model sparsity. Our results reveal a fundamental tradeoff between false positive and false negative control in all regression estimators and model selection criteria examined. Additionally, we numerically explore a transition point modulated by the signal-to-noise ratio and spectral properties of the design covariance matrix at which the selection accuracy of all considered algorithms degrades. Overall, we find that SCAD coupled with BIC or empirical Bayes model selection performs the best feature selection across the regression problems considered.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Bounding uncertainty in functional data: A case study

Functional data are fast becoming a preeminent source of information across a wide range of industries. A particularly challenging aspect of functional data is bounding uncertainty. In this unique case study, we present our attempts at creating bounding functions for selected applications at Sandia National Laboratories (SNL). The first attempt involved a simple extension of functional principal component analysis (fPCA) to incorporate covariates. Though this method was straightforward, the extension was plagued by poor coverage accuracy for the bounding curve. This led to a second attempt utilizing elastic methodology which yielded more accurate coverage at the cost of more complexity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Development of a metamodelling framework for building energy models with application to fifth-generation district heating and cooling networks

Fully defined physics-based building energy models can accurately represent building systems; however, generating models based on high-level parameters is time consuming and simulation time of complex models can be slow. This article discusses the development of a Metamodelling Framework to create metamodels from a building energy modelling dataset. The framework generates metamodels using either linear regression, random forests, or support vector regressions. A fifth-generation district heating and cooling system analysis use case was used to motivate the development of the framework. The use case required quick and accurate representations of annual building loads reported hourly. Typical annual building modelling approaches can result in a runtime of 10 min. The metamodels runtime was reduced to less than 10 s to load and run an annual simulation with user-defined covariates. The results of the metamodel performance and an abbreviated topology analysis based on the motivating use case will be presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗