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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Data-driven probability concentration and sampling on manifold

A new methodology is proposed for generating realizations of a random vector with values in a finite-dimensional Euclidean space that are statistically consistent with a dataset of observations of this vector. The probability distribution of this random vector, while a priori not known, is presumed to be concentrated on an unknown subset of the Euclidean space. A random matrix is introduced whose columns are independent copies of the random vector and for which the number of columns is the number of data points in the dataset. The approach is based on the use of (i) the multidimensional kernel-density estimation method for estimating the probability distribution of the random matrix, (ii) a MCMC method for generating realizations for the random matrix, (iii) the diffusion-maps approach for discovering and characterizing the geometry and the structure of the dataset, and (iv) a reduced-order representation of the random matrix, which is constructed using the diffusion-maps vectors associated with the first eigenvalues of the transition matrix relative to the given dataset. The convergence aspects of the proposed methodology are analyzed and a numerical validation is explored through three applications of increasing complexity. The proposed method is found to be robust to noise levels and data complexity as well as to the intrinsic dimension of data and the size of experimental datasets. Both the methodology and the underlying mathematical framework presented in this paper contribute new capabilities and perspectives at the interface of uncertainty quantification, statistical data analysis, stochastic modeling and associated statistical inverse problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Efficient reconstruction and validation of heterogeneous microstructures for energy applications

The digital reconstruction of microstructures is necessary for simulations in fields ranging from geology to electrochemistry, but the state-of-the-art digital reconstruction techniques often compromise between resolution and field of view. It is challenging to retain detailed microstructure information in large-scale reconstructions. Here, this study investigates different aspects of the Yeong-Torquato algorithm based on correlation functions to make it more efficient. We achieve this goal by reducing the computational complexity of the chord-length distribution function and the two-point correlation function, applying the random sphere-packing method as the initial condition, and restricting potential voxel swaps to interfaces. In addition, a novel superposition parallel scheme is introduced to aid in searching for potential voxel swaps. The algorithm proposed is validated by comparing the pore-size distributions of reconstructed 3D custom battery electrodes from a sample dataset obtained from transmission X-ray microscopy. From a sample image with 200 x 200 pixels, the code can reconstruct a 300 x 300 x 300 structure in under 22 h and reconstruct a 400 x 400 x 400 structure in 43 h with eight cores.

42 ENGINEERING↗
DOE OSTI2023

Use of Longitudinal Serum Analysis and Machine Learning to Develop a Classifier for Cancer Early Detection

Early detection of solid tumors through a simple screening process, such as the proteomic analysis of biofluids, has the potential to significantly alter the management and outcomes of cancers. The application of advanced targeted proteomics measurements and data analysis strategies to uniformly collected serum or plasma samples would enable longitudinal studies of cancer risk, progression, and response to therapy that have the potential to significantly reduce cancer burden in general. In this article, we describe a generalizable workflow combining robust, multiplexed targeted proteomics measurements applied to longitudinal samples from the Department of Defense Serum Repository with a Random Forest machine learning method for developing and initially evaluating the performance of candidate biomarker panels for early detection of cancers. The effectiveness of this approach was demonstrated in a cohort of 175 head and neck squamous cell carcinoma patients. The outlined protocols include methods for sample preparation, instrument analysis, and data analysis and interpretation using this workflow.

Longitudinal analysis, machine learning, cancer, e↗
DOE OSTI2023

Solving MaxCut with quantum imaginary time evolution

We introduce a method to solve the MaxCut problem efficiently based on quantum imaginary time evolution (QITE). We employ a linear Ansatz for unitary updates and an initial state involving no entanglement, as well as an imaginary-time-dependent Hamiltonian interpolating between a given graph and a subgraph with two edges excised. We apply the method to thousands of randomly selected graphs with up to fifty vertices. We show that our algorithm exhibits a 93% and above performance converging to the maximum solution of the MaxCut problem for all considered graphs. Our results compare favorably with the performance of classical algorithms, such as the greedy and Goemans–Williamson algorithms. We also discuss the overlap of the final state of the QITE algorithm with the ground state as a performance metric, which is a quantum feature not shared by other classical algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Simulation of dynamic crystal plasticity with a Lagrangian discontinuous Galerkin hydrodynamic method

Here we present a new Lagrangian modal discontinuous Galerkin (DG) hydrodynamic method that supports a dynamic dislocation based crystal plasticity model for simulating the mechanical behavior of crystallographic materials, both single crystal and polycrystalline, under dynamic conditions. A modal DG approach is used to evolve fields relevant to conservation laws. These fields are approximated by Taylor series polynomials of varying degree. These polynomials describe macro-scale hydrodynamic behavior while their evolution is determined by evaluating the dynamic crystal plasticity model at material points within the element. The dynamic crystal plasticity model is sensitive to the time increment size, with too large of time increments leading to instability in the model. To mitigate this, the temporal evolution of the dynamic crystal plasticity model is achieved with the combination of a sub-incrementing scheme with Heun’s third-order time integration scheme, which is also used to temporally evolve the governing equations. The implementation of the dynamic crystal plasticity model within the DG framework is tested using a 2D approximation of the Taylor impact test with a single crystal material, using quadratic elements that have faces that can bend. In addition to the standard continuous material modeling, we propose a new simulation method that would represent the heterogeneous behavior of polycrystalline microstructures within an element by varying the position and material properties of the material points within the element. This method is demonstrated using random orientation distributions on materials points that are arranged in both structured and random configurations.

42 ENGINEERING↗
DOE OSTI2023

Hierarchical reconstruction of 3D well-connected porous media from 2D exemplars using statistics-informed neural network

The relationships between porous microstructures and transport properties are of fundamental importance in various scientific and engineering applications. Due to the intricacy, stochasticity and heterogeneity of porous media, reliable characterization and modeling of transport properties often require a complete dataset of internal microstructure samples. However, it is often an unbearable cost to acquire sufficient 3D digital microstructures by purely using microscopic imaging systems. Herein this paper presents a machine learning-based technique to hierarchically reconstruct 3D well-connected porous microstructures from one isotropic or several anisotropic low-cost 2D exemplar(s). To compactly characterize the large-scale microstructural features, a Gaussian image pyramid is built for each 2D exemplar. Local morphology patterns are collected from the Gaussian image pyramids, and then they serve as the training data to embed the 2D morphological statistics into feed-forward neural networks at multiple length levels. By using a specially-developed morphology integration scheme, the 3D morphological statistics at different levels can be inferred from the statistics-informed neural networks. Gibbs sampling is adopted to hierarchically reconstruct 3D microstructures by using multi-level 3D morphological statistics, where the large-scale, regional and local morphological patterns are statistically generated and successively added to the same 3D random field. The proposed method is tested on a series of porous media with distinct morphologies, and the statistical equivalence between the reconstructed and the real microstructures is systematically evaluated by comparing morphological descriptors and transport properties. The results demonstrate that the proposed 2D-to-3D microstructure reconstruction method is a universal and efficient approach to generating morphologically and physically realistic samples of porous media.

42 ENGINEERING↗
DOE OSTI2016

Metal mixtures in urban and rural populations in the US: The Multi-Ethnic Study of Atherosclerosis and the Strong Heart Study

Background: Natural and anthropogenic sources of metal exposure differ for urban and rural residents. We searched to identify patterns of metal mixtures which could suggest common environmental sources and/or metabolic pathways of different urinary metals, and compared metal-mixtures in two population-based studies from urban/sub-urban and rural/town areas in the US: the Multi-Ethnic Study of Atherosclerosis (MESA) and the Strong Heart Study (SHS). Methods: We studied a random sample of 308 White, Black, Chinese-American, and Hispanic participants in MESA (2000–2002) and 277 American Indian participants in SHS (1998–2003). We used principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA) to evaluate nine urinary metals (antimony [Sb], arsenic [As], cadmium [Cd], lead [Pb], molybdenum [Mo], selenium [Se], tungsten [W], uranium [U] and zinc [Zn]). For arsenic, we used the sum of inorganic and methylated species (∑As). Results: All nine urinary metals were higher in SHS compared to MESA participants. PCA and CA revealed the same patterns in SHS, suggesting 4 distinct principal components (PC) or clusters (∑As-U-W, Pb-Sb, Cd-Zn, Mo-Se). In MESA, CA showed 2 large clusters (∑As-Mo-Sb-U-W, Cd-Pb-Se-Zn), while PCA showed 4 PCs (Sb-U-W, Pb-Se-Zn, Cd-Mo, ∑As). LDA indicated that ∑As, U, W, and Zn were the most discriminant variables distinguishing MESA and SHS participants. Conclusions: In SHS, the ∑As-U-W cluster and PC might reflect groundwater contamination in rural areas, and the Cd-Zn cluster and PC could reflect common sources from meat products or metabolic interactions. Among the metals assayed, ∑As, U, W and Zn differed the most between MESA and SHS, possibly reflecting disproportionate exposure from drinking water and perhaps food in rural Native communities compared to urban communities around the US. - Highlights: • We identified and compared environmental sources of urinary metals in MESA and SHS. • ∑As-U-W in SHS may reflect groundwater contamination in rural areas. • Cd-Zn in SHS may reflect common sources from meat products or metabolic interaction. • ∑As, U, W, and Zn differed the most between MESA and SHS participants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Determinants of serum cadmium levels in a Northern Italy community: A cross-sectional study

Introduction: Cadmium (Cd) is a heavy metal and a serious environmental hazard to humans. Some uncertainties still exist about major sources of Cd exposure in non-occupationally exposed subjects in addition to cigarette smoking, such as diet and outdoor air pollution. We sought to determine the influence of these sources on a biomarker of exposure, serum Cd concentration. Methods: We recruited 51 randomly selected residents from an Italian urban community, from whom we obtained detailed information about dietary habits and smoking habits, and a blood sample for serum Cd determination. We also assessed outdoor air Cd exposure, by modeling outdoor air levels of particulate matter ≤10 µm (PM{sub 10}) from motorized traffic at geocoded subjects’ residence. Results: In crude analysis, regression beta coefficients for dietary Cd, smoking and PM10 on serum Cd levels were 0.03 (95% CI -0.83 to 0.88), 6.96 (95% CI -0.02 to 13.95) and 0.62 (95% CI -0.19 to 1.43), respectively. In the adjusted analysis, regression beta coefficients were -0.34 (95% CI -1-40 to 0.71), 5.81 (95% CI -1.43 to 13.04) and 0.47 (95% CI -0.35 to 1.29), respectively. Conclusion: Cigarette smoking was the most important factor influencing serum Cd in our non-occupationally exposed population, as expected, while dietary Cd was not associated with this biomarker. Outdoor air pollution, as assessed through exposure to particulate matter generated by motorized traffic, was an additional source of Cd exposure. - Highlights: • Smoking markedly increases serum Cd levels in non-occupationally exposed individuals. • Overall dietary Cd intake shows little association with serum Cd levels. • Air pollution from motorized traffic increases serum Cd levels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Reduced Mortality With Partial-Breast Irradiation for Early Breast Cancer: A Meta-Analysis of Randomized Trials

Purpose: With earlier detection and more effective treatment, mortality from breast cancer continues to fall and it has become increasingly important to reduce the toxicity of treatments. Partial-breast radiation therapy, which focuses radiation to the tumor bed, may achieve this aim. We analyzed mortality differences in randomized trials of partial-breast irradiation (PBI). Methods and Materials: We included data from published randomized trials of PBI (alone or as part of a risk-adapted approach) versus whole-breast irradiation (WBI) for invasive breast cancer suitable for breast-conserving therapy. We identified trials using PubMed and Google searches with the terms “partial breast irradiation” OR “intraoperative radiotherapy” OR “IMRT” OR (“accelerated” AND “radiation”) AND “randomised/randomized,” as well as through discussion with colleagues in the field. We calculated the proportion of patients who had events in each randomized arm at 5 years' follow-up and created a forest plot using Stata, version 14.1. Results: We identified 9 randomized trials of PBI versus WBI in invasive breast cancer; 5-year outcomes were available for non–breast cancer mortality in 5 trials (n=4489) and for breast cancer mortality in 4 trials (n=4231). The overall mortality was 4.9%. There was no detectable heterogeneity between the trials for any of the outcomes. There was no difference in the proportion of patients dying of breast cancer (difference, 0.000% [95% confidence interval (CI), −0.7 to +0.7]; P=.999). Non–breast cancer mortality with PBI was lower than with WBI (difference, 1.1% [95% CI, −2.1% to −0.2%]; P=.023). Total mortality with PBI was also lower than with WBI (difference, 1.3% [95% CI, −2.5% to 0.0%]; P=.05). Conclusions: Use of PBI instead of WBI in selected patients results in a lower 5-year non–breast cancer and overall mortality, amounting to a 25% reduction in relative terms. This information should be included when breast-conserving therapy is proposed to a patient.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

CoTiO{sub 3} via cobalt oxalate-TiO{sub 2} precursor. Synthesis and characterization

Purely trigonal ilmenite type powder; CoTiO{sub 3} was successfully synthesized through the thermal decomposition of CoC{sub 2}O{sub 4}{center_dot}2H{sub 2}O-TiO{sub 2} precursor. The decomposition course was followed using differential thermal analysis-thermogravimetry measurements and the intermediate decomposition products as well as the titanate product were characterized using X-ray diffraction, Fourier transform infrared, transmission electron microscopy techniques. Thermal analysis study indicated that, the intermediate decomposition product; Co{sub 3}O{sub 4} decomposed endothermically into CoO at 963 Degree-Sign C with instantaneous formation of CoTiO{sub 3}. X-ray diffraction of the powder calcined at 950 Degree-Sign C showed characteristic lines for Co{sub 2}TiO{sub 4} besides secondary phase of rutile-TiO{sub 2}, which are disappeared by rising the calcination temperature to 1000 Degree-Sign C. Transmission electron microscopy image of CoTiO{sub 3} powder exhibit weak agglomerated particles with heterogeneous morphology in both shape and dimensions. Type II adsorption isotherm estimated using Brunauer-Emmett-Teller procedure indicated a very high specific surface area of 29.7 m{sup 2} g{sup -1}. Electrical properties of CoTiO{sub 3} measured as a function of temperature and frequency showed a semiconducting behavior at high temperatures with conduction activation energy of 0.73 eV. Kinetic analysis of the non-isothermal thermogravimetry curves, using different computational methods, illustrated that the random nucleation and growth mechanisms are the predominant one. - Graphical Abstract: The figure showed the variation of dielectric constant ({epsilon} Prime ) for CoTiO{sub 3} with temperature at different frequencies. {epsilon} Prime appeared to be temperature and frequency independent at low temperatures. By increasing the temperature, the orientation polarization plays a significant role and a gradual increase of {epsilon} Prime occurred. Highlights: Black-Right-Pointing-Pointer CoTiO{sub 3} was synthesized through decomposition of CoC{sub 2}O{sub 4}{center_dot}2H{sub 2}O-TiO{sub 2} precursor. Black-Right-Pointing-Pointer XRD, FT-IR, TEM techniques are used to characterize decomposition course. Black-Right-Pointing-Pointer Specific surface area of 29.7 m{sup 2} g{sup -1} was obtained. Black-Right-Pointing-Pointer The prepared titanate showed semiconducting behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2018

A Finite-Field Approach for GW Calculations beyond the Random Phase Approximation

In this work, we describe a finite-field approach to compute density response functions, which allows for efficient G0W0 and G0W0Γ0 calculations beyond the random phase approximation. The method is easily applicable to density functional calculations performed with hybrid functionals. Lastly, we present results for the electronic properties of molecules and solids and we discuss a general scheme to overcome slow convergence of quasiparticle energies obtained from G0W0Γ0 calculations, as a function of the basis set used to represent the dielectric matrix.

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

Significance of Low‐Velocity Zones on Solute Retention in Rough Fractures

Natural fractures are characterized by high internal heterogeneity. This internal variability is the cause of flow channeling, which in turn leads to contaminant transport taking place primarily along the high-velocity channels. Mass exchange between the high-velocity channels and the low-velocity zones has the potential to enhance contaminant retention, due to solute diffusion into the low-velocity zones and subsequent exposure to additional surface area for diffusion into the bordering rock matrix. Here, we derive a random walk particle tracking method for heterogeneous fractures, which includes an additional term to account for the aperture gradient. The method takes into account advection, diffusion in the fracture and matrix diffusion. The developed numerical framework is applied to assess the effect of low-velocity zones in rough self-affine fractures. The results show that diffusion into low-velocity zones has a visible but modest impact on contaminant retention. The magnitude of this impact does not change considerably, regardless of whether diffusion into the rock matrix is considered in the model, and increases for a decreasing average Péclet number of the fracture.

58 GEOSCIENCES↗
DOE OSTI2024

Analysis of multipacting threshold sensitivity to the random distributions of the secondary electron yield parameters

The way multipacting develops, depends strongly on the secondary emission property of the surface material. The knowledge of secondary electron yield is crucial for accurate prediction of the multipacting threshold. Variations in secondary electron yield parameters from experimental measurements create uncertainty, stemming from handling and surface preparation, and these uncertainties significantly affect multipacting threshold predictions. Despite their significance, the previous studies on the multipacting phenomenon did not adequately address the effect of an assumed random distribution of the secondary emission parameters on the multipacting threshold. Therefore, this paper aims to provide a comprehensive statistical study on how the different random distributions of the secondary emission parameters and, as a result, the uncertainty in the secondary electron yield affect multipacting thresholds. We focus on three commonly used distributions, namely uniform, normal, and truncated normal distributions, to define the uncertainty of random inputs. We use the chaos polynomial expansion method to determine how much each of the random parameters contributes to the multipacting threshold uncertainty. Additionally, we calculate Sobol sensitivity indices to evaluate the impact of the individual parameters or groups of parameters on the model outputs and study how different random distributions of these parameters affected the Sobol index results.

physics↗
DOE OSTI2025

Electric dipole excitations near the neutron separation energies in 96 Mo

Electric dipole strength near the neutron separation energy significantly impacts nuclear structure properties and astrophysical scenarios. These excitations are complex in nature and may involve the so-called pygmy dipole resonance (PDR). Transition densities play a crucial role in understanding the nature of nuclear excited states, including collective excitations, as well as in constructing transition potentials in DWBA or coupled-channels equations. In this work, we focus on electric dipole excitations in spherical molybdenum isotopes, particularly 96 Mo, employing fully consistent Hartree-Fock-Bogoliubov (HFB) and Quasiparticle Random Phase Approximation (QRPA) methods. We analyze the dipole strength near the neutron separation energy, which represents the threshold for neutron capture processes, and examine the isospin characteristics of PDR states through transition density calculations. Examination of proton and neutron transition densities reveals distinctive features of each dipole state, indicating their isoscalar and isovector nature. We observe that the primary component in the enhanced low-energy region exhibits isovector character. The PDR displays a mixture of isoscalar and isovector nature, distinguishing it from the isovector giant dipole resonance (IVGDR). These findings lay the groundwork for future investigations into the role of transition densities in reaction models and for their application to inelastic scattering calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

Spin wave theory for 2D disordered hard-core bosons

A spin-wave (SW) approach for hard-core bosons is presented to treat the problem of two dimensional boson localization in a random potential. After a short review of the method to compute 1/S-corrected observables, the case of random on-site energy is discussed. Whereas the mean-field solution does not display a Bose glass (BG) phase, 1/S corrections do capture BG physics. In particular, the localization of SW excitations is discussed through the inverse participation ratio.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Simulating Z 2 lattice gauge theory on a quantum computer

The utility of quantum computers for simulating lattice gauge theories is currently limited by the noisiness of the physical hardware. Various quantum error mitigation strategies exist to reduce the statistical and systematic uncertainties in quantum simulations via improved algorithms and analysis strategies. We perform quantum simulations of Z 2 gauge theory with matter to study the efficacy and interplay of different error mitigation methods: readout error mitigation, randomized compiling, rescaling, and dynamical decoupling. We compute Minkowski correlation functions in this confining gauge theory and extract the mass of the lightest spin-1 state from fits to their time dependence. Quantum error mitigation extends the range of times over which our correlation function calculations are accurate by a factor of 6 and is therefore essential for obtaining reliable masses.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Reduced peripheral activity leading to hepato‐preferential action of basal insulin peglispro compared with insulin glargine in patients with type 1 diabetes

Aims Basal insulin peglispro ( BIL ), a novel PEGylated basal insulin with a large hydrodynamic size, has a delayed absorption and reduced clearance that prolongs the duration of action. The current study compared the effects of BIL and insulin glargine ( GL ) on endogenous glucose production ( EGP ), glucose disposal rate ( GDR ) and lipolysis in patients with type 1 diabetes. Materials and Methods This was a randomized, open‐label, four‐period, crossover study. Patients received intravenous infusions of BIL and GL , each at two dose levels selected for partial and maximal suppression of EGP , during an 8 to 10 h euglycemic clamp procedure with d ‐[3‐ 3 H] glucose. Results Following correction for equivalent human insulin concentrations ( EHIC ), low‐dose GL infusion resulted in similar EGP at the end of the clamp compared to low‐dose BIL infusion ( GL / BIL ratio of 1.03) but a higher GDR ( GL / BIL ratio of 2.42), indicating similar hepatic activity but attenuated peripheral activity of BIL . Consistent with this, the EHIC ‐corrected GDR / EGP at the end of the clamp was 1.72‐fold greater for GL than BIL following low‐dose administration. At the lower dose of BIL and GL (concentrations in the therapeutic range), BIL produced less suppression of lipolysis compared with GL as indicated by free fatty acid and glycerol levels at the end of the clamp. Conclusions Compared with GL , BIL restored the hepato‐peripheral insulin action gradient seen in normal physiology via its peripherally restricted action on target tissues related to carbohydrate and lipid metabolism.

Mudaliar, S.↗