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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 OSTI2011

Confinement and correlation effects in the Xe-C{sub 60} generalized oscillator strengths

The impact of both confinement and electron correlation on generalized oscillator strengths (GOS's) of endohedral atoms, A-C{sub 60}, is theoretically studied choosing the Xe-C{sub 60} 4d, 5s, and 5p fast electron impact ionization as the case study. Calculations are performed in the transferred to the atom energy region beyond the 4d threshold, {omega}=75-175 eV. The calculation methodology combines the plane-wave Born approximation, Hartree-Fock approximation, and random-phase approximation with exchange in the presence of the C{sub 60} confinement. The confinement is modeled by a spherical {delta}-function-like potential as well as by a square well potential to evaluate the effect of the finite thickness of the C{sub 60} cage on the Xe-C{sub 60} GOS's. Dramatic distortion of the 4d, 5p, and 5s GOS's by the confinement is demonstrated, compared to the free atom. Considerable contributions of multipolar transitions beyond dipole transitions in the calculated GOS's are revealed, in some instances. The vitality of accounting for electron correlation in calculation of the Xe-C{sub 60} 5s and 5p GOS's is shown.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Evaluation of normalization strategies for mass spectrometry-based multi-omics datasets

Introduction Data normalization is crucial for multi-omics integration, reducing systematic errors and maximizing the likelihood of discovering true biological variation. Most studies assess normalization for a single omics type or use datasets from separate experiments. Few address time-course data, where normalization might bias temporal differentiation. In this study, we compared common normalization methods and a machine learning approach, Systematical Error Removal using Random Forest (SERRF), using multi-omics datasets generated from the same experiment—even from the same cell lysate. Objectives To develop a straightforward process to assess normalization effects and identify the most robust methods across multi-omics datasets. Methods We analyzed metabolomics, lipidomics, and proteomics datasets from primary human cardiomyocytes and motor neurons exposed to acetylcholine-active compounds over time. Normalization effectiveness was evaluated based on improvement in QC features consistency and observing the change in treatment and time-related variance. Results Probabilistic Quotient Normalization (PQN) and Locally Estimated Scatterplot Smoothing (LOESS) QC were identified as optimal for metabolomics and lipidomics, while PQN, Median, and LOESS normalization excelled for proteomics. These methods consistently enhanced QC feature consistency in metabolomics and lipidomics, and preserved time-related variance or treatment-related variance in proteomics, demonstrating their effectiveness and robustness. SERRF normalization, applied only to metabolomics in this study, outperformed other methods in some datasets but inadvertently masked treatment-related variance in others. Conclusion Our evaluation identified PQN and LoessQC as the top methods for metabolomics and lipidomics, and PQN, Median, and Loess normalization for proteomics, in multi-omics integration in a temporal study.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Coupling flux balance analysis with reactive transport modeling through machine learning for rapid and stable simulation of microbial metabolic switching

Integrating genome-scale metabolic networks with reactive transport models (RTMs) provides a detailed description of the dynamic changes in microbial growth and metabolism. Despite promising demonstrations in the past, computational inefficiency has been pointed out as a critical issue to overcome because it requires repeated application of linear programming (LP) to obtain flux balance analysis (FBA) solutions in every time step and spatial grid. To address this challenge, we propose a new simulation method where we train and validate artificial neural networks (ANNs) using randomly sampled FBA solutions and incorporate the resulting surrogate FBA model (represented as algebraic equations) into RTMs as source/sink terms. We demonstrate the efficiency of our method via a case study of Shewanella oneidensis MR-1. During aerobic growth on lactate, S. oneidensis produces metabolic byproducts (such as pyruvate and acetate), which are subsequently consumed as alternative carbon sources when the preferred nutrients are depleted. To effectively simulate these complex dynamics, we used a cybernetic approach that models metabolic switches as the outcome of dynamic competition among multiple growth options. In both zero-dimensional batch and one-dimensional column configurations, the ANN-based surrogate models achieved substantial reduction of computational time by several orders of magnitude compared to the original LP-based FBA models. Moreover, the ANN models produced robust solutions without any special measures to prevent numerical instability. These developments significantly promote our ability to utilize genome-scale networks in complex, multi-physics, and multi-dimensional ecosystem modeling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Probing the structural and dynamical properties of liquid water with models including non-local electron correlation

Water is a ubiquitous liquid that displays a wide range of anomalous properties and has a delicate structure that challenges experiment and simulation alike. The various intermolecular interactions that play an important role, such as repulsion, polarization, hydrogen bonding, and van der Waals interactions, are often difficult to reproduce faithfully in atomistic models. Here, electronic structure theories including all these interactions at equal footing, which requires the inclusion of non-local electron correlation, are used to describe structure and dynamics of bulk liquid water. Isobaric-isothermal (NpT) ensemble simulations based on the Random Phase Approximation (RPA) yield excellent density (0.994 g/ml) and fair radial distribution functions, while various other density functional approximations produce scattered results (0.8-1.2 g/ml). Molecular dynamics simulation in the microcanonical (NVE) ensemble based on Møller-Plesset perturbation theory (MP2) yields dynamical properties in the condensed phase, namely, the infrared spectrum and diffusion constant. At the MP2 and RPA levels of theory, ice is correctly predicted to float on water, resolving one of the anomalies as resulting from a delicate balance between van der Waals and hydrogen bonding interactions. For several properties, obtaining quantitative agreement with experiment requires correction for nuclear quantum effects (NQEs), highlighting their importance, for structure, dynamics, and electronic properties. A computed NQE shift of 0.6 eV for the band gap and absorption spectrum illustrates the latter. Giving access to both structure and dynamics of condensed phase systems, non-local electron correlation will increasingly be used to study systems where weak interactions are of paramount importance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2010

CORRECTING A STATISTICAL ARTIFACT IN THE ESTIMATION OF THE HUBBLE CONSTANT BASED ON TYPE IA SUPERNOVAE RESULTS IN A CHANGE IN ESTIMATE OF 1.2%

The Hubble constant enters big bang cosmology by quantifying the expansion rate of the universe. Existing statistical methods used to estimate Hubble's constant only partially take into account random measurement errors. As a consequence, estimates of Hubble's constant are statistically inconsistent and systematically too low. Using the simplest of the existing methods to illustrate the problem, we show that the change in estimate is 1.2%. The more advanced techniques suffer from the same deficiency.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

Investigation of advanced UQ for CRUD prediction with VIPRE.

This document summarizes the results from a level 3 milestone study within the CASL VUQ effort. It demonstrates the application of 'advanced UQ,' in particular dimension-adaptive p-refinement for polynomial chaos and stochastic collocation. The study calculates statistics for several quantities of interest that are indicators for the formation of CRUD (Chalk River unidentified deposit), which can lead to CIPS (CRUD induced power shift). Stochastic expansion methods are attractive methods for uncertainty quantification due to their fast convergence properties. For smooth functions (i.e., analytic, infinitely-differentiable) in L{sup 2} (i.e., possessing finite variance), exponential convergence rates can be obtained under order refinement for integrated statistical quantities of interest such as mean, variance, and probability. Two stochastic expansion methods are of interest: nonintrusive polynomial chaos expansion (PCE), which computes coefficients for a known basis of multivariate orthogonal polynomials, and stochastic collocation (SC), which forms multivariate interpolation polynomials for known coefficients. Within the DAKOTA project, recent research in stochastic expansion methods has focused on automated polynomial order refinement ('p-refinement') of expansions to support scalability to higher dimensional random input spaces [4, 3]. By preferentially refining only in the most important dimensions of the input space, the applicability of these methods can be extended from O(10{sup 0})-O(10{sup 1}) random variables to O(10{sup 2}) and beyond, depending on the degree of anisotropy (i.e., the extent to which randominput variables have differing degrees of influence on the statistical quantities of interest (QOIs)). Thus, the purpose of this study is to investigate the application of these adaptive stochastic expansion methods to the analysis of CRUD using the VIPRE simulation tools for two different plant models of differing random dimension, anisotropy, and smoothness.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

Statistical Approaches to Aerosol Dynamics for Climate Simulation

In this work, we introduce two general non-parametric regression analysis methods for errors-in-variable (EIV) models: the compound regression, and the constrained regression. It is shown that these approaches are equivalent to each other and, to the general parametric structural modeling approach. The advantages of these methods lie in their intuitive geometric representations, their distribution free nature, and their ability to offer a practical solution when the ratio of the error variances is unknown. Each includes the classic non-parametric regression methods of ordinary least squares, geometric mean regression, and orthogonal regression as special cases. Both methods can be readily generalized to multiple linear regression with two or more random regressors.

Compound regression↗
DOE OSTI2013

Amplification and cloning of single DNA molecules using rolling circle amplification

The present invention relates, e.g., to a method for amplifying a small number of copies (e.g. a single copy) of a single-stranded circular DNA molecule (e.g. having a size of about 5-6 kb) by an isothermal rolling circle mechanism, using random or partially random primers and a F29-type DNA polymerase. The method, which can also be used for amplifying DNAs by non-rolling types of multiple displacement amplification, comprises incubating the reaction components in a small volume, e.g. about 10 μl or less, such as about 0.6 μl or less. The degree of amplification can be about 109 fold, or higher. A method for cell-free cloning of DNA, using the rolling circle amplification method of the invention, is described.

Hutchison, III, Clyde A.↗
DOE OSTI2015

Coagulation kinetics beyond mean field theory using an optimised Poisson representation

Binary particle coagulation can be modelled as the repeated random process of the combination of two particles to form a third. The kinetics may be represented by population rate equations based on a mean field assumption, according to which the rate of aggregation is taken to be proportional to the product of the mean populations of the two participants, but this can be a poor approximation when the mean populations are small. However, using the Poisson representation, it is possible to derive a set of rate equations that go beyond mean field theory, describing pseudo-populations that are continuous, noisy, and complex, but where averaging over the noise and initial conditions gives the mean of the physical population. Such an approach is explored for the simple case of a size-independent rate of coagulation between particles. Analytical results are compared with numerical computations and with results derived by other means. In the numerical work, we encounter instabilities that can be eliminated using a suitable “gauge” transformation of the problem [P. D. Drummond, Eur. Phys. J. B 38, 617 (2004)] which we show to be equivalent to the application of the Cameron-Martin-Girsanov formula describing a shift in a probability measure. The cost of such a procedure is to introduce additional statistical noise into the numerical results, but we identify an optimised gauge transformation where this difficulty is minimal for the main properties of interest. For more complicated systems, such an approach is likely to be computationally cheaper than Monte Carlo simulation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Analysis of self-heating of thermally assisted spin-transfer torque magnetic random access memory

Thermal assistance has been shown to significantly reduce the required operation power for spin torque transfer magnetic random access memory (STT-MRAM). Proposed heating methods include modified material stack compositions that result in increased self-heating or external heat sources. Here, we analyze the self-heating process of a standard perpendicular magnetic anisotropy STT-MRAM device through numerical simulations in order to understand the relative contributions of Joule, thermoelectric Peltier and Thomson, and tunneling junction heating. A 2D rotationally symmetric numerical model is used to solve the coupled electro-thermal equations including thermoelectric effects and heat absorbed or released at the tunneling junction. We compare self-heating for different common passivation materials, positive and negative electrical current polarity, and different device thermal anchoring and boundaries resistance configurations. The variations considered are found to result in significant differences in maximum temperatures reached. Average increases of 3 K, 10 K, and 100 K for different passivation materials, positive and negative polarity, and different thermal anchoring configurations, respectively, are observed. Furthermore, the highest temperatures, up to 424 K, are obtained for silicon dioxide as the passivation material, positive polarity, and low thermal anchoring with thermal boundary resistance configurations. Interestingly it is also found that due to the tunneling heat, Peltier effect, device geometry, and numerous interfacial layers around the magnetic tunnel junction (MTJ), most of the heat is dissipated on the lower potential side of the magnetic junction. We have observed this asymmetry in heating and is important as thermally assisted switching requires heating of the free layer specifically and this will be significantly different for the two polarity operations, set and reset.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2016

Using Green's Functions to Compute 3 Dimensional Neutron Flux Maps - 16016

The knowledge of the radionuclide content of radioactive waste is of utmost importance for safety and waste management reasons. Numerical simulations are used by EDF-DP2D to anticipate dismantling and radioactive waste management. The main numerical activation schemes include the computing of a 3 dimensional neutron flux map, obtained on the basis of a neutron propagation calculation. The codes used solve the transport equation called the Boltzmann equation without simplification. The Monte Carlo method is used to solve the Boltzmann equation. Random series of numbers are used to simulate the lives of millions of neutrons. The codes follow each neutron individually, from its birth to its disappearance by leakage, absorption, or fission, and then the neutron flux map is calculated at the nominal power rating conditions. The main drawback of the Monte Carlo codes is the large computing time needed to converge within acceptable statistical criteria. This extended time period is incompatible with the multiple fuel managements used by EDF to operate its NPPs because it is necessary to make a Monte Carlo calculation for each fuel loading pattern. The solution developed by EDF-DP2D is to use the importance factors of Green's Functions to simplify the computing of the 3 dimensional neutron flux maps. Physicists talk about Green's Functions where mathematicians talk about elementary solutions of linear equations with constant coefficients. With an activation scheme based on the Green's Functions approach the 3D neutron flux is obtained on the basis of the convolution of the normalized source contributions (i.e. normalized response matrices) with the neutrons emitted by the fuel assemblies. The response matrices to unitary sources are computed with the Monte Carlo code MCNP for different tallies of interest and the neutron sources emitted by the fuel are computed with a core code for different fuel loading patterns. The principal interest of this approach is the calculation of the response matrices to unitary sources that can be used for all power distributions, thus limiting the Monte Carlo computing time. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Photovoltaic Hosting Capacity of Feeders with Reactive Power Control and Tap Changers

This paper proposes an algorithm to determine photovoltaic (PV) hosting capacity of power distribution networks as a function of number of PV injection nodes, reactive power support from the PVs, and the sub-station load tap changers (LTCs). In the proposed method, several minute by minute simulations are run based on randomly chosen PV injection nodes, daily PV output profiles, and daily load profiles from a pool of high-resolution realistic data set. The simulation setup is built using OpenDSS and MATLAB. The performance of the proposed method is investigated in the IEEE 123-node distribution feeder for multiple scenarios. The case studies are performed particularly for one, two, five and ten PV injection nodes, and looking at the maximum voltage deviations. Case studies show that the PV hosting capacity of the 123-node feeder greatly differs with the number of PV injection nodes. We have also observed that the PV hosting capacity increases with reactive power support and higher tap position of sub-station LTC.

14 SOLAR ENERGY↗
DOE OSTI2015

SU-D-201-06: Random Walk Algorithm Seed Localization Parameters in Lung Positron Emission Tomography (PET) Images

Purpose: The objective of this study was to find the best seed localization parameters in random walk algorithm application to lung tumor delineation in Positron Emission Tomography (PET) images. Methods: PET images suffer from statistical noise and therefore tumor delineation in these images is a challenging task. Random walk algorithm, a graph based image segmentation technique, has reliable image noise robustness. Also its fast computation and fast editing characteristics make it powerful for clinical purposes. We implemented the random walk algorithm using MATLAB codes. The validation and verification of the algorithm have been done by 4D-NCAT phantom with spherical lung lesions in different diameters from 20 to 90 mm (with incremental steps of 10 mm) and different tumor to background ratios of 4:1 and 8:1. STIR (Software for Tomographic Image Reconstruction) has been applied to reconstruct the phantom PET images with different pixel sizes of 2×2×2 and 4×4×4 mm{sup 3}. For seed localization, we selected pixels with different maximum Standardized Uptake Value (SUVmax) percentages, at least (70%, 80%, 90% and 100%) SUVmax for foreground seeds and up to (20% to 55%, 5% increment) SUVmax for background seeds. Also, for investigation of algorithm performance on clinical data, 19 patients with lung tumor were studied. The resulted contours from algorithm have been compared with nuclear medicine expert manual contouring as ground truth. Results: Phantom and clinical lesion segmentation have shown that the best segmentation results obtained by selecting the pixels with at least 70% SUVmax as foreground seeds and pixels up to 30% SUVmax as background seeds respectively. The mean Dice Similarity Coefficient of 94% ± 5% (83% ± 6%) and mean Hausdorff Distance of 1 (2) pixels have been obtained for phantom (clinical) study. Conclusion: The accurate results of random walk algorithm in PET image segmentation assure its application for radiation treatment planning and diagnosis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Using intrusive approaches as a step towards accounting for stochasticity in wind turbine design

Current wind turbine design methods require tens of thousands of time-domain simulations and use different random seeds to account for the stochasticity of the environmental conditions. The account of stochasticity is nonintrusive because the sampling method calls a deterministic model multiple times without changing its underlying equations. In this work, we investigate and demonstrate using simple proof of concepts how intrusive approaches can be used to directly account for stochasticity in the equations representing a mechanical system. Our long term goal is to apply such methodology to the design of wind turbines without requiring an excessive number of simulations. Intrusive methods manipulate stochastic variables directly to provide the probability density functions (PDFs) of the states and outputs at any time as functions of the PDFs of the inputs. We illustrate how different methods can be used with a reduced-order model of a wind turbine with one degree of freedom and for linear and nonlinear models. We discuss how the methods can be extended and what it will take to apply them to a level of fidelity similar to current state-of-the-art wind turbine design tools.

17 WIND ENERGY↗
DOE OSTI2011

Acute and Late Toxicity in a Randomized Trial of Conventional Versus Hypofractionated Three-Dimensional Conformal Radiotherapy for Prostate Cancer

Purpose: To compare the toxicity between hypofractionation vs. conventional fractionation schedules in patients with high-risk prostate cancer. Methods and Materials: Between January 2003 and December 2007, 168 patients were randomized to receive either hypofractionated (62 Gy in 20 fractions within 5 weeks, 4 fractions/wk) or conventionally fractionated (80 Gy in 40 fractions within 8 weeks) three-dimensional conformal radiotherapy to the prostate and seminal vesicles. All patients had undergone a 9-month course of total androgen deprivation, with radiotherapy starting 2 months after initiation of the total androgen deprivation. Results: The median follow-up was 32 and 35 months in the hypofractionation and conventional fractionation arms, respectively. For the patients developing acute toxicity, no difference between the two fractionation groups was found in either severity or duration of gastrointestinal or genitourinary toxicity. Also, no difference was found in the incidence and severity of late gastrointestinal and genitourinary toxicity between the two treatment schedules, with a 3-year rate of Grade 2 or greater toxicity of 17% and 16% for the hypofractionation arm and 14% and 11% for the conventional fractionation arm, respectively. A statistically significant correlation between acute and late gastrointestinal toxicity was found only in the conventional fractionation group. Conclusion: Our findings suggest that the hypofractionation regimen used in our study is safe, with only a slight, nonsignificant increase in tolerable and temporary acute toxicity compared with the conventional fractionation schedule. The severity and frequency of late complications was equivalent between the two treatment groups.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

Two datasets are better than one: method of double moments for 3D reconstruction in cryo-EM

Cryo-electron microscopy is a powerful imaging technique for reconstructing three-dimensional molecular structures from noisy tomographic projection images of randomly oriented particles. We introduce a new data fusion framework, termed the method of double moments, which reconstructs molecular structures from two instances of the second-order moment of projection images obtained under distinct orientation distributions: one uniform, the other non-uniform and unknown. We prove that these moments generically uniquely determine the underlying structure, up to a global rotation and reflection, and we develop a convex-relaxation-based algorithm that achieves accurate recovery using only second-order statistics. Our results demonstrate the advantage of collecting and modeling multiple datasets under different experimental conditions, illustrating that leveraging dataset diversity can substantially enhance reconstruction quality in computational imaging tasks.

Kam’s method↗
DOE OSTI2013

Neoadjuvant Chemoradiation for Distal Rectal Cancer: 5-Year Updated Results of a Randomized Phase 2 Study of Neoadjuvant Combined Modality Chemoradiation for Distal Rectal Cancer

Purpose: To assess the efficacy of 2 different approaches to neoadjuvant chemoradiation for distal rectal cancers. Methods and Materials: One hundred six patients with T3/T4 distal rectal cancers were randomized in a phase 2 study. Patients received either continuous venous infusion (CVI) of 5-Fluorouracil (5-FU), 225 mg/m{sup 2} per day, 7 days per week plus pelvic hyperfractionated radiation (HRT), 45.6 Gy at 1.2 Gy twice daily plus a boost of 9.6 to 14.4 Gy for T3 or T4 cancers (Arm 1), or CVI of 5-FU, 225 mg/m{sup 2} per day, Monday to Friday, plus irinotecan, 50 mg/m{sup 2} once weekly × 4, plus pelvic radiation therapy (RT), 45 Gy at 1.8 Gy per day and a boost of 5.4 Gy for T3 and 9 Gy for T4 cancers (Arm 2). Surgery was performed 4 to 10 weeks later. Results: All eligible patients (n=103) are included in this analysis; 2 ineligible patients were excluded, and 1 patient withdrew consent. Ninety-eight of 103 patients (95%) underwent resection. Four patients did not undergo surgery for either disease progression or patient refusal, and 1 patient died during induction chemotherapy. The median time of follow-up was 6.4 years in Arm 1 and 7.0 years in Arm 2. The pathological complete response (pCR) rates were 30% in Arm 1 and 26% in Arm 2. Locoregional recurrence rates were 16% in Arm 1 and 17% in Arm 2. Five-year survival rates were 61% and 75% and Disease-specific survival rates were 78% and 85% for Arm1 and Arm 2, respectively. Five second primaries occurred in patients on Arm 1, and 1 second primary occurred in Arm 2. Conclusions: High rates of disease-specific survival were seen in each arm. Overall survival appears affected by the development of unrelated second cancers. The high pCR rates with 5-FU and higher dose radiation in T4 cancers provide opportunity for increased R0 resections and improved survival.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2011

SLFP: A stochastic linear fractional programming approach for sustainable waste management

Highlights: > A new fractional programming (SLFP) method is developed for waste management. > SLFP can solve ratio optimization problems associated with random inputs. > A case study of waste flow allocation demonstrates its applicability. > SLFP helps compare objectives of two aspects and reflect system efficiency. > This study supports in-depth analysis of tradeoffs among multiple system criteria. - Abstract: A stochastic linear fractional programming (SLFP) approach is developed for supporting sustainable municipal solid waste management under uncertainty. The SLFP method can solve ratio optimization problems associated with random information, where chance-constrained programming is integrated into a linear fractional programming framework. It has advantages in: (1) comparing objectives of two aspects, (2) reflecting system efficiency, (3) dealing with uncertainty expressed as probability distributions, and (4) providing optimal-ratio solutions under different system-reliability conditions. The method is applied to a case study of waste flow allocation within a municipal solid waste (MSW) management system. The obtained solutions are useful for identifying sustainable MSW management schemes with maximized system efficiency under various constraint-violation risks. The results indicate that SLFP can support in-depth analysis of the interrelationships among system efficiency, system cost and system-failure risk.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗