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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Evolution of linear perturbations in Lemaítre-Tolman-Bondi void models

We study the evolution of linear perturbations in a Lemaítre-Tolman-Bondi (LTB) void model with realistic cosmological initial conditions. Linear perturbation theory in LTB models is substantially more complicated than in standard Friedmann universes as the inhomogeneous background causes gauge-invariant perturbations couple at first order. As shown by Clarkson et al. (2009) [1], the evolution is constrained by a system of linear partial differential equations which need to be integrated numerically. We present a new numerical scheme using finite element methods to solve this equation system and generate scalar initial conditions based on Gaussian random fields with an underlying power spectrum for the Bardeen potential. After spherical harmonic decomposition, the initial fluctuations are propagated in time and estimates of angular power spectra of each gauge invariant variable are computed as functions of redshift. This allows to analyse the coupling strength in a statistical way. We find significant couplings up to 25% for large and deep voids of Gpc scale as required to fit the distance redshift relations of SNe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Determining velocity of detonation using high-resolution time domain reflectometry

Two methods are presented to make measurements of the length of a coaxial cable with submillimeter and submicrosecond accuracy. If the cable is destroyed by the passage of a strong shock, for instance, from the detonation of an explosive, a measure of the phase velocity of the detonation (VOD) can be obtained with high accuracy (≈0.25%). The first method introduces a series of short Gaussian electrical pulses (<300 ps) into the cable that reflect off the cable end and are compared to the time of the original outgoing pulse by using a fast digitizer (100 gigasamples per second). By curve fitting a Gaussian form to the outgoing and reflected pulses, a measurement of the cable length can be made with subsample accuracy. So, as long as the pulses do not overlap, a large number may be present in the cable at any time allowing pulse repetition rates of 40–135 MHz depending on the cable length. Averaging allows accurate VOD measurements to be made that are comparable with the best traditional techniques. The second method replaces the pulser with a wide-spectrum (0.1–10 GHz) amplified random noise source. A similar setup and digitizer are used before autocorrelating short (80–160 ns) subsamples of the total waveform to measure the reflected delay time. That is, a smaller amplitude version of the original waveform appears time shifted by twice the transit time in the cable and this time is estimated via autocorrelation. The advantage of this method is that a new subsample needs only to be selected from a time increment in which new spectral information is present for a new measure of the length to be made. In this manner, many more length estimates can be averaged to establish the VOD than with the Gaussian approach. As a result, a detailed description of the implementation of both methods is presented together with an example of the type of data collected and limitations found.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

PARTICLE ACCELERATION IN SUPERLUMINAL STRONG WAVES

We calculate the electron acceleration in random superluminal strong waves (SLSWs) and radiation from them using numerical methods in the context of the termination shocks of pulsar wind nebulae. We pursue the orbit of electrons by solving the equation of motion in the analytically expressed electromagnetic turbulences. These consist of a primary SLS and isotropically distributed secondary electromagnetic waves. Under the dominance of the secondary waves, all electrons gain nearly equal energy. On the other hand, when the primary wave is dominant, selective acceleration occurs. The phase of the primary wave for electrons moving nearly along the wavevector changes very slowly compared with the oscillation of the wave, which is “phase-locked,” and such electrons are continuously accelerated. This acceleration by SLSWs may play a crucial role in pre-shock acceleration. In general, the radiation from the phase-locked population is different from the synchro-Compton radiation. However, when the amplitude of the secondary waves is not extremely weaker than that of the primary wave, the typical frequency can be estimated from synchro-Compton theory using the secondary waves. The primary wave does not contribute to the radiation because the SLSW accelerates electrons almost linearly. This radiation can be observed as a radio knot at the upstream of the termination shocks of the pulsar wind nebulae without counterparts in higher frequency ranges.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Pathologic Response, When Increased by Longer Interval, Is a Marker but Not the Cause of Good Prognosis in Rectal Cancer: 17-year Follow-up of the Lyon R90-01 Randomized Trial

Purpose: The Lyon R90-01 randomized trial investigated whether the interval between preoperative radiation therapy and surgery influenced rectal cancer outcome. Long-term results are reported here after a median follow-up of 17 years. Methods and Materials: Between February 1991 and December 1995, 210 patients from 29 French centers were randomly assigned (ratio of 1:1) to groups that waited either 2 weeks (short interval [SI]) or 6 to 8 weeks (long interval [LI]) between neoadjuvant radiation therapy and surgery. The primary endpoint was sphincter-preserving surgery. Results: LI group showed a better pathologic response (complete response or few residual cells) after radiation therapy than the SI group (26% vs 10.3%, P=.015). A better pathologic response was associated in multivariate analysis with significant improvement of overall survival (pT: P=.0293 and pN: P=.0048) but it was irrespective of the interval duration. The median follow-up was 17.2 years. The 5-, 10-, 15-, and 17-year overall survival rates were, respectively, 66.8%, 48.7%, 40.0%, and 34.0% for the SI group and, respectively, 67.1%, 53.5%, 41.9%, and 34.0% for the LI group. There were no significant differences between groups in terms of survival (P=.7656) or local recurrence rates (SI: 14.4% vs LI: 12.1%, respectively; P=.6202). Of 24 local disease recurrences, 20 (83%) occurred during the first 2 postoperative years, and all but one (96%) occurred during the first 5 postoperative years. The rate of second new malignancies was 9.4% (19 patients). Conclusions: The radiation-induced sterilization rate of the preoperative cancer specimen was a marker of good prognosis. The interval duration (the treatment being the same) although it is modifying the sterilization rate has no impact on survival. Radiation therapy did not postpone local recurrence, because the rate of local relapse after 5 years was low. Radiation-induced cancers after radiation therapy were unusual and should not influence treatment decisions in adults.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Transform-limited-pulse representation of excitation with natural incoherent light

The excitation of molecular systems by natural incoherent light relevant, for example, to photosynthetic light-harvesting is examined. We show that the result of linear excitation with natural incoherent light can be obtained using incident light described in terms of transform limited pulses, as opposed to conventional classical representations with explicit random character. The derived expressions allow for computations to be done directly for any thermal light spectrum using a simple wave function formalism and provide a route to the experimental determination of natural incoherent excitation using pulsed laser techniques. Pulses associated with solar and cosmic microwave background radiation are provided as examples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Uncertainty quantification methodology for hyperbolic systems with application to blood flow in arteries

We present a Stochastic Finite Volume - ADER (SFV-ADER) methodology for Uncertainty Quantification (UQ) in the general framework of systems of hyperbolic balance laws with uncertainty in parameters. The resulting method is weakly intrusive, meaning that deterministic solvers need minor modifications to include random contributions, and has no theoretical accuracy barrier. An illustration of the second-order version for the viscous Burgers equation with uncertain viscosity coefficient is first given in detail, under different deterministic initial conditions; the attainment of the theoretically expected convergence rate is demonstrated empirically; results and features of our scheme are discussed. We then extend the SFV-ADER method to a non-linear hyperbolic system with source terms that models one-dimensional blood flow in arteries, assuming uncertainties in a set of parameters of the problem. Results are then compared with measurements in a well-defined 1:1 experimental replica of the network of largest arteries in the human systemic circulation. For a 99%-confidence level, severe influence of parameter fluctuations is seen in pressure profiles, while secondary effects on flow rate profiles become visible, particularly in terminal branches. In conclusion, results suggest that the proposed methodology could successfully be applied to other physical problems involving hyperbolic balance laws.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Utilizing physics-based input features within a machine learning model to predict wind speed forecasting error

Machine learning is quickly becoming a commonly used technique for wind speed and power forecasting. Many machine learning methods utilize exogenous variables as input features, but there remains the question of which atmospheric variables are most beneficial for forecasting, especially in handling non-linearities that lead to forecasting error. This question is addressed via creation of a hybrid model that utilizes an autoregressive integrated moving-average (ARIMA) model to make an initial wind speed forecast followed by a random forest model that attempts to predict the ARIMA forecasting error using knowledge of exogenous atmospheric variables. Variables conveying information about atmospheric stability and turbulence as well as inertial forcing are found to be useful in dealing with non-linear error prediction. Streamwise wind speed, time of day, turbulence intensity, turbulent heat flux, vertical velocity, and wind direction are found to be particularly useful when used in unison for hourly and 3 h timescales. The prediction accuracy of the developed ARIMA–random forest hybrid model is compared to that of the persistence and bias-corrected ARIMA models. The ARIMA–random forest model is shown to improve upon the latter commonly employed modeling methods, reducing hourly forecasting error by up to 5 % below that of the bias-corrected ARIMA model and achieving an R 2 value of 0.84 with true wind speed.

17 WIND ENERGY↗
DOE OSTI2017

A practical method to test the validity of the standard Gumbel distribution in logit-based multinomial choice models of travel behavior

Most multinomial choice models (e.g., the multinomial logit model) adopted in practice assume an extreme-value Gumbel distribution for the random components (error terms) of utility functions. This distributional assumption offers a closed-form likelihood expression when the utility maximization principle is applied to model choice behaviors. As a result, model coefficients can be easily estimated using the standard maximum likelihood estimation method. However, maximum likelihood estimators are consistent and efficient only if distributional assumptions on the random error terms are valid. It is therefore critical to test the validity of underlying distributional assumptions on the error terms that form the basis of parameter estimation and policy evaluation. In this paper, a practical yet statistically rigorous method is proposed to test the validity of the distributional assumption on the random components of utility functions in both the multinomial logit (MNL) model and multiple discrete-continuous extreme value (MDCEV) model. Based on a semi-nonparametric approach, a closed-form likelihood function that nests the MNL or MDCEV model being tested is derived. The proposed method allows traditional likelihood ratio tests to be used to test violations of the standard Gumbel distribution assumption. Simulation experiments are conducted to demonstrate that the proposed test yields acceptable Type-I and Type-II error probabilities at commonly available sample sizes. The test is then applied to three real-world discrete and discrete-continuous choice models. For all three models, the proposed test rejects the validity of the standard Gumbel distribution in most utility functions, calling for the development of robust choice models that overcome adverse effects of violations of distributional assumptions on the error terms in random utility functions.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Block triangular preconditioning for stochastic Galerkin method

Here, in this paper, we develop a new block triangular preconditioner for solving partial differential equations with random coefficients. We prove spectral bounds for the preconditioned system. Several numerical examples are provided to demonstrate the efficiency of this preconditioner, especially for stochastic problems with large variance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Iterative self-organizing SCEne-LEvel sampling (ISOSCELES) for large-scale building extraction

Convolutional neural networks (CNN) provide state-of-the-art performance in many computer vision tasks, including those related to remote-sensing image analysis. Successfully training a CNN to generalize well to unseen data, however, requires training on samples that represent the full distribution of variation of both the target classes and their surrounding contexts. With remote sensing data, acquiring a sufficiently representative training set is a challenge due to both the inherent multi-modal variability of satellite or aerial imagery and the general high cost of labeling data. To address this challenge, we have developed ISOSCELES, an Iterative Self-Organizing SCEne LEvel Sampling method for hierarchical sampling of large image sets. Using affinity propagation, ISOSCELES automates the selection of highly representative training images. Compared to random sampling or using available reference data, the distribution of the training is principally data driven, reducing the chance of oversampling uninformative areas or undersampling informative ones. In comparison to manual sample selection by an analyst, ISOSCELES exploits descriptive features, spectral and/or textural, and eliminates human bias in sample selection. Using a hierarchical sampling approach, ISOSCELES can obtain a training set that reflects both between-scene variability, such as in viewing angle and time of day, and within-scene variability at the level of individual training samples. We verify the method by demonstrating its superiority to stratified random sampling in the challenging task of adapting a pre-trained model to a new image and spatial domain for country-scale building extraction. Using a pair of hand-labeled training sets comprising 1,987 sample image chips, a total of 496,000,000 individually labeled pixels, we show, across three distinct model architectures, an increase in accuracy, as measured by F1-score, of 2.2–4.2%.

42 ENGINEERING↗
DOE OSTI2021

van der Waals corrected density functionals for cylindrical surfaces: Ammonia and nitrogen dioxide adsorbed on a single-walled carbon nanotube

In this work, we extend the damped Zaremba-Kohn model (dZK) for long-range dispersion interaction between a molecule and a planar surface to molecules adsorbed on a curved cylindrical surface, and employ this extended model as an additive correction to the semilocal density functionals PBE (Perdew-Burke-Ernzerhof) and SCAN (strongly constrained and appropriately normed). The resulting PBE+vdW (van der Waals)-dZK and SCAN+vdW-dZK are applied to two systems, NH3 and NO2 molecules adsorbed on a single-wall carbon nanotube (CNT), for calculations of binding energies and equilibrium distances. For comparison, the results from vdW nonlocal functionals, such as SCAN+rVV10 and PBE+rVV10, are also presented. The binding energies from PBE+rVV10 (Vydrov and Van Voorhis), SCAN+rVV10, PBE+vdW-dZK, and SCAN+vdW-dZK are about 70–115 meV for the system of CNT + NH 3 and 300–500 meV for the system of CNT + NO 2 . The results from PBE+vdW-dZK and SCAN+vdW-dZK are closer to each other than those from PBE+rVV10 and SCAN+rVV10 are. The relatively closer results from PBE+vdW-dZK and SCAN+vdW-dZK indicate the consistency of our developed vdW–dZK model for cylindrical surfaces. All methods, including PBE, SCAN, PBE+rVV10, SCAN+rVV10, PBE+vdW-dZK, and SCAN+vdW-dZK, give approximately the same binding energy differences between two adsorption configurations (types I and II) for the two systems. This implies that the two adsorption sites have approximately the same adsorption stability. The exponent of the vdW interaction power law from our vdW-dZK model for the two systems is about 0 at short distance, largely due to the damping factor, and tends slowly to –4 to –4.5 at distances D about 20–50 Å. At even larger distances, the vdW power-law exponent approaches –5. This feature is very similar to the one calculated with random-phase approximation and renormalization group approaches, supporting the applicability of our methods. Our developed vdW-dZK method provides a highly efficient and reliable method for large systems with cylindrical surfaces, such as vdW interactions with nanotubes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Long-term missing value imputation for time series data using deep neural networks

We present an approach that uses a deep learning model, in particular, a MultiLayer Perceptron, for estimating the missing values of a variable in multivariate time series data. We focus on filling a long continuous gap (e.g., multiple months of missing daily observations) rather than on individual randomly missing observations. Our proposed gap filling algorithm uses an automated method for determining the optimal MLP model architecture, thus allowing for optimal prediction performance for the given time series. We tested our approach by filling gaps of various lengths (three months to three years) in three environmental datasets with different time series characteristics, namely daily groundwater levels, daily soil moisture, and hourly Net Ecosystem Exchange. We compared the accuracy of the gap-filled values obtained with our approach to the widely used R-based time series gap filling methods ImputeTS and mtsdi. The results indicate that using an MLP for filling a large gap leads to better results, especially when the data behave nonlinearly. Thus, our approach enables the use of datasets that have a large gap in one variable, which is common in many long-term environmental monitoring observations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A Monte Carlo volumetric-ray-casting estimator for global fluence tallies on GPUs

A Monte Carlo fluence estimator has been designed to take advantage of the computational power of graphical processing units (GPUs). This new estimator, termed the volumetric-ray-casting estimator, is an extension of the expectation estimator. It can be used as a replacement of the track-length estimator for the estimation of global fluence. Calculations for this estimator are performed on the GPU while the Monte Carlo random walk is performed on the central processing unit (CPU). This method lowers the implementation cost for GPU acceleration of existing Monte Carlo particle transport codes as there is little modification of the particle history logic flow. Three test problems have been evaluated to assess the performance of the volumetric-ray-casting estimator for neutron transport on GPU hardware in comparison to the standard track-length estimator on CPU hardware. Evaluation of neutron transport through air in a criticality accident scenario showed that the volumetric-ray-casting estimator achieved 23 times the performance of the track-length estimator using a single core CPU paired with a GPU and 15 times the performance of the track-length estimator using an eight core CPU paired with a GPU. Finally, simulation of a pressurized water reactor fuel assembly showed that the performance improvement was 6 times within the fuel and 7 times within the control rods using an eight core CPU paired with a single GPU.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Self-testing of a single quantum system from theory to experiment

Abstract Self-testing allows one to characterise quantum systems under minimal assumptions. However, existing schemes rely on quantum nonlocality and cannot be applied to systems that are not entangled. Here, we introduce a robust method that achieves self-testing of individual systems by taking advantage of contextuality. The scheme is based on the simplest contextuality witness for the simplest contextual quantum system—the Klyachko-Can-Binicioğlu-Shumovsky inequality for the qutrit. We establish a lower bound on the fidelity of the state and the measurements as a function of the value of the witness under a pragmatic assumption on the measurements. We apply the method in an experiment on a single trapped 40 Ca + using randomly chosen measurements and perfect detection efficiency. Using the observed statistics, we obtain an experimental demonstration of self-testing of a single quantum system.

Physics↗
DOE OSTI2015

Photoelectron circular dichroism in the multiphoton ionization by short laser pulses. I. Propagation of single-active-electron wave packets in chiral pseudo-potentials

A theoretical method to study the angle-resolved multiphoton ionization of polyatomic molecules is developed. It is based on the time-dependent formulation of the Single Center (TDSC) method and consists in the propagation of single-active-electron wave packets in the effective molecular potentials in the presence of intense laser pulses. For this purpose, the time-dependent Schrödinger equation for one electron, moving in a molecular field and interacting with an arbitrary laser pulse, is solved in spherical coordinates by an efficient numerical approach. As a test, the method is applied to the one- and two-photon ionizations of a model methane-like chiral system by circularly polarized short intense high-frequency laser pulses. Thereby, we analyze the photoelectron circular dichroism (PECD) in the momentum distribution. The considered model application illustrates the capability of the TDSC method to study multiphoton PECD in fixed-in-space and randomly oriented chiral molecules.

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

Transferring predictions of formation energy across lattices of increasing size*

In this study, we show the transferability of graph convolutional neural network (GCNN) predictions of the formation energy of the nickel-platinum solid solution alloy across atomic structures of increasing sizes. The original dataset was generated with the large-scale atomic/molecular massively parallel simulator using the second nearest-neighbor modified embedded-atom method empirical interatomic potential. Geometry optimization was performed on the initially randomly generated face centered cubic crystal structures and the formation energy has been calculated at each step of the geometry optimization, with configurations spanning the whole compositional range. Using data from various steps of the geometry optimization, we first trained our open-source, scalable implementation of GCNN called HydraGNN on a lattice of 256 atoms, which accounts well for the short-range interactions. Using this data, we predicted the formation energy for lattices of 864 atoms and 2048 atoms, which resulted in lower-than-expected accuracy due to the long-range interactions present in these larger lattices. We accounted for the long-range interactions by including a small amount of training data representative for those two larger sizes, whereupon the predictions of HydraGNN scaled linearly with the size of the lattice. Therefore, our strategy ensured scalability while reducing significantly the computational cost of training on larger lattice sizes.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Simulation of Rutherford backscattering spectrometry from arbitrary atom structures

Rutherford backscattering spectrometry in a channeling direction (RBS/C) is a powerful tool for analysis of the fraction of atoms displaced from their lattice positions. However, it is in many cases not straightforward to analyze what is the actual defect structure underlying the RBS/C signal. To reveal insights of RBS/C signals from arbitrarily complex defective atomic structures, we develop in this paper a method for simulating the RBS/C spectrum from a set of arbitrary read-in atom coordinates (obtained, e.g., from molecular dynamics simulations). We apply the developed method to simulate the RBS/C signals from Ni crystal structures containing randomly displaced atoms, Frenkel point defects, and extended defects, respectively. The RBS/C simulations show that, even for the same number of atoms in defects, the RBS/C signal is much stronger for the extended defects. Finally, comparison with experimental results shows that the disorder profile obtained from RBS/C signals in ion-irradiated Ni is due to a small fraction of extended defects rather than a large number of individual random atoms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

CHARACTERISTICS OF CYCLE-TO-CYCLE COMBUSTION VARIABILITY AT PARTIAL-BURN LIMITED AND MISFIRE LIMITED SPARK TIMING UNDER HIGHLY DILUTED CONDITIONS

Diluted combustion with exhaust gas recirculation (EGR) has been widely employed to improve the fuel economy of spark ignition engines. The combustion kinetics, however, are affected and the flame propagation speed is decreased. In order to compensate for this adverse effect, the spark timing needs to be recalibrated to achieve maximum brake torque (MBT). At high levels of EGR dilution, the spark timing is constrained by two ignition limits: 1) the partial-bum limit where the spark timing is retarded from MBT and 2) the misfire limit where the spark timing is too advanced. This work uses a probabilistic framework to capture the differences between both ignition limits. In particular, it introduces the concept of a nominal indicated mean effective pressure (IMEP) distribution based on the stochastic properties of the cycle-to-cycle variability (CCV) at nominal stable conditions. By defining a nominal band where fully burned cycles occur with high probability, we introduce a cycle classification method that can be used to 1) determine the level of randomness of misfire and partial-burn events, and 2) measure CCV. The new CCV metric based on the density of the nominal band is compared with the traditional coefficient of variation of IMEP (CoVIMEP). It is shown that the nominal band concept, together with the CoVIMEP, can help to discern between partial-burn limited and misfire limited conditions. Furthermore, the Kullback-Leibler divergence is used to demonstrate that the IMEP distribution is significantly different between nominal and partial-burn/misfire limited conditions. Experiments are carried at various EGR levels and spark timings while recoding in-cylinder pressure at steady state. Although the emphasis of this work is to characterize the differences of both ignition limits from a probabilistic point of view, similarities between partial-burn cycles at either limiting conditions are also discussed.

Maldonado, Bryan↗