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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 OSTI2017

Delineating Facies Spatial Distribution by Integrating Ensemble Data Assimilation and Indicator Geostatistics with Level Set Transformation.

A new approach is developed to delineate the spatial distribution of discrete facies (geological units that have unique distributions of hydraulic, physical, and/or chemical properties) conditioned not only on direct data (measurements directly related to facies properties, e.g., grain size distribution obtained from borehole samples) but also on indirect data (observations indirectly related to facies distribution, e.g., hydraulic head and tracer concentration). Our method integrates for the first time ensemble data assimilation with traditional transition probability-based geostatistics. The concept of level set is introduced to build shape parameterization that allows transformation between discrete facies indicators and continuous random variables. The spatial structure of different facies is simulated by indicator models using conditioning points selected adaptively during the iterative process of data assimilation. To evaluate the new method, a two-dimensional semi-synthetic example is designed to estimate the spatial distribution and permeability of two distinct facies from transient head data induced by pumping tests. The example demonstrates that our new method adequately captures the spatial pattern of facies distribution by imposing spatial continuity through conditioning points. The new method also reproduces the overall response in hydraulic head field with better accuracy compared to data assimilation with no constraints on spatial continuity on facies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Dibenzothiophene adsorption at boron doped carbon nanoribbons studied within density functional theory

The adsorption of dibenzothiophene (DBT) on bare and boron-doped armchair carbon nanoribbons (ACNRs) is being investigated in the framework of the density functional theory by implementing periodic boundary conditions that include corrections from dispersion interactions. The reactivity of the ACNRs is characterized by using the Fukui functions as well as the electrostatic potential as local descriptors. Non-covalent adsorption mechanism is found when using the local Perdew-Becke-Ernzerhof functional, regardless of the DBT orientation and adsorption location. The dispersion interactions addition is a milestone to describe the adsorption process. The charge defects introduced in small number (i.e., by doping with B atoms), within the ACNRs increases the selectivity towards sulfur mainly due to the charge depletion at B sites. The DBT magnitude in the adsorption energy shows non-covalent interactions. As a consequence, the configurations where the DBT is adsorbed on a BC{sub 3} island increase the adsorption energy compared to random B arrangements. The stability of these configurations can be explained satisfactorily in terms of dipole interactions. Nevertheless, from the charge-density difference analysis and the weak Bader charge-distribution interactions cannot be ruled out completely. This is why the electronic properties of the ribbons are analyzed in order to elucidate the key role played by the B and DBT states in the adsorbed configurations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Uncertainty quantification in application of the enrichment meter principle for nondestructive assay of special nuclear material

The various methods of nondestructive assay (NDA) of special nuclear material (SNM) have applications in nuclear nonproliferation, including detection and identification of illicit SNM at border crossings, and quantifying SNM at nuclear facilities for safeguards. No assay method is complete without “error bars,” which provide one way of expressing confidence in the assay result. Consequently, NDA specialists typically quantify total uncertainty in terms of “random” and “systematic” components, and then specify error bars for the total mass estimate in multiple items. Uncertainty quantification (UQ) for NDA has always been important, but it is recognized that greater rigor is needed and achievable using modern statistical methods. To this end, we describe the extent to which the guideline for expressing uncertainty in measurements (GUM) can be used for NDA. Also, we propose improvements over GUM for NDA by illustrating UQ challenges that it does not address, including calibration with errors in predictors, model error, and item-specific biases. A case study is presented using low-resolution NaI spectra and applying the enrichment meter principle to estimate the U-235 mass in an item. The case study illustrates how to update the current American Society for Testing and Materials guide for application of the enrichment meter principle using gamma spectra from a NaI detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

Evaluation of Automated Model Calibration Techniques for Residential Building Energy Simulation

This simulation study adapts and applies the general framework described in BESTEST-EX (Judkoff et al 2010) for self-testing residential building energy model calibration methods. BEopt/DOE-2.2 is used to evaluate four mathematical calibration methods in the context of monthly, daily, and hourly synthetic utility data for a 1960's-era existing home in a cooling-dominated climate. The home's model inputs are assigned probability distributions representing uncertainty ranges, random selections are made from the uncertainty ranges to define 'explicit' input values, and synthetic utility billing data are generated using the explicit input values. The four calibration methods evaluated in this study are: an ASHRAE 1051-RP-based approach (Reddy and Maor 2006), a simplified simulated annealing optimization approach, a regression metamodeling optimization approach, and a simple output ratio calibration approach. The calibration methods are evaluated for monthly, daily, and hourly cases; various retrofit measures are applied to the calibrated models and the methods are evaluated based on the accuracy of predicted savings, computational cost, repeatability, automation, and ease of implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Evaluation of Automated Model Calibration Techniques for Residential Building Energy Simulation

This simulation study adapts and applies the general framework described in BESTEST-EX (Judkoff et al 2010) for self-testing residential building energy model calibration methods. BEopt/DOE-2.2 is used to evaluate four mathematical calibration methods in the context of monthly, daily, and hourly synthetic utility data for a 1960's-era existing home in a cooling-dominated climate. The home's model inputs are assigned probability distributions representing uncertainty ranges, random selections are made from the uncertainty ranges to define "explicit" input values, and synthetic utility billing data are generated using the explicit input values. The four calibration methods evaluated in this study are: an ASHRAE 1051-RP-based approach (Reddy and Maor 2006), a simplified simulated annealing optimization approach, a regression metamodeling optimization approach, and a simple output ratio calibration approach. The calibration methods are evaluated for monthly, daily, and hourly cases; various retrofit measures are applied to the calibrated models and the methods are evaluated based on the accuracy of predicted savings, computational cost, repeatability, automation, and ease of implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Late Radiation and Cardiovascular Adverse Effects After Androgen Deprivation and High-Dose Radiation Therapy in Prostate Cancer: Results From the DART 01/05 Randomized Phase 3 Trial

Purpose: To present data on the late toxicity endpoints of a randomized trial (DART 01/05) conducted to determine whether long-term androgen deprivation (LTAD) was superior to short-term AD (STAD) when combined with high-dose radiation therapy (HDRT) in patients with prostate cancer (PCa). Patients and Methods: Between November 2005 and December 2010, 355 eligible men with cT1c-T3aN0M0 PCa and intermediate-risk and high-risk factors (2005 National Comprehensive Cancer Network criteria) were randomized to 4 months of AD combined with HDRT (median dose, 78 Gy) (STAD) or the same treatment followed by 24 months of AD (LTAD). Treatment-related complications were assessed using European Organization for Research and Treatment of Cancer–Radiation Therapy Oncology Group and Common Terminology Criteria for Adverse Events v3.0 scoring schemes. Multivariate analyses for late toxicity were done using the Fine-Gray method. Results: The 5-year incidence of grade ≥2 rectal and urinary toxicity was 11.1% and 8.2% for LTAD and 7.6% and 7.3% for STAD, respectively. Compared with STAD, LTAD was not significantly associated with a higher risk of late grade ≥2 rectal toxicity (hazard ratio [HR] 1.360, 95% confidence interval [CI] 0.660-2.790, P=.410) or urinary toxicity (HR 1.028, 95% CI 0.495-2.130, P=.940). The multivariate analysis showed that a baseline history of intestinal comorbidity (HR 3.510, 95% CI 1.560-7.930, P=.025) and the rectal volume receiving >60 Gy (Vr60) (HR 1.030, 95% CI 1.001-1.060, P=.043) were the only factors significantly correlated with the risk of late grade ≥2 rectal complications. A history of previous surgical prostate manipulations was significantly associated with a higher risk of grade ≥2 urinary complications (HR 2.427, 95% CI 1.051-5.600, P=.038). Long-term AD (HR 2.090; 95% CI 1.170-3.720, P=.012) and a history of myocardial infarction (HR 2.080; 95% CI 1.130-3.810, P=.018) were significantly correlated with a higher probability of cardiovascular events. Conclusion: Long-term AD did not significantly impact urinary or rectal radiation-induced toxicity, although it was associated with a higher risk of cardiovascular events. Longer follow-up is needed to measure the impact of AD on late morbidity and non-PCa mortality.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Growth and magnetoelectric properties of (00l)-oriented La{sub 0.67}Sr{sub 0.33}MnO{sub 3}/PbZr{sub 0.52}Ti{sub 0.48}O{sub 3} heterostructure films

C-axis oriented La{sub 0.67}Sr{sub 0.33}MnO{sub 3}(LSMO)/PbZr{sub 0.52}Ti{sub 0.48}O{sub 3}(PZT) films are fabricated successfully by sol-gel method on LaAlO{sub 3} (00l) substrates. The structure, composition and morphology of the films are investigated by X-ray diffractometer (XRD, θ-2θ scan, ω-scan and ϕ-scan), X-ray photoelectron spectroscope (XPS), field emission scanning electron microscope (FESEM) and high resolution transmission electron microscope (HRTEM). The electric and magnetic properties of randomly and c-axis oriented LSMO/PZT films are studied comparably using ferroelectric testing apparatus and physical property measurement system (PPMS). It is found that the epitaxial LSMO/PZT composite films show well controlled growth along c-axis, and much better magnetoelectric properties than the randomly oriented ones. The ME voltage coefficient increases from 23 mV cm{sup −1} Oe{sup −1} for the randomly oriented LSMO/PZT composite films to 52 mV cm{sup −1} Oe{sup −1} for c-axis oriented ones prepared using the low cost sol-gel method presented in this study, which shows high potential in promising applications. - Highlights: •Epitaxial LSMO/PZT films were fabricated successfully by sol-gel method on LAO (00l) substrate. •The prepared films exhibit well-defined multiferroic properties for the epitaxial LSMO/PZT films. •Epitaxial LSMO/PZT films show superior magnetoelectric properties to the randomly oriented ones.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Random mask optimization for fast neutron coded aperture imaging

In coded aperture imaging, one of the most important factors determining the quality of reconstructed images is the choice of mask/aperture pattern. In many applications, uniformly redundant arrays (URAs) are widely accepted as the optimal mask pattern. Under ideal conditions, thin and highly opaque masks, URA patterns are mathematically constructed to provide artifact-free reconstruction however, the number of URAs for a chosen number of mask elements is limited and when highly penetrating particles such as fast neutrons and high-energy gamma-rays are being imaged, the optimum is seldom achieved. In this case more robust mask patterns that provide better reconstructed image quality may exist. Through the use of heuristic optimization methods and maximum likelihood expectation maximization (MLEM) image reconstruction, we show that for both point and extended neutron sources a random mask pattern can be optimized to provide better image quality than that of a URA.

McMillan, Kyle↗
DOE OSTI2021

Large gradients via correlation in random parameterized quantum circuits

Scaling of variational quantum algorithms to large problem sizes requires efficient optimization of random parameterized quantum circuits. For such circuits with uncorrelated parameters, the presence of exponentially vanishing gradients in cost function landscapes is an obstacle to optimization by gradient descent methods. In this work, we prove that reducing the dimensionality of the parameter space by utilizing circuit modules containing spatially or temporally correlated gate layers can allow one to circumvent the vanishing gradient phenomenon. Here, examples are drawn from random separable circuits and asymptotically optimal variational versions of Grover's algorithm based on the quantum alternating operator ansatz. In the latter scenario, our bounds on cost function variation imply a transition between vanishing gradients and efficient trainability as the number of layers is increased toward $\mathcal{O}\left({2}^{n/2}\right)$, the optimal oracle complexity of quantum unstructured search.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Sequential Image Recovery Using Joint Hierarchical Bayesian Learning

Abstract Recovering temporal image sequences (videos) based on indirect, noisy, or incomplete data is an essential yet challenging task. We specifically consider the case where each data set is missing vital information, which prevents the accurate recovery of the individual images. Although some recent (variational) methods have demonstrated high-resolution image recovery based on jointly recovering sequential images, there remain robustness issues due to parameter tuning and restrictions on the type of sequential images. Here, we present a method based on hierarchical Bayesian learning for the joint recovery of sequential images that incorporates prior intra- and inter-image information. Our method restores the missing information in each image by “borrowing” it from the other images. More precisely, we couple sequential images by penalizing their pixel-wise difference. The corresponding penalty terms (one for each pixel and pair of subsequent images) are treated as weakly-informative random variables that favor small pixel-wise differences but allow occasional outliers. As a result, all of the individual reconstructions yield improved accuracy. Our method can be used for various data acquisitions and allows for uncertainty quantification. Some preliminary results indicate its potential use for sequential deblurring and magnetic resonance imaging.

Xiao, Yao↗
DOE OSTI2015

Introduction of a valence space in QRPA: Impact on vibrational mass parameters and spectroscopy

For the first time, using a unique finite range interaction (D1M Gogny force), a fully coherent and time-feasible calculation of the Bohr Hamiltonian vibrational mass is envisioned in a Hartree-Fock-Bogoliubov + Quasiparticle Random Phase Approximation (QRPA) framework. In order to reach handable computation time, we evaluate the feasibility of this method by considering the insertion of a valence space for QRPA. We validate our approach in the even-even tin isotopes comparing the convergence scheme of the mass parameter with those of built-in QRPA outputs: excited state energy and reduced transition probability. The seeming convergence of these intrinsic quantities is shown to be misleading and the difference with the theoretical expected value is quantified. This work is a primary step towards the systematic calculation of mass parameters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

SU-E-T-125: Dosimetric Comparison of Intensity Modulated Radiation Therapy Using Robotic Versus Traditional Linac Platform in Prostate Cancer

Purpose: Recent development of an MLC for robotic external beam radiotherapy has the potential of new clinical application in conventionally fractionated radiation therapy. This study offers a dosimetric comparison of IMRT plans using Cyberknife with MLC versus conventional linac plans. Methods: Ten prostate cancer patients treated on a traditional linac with IMRT to 7920cGy at 180cGy/fraction were randomly selected. GTVs were defined as prostate plus proximal seminal vesicles. PTVs were defined as GTV+8mm in all directions except 5mm posteriorly. Conventional IMRT planning was performed on Philips Pinnacle and delivered on a standard linac with CBCT and 10mm collimator leaf width. For each case a Cyberknife plan was created using Accuray Multiplan with same CT data set, contours, and dose constraints. All dosimetric data was transferred to third party software for independent computation of contour volumes and DVH. Delivery efficiency was evaluated using total MU, treatment time, number of beams, and number of segments. Results: Evaluation criteria including percent target coverage, homogeneity index, and conformity index were found to be comparable. All dose constraints from QUANTEC were found to be statistically similar except rectum V50Gy and bladder V65Gy. Average rectum V50Gy was lower for robotic IMRT (30.07%±6.57) versus traditional (34.73%±3.62, p=0.0130). Average bladder V65Gy was lower for robotic (17.87%±12.74) versus traditional (21.03%±11.93, p=0.0405). Linac plans utilized 9 coplanar beams, 48.9±3.8 segments, and 19381±2399MU. Robotic plans utilized 38.4±9.0 non-coplanar beams, 85.5±21.0 segments and 42554.71±16381.54 MU. The average treatment was 15.02±0.60 minutes for traditional versus 20.90±2.51 for robotic. Conclusion: The robotic IMRT plans were comparable to the traditional IMRT plans in meeting the target volume dose objectives. Critical structure dose constraints were largely comparable although statistically significant differences were found in favor of the robotic platform in terms of rectum V50Gy and bladder V65Gy at a cost of 25% longer treatment time.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-I-83: Error Analysis of Multi-Modality Image-Based Volumes of Rodent Solid Tumors Using a Preclinical Multi-Modality QA Phantom

Purpose: In our previous study a preclinical multi-modality quality assurance (QA) phantom that contains five tumor-simulating test objects with 2, 4, 7, 10 and 14 mm diameters was developed for accurate tumor size measurement by researchers during cancer drug development and testing. This study analyzed the errors during tumor volume measurement from preclinical magnetic resonance (MR), micro-computed tomography (micro- CT) and ultrasound (US) images acquired in a rodent tumor model using the preclinical multi-modality QA phantom. Methods: Using preclinical 7-Tesla MR, US and micro-CT scanners, images were acquired of subcutaneous SCC4 tumor xenografts in nude rats (3–4 rats per group; 5 groups) along with the QA phantom using the same imaging protocols. After tumors were excised, in-air micro-CT imaging was performed to determine reference tumor volume. Volumes measured for the rat tumors and phantom test objects were calculated using formula V = (π/6)*a*b*c where a, b and c are the maximum diameters in three perpendicular dimensions determined by the three imaging modalities. Then linear regression analysis was performed to compare image-based tumor volumes with the reference tumor volume and known test object volume for the rats and the phantom respectively. Results: The slopes of regression lines for in-vivo tumor volumes measured by three imaging modalities were 1.021, 1.101 and 0.862 for MRI, micro-CT and US respectively. For phantom, the slopes were 0.9485, 0.9971 and 0.9734 for MRI, micro-CT and US respectively. Conclusion: For both animal and phantom studies, random and systematic errors were observed. Random errors were observer-dependent and systematic errors were mainly due to selected imaging protocols and/or measurement method. In the animal study, there were additional systematic errors attributed to ellipsoidal assumption for tumor shape. The systematic errors measured using the QA phantom need to be taken into account to reduce measurement errors during the animal study.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Hybrid Ultrasonic TOFD Imaging of Weld Flaws Using Wavelet Transforms and Image Registration

Ultrasonic time of flight diffraction (TOFD) is an effective weld crack inspection technique. Due to the intensity of diffraction wave is rather weak compared with the lateral wave and the bottom echo wave, thus the signal-to-ratio (SNR) of TOFD image is low. A new dichotomous method is comprised of two steps that contains wavelet shrinkage and image registration is proposed in this paper to reduce the noise and improve the resolution of TOFD images as well. In order to evaluate the reliability of our proposed method in this paper, we have established the experiment system, and sampled a number of TOFD data with random distribution of noise characteristics. We adopted one-dimension wavelet transform and two-dimension wavelet transform in the very beginning of the first step of the proposed algorithm respectively. The SNR of the result obtained in this step is improved significantly compared with the classic algorithms. Next, the image registration is applied. After the registered images have been added to form a new one, then it comes to the final result that shows not only the SNR but also the definition of the image is enhanced effectively.

42 ENGINEERING↗
DOE OSTI2019

A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness

The challenge of quantifying uncertainty propagation in real-world systems is rooted in the high-dimensionality of the stochastic input and the frequent lack of explicit knowledge of its probability distribution. Traditional approaches show limitations for such problems, especially when the size of the training data is limited. To address these diculties, we have developed a general framework of constructing surrogate models on spaces of stochastic input with arbitrary probability measure irrespective of the mutual dependencies between individual components of the random inputs and the analytical form. The present Data-driven Sparsity-enhancing Rotation for Arbitrary Randomness (DSRAR) framework includes a data-driven construction of multivariate polynomial basis for arbitrary mutually dependent probability measure and a sparsity enhancement rotation procedure. This sparsity-enhancing rotation method was initially proposed in our previous work [1] for Gaussian density distributions, which may not be feasible for non-Gaussian distributions due to the loss of orthogonality after the rotation. To remedy such diculties, we developed a new data-driven approach to construct orthonormal polynomials for polynomials for arbitrary mutually dependent (amdP) randomness, ensuring the constructed basis maintains the orthogonality/near-orthogonality with respect to the density of the rotated random vector, where directly applying the regular polynomial chaos including arbitrary polynomial chaos (aPC) [2] shows limitations due to the assumption of the mutual independence between the components of the random inputs. The developed DSRAR framework leads to accurate recovery, with only limited training data, of a sparse representation of the target functions. The eectiveness of our method is demonstrated in challenging problems such as PDEs and realistic molecular systems within high-dimensional conformational space (O(10)) where the underlying density is implicitly represented by a large collection of sample data, as well as systems with explicitly given non-Gaussian probabilistic measures.

Lei, Huan↗
DOE OSTI2021

Analysis and prediction of intersection traffic violations using automated enforcement system data

We report that the automated enforcement system (AES) is an effective way of supplementing traditional traffic enforcement, and the traffic violation data from AES can also be effectively used for safety research. In this study, traffic violation data were used to analyze the influencing factors associated with traffic violations and to predict the probability of violations at intersections. The potential factors influencing violations include 24 independent factors related to time, space, traffic and weather. Results from a logistic model showed that the midday period, weekends, residential districts, collector roads, congested traffic conditions, high traffic flow, lower wind speed and low temperature would increase the probability of traffic violations. The probability of violations was predicted by the random forest algorithm, which was proven to be the best traffic violation prediction model among logistic regression, Gaussian naive Bayes, and support vector machine. Moreover, the proximity weighted synthetic oversampling technique (ProWSyn) method was applied to reduce the impact of the imbalance ratio (IR) and improve the model’s prediction performance. The receiver operating characteristics (ROC) curves and Precision-Recall (PR) curves illustrated that the random forest algorithm using oversampling data had the best classifier prediction performance than undersampling data. The area under curve (AUC) and out-of-bag (OOB) error with IR = 1 reached 0.914 and 0.0787, which showed the better performance of the random forest algorithm using ProWSyn in dealing with imbalanced traffic violation data.

42 ENGINEERING↗
DOE OSTI2022

Generation of Tunable Stochastic Sequences Using the Insulator–Metal Transition

Probabilistic computing is a paradigm in which data are not represented by stable bits, but rather by the probability of a metastable bit to be in a particular state. The development of this technology has been hindered by the availability of hardware capable of generating stochastic and tunable sequences of “1s” and “0s”. The options are currently limited to complex CMOS circuitry and, recently, magnetic tunnel junctions. Here, we demonstrate that metal–insulator transitions can also be used for this purpose. We use an electrical pump/probe protocol and take advantage of the stochastic relaxation dynamics in VO 2 to induce random metallization events. A simple latch circuit converts the metallization sequence into a random stream of 1s and 0s. The resetting pulse in between probes decorrelates successive events, providing a true stochastic digital sequence.

97 MATHEMATICS AND COMPUTING↗