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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Geometrical defect detection for additive manufacturing with machine learning models

This study proposed a scheme based on Machine Learning (ML) models to detect geometric defects of additively manufactured objects. The ML models are trained with synthetic 3D point clouds with defects and then applied to detect defects in actual production. Using synthetic 3D point clouds rather than experimental data could save a huge amount of training time and costs associated with many prints for each design. Besides distance differences of individual points between source and target point clouds, this scheme uses a new concept called “patch” to capture macro-level information about nearby points for ML training and implementation. Numerical comparisons of prediction results on experimental data with different shapes showed that the proposed scheme outperformed the existing Z-difference method in the literature. Five ML methods (Bagging of Trees, Gradient Boosting, Random Forest, K-nearest Neighbors and Linear Supported Vector Machine) were compared under various conditions, such as different point cloud densities and defect sizes. Bagging and Random Forest were found the two best models regarding predictability; and the right patch size was found to be at 20. The proposed ML-based scheme is applicable to in-situ defect detection during additive manufacturing with the aid of a proper 3D data acquisition system.

Additive manufacturing↗
DOE OSTI2021

Improving Prediction of Surface Solar Irradiance Variability by Integrating Observed Cloud Characteristics and Machine Learning

A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.

Riihimaki, Laura↗
DOE OSTI2023

Ultrasonic measurement of temperature distributions in extreme environments: Electrical power plants testing in utility-scale steam generators

Thermal heterogeneities within energy conversion and storage, material processing, nuclear processes, aerospace, and military applications are often inaccessible to characterization by insertion sensors. When sensor deployment is possible, conventional pointwise temperature probes quickly degrade when inserted into harsh environments typical of such processes. We developed spatially-resolved ultrasonic thermometry to noninvasively measure the spatial distributions of thermal properties in such applications, even when sizable thermal gradients are present. Our method divides the path of ultrasonic propagation into segments bound by echogenic features, which create echoes in pulse-echo mode, encoding the information about interior temperature distributions. We use the acquired ultrasonic responses to estimate the internal temperature distributions by solving an inverse problem or concatenating segmental estimates. This work describes the implementation and industrial testing of the developed method at a coal-fired electrical power generation plant. We inserted an echogenically segmented Inconel 625 waveguide into the combustion zone of the utility-scale boiler and continuously acquired ultrasonic data while keeping sensitive components away from the damaging combustion environment. The accuracy of the time-dependent temperature distributions reconstructed from the ultrasonic measurements was comparable to that of thermocouples. The resiliency of ultrasonic thermometry to harsh combustion conditions was far superior to conventional insertion sensors. The measurements obtained during plant operation captured daily steam generation cycles in response to changing customer demand and intermittent contributions of renewable power sources to the power grid. These measurements have revealed new insights into the relationship between the dynamic power generation load and the conditions inside the steam generator. As a result, the successful industrial testing of spatially-resolved ultrasonic thermometry in solids indicates that the developed technology has matured to become an attractive alternative to conventional sensing in solving challenging problems of long-term thermal characterizations in extreme environments.

42 ENGINEERING↗
DOE OSTI2010

Multiparameter Screening on SlipChip Used for Nanoliter Protein Crystallization Combining Free Interface Diffusion and Microbatch Methods

This paper describes two SlipChip-based approaches to protein crystallization: a SlipChip-based free interface diffusion (FID) method and a SlipChip-based composite method that simultaneously performs microbatch and FID crystallization methods in a single device. The FID SlipChip was designed to screen multiple reagents, each at multiple diffusion equilibration times, and was validated by screening conditions for crystallization of two proteins, enoyl-CoA hydratase from Mycobacterium tuberculosis and dihydrofolate reductase/thymidylate synthase from Babesia bovis, against 48 different reagents at five different equilibration times each, consuming 12 {micro}L of each protein for a total of 480 experiments using three SlipChips. The composite SlipChip was designed to screen multiple reagents, each at multiple mixing ratios and multiple equilibration times, and was validated by screening conditions for crystallization of two proteins, enoyl-CoA hydratase from Mycobacterium tuberculosis and dihydrofolate reductase/thymidylate synthase from Babesia bovis. To prevent cross-contamination while keeping the solution in the neck channels for FID stable, the plates of the SlipChip were etched with a pattern of nanowells. This nanopattern was used to increase the contact angle of aqueous solutions on the surface of the silanized glass. The composite SlipChip increased the number of successful crystallization conditions and identified more conditions for crystallization than separate FID and microbatch screenings. Crystallization experiments were scaled up in well plates using conditions identified during the SlipChip screenings, and X-ray diffraction data were obtained to yield the protein structure of dihydrofolate reductase/thymidylate synthase at 1.95 {angstrom} resolution. This free-interface diffusion approach provides a convenient and high-throughput method of setting up gradients in microfluidic devices and may find additional applications in cell-based assays.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Predicting Flow in Fracture Networks With Quantum Algorithms

Uncertainty quantification plays a crucial role in the modeling of subsurface flow. For instance, uncertainties in the properties of geologic fracture networks significantly impact flow, requiring numerous simulations to accurately estimate quantities of interest. However, each simulation is computationally expensive because it requires solving a large linear system to capture features that involve both small and large fractures. An example is in percolation, where the interaction of many small fractures (which cumulatively can have a large surface area) with the rock matrix must be modeled precisely. Quantum computing is an emerging tool with the potential to address this issue. Quantum algorithms offer a significant speedup in solving linear systems, achieving efficiencies that are challenging to match with classical approaches. These classical approaches include direct solvers, such as LU decomposition, and iterative methods, notably preconditioned conjugate gradient, commonly used in subsurface modeling to solve large sparse systems. However, applying quantum algorithms to geologic fracture flow requires careful attention to algorithmic and problem-specific constraints to fully realize this quantum advantage. In this work we describe a quantum algorithm for generalized Monte Carlo applications with a quadratic speedup over the classical approaches which can be combined with the quantum speedup, currently under investigation, for solving quantum linear systems for subsurface flow. We show that for quantum algorithms the computational cost of estimating a quantity of interest for a statistical ensemble of networks is roughly the same as that of a single realization, essentially implying that one can get uncertainty quantification for free.

58 GEOSCIENCES↗
DOE OSTI2014

Exchange integrals in magnetoelectric hexagonal ferrite (SrCo{sub 2}Ti{sub 2}Fe{sub 8}O{sub 19}): A density functional study

The exchange integrals in magnetoelectric hexagonal ferrite SrCo{sub 2}Ti{sub 2}Fe{sub 8}O{sub 19} have been calculated by using density functional theory. To get 10 inter-sublattice and 3 intra-sublattice exchange integrals, the electronic structures and total energies of 20 spin arrangements have been calculated with General Gradient Approximation (GGA) + U method. The dependence of exchange integrals on U has been studied. The comparison between the exchange integrals in SrFe{sub 12}O{sub 19} and those in SrCo{sub 2}Ti{sub 2}Fe{sub 8}O{sub 19} shows that substitution of Co and Ti decreases the most interactions involving the 12 k sites. The investigation based on our exchange integrals indicates that magnetic interaction between R and S blocks reduces significantly in SrCo{sub 2}Ti{sub 2}Fe{sub 8}O{sub 19}.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Optical properties of armchair (7, 7) single walled carbon nanotubes

Full potential linearized augmented plane waves method with the generalized gradient approximation for the exchange-correlation potential was applied to calculate the optical properties of (7, 7) single walled carbon nanotubes. The both x and z directions of the incident photons were applied to estimate optical gaps, dielectric function, electron energy loss spectroscopies, optical conductivity, optical extinction, optical refractive index and optical absorption coefficient. The results predict that dielectric function, ε (ω), is anisotropic since it has higher peaks along z-direction than x-direction. The static optical refractive constant were calculated about 1.4 (z-direction) and 1.1 (x- direction). Moreover, the electron energy loss spectroscopy showed a sharp π electron plasmon peaks at about 6 eV and 5 eV for z and x-directions respectively. The calculated reflection spectra show that directions perpendicular to the tube axis have further optical reflection. Moreover, z-direction indicates higher peaks at absorption spectra in low range energies. Totally, increasing the diameter of armchair carbon nanotubes cause the optical band gap, static optical refractive constant and optical reflectivity to decrease. On the other hand, increasing the diameter cause the optical absorption and the optical conductivity to increase. Moreover, the sharp peaks being illustrated at optical spectrum are related to the 1D structure of CNTs which confirm the accuracy of the calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Aerodynamic optimization of supersonic compressor cascade using differential evolution on GPU

Differential Evolution (DE) is a powerful stochastic optimization method. Compared to gradient-based algorithms, DE is able to avoid local minima but requires at the same time more function evaluations. In turbomachinery applications, function evaluations are performed with time-consuming CFD simulation, which results in a long, non affordable, design cycle. Modern High Performance Computing systems, especially Graphic Processing Units (GPUs), are able to alleviate this inconvenience by accelerating the design evaluation itself. In this work we present a validated CFD Solver running on GPUs, able to accelerate the design evaluation and thus the entire design process. An achieved speedup of 20x to 30x enabled the DE algorithm to run on a high-end computer instead of a costly large cluster. The GPU-enhanced DE was used to optimize the aerodynamics of a supersonic compressor cascade, achieving an aerodynamic loss minimization of 20%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

THE SPECTROSCOPIC DIVERSITY OF TYPE Ia SUPERNOVAE

We present 2603 spectra of 462 nearby Type Ia supernovae (SNe Ia), including 2065 previously unpublished spectra, obtained during 1993-2008 through the Center for Astrophysics Supernova Program. There are on average eight spectra for each of the 313 SNe Ia with at least two spectra. Most of the spectra were obtained with the FAST spectrograph at the Fred Lawrence Whipple Observatory 1.5 m telescope and reduced in a consistent manner, making this data set well suited for studies of SN Ia spectroscopic diversity. Using additional data from the literature, we study the spectroscopic and photometric properties of SNe Ia as a function of spectroscopic class using the classification schemes of Branch et al. and Wang et al. The width-luminosity relation appears to be steeper for SNe Ia with broader lines, although the result is not statistically significant with the present sample. Based on the evolution of the characteristic Si II {lambda}6355 line, we propose improved methods for measuring velocity gradients, revealing a larger range than previously suspected, from {approx}0 to {approx}400 km s{sup -1} day{sup -1} considering the instantaneous velocity decline rate at maximum light. We find a weaker and less significant correlation between Si II velocity and intrinsic B - V color at maximum light than reported by Foley et al., owing to a more comprehensive treatment of uncertainties and host galaxy dust. We study the extent of nuclear burning and the presence of unburnt carbon in the outermost layers of the ejecta and report new detections of C II {lambda}6580 in 23 early-time SN Ia spectra. The frequency of C II detections is not higher in SNe Ia with bluer colors or narrower light curves, in conflict with the recent results of Thomas et al. Based on nebular spectra of 27 SNe Ia, we find no relation between the FWHM of the iron emission feature at {approx}4700 A and {Delta}m{sub 15}(B) after removing the two low-luminosity SN 1986G and SN 1991bg, suggesting that the peak luminosity is not strongly dependent on the kinetic energy of the explosion for most SNe Ia. Finally, we confirm the correlation of velocity shifts in some nebular lines with the intrinsic B - V color of SNe Ia at maximum light, although several outliers suggest a possible non-monotonic behavior for the largest blueshifts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Degenerate doping in B-Ga 2 O 3 Single Crystals through Hf-doping

n-type conductivity of β-Ga 2 O 3 grown from the melt is typically achieved using Sn and Si. In this paper, we experimentally and computationally investigate Hf doping of β-Ga 2 O 3 single crystals using UV–vis-NIR absorption and Hall effect measurements and hybrid functional calculations. Unintentionally-doped and Hf-doped samples with a nominal concentration of 0.5at% were grown from the melt using vertical gradient freeze and Czochralski method in mixed Ar + O 2 atmosphere. We demonstrate Hf dopants, predicted to incorporate on the octahedral GaII site as a shallow donor, achieve degenerate doping in β-Ga 2 O 3 with a measured electron concentration ~2 × 10 19 cm -3 , mobility 80–65 cm 2 V -1 s -1 , and resistivity down to 5 mΩ cm in our samples. The concentration of Hf was measured to be 1.3 × 10 19 atoms cm -3 using glow discharge mass spectroscopy on doped samples, confirming Hf to be the cause of n-type conductivity (electron concentration ~2 × 10 19 cm -3 ).

36 MATERIALS SCIENCE↗
DOE OSTI2020

A log-barrier Newton-CG method for bound constrained optimization with complexity guarantees

Abstract We describe an algorithm based on a logarithmic barrier function, Newton’s method and linear conjugate gradients that seeks an approximate minimizer of a smooth function over the non-negative orthant. We develop a bound on the complexity of the approach, stated in terms of the required accuracy and the cost of a single gradient evaluation of the objective function and/or a matrix-vector multiplication involving the Hessian of the objective. The approach can be implemented without explicit calculation or storage of the Hessian.

O’Neill, Michael↗
DOE OSTI2023

Infrared fixed point of the SU(3) gauge theory with 𝑁 𝑓 = 10 flavors

We use lattice simulations and the continuous renormalization group method, based on the gradient flow, to calculate the 𝛽 function and anomalous dimensions of the SU(3) gauge theory with 𝑁 𝑓 = 10 flavors of fermions in the fundamental representation. We employ several improvements to extend the range of available renormalized couplings, including the addition of heavy Pauli-Villars bosons to reduce cutoff effects and the combination of a range of gradient flow transformations. While in the weak coupling regime our result is consistent with those of earlier studies, our techniques allow us to study the system at much stronger couplings than previously possible. We find that the renormalization group 𝛽 function develops a zero, corresponding to an infrared-stable fixed point, at gradient-flow coupling 𝑔 2 = 15.0⁢(5). We also determine the mass and tensor anomalous dimensions: At the fixed point we find 𝛾 𝑚 ≃0.6, suggesting that this system might be deep inside the conformal window.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

AlignOT: An Optimal Transport Based Algorithm for Fast 3D Alignment With Applications to Cryogenic Electron Microscopy Density Maps

Aligning electron density maps from Cryogenic electron microscopy (cryo-EM) is a first key step for studying multiple conformations of a biomolecule. As this step remains costly and challenging, with standard alignment tools being potentially stuck in local minima, we propose here a new procedure, called AlignOT, which relies on the use of computational optimal transport (OT) to align EM maps in 3D space. By embedding a fast estimation of OT maps within a stochastic gradient descent algorithm, our method searches for a rotation that minimizes the Wasserstein distance between two maps, represented as point clouds. Here, we quantify the impact of various parameters on the precision and accuracy of the alignment, and show that AlignOT can outperform the standard local alignment methods, with an increased range of rotation angles leading to proper alignment. We further benchmark AlignOT on various pairs of experimental maps, which account for different types of conformational heterogeneities and geometric properties. As our experiments show good performance, we anticipate that our method can be broadly applied to align 3D EM maps.

3D alignment↗
DOE OSTI2015

Theoretical study of oxygen sorption and diffusion in the volume and on the surface of a γ-TiAl alloy

The oxygen sorption on the low-index (001), (100), and (110) surfaces of a γ-TiAl alloy is studied by the pseudopotential method with the generalized gradient approximation for the exchange-correlation functional. The most preferred sites for oxygen sorption in the bulk and on the surface of the alloy are determined. The titanium-rich octahedral site is shown to be preferred for oxygen sorption in the bulk material. The effect of the oxygen concentration on the atomic and electronic structures of the stoichiometric TiAl(100) surface is studied. It is shown that, at the first stage of oxidation, oxygen prefers to form bonds with titanium. The energy barriers for oxygen diffusion on the stoichiometric (100) surface and in the bulk of the material are calculated. The energy barriers are shown to depend substantially on the local environments of oxygen and to increase during diffusion from titanium-rich sites. The most possible mechanism of oxygen diffusion from the (100) surface to the bulk of the material is oxygen migration through tetrahedral sites.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Framework for Error-Bounded Approximate Computing, with an Application to Dot Products

Approximate computing techniques, which trade off the computation accuracy of an algorithm for better performance and energy efficiency, have been successful in reducing computation and power costs in several domains. However, error sensitive applications in high-performance computing are unable to benefit from existing approximate computing strategies that are not developed with guaranteed error bounds. While approximate computing techniques can be developed for individual high-performance computing applications by domain specialists, this often requires additional theoretical analysis and potentially extensive software modification. Hence, the development of low-level error-bounded approximate computing strategies that can be introduced into any high-performance computing application without requiring additional analysis or significant software alterations is desirable. In this paper, we provide a contribution in this direction by proposing a general framework for designing error-bounded approximate computing strategies and apply it to the dot product kernel to develop \bf qdot---an error-bounded approximate dot product kernel. Following the introduction of qdot, here we perform a theoretical analysis that yields a deterministic bound on the relative approximation error introduced by qdot. Empirical tests are performed to illustrate the tightness of the derived error bound and to demonstrate the effectiveness of qdot on a synthetic dataset, as well as two scientific benchmarks---the conjugate gradient (CG) and power methods. In some instances, using qdot for the dot products in CG can result in many components being quantized to half precision without increasing the iteration count required for convergence to the same solution as CG using a double precision dot product.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Gradient Flow Renormalization of Quasi-Parton Distributions

Gradient flow, is a method that allows us to construct lattice quasi PDFs that are finite in the continuum limit. In the small flow time limit, the moments of the smeared quasi PDFs are proportional to those of the light-front PDFs. These proportionality coefficient obey a renormalization group equation that allows us to derive renormalization scale evolution equations for the matching kernel that relates the smeared quasi PDF and the renormalized light-front PDFs. Based on these results we propose a new methodology for obtaining light-front PDFs from lattice QCD.

Orginos, K.↗