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At least 1,279 records · Page 71

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Optimization of leaf morphology in relation to leaf water status: A theory

The leaf economic traits such as leaf area, maximum carbon assimilation rate, and venation are all correlated and related to water availability. Furthermore, leaves are often broad and large in humid areas and narrower in arid/semiarid and hot and cold areas. We use optimization theory to explain these patterns. We have created a constrained optimization leaf model linking leaf shape to vein structure that is integrated into coupled transpiration and carbon assimilation processes. The model maximizes net leaf carbon gain (NPPleaf) over the loss of xylem water potential. Modeled relations between leaf traits are consistent with empirically observed patterns. As the results of the leaf shape-venation relation, our model further predicts that a broadleaf has overall higher NPPleaf compared to a narrowleaf. In addition, a broadleaf has a lower stomatal resistance compared to a narrowleaf under the same level of constraint. With the same leaf area, a broadleaf will have, on average, larger conduits and lower total leaf xylem resistance and thus be more efficient in water transportation but less resistant to cavitation. By linking venation structure to leaf shape and using water potential as the constraint, our model provides a physical explanation for the general pattern of the covariance of leaf traits through the safety-efficiency trade-off of leaf hydraulic design.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

A neural network‐enhanced reproducing kernel particle method for modeling strain localization

Abstract Modeling the localized intensive deformation in a damaged solid requires highly refined discretization for accurate prediction, which significantly increases the computational cost. Although adaptive model refinement can be employed for enhanced effectiveness, it is cumbersome for the traditional mesh‐based methods to perform while modeling the evolving localizations. In this work, neural network‐enhanced reproducing kernel particle method (NN‐RKPM) is proposed, where the location, orientation, and shape of the solution transition near a localization is automatically captured by the NN approximation via a block‐level neural network (NN) optimization. The weights and biases in the blocked parameterization network control the location and orientation of the localization. The designed basic four‐kernel NN block is capable of capturing a triple junction or a quadruple junction topological pattern, while more complicated localization topological patters are captured by the superposition of multiple four‐kernel NN blocks. The standard RK approximation is then utilized to approximate the smooth part of the solution, which permits a much coarser discretization than the high‐resolution discretization needed to capture sharp solution transitions with the conventional methods. A regularization of the NN approximation is additionally introduced for discretization‐independent material responses. The effectiveness of the proposed NN‐RKPM is verified by a series of numerical verifications.

Baek, Jonghyuk↗

DOE OSTI2025

An experimental study on gas-liquid phase fluid migration in hydrate-bearing sediments during hydrate dissociation

Natural gas hydrate production tests face problems such as severe sand blockage, poor gas-liquid phase separation, and significant land subsidence. This is because of the insufficient understanding of the complex phase transition and gas-liquid multi-phase fluid migration during hydrate dissociation. In hydrate-bearing sediment systems, hydrate phase transition couples with gas-liquid fluid migration. The phase transition causes changes in pore structure, which in turn modifies porous infiltration parameters and fluid flow capacity. Meanwhile, alterations in phase interfaces affect key parameters like surface tension and wettability. Gas-liquid fluid migration influences heat and mass transfer, thus affecting phase equilibrium and dissociation rates. To bridge the gap in describing gas-liquid fluid migration during hydrate dissociation in experiments, this research innovatively integrated an unsteady-state gas displacement by water and a quantitative hydrate dissociation process, independently developed a multi-phase seepage experimental system suitable for hydrate dissociation and determined the relationship between seepage parameters and hydrate saturation under different porosity. The results are as follows: a) Core samples with higher initial porosity show a greater recovery rate of fluid flow capacity. b) The retarding effect of multi-phase fluid has a more significant impact on the migration of the wetting phase fluid (water) than that of the non-wetting phase fluid (methane). c) During hydrate dissociation, the evolution of absolute permeability shows an “S-shaped” pattern, and the evolution of relative permeability shows a “wiring-harness” pattern. In conclusion, the findings can provide a theoretical basis for preventing geological disasters and for geotechnical engineering design during hydrate production.

58 GEOSCIENCES↗

DOE OSTI2025

Geometric control of magnetic domain morphology in electrodeposited Ni rich NiPt mesoscopic wires

In this paper, we study the electrodeposited Ni—Pt magnetic wires, exploring the mechanisms by which changes in geometry and composition influence the local magnetic structure. The samples were prepared by electrodeposition, their elemental composition was analyzed by energy dispersive X-ray spectroscopy, and phase analysis was carried out by an X-ray diffraction measurement. Magnetic force microscopy measurements, and magnetic force microscopy measurements in magnetic field were carried out to study the domain morphology of the prepared samples. Magnetization characteristics were measured via vibrating sample magnetometry mode of a superconducting quantum interference device. Here, the results were analyzed and compared with micromagnetic simulations. It is shown that a strong perpendicular magnetic anisotropy is responsible for the characteristic checkerboard-like surface magnetization patterns, which can be tuned by geometrical constriction and Ni content. Understanding these relationships provides insights into the design of magnetic materials with tailored properties for advanced applications, like magnetic sensors.

Binary alloy↗

DOE OSTI2018

Bragg coherent diffractive imaging of ferromagnetic nickel nanoparticles

We synthesized Ni nanoparticles with an atmospheric-thermal chemical vapor deposition method and investigated their lattice distortions and defects using Bragg coherent diffractive imaging. Because the strain can alter the magnetic properties of ferromagnetic materials, it is important to study the lattice distortion and structural imperfection. As a result of the Bragg coherent diffractive imaging, we found that the Ni nanocubes have a deformation-free zone in the middle. In addition, the defects such as dislocation and twin domain defects were detected in other Ni nanoparticles based on the Bragg diffraction images and patterns. The imaging of nanoparticles can contribute to a better understanding of their growth mechanisms and improve the design and synthesis of nanoparticles.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2021

Chemically controlled pattern formation in self-oscillating elastic shells

Significance Mechanochemical processes drive various biological functions including morphogenesis and morphological changes in responsive synthetic hydrogels. By allowing reactive components to function in an elastic closed shell, autonomous chemical reactions generate microcompartments capable of expanding or contracting or induce buckled patterns. This capability can drive specific functions such as enhancing the rate of diffusion of compartmentalized chemicals or releasing cargos at specific rates. Here, we demonstrate the coupling of mechanical response to autonomous chemical reactions in elastic shells which swell and deswell to generate specific dynamic patterns and morphologies. The reverse feedback from the mechanical input triggers the chemical reaction. Our work inspires the design of responsive shells to enhance the functionality of microcompartments and nanoreactors.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Superconducting quasiparticle-amplifying transmon: A qubit-based sensor for meV-scale phonons and single terahertz photons

With great interest from the quantum computing community, an immense amount of R&D effort has been invested into improving superconducting qubits. The technologies developed for the design and fabrication of these qubits can be directly applied to applications for ultralow-threshold particle detectors, e.g., low-mass dark matter and far-infrared photon sensing. We propose a novel energy-resolving sensor based on the transmon qubit architecture combined with a signal-enhancing superconducting quasiparticle amplification stage. We refer to these sensors as SQUATs: superconducting quasiparticle-amplifying transmons. We detail the operating principle and design of this new sensor and predict that, with minimal R&D effort, solid-state-based detectors patterned with these sensors can achieve sensitivity to single terahertz photons, and sensitivity to 1 meV phonons in the detector absorber substrate on the microsecond timescale. Published by the American Physical Society 2024

47 OTHER INSTRUMENTATION↗

DOE OSTI2018

Correct interpretation of diffraction properties of quartz crystals for X-ray optics applications

Quartz has hundreds of strong Bragg reflections that may offer a great number of choices for making fixed-angle X-ray analyzers and polarizers at virtually any hard X-ray energies with selectable resolution. However, quartz crystals, unlike silicon and germanium, are chiral and may thus appear in two different forms of handedness that are mirror images. Furthermore, because of the threefold rotational symmetry along the c axis, the { h 1 h 2 h 3 L } and { h 2 h 1 h 3 L } Bragg reflections may have quite different Darwin bandwidth, reflectivity and angular acceptance, although they have the same Bragg angle. The design of X-ray optics from quartz crystals therefore requires unambiguous determination of the orientation, handedness and polarity of the crystals. The Laue method and single-axis diffraction technique can provide such information, but the variety of conventions used in the literature to describe quartz structures has caused widespread confusion. The current studies give detailed guidelines for design and fabrication of quartz X-ray optics, with special emphasis on the correct interpretation of Laue patterns in terms of the crystallography and diffraction properties of quartz. Furthermore, the quartz crystals examined were confirmed by X-ray topography to have acceptably low densities of dislocations and other defects, which is the foundation for developing high-resolution quartz-based X-ray optics.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Electromagnetic Modeling of Lattice Structures in Additively Manufactured Electric Machines

Recent advancements in metal additive manufacturing (AM) show great potential to revolutionize the design and manufacturing of electromagnetic components used in the field of electrical engineering. Lattice structures directly printed by AM processes typically offer better structural performance with reduced weight, such as high stiffness, surface area, elongation, energy absorption, and porosity, than the solid counterpart. This paper aims to study the electromagnetic modeling of lattice structures used in additively manufactured magnetic cores or windings for electric machines. Three dimensional (3D) electromagnetic finite element (FE) analysis with high performance computing (HPC) shows the highest fidelity in predicting the electromagnetic performance of designs with lattice structures by preserving complex geometry details. FE-based homogenization methods have also been explored to potentially speed up concept design. A case study based on an additively manufactured axial-flux permanent magnet machine with a Hilbert pattern stator validates the discussed electromagnetic modeling approaches.

Chung, Shi-Uk [ANSYS, Inc.]↗

DOE OSTI2021

Assessing Machine Learning as a Tool to Explain Variance in Deployed Photovoltaic (PV) System Degradation

Degradation remains a large uncertainty in forecasting production for PV plants, creating significant risk for developers and financiers. This study aims to quantify the distribution and drivers of degradation across 10,000 PV systems deployed for distributed or utility generation by training a machine learning model to predict year-over-year degradation rates from metadata characteristics. A combination of K-Means clustering and random forest regressor were found to associate multiple metadata features as potential drivers of degradation, including module characteristics, system design, and climate features. From this, it is inferred that if machine learning is able to find complex patterns between metadata features and system performance loss, such methods can be employed to help developers and financiers make data-informed decisions when estimating long-term energy production forecasts in financial models.

Dunn, Jimmy C.↗

DOE OSTI2017

vis-react-components

In developing visualizations for different data sets, the end solution often become dependent on the data being visualized. This causes engineers to have to re-develop many common components multiple times. The vis-react components library was designed to help enable creating visualizations that are independent of the underlying data. This library utilizes the React.js pattern of instantiating components that may be re-used. The library exposes an example application that allows other developers to understand how to use the components in the library.

Richardson, GregoryD↗

DOE OSTI2026

OPEN ALPHADIFFRACT

Open-source release of the AlphaDiffract data generation and training system. Includes only the public Materials Project dataset retrievers.AlphaDiffract is a deep learning framework that achieves state-of-the-art performance in predicting the crystal system, space group, and lattice parameters directly from PXRD patterns. AlphaDiffract utilizes a 1D adaptation of the ConvNeXt architecture, a modern convolutional neural network that integrates key design principles from transformers, coupledwith dedicated prediction heads for each crystallographic property.

Prince, Michael [Argonne National Laboratory (ANL)↗

DOE OSTI2017

Epidemiologic Study of One Million U.S. Radiation Workers and Veterans

The single most important question in radiation epidemiology is determining the level of health risks associated with radiation exposures that occur gradually over time. The study of one million early U.S. radiation workers and veterans has been designed to provide information on risk following chronic exposures by focusing on occupational groups with differing radiation exposure patterns, including intakes of radionuclides. The cost-efficient study builds on the investments made and foundations laid by investigators and government agencies over the past 30-40 years, which have established early worker cohorts that can now provide answers to questions on the lifetime human health risks associated with low-level radiation exposures. Within the overall goal of the epidemiologic study of one million U.S. radiation workers and veterans, this project had a total of nine specific aims which included studies of six populations for multiple endpoints including cancer overall mortality, leukemia and non-cancer mortality. The six populations included: Mound, Ohio, workers exposed to polonium, tritium and plutonium; nuclear power plant workers within the Landauer dosimetry and Nuclear Regulatory Commission data files; industrial radiographers; Mallinckrodt uranium workers; uranium workers who linked with the US Renal Data System; and nuclear weapons test participants. Over 400,000 workers and atomic veterans are included in these populations, with vital status being determined and analyses of all causes of death undertaken. A critical, integral component of the studies has been comprehensive evaluations of dosimetry involving, in many cases, complex dose reconstructions, and assessments of uncertainties. The work has also involved development of state-of-the art statistical approaches and modeling. All nine aims were accomplished successfully, resulting in publication of two NCRP documents, 13 literature papers, numerous Boice Reports in Health Physics News and many presentations of the work at scientific meetings. Furthermore, recommendations have been developed for an approach to integrate epidemiologic observations with radiation biology for risk assessment and biological models of radiation effect. The results of this project, as well as ongoing epidemiology studies of US radiation workers and veterans, are providing much-needed insights into complex issues regarding cancer and non-cancer health risks from low dose irradiation, how to apply a "dose and dose rate effectiveness factor" to scale the risks from the A-bomb survivor data to current occupational and environmental circumstances, and how to evaluate risk following intakes of radioactive substances. In addition to application to radiation workers, the results of the studies can provide guidance as society debates the role of nuclear energy and deals with nuclear waste, threats of terrorist attacks with nuclear/radiological devices, the remarkable increase in medical exposures to CT scans and nuclear imaging, and to NASA as radiation protection for astronauts on long-duration mission beyond low-Earth orbit is planned.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2018

Fundamental study of key issues related to advanced sCO 2 Brayton cycle: Prototypic HX development and cavitation

Diffusion bonded heat exchangers are the leading candidates for the sCO 2 Brayton cycles in next generation nuclear power plants. Commercially available diffusion bonded heat exchangers utilize set of continuous semi-circular zigzag micro channels to increase the heat transfer area and enhance heat transfer through increased turbulence production. Such heat exchangers can lead to excessive pressure drop as well as flow maldistribution in the case of poorly designed flow distribution headers. The goal of the current project is to fabricate and test potential discontinuous fin patterns for diffusion bonded heat exchangers; which can achieve desired thermal performance at lower pressure drops. Prototypic discontinuous offset rectangular and Airfoil fin surface geometries were chemically etched on to 316 stainless steel plate and sealed against an un-etched flat pate using O-ring seal emulating diffusion bonded heat exchangers. Thermal-hydraulic performance of these prototypic discontinuous fin geometries was experimentally evaluated and compared to the existing data for the continuous zigzag channels. The data generated from this project will serve as the database for future testing and validation of numerical models.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Report on PTT Imaging of Defects in AM Metallic Materials-Part 2

36 MATERIALS SCIENCE↗

DOE OSTI2022

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components (Final Technical Report)

Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study. In another approach, we investigate Thermal Tomography (TT), which is a computational method for reconstruction of depth profile of internal material defects from PIT nondestructive evaluation (NDE). TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of subsurface internals defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. Performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shape imported from scanning electron microscopy (SEM) images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on TT images of elliptical defects is capable of classifying shape and orientation of irregular defects. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN. We also investigate several approaches based on Gaussian Random Circle and Bezier Curves for constructing parametric models of irregular-shape defects.

36 MATERIALS SCIENCE↗

NASA NTRS1972

Improved feedback shift register

Design of feedback shift register with three tap feedback decoding scheme is described. Application for obtaining sequence synchronization patterns is examined. Operation of the circuitry is described and drawings of the systems are included.

Perlman, M.↗

NASA NTRS1973

High performance S-band horn antennas for radiometer use

Horn antennas of four types: pyramidal corrugated, conical corrugated, pyramidal dual mode and conical dual mode, have been constructed and evaluated for use as S-band radiometer antennas. Each of the structures is described and radiation patterns and impedance and resistive loss measurements including a layer of foreign material on a thin radome, are presented. A precision method for determining reflection losses is described using a multiprobe reflectometer technique. The same technique is also applied to the measurement of resistive losses by closing the ends of the antennas with short circuit plates and determining the losses from an accurate measurement of the reflection coefficient. The radiation patterns were recorded with the aid of a real-time digital computer. The stored patterns were then processed to yield gain and beam efficiency. It was concluded that it is possible to design a highly efficient antenna for radiometer use and to measure its parameters precisely. However, it was found that it is necessary to modify the conventional definition of beamwidth somewhat if this term is to be meaningful for radiometer applications.

Caldecott, R.↗