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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Single Transducer Ultrasonic Imaging Method that Eliminates the Effect of Plate Thickness Variation in the Image

This article describes a single transducer ultrasonic imaging method that eliminates the effect of plate thickness variation in the image. The method thus isolates ultrasonic variations due to material microstructure. The use of this method can result in significant cost savings because the ultrasonic image can be interpreted correctly without the need for machining to achieve precise thickness uniformity during nondestructive evaluations of material development. The method is based on measurement of ultrasonic velocity. Images obtained using the thickness-independent methodology are compared with conventional velocity and c-scan echo peak amplitude images for monolithic ceramic (silicon nitride), metal matrix composite and polymer matrix composite materials. It was found that the thickness-independent ultrasonic images reveal and quantify correctly areas of global microstructural (pore and fiber volume fraction) variation due to the elimination of thickness effects. The thickness-independent ultrasonic imaging method described in this article is currently being commercialized under a cooperative agreement between NASA Lewis Research Center and Sonix, Inc.

Roth, Don J.↗
NASA NTRS2004

Optical Metrology for the Segmented Optics on the Constellation-X Spectroscopy X-Ray Telescope

We present the metrology requirements and metrology implementation necessary to prove out the reflector technology for the Constellation X(C-X) spectroscopy X-ray telescope (SXT). This segmented, 1.6m diameter highly nested Wolter-1 telescope presents many metrology and alignment challenges. In particular, these mirrors have a stringent imaging error budget as compared to their intrinsic stiffness; This is required for Constellation-X to have sufficient effective area with the weight requirement. This has implications for the metrology that can be used. A variety of contract and noncontact optical profiling and interferometric methods are combined to test the formed glass substrates before replication and the replicated reflector segments.The reflectors are tested both stand-alone and in-situ in an alignment tower.Some of these methods have not been used on prior X-ray telescopes and some are feasible only because of the segmented approach used on the SXT. Methods discussed include high precision coordinate measurement machines using very low force or optical probe axial interferometric profiling azimuthal circularity profiling and use of advanced null optics such as conical computer generated hologram (CGHs).

Content, David↗
NASA NTRS2003

MEMS-Based Piezoelectric/Electrostatic Inchworm Actuator

A proposed inchworm actuator, to be designed and fabricated according to the principles of microelectromechanical systems (MEMS), would effect linear motion characterized by steps as small as nanometers and an overall range of travel of hundreds of microns. Potential applications for actuators like this one include precise positioning of optical components and active suppression of noise and vibration in scientific instruments, conveyance of wafers in the semiconductor industry, precise positioning for machine tools, and positioning and actuation of micro-surgical instruments. The inchworm motion would be generated by a combination of piezoelectric driving and electrostatic clamping. The actuator (see figure), would include a pair of holders (used for electrostatic clamping), a slider (the part that would engage in the desired linear motion), a driver, a piezoelectric stack under the driver, and a pair of polymer beams centrally clamped to the flexure beam via a T bar. The holders would be held stationary. One end of the piezoelectric stack would be held stationary; the other end would be connected to the bottom of the driver, which would be free to move up and down. All of these components except the piezoelectric stack and the polymer beams would be micromachined from a 500- m-thick silicon wafer by deep reactive-ion etching. The inchworm motion would be perpendicular to the broad faces of the wafer (perpendicular to the plane of the figure). The combination of the polymer beams and the centrally clamped flexure beam would spring-bias the slider into a position such that, in the absence of electrostatic clamping, the gap between the slider on the one hand and both the driver and the holder on the other hand would be no more than a few microns. This arrangement would make it possible to electrostatically pull the slider into contact with either the holders or the driver at a clamping force of the order of 1 N by applying a reasonably small voltage (of the order of 100 V).

Yang, Eui-Hyeok↗
NASA NTRS2024

Findings From 9 Years of Renal Stone Surveillance of U.S. Astronauts

INTRODUCTION: Renal stones are thought to be a risk for spaceflight due to urine chemistry changes, induced by microgravity. Asymptomatic stone can have significant medical and mission impact.For9 years astronauts travelling to the International Space Station (ISS) have received pre-and post-flight renal ultrasound, according to a specialized renal ultrasound protocol, to look for renal calcifications or mineralized renal material (MRM).A new ultrasound machine was put in service halfway through the screening program. METHODS: MRM screening data from pre-and post-flight were compared, to look for changes in MRM burden. Pre-and post-flight scans from 42 subjects were compared. Studies were further stratified by old versus new machine. Measures were modeled using the analysis of variance (ANOVA) as defined by the interaction between pre-to postflight, and machine-to-machine. RESULTS: There was no observed statistical change seen in MRM burden pre-to postflight using the new higher-resolution ultrasound machine. DISCUSSION: The new machine gives more precise measurements and allows us to differentiate MRMs from other bright objects seen on ultrasound, such as small blood vessels. These data imply that spaceflight does not increase the risk of renal stone formation, at least out to the length of ISS missions. This data will be used to update NASA’s Human System Risk Board’s risk tracking documents, in-flight Concept of Operations, and our Clinical Practice Guideline.

K Cortez↗
DOE OSTI2024

Investigating the effects of precise mass measurements of Ru and Pd isotopes on machine learning mass modeling

Atomic masses are a foundational quantity in our understanding of nuclear structure, astrophysics, and fundamental symmetries. The longstanding goal of creating a predictive global model for the binding energy of a nucleus remains a significant challenge, however, and prompts the need for precise measurements of atomic masses to serve as anchor points for model developments. We present precise mass measurements of neutron-rich Ru and Pd isotopes performed at the Californium Rare Isotope Breeder Upgrade facility at Argonne National Laboratory using the Canadian Penning Trap mass spectrometer. The masses of 108 Ru, 110 Ru, and 116 Pd were measured to a relative mass precision $\delta$⁢$m/m$ ≈ 10 -8 via the phase-imaging ion-cyclotron-resonance technique, and represent an improvement of approximately an order of magnitude over previous measurements. Further, these mass data were used in conjunction with the physically interpretable machine learning (PIML) model, which uses a mixture density neural network to model mass excesses via a mixture of Gaussian distributions. The effects of our new mass data on a Bayesian-updating of a PIML model are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Vehicle Position Detection Based on Machine Learning Algorithms in Dynamic Wireless Charging

Dynamic wireless charging (DWC) has emerged as a viable approach to mitigate range anxiety by ensuring continuous and uninterrupted charging for electric vehicles in motion. DWC systems rely on the length of the transmitter, which can be categorized into long-track transmitters and segmented coil arrays. The segmented coil array, favored for its heightened efficiency and reduced electromagnetic interference, stands out as the preferred option. However, in such DWC systems, the need arises to detect the vehicle’s position, specifically to activate the transmitter coils aligned with the receiver pad and de-energize uncoupled transmitter coils. This paper introduces various machine learning algorithms for precise vehicle position determination, accommodating diverse ground clearances of electric vehicles and various speeds. Through testing eight different machine learning algorithms and comparing the results, the random forest algorithm emerged as superior, displaying the lowest error in predicting the actual position.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Climbing the Summit and Pushing the Frontier of Mixed Precision Benchmarks at Extreme Scale

The rise of machine learning (ML) applications and their use of mixed precision to perform interesting science are driving forces behind AI for science on HPC. The convergence of ML and HPC with mixed precision offers the possibility of transformational changes in computational science. The HPL-AI benchmark is designed to measure the performance of mixed precision arithmetic as opposed to the HPL benchmark which measures double precision performance. Pushing the limits of systems at extreme scale is nontrivial -little public literature explores optimization of mixed precision computations at this scale. In this work, we demonstrate how to scale up the HPL-AI benchmark on the pre-exascale Summit and exascale Frontier systems at the Oak Ridge Leadership Computing Facility (OLCF) with a cross-platform design. We present the implementation, performance results, and a guideline of optimization strategies employed for delivering portable performance on both AMD and NVIDIA GPUs at extreme scale.

Lu, Hao↗
DOE OSTI2024

Impedance-Aware Graph Convolutional Networks for Voltage Estimation in Active Distribution Networks

Voltage estimation plays a key role in ensuring the effective control and reliability of distribution networks. However, traditional machine learning methods often fail to capture the details of the distribution network’s topology. To overcome this challenge, graph convolutional networks (GCN) have emerged as an alternative. Graph convolutional networks inherently capture the topology of the grid, utilizing correlations to achieve precise voltage estimation. Other machine learning models and conventional GCNs fail to account for the distribution line characteristics found in the real world, limiting their effectiveness. This paper proposes an advanced variant of GCN called the Impedance-Aware Graph Convolutional Network (IA-GCN). The IA-GCN layer incorporates the magnitude of the impedance into the graph convolution mechanism, allowing it to capture topological nuances and provide valuable insights into node interrelationships by considering impedance as an intrinsic dimension. The performance of the IA-GCN layer is then compared with that of GCN and GraphSAGE layers through a surrogate model for voltage estimation. The performance analysis demonstrates that IA-GCN outperforms GCN by reducing the MAE by 87.55% and improving the R-squared value by 98%.

Ravi, Abhijith↗
NASA NTRS2020

Measuring Large Amplitude Surface Figure Error using Coordinate Metrology

Advances in optical testing are as important as advances in optical fabrication, because one can make only what one can measure, particularly for unconventional prescriptions, like aspheric and freeform optics. A new, high-precision metrology capability is utilized to close the gap between interferometric testing and lower precision, contact-probe-based coordinate measuring machines or laser tracker/radar metrology to accurately measure surfaces with large figure error. This nearly universal optical testing method employs an ultra-precision coordinate measuring machine equipped with a non-contact probe. It was developed to characterize a broad spectrum of optical surfaces including ones with high slopes, impossible to measure using traditional interferometric testing. Optical components, covering a wide range of prescriptions, such as large convex conics, high-sloped aspherics, grazing-incidence x-ray optics, and highly deformed flats, were successfully measured. The resulting data were reduced using custom-developed routines to determine the optic’s alignment, surface departure from design, and the as-built optical prescription. This information guided the fabrication and modeling of these optical components

Manal Khreishi↗
DOE OSTI2020

Modular performance prediction for scientific workflows using Machine Learning

Scientific workflows provide an opportunity for declarative computational experiment design in an intuitive and efficient way. A distributed workflow is typically executed on a variety of resources, and it uses a variety of computational algorithms or tools to achieve the desired outcomes. Such a variety imposes additional complexity in scheduling these workflows on large scale computers. As computation becomes more distributed, insights into expected workload that a workflow presents become critical for effective resource allocation. In this paper, we present a modular framework that leverages Machine Learning for creating precise performance predictions of a workflow. The central idea is to partition a workflow in such a way that makes the task of forecasting each atomic unit manageable and gives us a way to combine the individual predictions efficiently. We recognize a combination of an executable and a specific physical resource as a single module. This gives us a handle to characterize workload and machine power as a single unit of prediction. Overall, our modular technique of creating atomic modules and deployment of longest-path approach to estimate workflow performance, allows the framework to adapt to highly complex nested directed acyclic workflows and scale to new scenarios, since it does not make assumptions of underlying workflow structure. We present performance estimation results of independent workflow modules executed on the XSEDE SDSC Comet cluster using various Machine Learning algorithms. The results provide insights into the behavior and effectiveness of different algorithms in the context of scientific workflow performance prediction.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

Manufacturing methods for machining spring ends parallel at loaded length

A first end surface of a coiled compression spring at its relaxed length is machined to a plane transverse to the spring axis. The spring is then placed in a press structure having first and second opposed planar support surfaces, with the machined spring end surface bearing against the first support surface, the unmachined spring end surface bearing against a planar first surface of a lateral force compensation member, and an opposite, generally spherically curved surface of the compensation member bearing against the second press structure support surface. The spring is then compressed generally to its loaded length, and a circumferentially spaced series of marks, lying in a plane parallel to the second press structure support surface, are formed on the spring coil on which the second spring end surface lies. The spring is then removed from the press structure, and the second spring end surface is machined to the mark plane. When the spring is subsequently compressed to its loaded length the precisely parallel relationship between the machined spring end surfaces substantially eliminates undesirable lateral deflection of the spring.

Hinke, Patrick Thomas↗
NASA NTRS1995

Manufacturing methods for machining spring ends parallel at loaded length

A first end surface of a coiled compression spring at its relaxed length is machined to a plane transverse to the spring axis. The spring is then placed in a press structure having first and second opposed planar support surfaces, with the machined spring end surface bearing against the first support surface, the unmachined spring end surface bearing against a planar first surface of a lateral force compensation member, and an opposite, generally spherically curved surface of the compensation member bearing against the second press structure support surface. The spring is then compressed generally to its loaded length, and a circumferentially spaced series of marks, lying in a plane parallel to the second press structure support surface, are formed on the spring coil on which the second spring end surface lies. The spring is then removed from the press structure, and the second spring end surface is machined to the mark plane. When the spring is subsequently compressed to its loaded length the precisely parallel relationship between the machined spring end surfaces substantially eliminates undesirable lateral deflection of the spring.

Hinke, Patrick Thomas↗
NASA NTRS1988

Sine-Bar Attachment For Machine Tools

Sine-bar attachment for collets, spindles, and chucks helps machinists set up quickly for precise angular cuts that require greater precision than provided by graduations of machine tools. Machinist uses attachment to index head, carriage of milling machine or lathe relative to table or turning axis of tool. Attachment accurate to 1 minute or arc depending on length of sine bar and precision of gauge blocks in setup. Attachment installs quickly and easily on almost any type of lathe or mill. Requires no special clamps or fixtures, and eliminates many trial-and-error measurements. More stable than improvised setups and not jarred out of position readily.

Mann, Franklin D.↗
DOE OSTI2023

Elsa: enhanced latent spaces for improved collider simulations

Abstract Simulations play a key role for inference in collider physics. We explore various approaches for enhancing the precision of simulations using machine learning, including interventions at the end of the simulation chain (reweighting), at the beginning of the simulation chain (pre-processing), and connections between the end and beginning (latent space refinement). To clearly illustrate our approaches, we use W + jets matrix element surrogate simulations based on normalizing flows as a prototypical example. First, weights in the data space are derived using machine learning classifiers. Then, we pull back the data-space weights to the latent space to produce unweighted examples and employ the Latent Space Refinement ( Laser ) protocol using Hamiltonian Monte Carlo. An alternative approach is an augmented normalizing flow, which allows for different dimensions in the latent and target spaces. These methods are studied for various pre-processing strategies, including a new and general method for massive particles at hadron colliders that is a tweak on the widely-used RamboOnDiet mapping. We find that modified simulations can achieve sub-percent precision across a wide range of phase space.

Nachman, Benjamin (ORCID:0000000310240932)↗
DOE OSTI2024

Event Detection and Classification Using Machine Learning Applied to PMU Data for the Western US Power System

Smart grid technology enhances our comprehension and reliability of the power grid, leveraging Phasor Measurement Unit (PMU) data—time-synchronized, high-frequency measurements gathered across the US power grid. This paper employs machine learning techniques to effectively analyze the vast PMU data in Wide Area Monitoring Systems (WAMS) for power grid event detection and classification. Analyzing several months of real-world PMU data, the paper focuses on machine learning for fast, precise event detection and classification, corroborated by utility event logs. Practical challenges like feature extraction, dimensionality reduction, and model selection are addressed. A novel feature yielding improved results is discovered, and a supplementary algorithm for detecting small power grid faults is developed. The final algorithm is validated using a month-long real PMU data set, demonstrating its capability in accurately identifying power grid events in near real-time.

machine learning, event detection, PMU↗
DOE OSTI2026

Improving statistical precision in Monte Carlo samples with negative weights via reweighting and uncertainty quantification

High statistical precision is critical for Monte Carlo (MC) samples in high energy physics and is degraded by negatively weighted events. This paper investigates a procedure to learn the relationship between the negative and positive weight distributions of any sample, allowing the reduction of statistical uncertainty by reweighting kinematically equivalent events with the same sign. A robust uncertainty quantification method is required for the practical application of such method. Two methods for the estimation of the reweighting uncertainty are developed: one at the event and another one at the final observable level. The latter method is strongly favored. The gains in statistical precision are then quantified. The method is demonstrated on Sherpa vector boson plus jets samples when using all generated events and when restricted to the signal region of a mock analysis. It is demonstrated to significantly reduce stochastic behavior in sparse MC samples while decreasing the overall uncertainty with a sufficiently well-known reweighting function.

Monte Carlo methods↗