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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Measuring 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography

Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that destroys the specimen before an experiment is completed. Even larger doses are required to achieve high resolution. Thus, chemical mapping in 3D has been unachievable except at lower resolution with the most radiation-hard materials. Here, high-resolution 3D chemical imaging is achieved near or below one-nanometer resolution in an Au-Fe3O4 metamaterial within an organic ligand matrix, Co3O4-Mn3O4 core-shell nanocrystals, and ZnS-Cu0.64S0.36 nanomaterial using fused multi-modal electron tomography. Multi-modal data fusion enables high-resolution chemical tomography often with 99% less dose by linking information encoded within both elastic (HAADF) and inelastic (EDX/EELS) signals. We thus demonstrate that sub-nanometer 3D resolution of chemistry is measurable for a broad class of geometrically and compositionally complex materials.

Schwartz, Jonathan↗
DOE OSTI2024

Using Multi-shaker Force Control Methods in Modal Analysis

Shaker excitation is a popular technique in experimental modal analysis because it gives the practitioner a high level of control and repeatability in the excitation. However, the dynamics of the shaker and test article can fundamentally limit the excitation bandwidth when a voltage-mode amplifier is used to power the shaker. Further, multiple shakers can couple to one-another through the test article and correlate the forces during a multiple-input / multiple-output modal test, potentially impa

Carter, Steven Phillip↗
DOE OSTI2024

A data-driven framework for predicting machining stability: employing simulated data, operational modal analysis, and enhanced transfer learning

Chatter, a self-excited vibration phenomenon, presents a significant challenge in machining operations, particularly in high-speed milling, where it can degrade tool life, reduce material removal efficiency, and compromise workpiece quality. Addressing this challenge requires a reliable predictive model that can accommodate the complex dynamics of various machining scenarios. This study introduces a novel, data-driven approach to predicting machining stability, leveraging over 140,000 simulated datasets and employing advanced techniques such as operational modal analysis (OMA), enhanced transfer learning (TL), and receptance coupling substructure analysis (RCSA). By integrating these methodologies, the framework effectively classifies and predicts chatter across diverse operational modes, achieving robust and accurate outcomes. Our model utilizes a Random Forest (RF) classifier trained with the comprehensive dataset, which demonstrates substantial improvements in both predictive accuracy and robustness. Specifically, the RF model achieved an accuracy rate of 85%, an area under the curve (AUC) of 0.90, and an F1 score of 0.88, underscoring its capability to adapt to varying machining configurations. These results highlight the framework’s potential to enhance operational efficiency and machining quality by providing reliable chatter predictions across a broad range of machining parameters. In conclusion, this research thus offers a significant advancement in predictive maintenance for machining processes, enabling more stable and efficient manufacturing operations.

42 ENGINEERING↗
DOE OSTI2024

Microscopy modality transfer of steel microstructures: Inferring scanning electron micrographs from optical microscopy using generative AI

Scanning electron microscopy (SEM) is resource intensive, which limits its throughput in some applications. As an alternative, we propose applying computer vision and machine learning to generate high-quality synthetic SEM micrographs from micrographs obtained using light optical microscopy (LOM). Working with a correlated LOM/SEM dataset of dual-phase steel images, we test generative models of various architectures, including encoder-decoder networks, generative adversarial networks (GANs), and diffusion-based models. We find that the diffusion models significantly outperform other methods on both qualitative and quantitative assessments, while preserving key metallurgical meaning. This work establishes diffusion as the state-of-the-art for microscopy modality transfer and demonstrates the potential of AI-powered microscopy to enhance LOM with micron scale structural recreation.

Computer vision↗
NASA NTRS1995

Research Problems Associated with Limiting the Applied Force in Vibration Tests and Conducting Base-Drive Modal Vibration Tests

The intent of this paper is to make a case for developing and conducting vibration tests which are both realistic and practical (a question of tailoring versus standards). Tests are essential for finding things overlooked in the analyses. The best test is often the most realistic test which can be conducted within the cost and budget constraints. Some standards are essential, but the author believes more in the individual's ingenuity to solve a specific problem than in the application of standards which reduce problems (and technology) to their lowest common denominator. Force limited vibration tests and base-drive modal tests are two examples of realistic, but practical testing approaches. Since both of these approaches are relatively new, a number of interesting research problems exist, and these are emphasized herein.

tests tailored tests cost analysis↗
NASA NTRS2007

Modal Analysis of MARS Solar Panel and Planar Vibrations

This slide presentation reviews the modal analysis of MARS solar panels and the planar vibrations. Included are views of the solar panels mock-up assembly, a view of the test seup,a view of the plot from the test, with the raw numbers of the frequencies in Hz values with the mode number, the spatial acceleration plots of Center sub panel at resonant frequencies, predictions from the Finite element models, an explanation of the two test that were done on the plate and the results from both tests,

modal analysis↗
NASA NTRS2014

Robust Modal Filtering and Control of the X-56A Model with Simulated Fiber Optic Sensor Failures

The X-56A aircraft is a remotely-piloted aircraft with flutter modes intentionally designed into the flight envelope. The X-56A program must demonstrate flight control while suppressing all unstable modes. A previous X-56A model study demonstrated a distributed-sensing-based active shape and active flutter suppression controller. The controller relies on an estimator which is sensitive to bias. This estimator is improved herein, and a real-time robust estimator is derived and demonstrated on 1530 fiber optic sensors. It is shown in simulation that the estimator can simultaneously reject 230 worst-case fiber optic sensor failures automatically. These sensor failures include locations with high leverage (or importance). To reduce the impact of leverage outliers, concentration based on a Mahalanobis trim criterion is introduced. A redescending M-estimator with Tukey bisquare weights is used to improve location and dispersion estimates within each concentration step in the presence of asymmetry (or leverage). A dynamic simulation is used to compare the concentrated robust estimator to a state-of-the-art real-time robust multivariate estimator. The estimators support a previously-derived mu-optimal shape controller. It is found that during the failure scenario, the concentrated modal estimator keeps the system stable.

Modal Filtering↗
NASA NTRS2016

Robust Modal Filtering and Control of the X-56A Model with Simulated Fiber Optic Sensor Failures

The X-56A aircraft is a remotely-piloted aircraft with flutter modes intentionally designed into the flight envelope. The X-56A program must demonstrate flight control while suppressing all unstable modes. A previous X-56A model study demonstrated a distributed-sensing-based active shape and active flutter suppression controller. The controller relies on an estimator which is sensitive to bias. This estimator is improved herein, and a real-time robust estimator is derived and demonstrated on 1530 fiber optic sensors. It is shown in simulation that the estimator can simultaneously reject 230 worst-case fiber optic sensor failures automatically. These sensor failures include locations with high leverage (or importance). To reduce the impact of leverage outliers, concentration based on a Mahalanobis trim criterion is introduced. A redescending M-estimator with Tukey bisquare weights is used to improve location and dispersion estimates within each concentration step in the presence of asymmetry (or leverage). A dynamic simulation is used to compare the concentrated robust estimator to a state-of-the-art real-time robust multivariate estimator. The estimators support a previously-derived mu-optimal shape controller. It is found that during the failure scenario, the concentrated modal estimator keeps the system stable.

Modal Filtering↗
NASA NTRS2018

Modal Test and Analysis of the NASA Tiltrotor Test Rig

The Tiltrotor Test Rig (TTR) is being developed at the NASA Ames Research Center for testing full-scaleproprotors in the National Full-scale Aerodynamics Complex (NFAC) wind tunnel. The TTR is currentlyundergoing checkout testing to ensure its proper functionality. Part of the checkout process is a groundvibration test, or shake test, to characterize the modal characteristics of the test rig once it is installed in the wind tunnel. This paper presents a summary of the shake test procedure and an overview of the test results. The results include frequency response functions for a number of different test configurations as well as visualizations of the major mode shapes. Excitation methods included random and swept sine shaking as well as hammer impacts. At the conclusion of this paper, some recommendations are given for future shake tests.

Tiltrotor Test Rig↗
NASA NTRS1997

Modal Test of the Cassini Spacecraft

Modal testing of the Cassini spacecraft posed a challenge due to the large size and dynamic complexity of the spacecraft...This paper presents an overview of the test methods and results, with an emphasis on lessons learned.

modal↗
NASA NTRS1997

Structural Damage Detection using Modal Test Data

This study presents a methodology for updating the finite element model of a structure for damage detection purposes using an incomplete set of experimentally obtained modal frequencies and modeshapes.

Damage↗
NASA NTRS2022

Implementation of the NASA High Intensity Modal Impedance Tube

This paper provides a description of the High Intensity Modal Impedance Tube (HIMIT) and its use for the evaluation of acoustic liners. Tests conducted with two liners, one linear and one nonlinear, are used to evaluate the suitability of the HIMIT for evaluation at frequencies up to 6 kHz and sound pressure levels up to 155 dB. Two impedance eduction methods are used, one suitable for plane wave frequencies and the other applicable over the full frequency range. These educed impedances are compared against those computed with an impedance prediction method and against those educed with another zero-flow test rig. These results confirm that the HIMIT can be used with confidence for the evaluation of acoustic liners over these frequency and sound pressure level ranges.

impedance↗
DOE OSTI2024

L-PBF High-Throughput Data Pipeline Approach for Multi-modal Integration

Abstract Metal-based additive manufacturing requires active monitoring solutions for assessing part quality. Multiple sensors and data streams, however, generate large heterogeneous data sets that are impractical for manual assessment and characterization. In this work, an automated pipeline is developed that enables feature extraction from high-speed camera video and multi-modal data analysis. The framework removes the need for manual assessment through the utilization of deep learning techniques and training models in a weakly supervised paradigm. We demonstrate this pipeline’s capability over 700,000 high-speed camera frames. The pipeline successfully extracts melt pool and spatter geometries and links them to corresponding pyrometry, radiography, and processparameter information. 715 individual prints are examined to reveal melt pool areas that exceeds 0.07 mm 2 and pyrometry signal over a threshold (375 pyrometry units) were more likely to have defects. These automated processes enable massive throughput of characterization techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Packing density optimization and recyclability of multi-modal SiC powder feedstocks in binder jet additive manufacturing

Achieving high powder packing density is critical in binder jet additive manufacturing (BJAM), as it directly influences the final part density, mechanical properties, and sintering behavior. Multi-modal powder blends, which combine particles of different sizes, have been explored as a strategy to optimize packing efficiency and minimize defects. In this study, bimodal and trimodal powder blends were obtained by mixing silicon carbide powder in three different sizes. These results show that increased powder density is achievable with bimodal powder blends but is reduced in trimodal blends, and it was found that a 13 % increase in the powder tap density was achieved using a bimodal blend of powder. The powder size distribution of the bimodal blend was measured at various stages during binder jet additive manufacturing and, no measurable powder separation occurred even after eight prints. Altogether, this study shows limited advantage to trimodal powder blends but good promise for bimodal blends for increasing printed density while maintaining reusability in the binder jet process.

Aguirre, Trevor G. [Oak Ridge National Laboratory ↗
DOE OSTI2025

Comparison of three measurement modalities for 3D characterization of manufactured features and process-induced porosity in titanium alloy additively manufactured parts

Nondestructive characterization of internal features and defects within complex components is vital for many industrial applications, particularly with the advent of additive manufacturing (AM) technologies. However, community understanding of the limitations of nondestructive methods such as X-ray Computed Tomography (CT) can be limited in certain industrial sectors as these may be emergent applications. In this paper, we investigate the limits of X-ray CT measurements and compare extracted data with mechanical polishing serial sectioning (MPSS) and confocal laser scanning microscopy (CLSM). The test object is an additively manufactured titanium alloy disk that contains both process-induced porosity and machined features, including focused ion beam milled features designed to probe the resolution limits of X-ray CT. Results show that each of these characterization techniques has advantages and disadvantages. We compare data acquisition times, spatial resolution, geometric measurement accuracy and defect visualization fidelity across these modalities to establish a practical framework.

Additive manufacturing↗