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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Nondestructive Analysis of Apollo Samples by Micro-CT and Micro-XRF Analysis: A PET Style Examination

An integral part of any sample return mission is the initial description and classification of returned samples by the preliminary examination team (PET). The goal of a PET is to characterize and classify the returned samples, making this information available to the general research community who can then conduct more in-depth studies on the samples. A PET strives to minimize the impact their work has on the sample suite, which often limits the PET work to largely visual measurements and observations like optical microscopy. More modern techniques can also be utilized by future PET to nondestructively characterize astromaterials in a more rigorous way. Here we present our recent analyses of Apollo samples 14321 and 14305 by micro-CT and micro-XRF (respectively), assess the potential for discovery of "new" Apollo samples for scientific study, and evaluate the usefulness of these techniques in future PET efforts.

Zeigler, Ryan A.↗
NASA NTRS2015

Comprehensive Non-Destructive Conservation Documentation of Lunar Samples Using High-Resolution Image-Based 3D Reconstructions and X-Ray CT Data

Established contemporary conservation methods within the fields of Natural and Cultural Heritage encourage an interdisciplinary approach to preservation of heritage material (both tangible and intangible) that holds "Outstanding Universal Value" for our global community. NASA's lunar samples were acquired from the moon for the primary purpose of intensive scientific investigation. These samples, however, also invoke cultural significance, as evidenced by the millions of people per year that visit lunar displays in museums and heritage centers around the world. Being both scientifically and culturally significant, the lunar samples require a unique conservation approach. Government mandate dictates that NASA's Astromaterials Acquisition and Curation Office develop and maintain protocols for "documentation, preservation, preparation and distribution of samples for research, education and public outreach" for both current and future collections of astromaterials. Documentation, considered the first stage within the conservation methodology, has evolved many new techniques since curation protocols for the lunar samples were first implemented, and the development of new documentation strategies for current and future astromaterials is beneficial to keeping curation protocols up to date. We have developed and tested a comprehensive non-destructive documentation technique using high-resolution image-based 3D reconstruction and X-ray CT (XCT) data in order to create interactive 3D models of lunar samples that would ultimately be served to both researchers and the public. These data enhance preliminary scientific investigations including targeted sample requests, and also provide a new visual platform for the public to experience and interact with the lunar samples. We intend to serve these data as they are acquired on NASA's Astromaterials Acquisistion and Curation website at http://curator.jsc.nasa.gov/. Providing 3D interior and exterior documentation of astromaterial samples addresses the increasing demands for accessability to data and contemporary techniques for documentation, which can be realized for both current collections as well as future sample return missions.

Blumenfeld, E. H.↗
NASA NTRS2021

Large-Scale CT Inspection of Feed-Through EMI Filters for Space Application

A type of Feed-Through Electromagnetic Interference (EMI) Filter incorporating two discoidal (ring-shaped) capacitors was selected for use in the Europa Clipper spacecraft propulsion systems. Parts reliability specialists were aware of historical problems with the attach materials used to provide mechanical support and electrical contact within these devices, so therefore a screening was desired to ensure the longevity and ruggedness of units intended for the space environment. CT X-ray, a technology which has become more widely available thanks to the compelling depth of information it offers, was selected as the inspection method of choice.

Bescup, John C↗
DOE OSTI2022

Automatic threat recognition system and method using material disambiguation informed by physics in x-ray CT images of baggage

An automatic threat recognition (ATR) system is disclosed for scanning an article to recognize contraband items or items of interest contained within the article. The ATR system uses a CAT scanner to obtain a CT image scan of objects within the article, representing a plurality of 2D image slices of the article and its contents. Each 2D image slice includes information forming a plurality of voxels. The ATR system includes a computer and determines which voxels have a likelihood of representing materials of interest. It then aggregates those voxels to produce detected objects. The detected objects are further classified as items of interest vs. not of interest. The ATR system is based on learned parameters for a novel interaction of global and object context mechanisms. ATR system performance may be optimized by using jointly optimal global and object context parameters learned during training. The global context parameters may apply to the article as a whole and facilitate object detection. The object context parameters may apply to the individual object detections.

Paglieroni, David W.↗
DOE OSTI2024

DETERMINATION OF STRUT QUALITY FACTORS IN ADDITIVELY MANUFACTURED LATTICES USING IN-SITU COMPRESSION TESTING µ-CT

In response to the need for an automated, commercial method to qualify additively manufactured (AM) lattice components, an experiment was conducted to evaluate the effects of defective lattice struts on the structural compression strength. Lattice samples with known defective or missing struts were compressed using a Deben CT5000RT and imaged using xray µ-CT. The compressive force and x-ray computed tomography results were compared to defect free standards to evaluate the impact of each defect type on the overall structure’s compressive strength. This analysis will allow for simplifications to Finite Element Analysis (FEA) on AM parts without sacrificing model fidelity. Understanding the contribution of each defect type and severity will also better inform non-destructive evaluation (NDE) personnel of the inspection parameters necessary to detect the smallest feature of importance.

Dinova, Vincent A.↗
DOE OSTI2025

District Geothermal Heating + Cooling Deployment in a CT Environmental Justice Community

The report marks the team’s completion of all required tasks and milestones. Work completed for Task 1 (Technical and Economic Feasibility Assessment & Procurement Drafting) included development of analysis and design model; completion of technical, economic, and environmental assessments; and technical outreach and coalition design. Components for Task 2 (Outreach & Community Engagement) involved broad outreach and community-engagement efforts (including stakeholder meetings and a webinar as well as development of a formal engagement plan) and development of a web page and a case study. For Task 3 (Workforce Transition, Development, & Training Plan), the team undertook a formal statewide geothermal workforce needs assessment, developed corresponding recommendations for both the state as a whole and the Wallingford project, and held several workshops. For Task 4 (Project Management & Data Sharing), the team drafted a data-sharing plan.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Combining Deep Learning and scatterControl for High-Throughput X-ray CT Based Non-Destructive Characterization of Large-Scale Casted Metallic Components

X-ray computed tomography (XCT) is essential for nondestructive evaluation and quality control of large-scale metal components. XCT imaging, however, faces significant challenges from metal artifacts, particularly those caused by Compton scattering, which degrade image quality and obscure critical details. Hardware-based solutions (e.g. scatterControl) offer advancements by intercepting scattered photons and reducing artifacts, but they can be time-consuming and require additional processing. Here, we propose modifying and leveraging a novel deep learning (DL) framework, Simurgh, to enhance and accelerate scatter correction in XCT. By combining scatterControl with DL-based artifact removal, we demonstrate significant reduction in scan time while producing high-quality reconstructions. Through extensive evaluation on industrial XCT data, we show that our methods reduce scan time by up to more than 10 x while preserving flaw detectability. Quantitative analysis across multiple segmentation techniques confirms that Simurgh-based reconstructions consistently outperform traditional Feldkamp-Davis-Kress, model-based iterative reconstruction, and commercial DL models in both pixel-level and task-specific evaluations, enabling scalable, high-throughput XCT workflows for characterization of large scale components in applications such as casting and metal additive manufacturing.

Complex metal parts↗
DOE OSTI2024

Mt. Simon Sandstone - High Resolution CT

High resolution micro-computed tomography images of sandstone from the Mt. Simon formation at different depths. These images provide an insight into the pore structure of the Mt. Simon sandstone (e.g. for potential CO2 storage). This data set can be used to further study the formation as a whole..

CCS,CO2 Sequestration,Carbon Sequestration,Compute↗
DOE OSTI2023

Enabling rapid X-ray CT characterisation for additive manufacturing using CAD models and deep learning-based reconstruction

Metal additive manufacturing (AM) offers flexibility and cost-effectiveness for printing complex parts but is limited to few alloys. Qualifying new alloys requires process parameter optimisation to produce consistent, high-quality components. High-resolution X-ray computed tomography (XCT) has not been effective for this task due to artifacts, slow scan speed, and costs. We propose a deep learning-based approach for rapid XCT acquisition and reconstruction of metal AM parts, leveraging computer-aided design models and physics-based simulations of nonlinear interactions between X-ray radiation and metals. This significantly reduces beam hardening and common XCT artifacts. We demonstrate high-throughput characterisation of over a hundred AlCe alloy components, quantifying improvements in characterisation time and quality compared to high-resolution microscopy and pycnometry. Our approach facilitates investigating the impact of process parameters and their geometry dependence in metal AM.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Complexity analysis of a CT injection experiment on BRB

In this work, we use Jensen–Shannon complexity and permutation entropy to analyze the magnetic field fluctuations of an astrophysically scaled plasma experiment. The experiment was intended to emulate an interplanetary coronal mass ejection event in the lab, recreating the major sections seen in satellite data. We also use a technique called “delay,” in which we use select elements, skipping one or more data points at a time, in our time series data to obtain Jensen–Shannon complexity as a function of frequency and investigate the frequency of maximized complexity. We then compare the delay frequencies to other frequencies in the plasma. We found that the frequencies for maximum complexity do not correspond to the frequencies investigated, implying that other physical mechanisms lead to an increase in complexity at these frequencies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

A hybrid machine-learning approach for analysis of methane hydrate formation dynamics in porous media with synchrotron CT imaging

Fast multi-phase processes in methane hydrate bearing samples pose a challenge for quantitative micro-computed tomography study and experiment steering due to complex tomographic data analysis involving time-consuming segmentation procedures. This is because of the sample's multi-scale structure, which changes over time, low contrast between solid and fluid materials, and the large amount of data acquired during dynamic processes. Here, a hybrid approach is proposed for the automatic segmentation of tomographic data from time-resolved imaging of methane gas-hydrate formation in sandy granular media, which includes a deep-learning 3D U-Net model. To prepare a training dataset for the 3D U-Net, a technique to automate data labeling based on sample-specific information about the mineral matrix immobility and occasional fluid movement in pores is proposed. Automatic segmentation allowed for studying properties of the hydrate growth in pores, as well as dynamic processes such as incremental flow and redistribution of pore brine. Results of the quantitative analysis showed that for typical gas-hydrate stability parameters (100 bar methane pressure, 7°C temperature) the rate of formation is slow (less than 1% per hour), after which the surface area of contact between brine and gas increases, resulting in faster formation (2.5% per hour). Hydrate growth reaches the saturation point after 11 h of the experiment. Finally, the efficacy of the proposed segmentation scheme in on-the-fly automatic data analysis and experiment steering with zooming to regions of interest is demonstrated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗