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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Open Source Visualization and Analysis Platform for 3D Reconstructions of Materials by Transmission Electron Microscopy

Three-dimensional characterization of materials at the nano- and meso-scale has become possible with transmission and scanning transmission electron microscopes (S/TEM). Its importance has extended to a wide class of nanomaterials such as hydrogen fuel cells, solar cells, industrial catalysts, new battery materials and semiconductor devices, as well as spanning high-tech industry, universities, and national labs. While capable instrumentation is abundant, this rapidly expanding demand for high-resolution tomography is bottlenecked by software that is instead tailored for lower-dose, biological applications and not optimized for higher-resolution materials applications. Existing tomography fails to utilize the chemical information provided by S/TEM spectrometers. To address this problem, this project delivered a scalable, fully functional, freely-distributable, open Source materials tomography package with a modern user interface that enables automated acquisition, alignment, and real-time reconstruction of raw tomography data, and provides advanced segmentation, three-dimensional chemical visualization and analysis optimized for materials applications. It has established an extendable framework capable of automation for high-throughput for the tomography of materials from data acquisition to visualization. Phase I and II delivered a full-featured, cross-platform, clean and integrated application for S/TEM materials tomography. It can read projection data from the microscope, with graphical tools for alignment of data, tomographic reconstruction, segmentation, and visualization of the reconstructed 3D volume. The entire pipeline can be saved to an XML state file, enabling fully reproducible data processing and analysis with a full Python environment for the development of custom algorithms, processing, and analysis requirements. Phase IIB extended the application to provide real-time tomographic reconstruction, "live updates" as data is processed, analysis of big data, and multi-channel tomography. Here, quantitative assessment of nanomaterials can occur as data is being recorded on a 3D visualization platform that accommodates additional information from multiple channels. We provide unmatched high-throughput tomography, where the complete tomographic pipeline from measurement to 3D visualization can occur rapidly. The project was extended to offer capabilities for micro-CT, X-ray, neutron, focused ion beam, atomic electron tomography and atom probe tomography. With over 600 transmission electron microscopes worldwide and approximately 50 coming online each year, the demand and impact of an open-source tomography tool is large. Significant opportunities exist in high-tech industry, universities, and national labs to enable or enhance three-dimensional imaging at the nanoscale and bring automated high-throughput approaches that will accelerate progress in materials characterization and metrology. The project supports a service based business model by enabling lab-specific acquisition and processing customization and integration-support and development that will be provided into Phase III and beyond.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

In-Depth Modeling and Simulation Analysis of Artemis Missions Using the Impact Probabilistic Risk Assessment Tool

BACKGROUND The Artemis campaign is a Moon exploration program with a series of six planned missions, five of which will be crewed. These five crewed missions will contain a single mission segment (space flight), or multiple mission segments involving space flight (Orion), lunar landing (LTV) and/or space habitat (Gateway). Each crewed segment faces the risk of unique medical conditions, necessitating medical sets/kits tailored to those specificities. To support and enable a data-driven and evidence-based decision-making process through out a mission’s life cycle, a software tool called IMPACT was developed. Using probabilistic risk assessment (PRA) methodologies, IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a novel tool built for analyzing the possibility of encountering complex medical risks during space flight, and for identifying the medical resources and capabilities needed to treat those potential at-risk medical conditions. IMPACT achieves this by performing hundreds of thousands of Monte Carlo simulations of missions to build aggregate pictures of medical risk. During an extended simulation modeling phase, IMPACT generated analytical results for medical risks, and the medical resources and capabilities to address those risks, for every segment of every crewed Artemis mission. This presentation will highlight the reliability, consistency and validity of IMPACT’s computational modeling techniques and will showcase the library of analytical outcomes generated for the Artemis missions. OVERVIEW During the early stages of IMPACT’s design, architecture and technical requirements collection, “scenarios” (use cases) - achievement goals required for acceptance testing, were identified by stakeholders. IMPACT successfully completed the scenario testing requirements and undertook an extensive operational run phase utilizing a wide range of input combinations with a goal of delivering a cohesive, trustworthy, reliable, vast, and diverse body of evidence. The intent of these modeling runs was to validate consistency in output, ensure solidity of executable operations and to streamline processes by identifying areas requiring efficiency improvements. Using the many missions of Artemis, IMPACT ran variations of operational runs to assess the output for acceptable, as well as unusual characteristics. This rigorous long-term “shakedown” analysis was implemented to help build a collective body of evidence to aid in securing a high level of confidence, reliability, and validity in the output, whether from the applicational components of IMPACT, or the entirety of the operational process. ANTICIPATED ANALYSIS AND CONCLUSION This presentation will discuss the various categories of input criteria; the comparisons in the application of these input criteria to various Artemis missions; the preparation and collection of the body of evidence, and reliability of the computational modeling techniques. This paper serves as an initial analytical overview of IMPACT’s probabilistic risk assessment (PRA) medical risk outputs covering Artemis missions and is not intended to be deemed the official medical response for the Artemis campaign.

Crew Composition↗
DOE OSTI2024

A mesoscale 3D model of irradiated concrete informed via a 2.5 U-Net semantic segmentation

The concrete biological shield in light-water reactors is exposed to neutron and gamma irradiation, which deteriorates the concrete’s mechanical properties in the long term. To assess the irradiation-induced damage, predictive mechanical models are developed and used in parallel with the characterization of irradiated concrete samples. Realistic 3D simulation domains can drastically improve a model’s prediction. In this work, we utilized x-ray computed tomography (XCT) data of a concrete specimen to reconstruct its 3D microstructure. The XCT data shows low contrast between the concrete’s aggregates and cement paste, resulting in poor image segmentation when using traditional unsupervised techniques. To address this issue, we developed and trained a 2.5D U-Net model on only 24 pre-labeled XCT layers to segment 651 layers of the XCT data. The overall F1-score of the model is approximately 96%. Then, we created a 3D finite element (FE) mesh based on the stack of segmented images. The FE model contains radiation-induced expansion, damage, and creep. The constitutive equations are adapted to each phase (aggregates and cement paste). Here, we simulated the effects of neutron irradiation in the concrete specimen as well as the specimen’s mechanical response to uniaxial compression. Finally, model validation was performed using experimental data on similar concrete specimens in the literature.

2.5D U-Net↗
NASA NTRS2009

Ultrasonic Phased Array Inspection Simulations of Welded Components at NASA

Comprehensive and accurate inspections of welded components have become of increasing importance as NASA develops new hardware such as Ares rocket segments for future exploration missions. Simulation and modeling will play an increased role in the future for nondestructive evaluation in order to better understand the physics of the inspection process and help explain the experimental results. It will also help to prove or disprove the feasibility for an inspection method or inspection scenario, help optimize inspections, and allow to a first approximation limits of detectability. This study presents simulation and experimental results for an ultrasonic phased array inspection of a critical welded structure important for NASA future exploration vehicles.

Roth, D. J.↗
NASA NTRSDate not supplied

Exoplanet Yield Sensitivity for the Hybrid Lyot Coronagraph from end-to-end modeling for LUVOIR-A

NASA's future direct imaging flagship mission will require telescope mirror segmentation to survey a statistically significant population of nearby stellar systems by increasing aperture size beyond that achieved by traditional space-borne monolithic apertures thus far. Segmentation is a path for the size expansion, but it entails a challenge due to the introduction of temporal pupil discontinuities in terms of both phase and amplitude. Modeling of the integrated performance of the coronagraph with the expected dynamical profile of the telescope is an important step toward realistic estimates of the mission's science yields. We present results of a study using the hybrid Lyot coronagraph with a 15-m on-axis, segmented optical telescope assembly (OTA), akin to that of the Large UV / Optical /Infrared (LUVOIR) mission. We have developed a high-fidelity, end-to-end model as part of the Segmented Coronagraph Design and Analysis (SCDA) study that includes diffractive optical propagations at the 1e-10 contrast level across the optical train including the OTA and intermediate re-imaging optics inside the coronagraph instrument. We perform a sensitivity analysis to assess the contrast sensitivity to wavefront perturbations for a set of key telescope terms including segment-level low-order and mid-spatial frequency aberrations and evaluate expected exoplanet yields obtained in the presence of these sets of aberrations. Industry-provided segment-level wavefront aberration datasets involving the following mechanisms are input to the end-to-end model: (1) thermal gradients and accelerations, and (2) telescope dynamics including line-of-sight and segment jitter. Dynamically averaged PSFs in the presence of aberrations are fed into AYO (Altruistic Yield Optimization), a mission design reference simulator, that then assesses the scientific yield sensitivities.

high-contrast imaging↗
DOE OSTI2021

Adoption of image-driven machine learning for microstructure characterization and materials design: A Perspective

Microstructure characterization enables the development of structure-processing-property relationships critical to several research areas within the broad field of materials science, from alloy design to the assessment of corrosion resistance, and failure analysis. Conventional approaches to material characterization have relied on either qualitative inference by the human ex-pert or software applications that can extract high-level features from images, such as boundary segmentation, average grain diameter, etc. Such approaches rely heavily on subject matter expert user intervention and knowledge of what phases or more generally, what microstructural features, are of interest. The recent surge in the adoption of machine learning techniques to address problems in materials engineering has brought with it an increased interest and application of Image Driven Machine Learning (IDML) approaches. In this work, we review the applications of IDML to the field of materials characterization. A canonical hierarchy of stages is defined, which when put sequentially together completes an IDML study: problem definition, dataset building, model selection and training, model evaluation, and integration with existing instrumentation or simulation workflow. The studies reviewed in this work are analyzed from the perspective of each of these stages. Such a review permits agranular assessment of the field, for example the impact of IDML on materials characterization at the nanoscale, the size of a typical dataset required to train a semantic segmentation model on electron microscopy images, ubiquitousness of transfer learning in the domain, etc. Finally, we discuss the importance of interpretability and explainability in the field of IDML for materials characterization, and provide an overview of two emerging techniques in the field: semantic segmentation and generative adversarial networks.

Baskaran, Arun↗
DOE OSTI2020

The CMS Outer Tracker for the High Luminosity LHC

The High Luminosity LHC (HL-LHC) is expected to deliver an integrated luminosity of 3000 - 4000 fb -1 in its lifetime with peak instantaneous luminosities reaching about 5 - 7.5×10 34 cm -2 s -1 . During the Long Shutdown 3 period, several components of the CMS detector will undergo major changes, called Phase-2 upgrades, to be able to operate in the challenging environment of the HL-LHC, including the replacement of the entire silicon tracker. The Phase-2 Outer Tracker (OT) will have high radiation tolerance, higher granularity, and the capability to handle higher data rates. Another key feature of the OT will be to provide tracking information to the Level-1 trigger, allowing trigger rates to be kept at a sustainable level without sacrificing physics potential. For this, the OT will be made of modules that have two closely spaced sensors read out by the same electronics, which can correlate hits in the two sensors creating short track segments called ”stubs”. The stubs will be used for tracking at 40 MHz. In this contribution, the design of the CMS Phase-2 OT, the technological choices, and highlights of research and development activities are reported.

Ravera, Fabio↗
DOE OSTI2023

Automated extraction of interfacial dislocations and disconnections from atomistic data

Here we introduce Interfacial Line Defect Analysis (ILDA), a method for identifying and extracting interfacial dislocations and disconnections with little-to-no input from the user on the nature of the interface. By simply providing the orientations and coherency strains for the crystals in a coherent reference state of the interface, ILDA provides exact Burgers vectors. Alternatively, these orientations and strains can be estimated using local atomic deformation gradients, making ILDA fully automated and providing estimated Burgers vectors. ILDA also determines the step height associated with each defect line segment, in case the associated defect is a disconnection. The heart of the method is the identification of atoms residing at co-incidence sites between the two crystals and the construction of a surface mesh connecting these sites that is used to compose Burgers circuits and insert defect line segments. We demonstrate the performance of ILDA in two test cases: a twist grain boundary and a phase boundary.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Reconstruction of concrete microstructure using complementarity of X-ray and neutron tomography

The concrete microstructure was successfully reconstructed using the complementarity of X-ray and neutron computed tomography (CT). Neither tomogram alone was found to be suitable to properly describe the microstructure of concrete under this study. However, by merging the information revealed by the two modalities, and using image segmentation, noise reduction, and image registration techniques we reconstruct the concrete microstructure. Void, aggregate, and cement paste phases are successfully captured down to the images' spatial resolution, even though the aggregate consists of multiple minerals. The coarse-aggregate volume fraction of the reconstructed microstructure was similar to that of the mixing proportions. Furthermore, image-based finite element analysis is performed to demonstrate the effects of microstructure on stress concentration and strain localization.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Synthesis of Large Mesoporous–Macroporous and High Pore Volume, Mixed Crystallographic Phase Manganese Oxide, Mn 2 O 3 /Mn 3 O 4 Sponge

The controlled synthesis of mixed crystallographic phase Mn 2 O 3 /Mn 3 O 4 sponge material by varying heating rates and isothermal segments provides valuable information about the morphological and physical properties of the obtained sample. The well-characterized Mn 2 O 3 /Mn 3 O 4 sponge and applicability of difference in reactivity of H 2 and CO 2 desorbed during the synthesis provide new developments in the synthesis of metal oxide materials with unique morphological and surface properties. Here, we report the preparation of a Mn 2 O 3 /Mn 3 O 4 sponge using a metal nitrate salt, water, and Dextran, a biopolymer consisting of glucose monomers. The Mn 2 O 3 /Mn 3 O 4 sponge prepared at 1 °C·min –1 heating rate to 500 °C and held isothermally for 1 h consisted of large mesopores–macropores (25.5 nm, pore diameter) and a pore volume of 0.413 mL/g. Furthermore, the prepared Mn 2 O 3 /Mn 3 O 4 and 5 mol %–Fe-Mn 2 O 3 /Mn 3 O 4 sponges provide potential avenues in the development of solid-state catalyst materials for alcohol and amine oxidation reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Relativistic hybrid stars in light of the NICER PSR J0740 + 6620 radius measurement

We explore the implications of the recent radius determination of PSR J0740 + 6620 by the NICER experiment, combined with the neutron skin measurement by the PREX-II experiment and the associated inference of the slope of symmetry energy, for the structure of hybrid stars with a strong first-order phase transition from nucleonic to quark matter. Furthermore, we combine a covariant density-functional nucleonic equation of state (EOS) with a constant-speed-of-sound EOS for quark matter. We show that the radius and tidal deformability ranges obtained from GW170817 can be reconciled with the implication of the PREX-II experiment if there is a phase transition to quark matter in the low-mass compact star. In the high-mass segment, the EOS needs to be stiff to comply with the large-radius inference for PSR J0740 + 6620 and J0030 + 0451 with masses M ≃ 2 M ⊙ and M≃1.4 M⊙. We show that twin stars are not excluded, but the mass and radius ranges (with M ≥ M ⊙ ) are restricted to narrow domains ΔM twin ≲ 0.05 M ⊙ and ΔR twin ~ 1.0 km. We also show that the existence of twin configurations is compatible with the light companion in the GW190814 event being a hybrid star in the case of values of the sound-speed square s = 0.6 and s = 1/3.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1991

Surface control techniques for the segmented primary mirror in the large lunar telescope

The large lunar telescope is a proposed moon-based telescope which incorporates a sixteen-meter segmented primary mirror. An error budget is developed for the active control system of the primary mirror. A control methodology for the primary mirror is then described which utilizes piston sensors for measuring the relative piston error between adjacent segments as well as a separate sensor which measures the tilt of each segment with respect to the pointing direction of the telescope. A trade study is conducted in which the following types of tilt sensors are examined to determine their applicability to this program: stellar wavefront sensors, such as a Hartmann-Shack or a shearing interferometer; holographic optical elements; interferometers; scanning systems; and some nonoptical systems which electronically measure the relative tilt between adjacent segments. In addition, two independent methods of quantitatively verifying the performance of the telescope using either a phase retrieval algorithm or an image sharpening technique, both of which are based on the quality of a stellar image, are presented.

Gleckler, Anthony D.↗
NASA NTRS2010

Wavefront Sensing of X-Ray Telescopes

Phase Retrieval analysis of off-axis or defocused focal-plane data from telescope optics has been proven effective in understanding misalignments and optical aberrations in normal incidence telescopes. The approach is used, e.g., in commissioning of the James Webb Space Telescope (JWST) segmented primary mirror. There is a similar need for evaluating low-order figure errors of grazing incidence mirrors and nested telescope assemblies. When implemented in these systems, phase retrieval does not depend on normal incidence access to each mirror (shell) surface and, therefore, provides an effective means for evaluating nested x-ray telescopes during integration and test. We have applied a well-known phase retrieval algorithm to grazing incidence telescopes. The algorithm uses the Levenberg-Marquardt optimization procedure to perform a non-linear least-squares fit of the telescope Point Spread Function (PSF). The algorithm can also retrieve low order figure errors at visible wavelengths where optical diffraction is the dominant defect in the PSF. In this paper we will present the analytical approach and its implementation for grazing incidence mirrors of the International X-Ray Observatory (IXO). We analyze the effects of low order axial surface errors individually, and in combination on the system PSF at 633 nanometers. We demonstrate via modeling that the wavefront sensing algorithm can recover axial errors (of the grazing incidence mirrors) to a small fraction of the known axial figure errors using simulated PSFs as input data to the algorithm.

Saha, Timo↗
NASA NTRSDate not supplied

Quantum-Assisted Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data

The quantum computing community has been searching for suitable applications to demonstrate the potential of near-term quantum devices. Quantum machine learning is a potential candidate, particularly using models that cannot be efficiently simulated with classical computers [1, 2]. This work focuses on a transition phase of quantum computers where the quantum machine learning model is still simulable classically but projected not to be simulable as the size of the model grows. Ultimately quantum computers may have advantages for high-dimensional real-world problems. Due to the limited number of qubits in current noisy intermediate-scale quantum (NISQ) devices, the direct application of quantum computers in high dimensional data is not feasible. To remedy this problem, an encoder-decoder architecture can be utilized. The encoder model would transform the high-dimensional data into a compact representation, to a level that small quantum computers can be used today (or in the near future), and the decoder would take the quantum processed outputs back to the high-dimensional space. Addressing the two challenges of quantum machine learning, this work investigates a hybrid supervised generative model with a quantum Ising Born machine embedded as the latent distribution. The model contains four main parts (Figure 1.a.): (1) a U-NET architecture responsible for learning segmentation flow, (2) a Prior network responsible for learning an encoded latent distribution of the input data, (3) a Born machine which represents the latent distribution, and (4) a Posterior network in charge of learning the joint encoded latent distribution of inputs and target data. The initial model, proposed by [3], is optimized by (1) maximizing the overlap of the prior and posterior latent distributions, and (2) minimizing the segmentation loss. The proposed model is designed to be investigated in a simulation environment applied to the real-world application of wildfire segmentation. Specifically, the model is designed to solve the patchy wildfire segmentations of Moderate Resolution Imaging Spectroradiometer (MODIS) by taking the MODIS observations and using Visible Infrared Imaging Radiometer Suite’s (VIIRS) consistent wildfire product as the target. The model solves patchy wildfire segmentations and provides insight into the epistemic errors sourced from model variation. The model utilizes the Born machine as a QUBO solver to represent the latent space as a Bernoulli distribution. The proposed configuration allows the variational segmentation model to leverage the true quantum probabilistic nature and derive a more expressive latent configuration, increasing the model performance in describing wildfire segmentations. The quantum probabilistic information of the Born machine is directly incorporated in the Kullback-Leibler divergence loss in the prior and posterior distributions, forcing the Bernoulli latent distribution to maximize the overlap of input and joint input-target distributions. The proposed model is then trained and compared with a baseline only consisting of direct Bernoulli latent distribution with no Born machine representing the latent space. The models are evaluated based on the segmentation metrics, such as precision, recall, intersect of union, with uncertainty boundaries accounting for the stochastic nature of the model. Our findings show that even in low latent-dimensional space (due to the limit in computational power of the classical quantum simulator), we are able to effectively capture the latent representation and hence the model performs better than the baseline. The findings are a projection for scaling the model into higher dimensional latent space with the Born machine surpassing the baseline performance. Figure 1. Sub-figure (a) demonstrates the architecture for the training phase. The model consists of a Prior and Posterior network that encode inputs and joint input-target data into compact representations, respectively. The Born machine represents the latent distribution, and the U-NET branch learns the segmentation patterns of the data. The stochasticity is introduced to the U-NET through its last layer to create meaningful but stochastic segmentations. Sub-figure (b) represents the inference phase where the model takes the stochastic behavior from the prior network and injects that into the U-NET. Each attempt of inference will generate different but similar segmentations from the same distribution of the wildfire event. REFERENCES [1] Coyle, B., Mills, D., Danos, V., & Kashefi, E. (2020). The Born supremacy: quantum advantage and training of an Ising Born machine. npj Quantum Information, 6(1), 1-11. [2] Liu, J. G., & Wang, L. (2018). Differentiable learning of quantum circuit born machines. Physical Review A, 98(6), 062324. [3] Kohl, S., Romera-Paredes, B., Meyer, C., De Fauw, J., Ledsam, J. R., Maier-Hein, K., ... & Ronneberger, O. (2018). A probabilistic u-net for segmentation of ambiguous images. Advances in neural information processing systems, 31.

quantum machine learning↗
DOE OSTI2019

Wettability Effects on Primary Drainage Mechanisms and NAPL Distribution: A Pore-Scale Study

The pore-scale processes governing water drainage behavior in porous media have implications for geoscience multiphase scenarios including carbon capture and storage, contaminant site remediation, oil recovery, and vadose zone processes. However, few studies report directly observed pore-scale water drainage phenomena in 3-D soils. This knowledge gap limits our ability to verify assumptions underlying existing models and develop optimal solutions. As such, this paper utilizes synchrotron X-ray microtomography to present an experimental pore-scale examination of nonaqueous phase liquid (NAPL)/water distribution along a primary drainage front as dense NAPL was injected upward into water wetting (WW) and intermediate wetting (IW) sand-packed columns. Pore-network structures were extracted from imaged data sets and mapped onto segmented NAPL/water data sets which allowed quantitative examinations of wettability impacts on (a) the extent to which NAPL fills individual pore bodies and (b) relationships between pore size and the phase occupying the pore, with both considered as a function of distance (and capillary pressure) relative to the NAPL front. These results revealed that several hypotheses treating IW sand similarly to WW sands are simplistic. IW systems exhibited a sequence of pore filling that deviated from traditional capillary pressure-based model predictions: NAPL invades smaller pores, while larger, adjacent pores are bypassed leaving multipore residual water ganglia. NAPL pore saturations were close to 1 and did not change with capillary pressure in IW systems. Overall, the results illustrate how a relatively small change in operative contact angle alters NAPL distribution during water drainage, with important implications for geoscience multiphase flow scenarios.

58 GEOSCIENCES↗
DOE OSTI2025

Using X-ray radiography to study oxygen flow in a proton exchange membrane electrolyzer operating under balanced pressure conditions

Of the various water electrolyzer technologies, the proton exchange membrane electrolyzer (PEMWE) is one of the best solutions for producing clean hydrogen without releasing CO 2 . In order to allow for widespread use of clean hydrogen, it is necessary to decrease its cost, which is intrinsically related to system operation. Current PEMWE plants operate in differential mode, directly pressurizing hydrogen and benefiting from thermodynamic compression, which increases overall system efficiency. However, high differential pressure above 30 bar can cause membrane stress, resulting in membrane creeping and failure. Pressurizing the water and operating at balanced pressure allows hydrogen to be produced at higher pressures while preserving the integrity of the membrane and porous layers. Nevertheless, the impact of pressurizing water on PEMWE performance must be better understood to maximize performance under balanced pressure conditions. Here, this study examined the impact of water pressure on two-phase flow. A high-pressure electrolyzer setup was developed to perform operando X-ray radiography and examine oxygen transport with high temporal resolution. The imaging segmentation process, developed to capture bubble properties in the channel, was applied to a specific experiment. The results clearly showed that as pressure increased up to 30 bars, the initial bubbly flow transitioned to slug flow, which led to channel saturation with oxygen. This work demonstrates that two-phase flow in an electrolyzer can be studied using X-ray radiography, which has the advantages of fast measurements and the ability to probe dense materials, such as those required for pressurized electrolyzers.

Balanced high pressure operation↗
NASA NTRS2010

Reverberation Chamber Uniformity Validation and Radiated Susceptibility Test Procedures for the NASA High Intensity Radiated Fields Laboratory

The NASA Langley Research Center's High Intensity Radiated Fields Laboratory has developed a capability based on the RTCA/DO-160F Section 20 guidelines for radiated electromagnetic susceptibility testing in reverberation chambers. Phase 1 of the test procedure utilizes mode-tuned stirrer techniques and E-field probe measurements to validate chamber uniformity, determines chamber loading effects, and defines a radiated susceptibility test process. The test procedure is segmented into numbered operations that are largely software controlled. This document is intended as a laboratory test reference and includes diagrams of test setups, equipment lists, as well as test results and analysis. Phase 2 of development is discussed.

Koppen, Sandra V.↗