XHAB University of South Alabama: Development of a Concentration Swing Frequency Response Device
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Grant Glover↗
SEARCH · Engineering Papers
Results for “Brady”
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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Using Virtual Reality to Envision Deployment of Spacesuit-Compatible Augmented Reality Displays for Lunar Surface Operations
The National Aeronautics and Space Administration (NASA) aims to land crew on the lunar surface to establish a sustainable presence and develop operational concepts for future long-duration missions. New technologies will be necessary to extend planning and execution capabilities for lunar surface activities. NASA’s Joint Augmented Reality Visual Informatics System (Joint AR) is one such technology. Joint AR is a suit-mounted augmented reality (AR) display and computes system which facilitates unprecedented information exchange and data visualization capabilities between mission support operators and suited crew. This paper describes challenges associated with developing AR technology for an envisioned work domain by applying a sociotechnical lens to the iterative testing and development of novel AR technology through virtual reality (VR). A VR testbed was established to simulate a representative lunar surface environment, enabling a series of three human-in-the-loop (HITL) tests evaluating AR navigation interfaces for exploration extravehicular activity (xEVA). Our findings identify several considerations for future Joint AR design and testing efforts, including challenges with data overload, attentional demands, and environment-related perceptual challenges. Trade-offs and potential approaches are discussed to mitigate these challenges and improve future Joint AR testing fidelity.
Matthew Miller↗
The James Webb Space Telescope Mission: Optical Telescope Element Design, Development, and Performance
The James Webb Space Telescope (JWST) is a large, infrared space telescope that has recently started its science program which will enable breakthroughs in astrophysics and planetary science. Notably, JWST will provide the very first observations of the earliest luminous objects in the universe and start a new era of exoplanet atmospheric characterization. This transformative science is enabled by a 6.6 m telescope that is passively cooled with a 5 layer sunshield. The primary mirror is comprised of 18 controllable, low areal density hexagonal segments, that were aligned and phased relative to each other in orbit using innovative image-based wave front sensing and control algorithms. This revolutionary telescope took more than two decades to develop with a widely distributed team across engineering disciplines. We present an overview of the telescope requirements, architecture, development, superb on-orbit performance, and lessons learned. JWST successfully demonstrates a segmented aperture space telescope and establishes a path to building even larger space telescopes.
Infrared observatories↗
Phasing the Webb Telescope
The James Webb Space Telescope (JWST) is a segmented deployable telescope, currently operating at L2. The telescope utilizes 6 degrees of freedom for adjustment of the Secondary Mirror (SM) and 7 degrees of freedom for adjustment of each of its 18 segments in the Primary Mirror (PM). After deployment, the PM segments and the SM arrived in their correct optical positions to within a ~1 mm, with accordingly large wavefront errors. A Wavefront Sensing and Controls (WFSC) process was executed to adjust each of these optical elements in order to correct the deployment errors and produce diffraction-limited images across the entire science field. This paper summarizes the application of the WFSC process.
JWST↗
LGM Paleoclimate Constraints Inform Cloud Parameterizations and Equilibrium Climate Sensitivity in CESM2
The Community Earth System Model version 2 (CESM2) simulates a high equilibrium climate sensitivity (ECS > 5°C) and a Last Glacial Maximum (LGM) that is substantially colder than proxy temperatures. In this study, we examine the role of cloud parameterizations in simulating the LGM cooling in CESM2. Through substituting different versions of cloud schemes in the atmosphere model, we attribute the excessive LGM cooling to the new CESM2 schemes of cloud microphysics and ice nucleation. Further exploration suggests that removing an inappropriate limiter on cloud ice number (NoNimax) and decreasing the time-step size (substepping) in cloud microphysics largely eliminate the excessive LGM cooling. NoNimax produces a more physically consistent treatment of mixed-phase clouds, which leads to an increase in cloud ice content and a weaker shortwave cloud feedback over mid-to-high latitudes and the Southern Hemisphere subtropics. Microphysical substepping further weakens the shortwave cloud feedback. Based on NoNimax and microphysical substepping, we have developed a paleoclimate-calibrated CESM2 (PaleoCalibr), which simulates well the observed twentieth century warming and spatial characteristics of key cloud and climate variables. PaleoCalibr has a lower ECS (∼4°C) and a 20% weaker aerosol-cloud interaction than CESM2. PaleoCalibr represents a physically more consistent treatment of cloud microphysics than CESM2 and is a valuable tool in climate change studies, especially when a large climate forcing is involved. Our study highlights the unique value of paleoclimate constraints in informing the cloud parameterizations and ultimately the future climate projection.
equilibrium climate sensitivity↗
In Space Manufacturing
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In Space Manufacturing↗
Basic Research in Functional Ceramics for Power and Energy Applications
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Ceramics↗
Waste Materials Recycling for In-Space Manufacturing
Logistics and resupply payloads for Lunar and Martian missions are significantly more cost and schedule prohibitive compared to LEO destinations. Recycling of waste and trash materials generated throughout the duration of a mission, as well as previous missions, will be critical for enabling sustained human presence beyond Earth. Currently, no solution exists for recycling waste materials back into feedstock form beyond entry-level hobbyist equipment, which is not suitable nor practical for space applications. There is already over 500,000 lbs of trash on the moon from previous missions, which will grow exponentially over the coming decade throughout the Artemis Campaign. Waste and trash materials are precious resources that, if recovered and properly processed, can be transformed back into usable feedstock form for on-demand manufacturing capabilities. Hence, it is prudent that a recycling solution be developed and infused into future missions.
additive↗
Validation Testing and Statistical Analysis of the Rotary Tumbler Fabric Abrasion Method
The Artemis space suit glove environmental protection garment (EPG) will be the first line of protection used to shield the crewmember’s hands from the environments encountered during extravehicular activity (EVA). As the Artemis missions will include more extreme environments than those experienced on the International Space Station, development, verification, and validation of gloves requires the development of new test methods. A previous paper focused on the development of a test procedure to characterize lunar EVA glove fabrics using ASTM standardized test methods and the design and validation of a new standardized test procedure for comparing abrasion resistance between fabrics using a dust and rock filled rotary tumbler. Preliminary results of testing were presented in that paper. This paper reports on the validation testing and statistical analysis of the newly developed tumbler abrasion test method.
Robert J Jones↗
Establishing a Standardized Test Method for Evaluating the Cut Resistance of Space Suit Glove Fabrics
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Robert J Jones↗
Integrating Forest Structural Diversity Measurement Into Ecological Research
The measurement of forest structure has evolved steadily due to advances in technology, methodology, and theory. Such advances have greatly increased our capacity to describe key forest structural elements and resulted in a range of measurement approaches from traditional analog tools such as measurement tapes to highly derived and computationally intensive methods such as advanced remote sensing tools (e.g., lidar, radar). This assortment of measurement approaches results in structural metrics unique to each method, with the caveat that metrics may be biased or constrained by the measurement approach taken. While forest structural diversity (FSD) metrics foster novel research opportunities, understanding how they are measured or derived, limitations of the measurement approach taken, as well as their biological interpretation is crucial for proper application. We review the measurement of forest structure and structural diversity—an umbrella term that includes quantification of the distribution of functional and biotic components of forests. We consider how and where these approaches can be used, the role of technology in measuring structure, how measurement impacts extend beyond research, and current limitations and potential opportunities for future research.
Jeff W. Atkins↗
Wildfire Segmentation From Remotely Sensed Data Using Quantum-Compatible Conditional Vector Quantized-Variational Autoencoders
Wildfires represent a critical environmental hazard with multifaceted implications for ecosystems, communities, and public health [1]. The escalating frequency and intensity of wildfires globally have intensified the urgency for robust segmentation methodologies to facilitate effective mitigation, response, and recovery strategies [2]. Accurate wildfire segmentation is pivotal for delineating fire boundaries, assessing progression patterns, and prioritizing resource allocation during emergency scenarios. Furthermore, precise segmentation enables stakeholders, including policymakers, environmental scientists, and emergency responders, to formulate evidence-based strategies, thereby minimizing socio-economic disruptions and ecological degradation. Consequently, advancing wildfire segmentation techniques through innovative technological interventions remains a paramount research imperative. Although foundational in wildfire segmentation, traditional deterministic models exhibit inherent limitations that compromise their efficacy in dynamic and uncertain environments. These models often operate on rigid algorithms prioritizing deterministic classifications, thereby overlooking the inherent complexities and uncertainties associated with wildfire behavior and satellite data variability. Such deterministic frameworks tend to produce oversimplified representations that fail to capture the intricate nuances of evolving fire dynamics, spatial heterogeneity, and environmental interactions [1]. Consequently, the deterministic approach’s propensity for uncertainty collapsing [1, 3] hampers the accuracy, reliability, and applicability of segmentation outcomes in real-world scenarios. Contrastingly, stochastic models offer a more nuanced and adaptable framework for wildfire segmentation. By integrating probabilistic elements into the modeling paradigm, stochastic approaches, particularly probabilistic approaches such as variational auto encoders (VAEs) [4], facilitate comprehensive uncertainty assessment, enabling researchers to quantify and incorporate uncertainties into segmentation outcomes effectively. This probabilistic nature empowers stochastic models to encapsulate variability, account for data inconsistencies, and adapt to evolving environmental conditions, enhancing segmentation accuracy, reliability, and robustness. Embracing stochastic methodologies thus catalyzes advancements in wildfire science by fostering a more holistic, adaptive, and resilient segmentation framework. Despite VAEs demonstrating significant promise in various applications, they come with inherent limitations that have garnered attention within the machine learning community. One of the primary drawbacks lies in their reliance on static priors, which essentially assume a fixed distribution for latent variables, thereby limiting the model’s flexibility to capture complex data structures effectively [5]. This static nature leads to suboptimal representations, especially when dealing with complex and high-dimensional data. Additionally, VAEs often struggle with generating sharp and realistic samples, a phenomenon commonly referred to as mode collapse [5, 7, 6]. Furthermore, the optimization process in VAEs, which involves balancing the reconstruction loss and the regularization term, can sometimes be challenging to fine-tune [7]. In recent efforts to address these shortcomings, alternative approaches like Vector Quantized Variational Auto encoders(VQ-VAEs) [7], address the challenges by incorporating discrete latent variables and leveraging techniques that enhance the quality and diversity of generated samples while maintaining efficient training dynamics. VQ-VAEs propose a dynamic prior distribution generation mechanism that diverges from the static priors commonly associated with traditional VAEs. This dynamic approach allows for more adaptive and context-aware latent variable representations, thereby potentially capturing complex data structures more effectively. Unlike autoregressive prior models such as PixelCNN, which, despite their ability to model dependencies across data dimensions, suffer from significant computational inefficiencies and lack flexibility in handling diverse datasets. In our work, we propose to use a generative quantum-compatible approach to help alleviate the shortcomings of autoregressive prior model in VQ-VAEs. Restricted Boltzmann Machines (RBMs) are a viable alternative prior model that can learn prior distributions in a faster and more flexible manner. In this research endeavor, we meticulously curate a state-of-the-art dataset leveraging satellite MODIS data in conjunction with VIIRS fire masks, derived from Fire Radiative Power (FRP), thereby encapsulating diverse wildfire scenarios and environmental contexts. We developed a conditional VQ-VAE architecture with the RBM prior model that is trained in a supervised manner for segmenting wildfire masks. This innovative approach synergistically harnesses deep learning capabilities, enabling the generation of segmentation maps characterized by heightened precision, granularity, and contextual relevance. Furthermore, replacing the autoregressive prior learning method proposed by the original VQ-VAE with a prior density approximation via quantum-compatible RBM facilitates expedited inference processes, augments flexibility in prior sampling, optimizes computational efficiency and establishes a groundbreaking benchmark in wildfire segmentation methodologies.
quantum machine learning↗
Establishing A Standardized Test Method for Evaluating the Cut Resistance of Space Suit Glove Fabrics
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Bobby Jones↗
Validation Testing and Statistical Analysis of the Rotary Tumbler Fabric Abrasion Method
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Bobby Jones↗
Code Migration and Optimization for Calculating Atmospheric Attenuation in RF Link Budgets Within Glenn Research Center Communication Analysis Suite
This technical report encompasses development completed on the Glenn Research Center Communication Analysis Suite (GCAS) as it relates to converting code from the original MATLAB® (The MathWorks, Inc.) GCAS code base to C++. It focuses on a majority of the functions, structures, and intermediate code developed for radiofrequency (RF) links calculations. The report serves as a comprehensive record of the current progress and outlines the technical challenges overcome during this research. Throughout this project, numerous functions and structures were migrated end to end, with validation test cases conducted in accordance with NASA’s verification and validation efforts for standard models and simulations. This report also provides high-level documentation of the completed work; detailed low-level documentation has been compiled to provide a line-by-line explanation in alignment with NASA’s goals. This development has largely been focused on atmospheric effects on calculating RF link budgets for both terrestrial and space links. Some developments outside of atmospheric functions are also covered throughout this report; these are largely related to mathematical operations and MATLAB® structure conversions.
Brady M. Phelps↗
Parameter, Post-Processing Sensitivities, and Qualification Approach of Laser Powder Bed Fusion Hydrogen Resistant Alloy NASA HR-1
Metal additive manufacturing (AM) processes are being used to enable economical manufacturing of legacy alloys as well as advancing new alloys. Laser powder bed fusion (L-PBF) is a metal AM process that has high maturity and being used to produce a variety of parts for space applications including complex propulsion components. The National Aeronautics and Space Administration (NASA) has identified the need to develop and advance new materials in unique space applications such as high-pressure hydrogen environments. NASA HR-1 is a high strength Fe-Ni based superalloy designed to resist high pressure hydrogen environment embrittlement (HEE), oxidation, and corrosion that has been successfully adapted to laser powder directed energy deposition (LP-DED). Insights gained from the NASA HR-1 development for LP-DED have guided the development process for L-PBF. However, adapting NASA HR-1 to L-PBF posed new challenges due to the distinct differences between the additive manufacturing processes. During parameter development, sensitivities were observed in post-processing that necessitated additional optimization of heat treatments. Additionally, the variations in thickness and how it influenced the microstructural response during heat treatment was characterized. Understanding these sensitivities is important to qualification of the material in a L-PBF machine. This ensures that the microstructures and properties of the material maintain consistency in production. This presentation will cover parameter development along with post-processing challenges and solutions will be discussed in addition to key material properties as it pertains to application performance and qualification per NASA-STD-6030. Improvements made by developing a derivative alloy, NASA HR-2, will be highlighted through preliminary small scale parameter development, material characterization, and initial property testing.
NASA HR-1↗
TOI-3568 b: A Super-Neptune in the Sub-Jovian Desert
The sub-Jovian desert is a region in the mass–period and radius–period parameter space that typically encompasses short-period ranges between super-Earths and hot Jupiters, and exhibits an intrinsic dearth of planets. This scarcity is likely shaped by photo evaporation caused by the stellar irradiation received by giant planets that have migrated inward. We report the detection and characterization ofTOI-3568 b, a transiting super-Neptune with a mass of26.4±1.0M⊕, a radius of5.30±0.27R⊕, a bulk density of0.98±0.15g cm−3, and an orbital period of 4.417965 (5) d situated in the vicinity of the sub-Jovian desert. This planet orbiting a K dwarf star with solar metallicity was identified photometrically by the Transiting Exoplanet Survey Satellite (TESS). It was characterized as a planet by our high-precision radial-velocity (RV) monitoring program using MAROON-X at Gemini North, supplemented with additional observations from the SPICE large program with SPIRou at CFHT. We performed a Bayesian MCMC joint analysis of the TESSand ground-based photometry, and MAROON-X and SPIRou RVs, to measure the orbit, radius, and mass of the planet, as well as a detailed analysis of the high-resolution flux and polarimetric spectra to determine the physical parameters and elemental abundances of the host star. Our results reveal TOI-3568 b to be a hot super-Neptune rich in hydrogen and helium, with a core of heavier elements of between 10 and 25 M⊕in mass. We analyzed the photo evaporation status of TOI-3568 b and find that it experiences one of the highest extreme-ultraviolet (EUV) luminosities among planets with a mass of Mp<2MNep, yet it has an evaporation lifetime exceeding 5 Gyr. Positioned in the transition between two significant populations of exoplanets on the mass–period and energy diagrams, this planet presents an opportunity to test theories concerning the origin of the sub-Jovian desert.
stars: individual: TOI-3568↗
Assessing and Advancing the Potential of Quantum Computing: A NASA Case Study
Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, these noisy- intermediate scale quantum (NISQ) processors are still too small and non-robust to be directly useful for any real-world applications. In this paper, we describe NASA’s work in assessing and advancing the potential of quantum computing. We discuss advances in algorithms, both near- and longer-term, and the results of our explorations on current hardware as well as with simulations, including illustrating the benefits of algorithm-hardware codesign in the NISQ era. This work also includes physics-inspired classical algorithms that can be used at application scale today. We discuss innovative tools supporting the assessment and advancement of quantum computing, and describe improved methods for simulating quantum systems of various types on high performance computing systems that incorporate realistic error models. We provide an overview of recent methods for benchmarking, evaluating, and characterizing quantum hardware for error mitigation, computational purposes.
quantum computing↗