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NASA NTRS2012

Projection of Stabilized Aerial Imagery Onto Digital Elevation Maps for Geo-Rectified and Jitter-Free Viewing

As imagery is collected from an airborne platform, an individual viewing the images wants to know from where on the Earth the images were collected. To do this, some information about the camera needs to be known, such as its position and orientation relative to the Earth. This can be provided by common inertial navigation systems (INS). Once the location of the camera is known, it is useful to project an image onto some representation of the Earth. Due to the non-smooth terrain of the Earth (mountains, valleys, etc.), this projection is highly non-linear. Thus, to ensure accurate projection, one needs to project onto a digital elevation map (DEM). This allows one to view the images overlaid onto a representation of the Earth. A code has been developed that takes an image, a model of the camera used to acquire that image, the pose of the camera during acquisition (as provided by an INS), and a DEM, and outputs an image that has been geo-rectified. The world coordinate of the bounds of the image are provided for viewing purposes. The code finds a mapping from points on the ground (DEM) to pixels in the image. By performing this process for all points on the ground, one can "paint" the ground with the image, effectively performing a projection of the image onto the ground. In order to make this process efficient, a method was developed for finding a region of interest (ROI) on the ground to where the image will project. This code is useful in any scenario involving an aerial imaging platform that moves and rotates over time. Many other applications are possible in processing aerial and satellite imagery.

Ansar, Adnan I.↗

DOE OSTI2025

Neural Network Analysis of Nuclear Magnetic Resonance and Infrared Spectra

Nuclear magnetic resonance (NMR) spectroscopy and infrared (IR) spectroscopy are powerful chemical characterization techniques with broad general usage. However, the manual evaluation of the resulting spectra is time-consuming and requires significant expertise, preventing insights from being used in real-time applications. With recent advances in computation and artificial intelligence (AI), new tools are available for automating spectral interpretation. In this work, machine learning (ML) algorithms using 1-dimensional convolutional neural networks (CNNs) were applied to identify common functional groups from spectral information. Raw spectra were collected virtually from the Human Metabolome Database (HMDB) and National Institute of Standards and Technology (NIST) Chemistry WebBook and processed into a suitable standard. Algorithm design was tailored to best fit the nature of the problem, with built-in flexibility to accommodate relevant parameters beyond the raw spectral input, specifically solvent identity and magnetic frequency for NMR. The predictive capability of the algorithm in identifying functional groups is displayed in several examples. This methodology has been compiled into a code repository and could easily be modified to adapt alternative data sources, including other spectrum types. To mitigate overfitting, a common problem in mathematical modeling where overfamiliarity with training data produces trends that are not representative of the general data, a novel metric was developed, referred to as Accufit. Accufit includes a parameter that penalizes substantial differences in the training accuracy and the accuracy of an independent validation set. Examples are presented showing the effectiveness of Accufit in maintaining the model’s predictive capability while controlling the overfitting when used as a custom metric for hyperparameter tuning.

Sturgill, James↗

DOE OSTI2023

Opinions from Users Across the Lifespan about Fully Autonomous and Rideshare Vehicles with Associated Features

Fully autonomous vehicles have the potential to fundamentally transform the future transportation system. While previous research has examined individuals’ perceptions towards fully autonomous vehicles, a complete understanding of attitudes and opinions across the lifespan is unknown. Therefore, individuals’ awareness, acceptance, and preferences towards autonomous vehicles were obtained from 75 participants through interviews with three diverse groups of participants: 20 automotive engineering graduate students who were building an autonomous concept vehicle, 21 non-technical adults, and 34 senior citizens. The results showed that regardless of age, an individual’s readiness to ride in a fully autonomous vehicle and the vehicle’s requirements were influenced by the users’ understanding of autonomous vehicles. All of the engineering students understand what a fully autonomous vehicle is and this group was the most willing to ride especially compared to the seniors, where only half of the seniors knew what a fully autonomous vehicle is and 58.8% were not at all ready to ride one. The desire to have a manual control option or the ability to override the vehicle was common (90% of the engineering students, 95.2% of the adults, and 82.4% of the seniors), especially for individuals who reported not being ready to ride in a fully autonomous vehicle. The majority of all three groups of participants (85% of the engineering students, 81% of the adults, and 52.9% of the seniors) considered it essential that the vehicle should convey information about the vehicle’s status and intended behavior. Diagnostic information about the vehicle was desired by the engineering students (71.4%), who had a technical understanding of autonomous vehicles and current automotive related technologies. When autonomous vehicles are available, most participants anticipate preferring to use them as a rideshare service model (75% of the engineering students, 38% of the adults, and 27% of the seniors) rather than owning (5% of the engineering students, 19% of the adults, and 21% of the seniors) the autonomous vehicle themselves. Regarding the topic of sharing rides with strangers, both the automotive engineering students (90%) and the adults (52.6%) were comfortable with the idea of pooled rideshare in comparison to the seniors (29.4%). In future efforts, it will be important to include potential autonomous vehicle users of a wide age range as well as physical, cognitive, and visual abilities.

Gangadharaiah, Rakesh↗

NASA NTRS2001

Techniques for Improving Pilot Recovery from System Failures

This project examined the application of intelligent cockpit systems to aid air transport pilots at the tasks of reacting to in-flight system failures and of planning and then following a safe four dimensional trajectory to the runway threshold during emergencies. Two studies were conducted. The first examined pilot performance with a prototype awareness/alerting system in reacting to on-board system failures. In a full-motion, high-fidelity simulator, Army helicopter pilots were asked to fly a mission during which, without warning or briefing, 14 different failures were triggered at random times. Results suggest that the amount of information pilots require from such diagnostic systems is strongly dependent on their training; for failures they are commonly trained to react to with a procedural response, they needed only an indication of which failure to follow, while for 'un-trained' failures, they benefited from more intelligent and informative systems. Pilots were also found to over-rely on the system in conditions were it provided false or mis-leading information. In the second study, a proof-of-concept system was designed suitable for helping pilots replan their flights in emergency situations for quick, safe trajectory generation. This system is described in this report, including: the use of embedded fast-time simulation to predict the trajectory defined by a series of discrete actions; the models of aircraft and pilot dynamics required by the system; and the pilot interface. Then, results of a flight simulator evaluation with airline pilots are detailed. In 6 of 72 simulator runs, pilots were not able to establish a stable flight path on localizer and glideslope, suggesting a need for cockpit aids. However, results also suggest that, to be operationally feasible, such an aid must be capable of suggesting safe trajectories to the pilot; an aid that only verified plans entered by the pilot was found to have significantly detrimental effects on performance and pilot workload. Results also highlight that the trajectories suggested by the aid must capture the context of the emergency; for example, in some emergencies pilots were willing to violate flight envelope limits to reduce time in flight - in other emergencies the opposite was found.

Pritchett, Amy R.↗

NASA NTRS2004

From the Cluster Temperature Function to the Mass Function at Low Z

This XMM project consisted of three observations of the nearby, hot galaxy cluster Triangulum Australis, one of the cluster center and two offsets. The goal was to measure the radial gas temperature profile out to large radii and derive the total gravitating mass within the radius of average mass overdensity 500. The central pointing also provides data for a detailed two-dimensional gas temperature map of this interesting cluster. We have analyzed all three observations. The derivation of the temperature map using the central pointing is complete, and the paper is soon to be submitted. During the course of this study and of the analysis of archival XMM cluster observations, it became apparent that the commonly used XMM background flare screening techniques are often not accurate enough for studies of the cluster outer regions. The information on the cluster's total masses is contained at large off-center distances, and it is precisely the temperatures for those low-brightness regions that are most affected by the detector background anomalies. In particular, our two offset observations of the Triangulum have been contaminated by the background flares ("bad cosmic weather") to a degree where they could not be used for accurate spectral analysis. This forced us to expand the scope of our project. We needed to devise a more accurate method of screening and modeling the background flares, and to evaluate the uncertainty of the XMM background modeling. To do this, we have analyzed a large number of archival EPIC blank-field and closed-cover observations. As a result, we have derived stricter background screening criteria. It also turned out that mild flares affecting EPIC-pn can be modeled with an adequate accuracy. Such modeling has been used to derive our Triangulum temperature map. The results of our XMM background analysis, including the modeling recipes, are presented in a paper which is in final preparation and will be submitted soon. It will be useful not only for our future analysis but for other XMM cluster observations as well.

Mushotzky, Richard↗

NASA NTRS2020

Toward Transition Modeling in a Hypersonic Boundary Layer at Flight Conditions

An accurate physics-based transition prediction method integrated with computational fluid dynamics (CFD) solvers is pursued for hypersonic boundary layer flows over slender hypersonic vehicles at flight conditions. The geometry and flow conditions are selected to match relevant trajectory locations from the ascent phase of the HIFiRE-1 flight experiment, namely, a 7-degree half-angle cone with 2.5 mm nose radius, freestream Mach numbers in the range of 3.8 – 5.5 and freestream unit Reynolds numbers in the range of 3.3 × 10(exp 6) – 21.4 × 10(exp 6) m(exp -1). Earlier research had shown that the onset of transition during the HIFiRE-1 flight experiment correlated with an amplification factor of N ≈ 13.5 for the planar Mack modes. However, to incorporate the N-factor correlations into a CFD code, we investigate surrogate models for disturbance amplification that avoid the direct computation of stability characteristics. A commonly used approach for low-speed flows is based on an a priori database of stability characteristics for locally similar profiles. However, the results presented in this paper demonstrate that the application of this approach to hypersonic boundary layers over blunt spherical nose-tip cones leads to large, unacceptable errors in the predictions of amplification factors, mainly due to its failure in accounting for the effects of the entropy layer on the boundary-layer profiles along the length of the model. We propose and demonstrate an alternate approach that employs the stability computations for a canonical set of blunt cone configurations to train a physics-informed convolutional neural network model that is shown to provide substantially improved transition predictions for hypersonic flow configurations with entropy-layer effects. Furthermore, the excellent performance of the neural network model is also confirmed for cone configurations with nose radius and half-angle values that do not correspond to those used to build the database. Finally, the convolutional neural network model is shown to outperform the linear stability calculations for underresolved basic states.

Pedro Paredes↗

NASA NTRS2022

Natural Language Processing Techniques for Intelligent Knowledge Management of Safety Reports

Safety, failure, and incident reports are common artifacts across various domains, including aviation and wildfire response. These reports are often mandatory to submit, resulting in the culmination of large repositories of text-based documents. Simultaneously, these reports and corresponding repositories are often only manually analyzed and queried by users via out-of-date search engines. As a consequence, we have been developing the Manager for Intelligent Knowledge Access (MIKA) toolkit, which uses natural language processing to improve information access and reuse. In this presentation, we discuss natural language processing techniques for knowledge discovery and apply these methods to a repository of aerial wildfire mishap reports. Two methods are used for knowledge discovery: topic modeling and named-entity recognition. We use topic modeling to identify hazards and perform a trend analysis to produce a data-driven risk matrix. A custom named-entity recognition model, build from fine tuning a pre-trained language model, is used to identify failure modes, failure causes, failure effects, control processes, and recommendations to aid in failure modes and effects analysis (FMEA). Throughout the presentation, we discuss and apply natural language processing techniques to better leverage the vast amount of information contained in report repositories.

Machine learning↗

DOE OSTI2020

Technical Performance of Refractory Liners for Molten Chloride Salt Thermal Energy Storage Systems

A chloride-based molten-salt system that uses a ternary blend of MgCl2/KCl/NaCl is investigated to provide higher temperature thermal energy storage capability. Despite higher thermal stability, molten chlorides present several unique challenges, including the design of internal refractory-ceramic liners to prevent the corrosion and thermal stress of alloy tank shells. This work discusses issues and potential solutions related to containment of molten chloride salt, specifically the optimization of the refractory material at the molten salt interface (hot face). The down-selected hot face candidate limits permeation of salt through the material and forms a highly stable secondary surface phase in equilibrium with the molten salt. A mortar is created using the corrosion resistant hot face brick. Brick and mortar composite are subjected to mechanical stress/strain analysis, in order to calculate composite material properties and better inform thermomechanical models. The U.S. Department of Energy Generation 3 (DOE Gen3) program seeks to develop higher efficiency CSP plants that can provide cost-competitive, flexible power in the U.S. electric grid. The proposed Gen3 Liquid Pathway CSP plant closely resembles the configuration of current nitrate salt power towers with two-tank storage (Gen2). The differences between Gen2 and Gen3 include the types of compatible materials used in salt storage tank construction. Stainless steel loses strength at Gen3 temperatures, and although nickel superalloys would be capable of withstanding sustained high temperatures, these materials are prohibitively expensive at scale. Uninsulated tank shells also pose a significant risk as common steels are highly susceptible to chemical attack from molten chloride salt. To address these concerns, refractory-ceramic based containment materials are proposed to line the inside of the hot and cold storage tanks. In doing so, stainless or carbon steel shells may be used in construction depending on the level of insulation provided. The composition of the internal liner requires careful consideration to maximize the efficacy of multiple parameters including corrosion resistance, strength at operating temperature, durability, and cost. This is particularly true for the material at the interface with the salt, known as the "hot face", which is responsible for protecting the insulating layers between the tank shell and the hot face brick layer. The molten salt in this system is superheated over 300 °C above its freeze temperature. Therefore, unlike other industrial processes which use refractory-lined vessels, it is not expected that a freeze plane will develop in the hot face. Therefore, the hot face must be designed to withstand chemical corrosion and inhibit permeation of molten salt into the insulating layers. A down selection was performed to identify a hot face candidate best equipped to maintain thermal, mechanical, and chemical integrity when exposed to molten salt over extended periods of time. Long-duration chemical capability experiments were conducted with the down selected hot face refractory fully immersed in molten chloride salt for up to 3000 hours. The average salt penetration does not exceed 100 microns. When extrapolated to 20 and 30 years of continuous exposure, the expected salt penetration depth is approximately 2.0 mm and 2.9 mm, respectively. A magnesium-rich secondary phases develops at the salt/refractory interface. X-ray diffraction identifies the material as forsterite (Mg2SiO4), which is reported to form synthetically in molten chloride salt solutions. These results suggest the selected hot face will adequately inhibit salt permeation. While there is optimism that the hot face brick will inhibit salt permeation, mortar joints are typically the weakest point of a refractory brick liner. From a thermochemical perspective, differing thermal expansion coefficients may result in the mortar and brick to grow independent of each other, creating gaps through which molten salt can penetrate. To address this issue, NREL has developed an in-house mortar composed of the down selected hot face brick that has been shown to be compatible with the salt. Compressive stress/strain analyses have been performed on the brick/ mortar composites to generate stress/strain curves. Modulus of elasticity and Poisson's ratio of the composite may be calculated from the stress/strain curves, in order to provide more representative data to finite element mechanical models for accurate approximation of stress on the tank shell and the amount of thermal expansion expected within the tank liner.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2023

Beyond Generalized Eigenvalues in Lattice Quantum Field Theory

Two analysis techniques, the generalized eigenvalue method (GEM) or Prony's (or related) method (PM), are commonly used to analyze statistical estimates of correlation functions produced in lattice quantum field theory calculations. GEM takes full advantage of the matrix structure of correlation functions but only considers individual pairs of time separations when much more data exists. PM can be applied to many time separations and many individual matrix elements simultaneously but does not fully exploit the matrix structure of the correlation function. We combine both these methods into a single framework based on matrix polynomials. As these algebraic methods are well known for producing extensive spectral information about statistically-noisy data, the method should be paired with some information criteria, like the recently proposed Bayesean model averaging.

Fleming, George T.↗

DOE OSTI2021

An overview of data tools for representing and managing building information and performance data

Building information modeling (BIM) has been widely adopted for representing and exchanging building data across disciplines during building design and construction. However, BIM's use in the building operation phase is limited. With the increasing deployment of low-cost sensors and meters, as well as affordable digital storage and computing technologies, growing volumes of data have been collected from buildings, their energy services systems, and occupants. Such data are crucial to help decision makers understand what, how, and when energy is consumed in buildings—a critical step to improving building performance for energy efficiency, demand flexibility, and resilience. However, practical analyses and use of the collected data are very limited due to various reasons, including poor data quality, ad-hoc representation of data, and lack of data science skills. To unlock value from building data, there is a strong need for a toolchain to curate and represent building information and performance data in common standardized terminologies and schemas, to enable interoperability between tools and applications. This study selected and reviewed 24 data tools based on common use cases of data across the building life cycle, from design to construction, commissioning, operation, and retrofits. The selected data tools are grouped into three categories: (1) data dictionary or terminology, (2) data ontology and schemas, and (3) data platforms. The data are grouped into ten typologies covering most types of data collected in buildings. This study resulted in five main findings: (1) most data representation tools can represent their intended data typologies well, such as Green Button for smart meter data and Brick schema for metadata of sensors in buildings and HVAC systems, but none of the tools cover all ten types of data; (2) there is a need for data schemas to represent the basis of design data and metadata of occupant data; (3) standard terminologies such as those defined in BEDES are only adopted in a few data tools; (4) integrating data across various stages in the building life cycle remains a challenge; and (5) most data tools were developed and maintained by different parties for different purposes, their flexibility and interoperability can be improved to support broader use cases. Finally, recommendations for future research on building data tools are provided for the data and buildings community based on the FAIR principles to make data Findable, Accessible, Interoperable, and Reusable.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Innovative Strategies for Long-Term Monitoring of Complex Groundwater Plumes at DOE’s Legacy Sites (Workshop Report)

Most remaining Department of Energy (DOE) sites will require extended periods of institutional control, especially at complex groundwater sites where attenuation-based strategies have been implemented to facilitate closure. The current practice of monitoring—obtaining and analyzing contaminant concentration in groundwater samples at numerous wells—will account for a large portion of the projected life-cycle at these DOE sites unless a new approach is adopted. State-of-the-art technologies are being developed, including in situ sensors, geophysics, radiation mapping, numerical modeling and AI/ML. These technologies can optimize monitoring strategies in space and time, provide spatially extensive information at vulnerable regions and/or provide more continuous monitoring at lower cost. As part of DOE’s Office of Environmental Management (DOE-EM’s) efforts to advance long-term monitoring systems, an in-person/virtual hybrid workshop was hosted by Savannah River National Laboratory (SRNL) on January 24 and 25, 2023, in Augusta, Georgia. Because DOE-EM’s complex sites will eventually be transferred to DOE’s Office of Legacy Management (DOE-LM), representatives of DOE-LM were important participants in the workshop. The purpose of the workshop was to identify challenges and opportunities for deploying advanced technologies for long-term monitoring at DOE sites. The key questions during the workshop were: 1) the regulatory acceptance of replacing a process that traditionally has used laboratory sampling and analysis of groundwater samples, and 2) the application of this strategy to the southwestern arid sites that include many of the remaining DOE-EM and DOE-LM complex groundwater plumes. Characteristics common to most arid sites present both limitations and opportunities for advanced technologies. DOE-EM has funded a National Laboratory team from SRNL, Lawrence Berkeley National Laboratory (LBNL), and Pacific Northwest National Laboratory (PNNL) to establish the overarching framework of long-term monitoring by systematically combining advanced hardware and software technologies. This project is titled “Advanced Long-Term Environmental Monitoring Systems (ALTEMIS)” and is sponsored by the DOE-EM Technology Development Program. The multi-laboratory team is currently developing and testing innovative monitoring strategies, including the use of in situ groundwater sensors, geophysics, drone/satellite-based remote sensing, reactive transport modeling, and artificial intelligence/machine learning (AI/ML). The project’s demonstration testbed is at the Savannah River Site (SRS) F-Area Seepage Basins, where a well-characterized complex groundwater plume composed of uranium and other radionuclides is in the latter stages of remediation. The workshop included more than 70 participants, presentations, a field visit to F-Area, breakout working groups, and large group discussion. Participants developed recommendations on five topics: in situ sensors, spatially integrative tools, challenges to regulatory acceptance, AI/ML strategies, and transitioning sites to DOE-LM.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover

Feedstock heterogeneity is a key challenge impacting the deconstruction and conversion of herbaceous lignocellulosic biomass to biobased fuels, chemicals, and materials. Upstream processing to homogenize biomass feedstock streams into their anatomical components via air classification allows for a more tailored approach to subsequent mechanical and chemical processing. Here, we show that differing corn stover anatomical tissues respond differently to pretreatment and enzymatic hydrolysis and therefore, a one-size-fits-all approach to chemical processing biomass is inappropriate. To inform on-line downstream processing, a robust and high-throughput analytical technique is needed to quantitatively characterize the separated biomass. Predictive correlation of near-infrared spectra to biomass chemical composition is such a technique. Here, we demonstrate the capability of models developed using an “off-the-shelf,” industrially relevant spectrometer with limited spectral range to make strong predictions of both cell wall chemical composition and the relative abundance of anatomical components of the corn stover, the latter for the first time ever. Gaussian process regression (GPR) yields stronger correlations (average R 2 v = 88% for chemical composition and 95% for anatomical relative abundance) than the more commonly used partial least squares (PLS) regression (average R 2 v = 84% for chemical composition and 92% for anatomical relative abundance). In nearly all cases, both GPR and PLS outperform models generated using neural networks. These results highlight the potential for coupling NIRS with predictive models based on GPR due to the potential to yield more robust correlations.

09 BIOMASS FUELS↗

NASA NTRS1994

General purpose architecture for intelligent computer-aided training

An intelligent computer-aided training system having a general modular architecture is provided for use in a wide variety of training tasks and environments. It is comprised of a user interface which permits the trainee to access the same information available in the task environment and serves as a means for the trainee to assert actions to the system; a domain expert which is sufficiently intelligent to use the same information available to the trainee and carry out the task assigned to the trainee; a training session manager for examining the assertions made by the domain expert and by the trainee for evaluating such trainee assertions and providing guidance to the trainee which are appropriate to his acquired skill level; a trainee model which contains a history of the trainee interactions with the system together with summary evaluative data; an intelligent training scenario generator for designing increasingly complex training exercises based on the current skill level contained in the trainee model and on any weaknesses or deficiencies that the trainee has exhibited in previous interactions; and a blackboard that provides a common fact base for communication between the other components of the system. Preferably, the domain expert contains a list of 'mal-rules' which typifies errors that are usually made by novice trainees. Also preferably, the training session manager comprises an intelligent error detection means and an intelligent error handling means. The present invention utilizes a rule-based language having a control structure whereby a specific message passing protocol is utilized with respect to tasks which are procedural or step-by-step in structure. The rules can be activated by the trainee in any order to reach the solution by any valid or correct path.

Loftin, R. Bowen↗

DOE OSTI2023

Neural Image Compression: Generalization, Robustness, and Spectral Biases

Recent advances in neural image compression (NIC) have resulted in models which are starting to outperform traditional codecs. While this has led to growing excitement about using these methods in real-world applications, the successful adoption of any machine learning system (including NIC) in the wild requires it to generalize (and be robust) to unseen distribution shifts at deployment time. Unfortunately, current research lacks comprehensive datasets and informative tools to evaluate and understand compression performance in real-world settings. To bridge this crucial gap, first, this paper presents a comprehensive benchmark suite to evaluate the out-of-distribution (OOD) performance of image compression methods. Specifically, we design CLIC-C and Kodak-C by introducing 15 common corruptions to popular CLIC and Kodak benchmarks. Next, we propose spectrally inspired introspection tools to gain a deeper understanding of errors introduced by image compression methods as well as their OOD performance. To this end, we carry out a detailed performance comparison of the classical codec with various variants of NIC (e.g., original, variable rate, pruned), revealing intriguing findings that challenge our current understanding of the strengths and limitations of NIC. Finally, we corroborate our empirical findings with theoretical analysis, providing an in-depth view of the OOD performance of NIC. Our benchmarks, spectral introspection tools, and findings provide a crucial bridge to the real-world adoption of NIC. We hope that our work will propel future efforts in designing more robust and generalizable NIC methods.

neural networks, Variational Autoencoder, robustne↗

NASA NTRSDate not supplied

A Crew Seat for Human Exploration in Multiple Gravity Environments

This work attempts to develop a single crew seating solution that is applicable across a range of gravity environments encountered by spacecraft proposed in several conceptual spacecraft architectures. All of these spacecraft will need to provide some sort of stationary accommodation for the crew for performing various activities such as work in science laboratories, maintenance and repair facilities, medical care facilities, and spacecraft operations centers, as well as for basic habitation in crew quarters, entertainment / relaxation facilities, and crew dining facilities. Depending on the spacecraft or architecture, this stationary accommodation may be experienced continuously in microgravity, such as would be the case for the Deep Space Exploration Vehicle. Alternately, it could be in continuous lunar or Martian gravity, such as the Common Habitat base camps. It could experience fractional gravity, such as a Pressurized Rover for In-Space Missions at a Near Earth Asteroid or one of the Martian moons. It could alternate between artificial gravity and microgravity, such as the Nautilus-X. It could alternate between microgravity and lunar or Martian gravity, such as the SpaceX Starship Human Landing System or the Blue Origin Blue Moon Block 2 Human Landing System. Or it could be in continuous Earth gravity, such as ground trainer systems. Prior human spaceflight systems for stationary accommodation have been focused on microgravity applications. These systems have their own limitations and cannot be used in a gravity environment. A public crowdsourcing campaign generated dozens of ideas, which ultimately generated a Gecko Mobility Aids system for crew translation and the Multi-Gravity Crew Seat (MGCS) for stationary accommodation. The MGCS functions in gravity as a traditional terrestrial seat, performing functions of load for the overall body and forearms, as well as head and neck load relief while positioning the body within range of an intended task. In microgravity, the MGCS functions as a body restraint, securing the body against inadvertent drifting by applying a restraining pressure at the front and back of the thighs, shoulders, and back. The initial MGCS concept was developed in a NASA hackathon and was refined through a review of dozens of terrestrial seating styles. Additionally, a review of anthropometry and biomechanics data related to the neutral body posture was conducted to help inform the microgravity configuration of the MGCS. A series of CAD models were iteratively developed, with subject matter expert reviews leading to design improvements. A scale model was constructed and used with a humanoid model to demonstrate MGCS accommodation of a human-like body in both gravity and microgravity modes. Work to develop a full-scale protype of the MGCS is discussed, including design and fabrication of the headrest, arm rest, seat back, seat pan, seat base, and the conversion mechanisms. A 1-g human-in-the-loop evaluation of the prototype assessed the acceptability of performing seated activities in the MGCS, collecting data on the usability, comfort, and ease of ingress/egress. Based on the evaluation results, design modifications needed for reduced gravity testing are documented and initial work is indicated for a reduced gravity test plan.

Restraints and Mobility Aids↗

DOE OSTI2024

Common Electric Power Transmission System Model JSON Schema Specification

The Common Electric Power Transmission System Model (CTM) is an intuitive, extensible, language-agnostic, and error-resistant specification of electric power network components parameter names and units, and relation between components, intended for use by the research community developing new computational methods for power systems operations and simulation. Power system datasets following the CTM specification can be read as dictionaries and manipulated in that form in most programming languages (e.g., Python, Julia, C++). This standard data structure in CTM makes it easy to work in multiple power systems domains (e.g., economic operation, reliability assessment, electricity markets, stability assessment, etc.) without requiring conversions between use-case-specific file formats with information loss in the process. This repository specifies CTM as a JSON Schema, provides documentation, derivate (code-generated) implementations of CTM, and example data and usage of the schema for important use cases.

Aravena Solis, Ignacio↗

NASA NTRS1999

Combustion of Metals in Reduced-Gravity and Extra Terrestrial Environments

The combustion of metals is a field with important practical applications in rocket propellants, high-temperature flames, and material synthesis. Also, the safe operation of metal containers in high-pressure oxygen systems and with cryogenic fuels and oxidizers remains an important concern in industry. The increasing use of metallic components in spacecraft and space structures has also raised concerns about their flammability properties and fire suppression mechanisms. In addition, recent efforts to embark on unmanned and manned planetary exploration, such as on Mars, have also renewed the interest in metal/carbon-dioxide combustion as an effective in situ resource utilization technology. In spite of these practical applications, the understanding of the combustion properties of metals remains far behind that of the most commonly used fuels such as hydrocarbons. The lack of understanding is due to the many problems unique to metal- oxidizer reactions such as: low-temperature surface oxidation prior to ignition, heterogeneous reactions, very high combustion temperatures, product condensation, high emissivity of products, and multi-phase interactions. Very few analytical models (all neglecting the influence of gravity) have been developed to predict the burning characteristics and the flame structure details. Several experimental studies attempting to validate these models have used small metal particles to recreate gravity-free conditions. The high emissivity of the flames, rapid reaction, and intermittent explosions experienced by these particles have made the gathering of any useful information on burning rates and flame structure very difficult. The use of a reduced gravity environment is needed to clarify some of the complex interactions among the phenomena described above. First, the elimination of the intrusive buoyant flows that plague all combustion phenomena is of paramount importance in metal reactions due to the much higher temperatures reached during combustion. Second, a low-gravity environment is absolutely essential to remove the destructive effect of gravity on the shape of a molten metal droplet in order to study a spherically symmetric condition with large bulk samples. The larger size of the spherical metal droplet and the longer burning times available in reduced gravity extend the spatial and temporal dimensions to permit careful probing of the flame structure and dynamics. Third, the influence of the radiative heat transfer from the solid oxides can be studied more carefully by generating a stagnant spherical shell of condensed products undisturbed by buoyancy.

Branch, M.C.↗

NASA NTRS2009

Applications of Meteorological Tower Data at Kennedy Space Center

Members of the National Aeronautics and Space Administration (NASA) design and operation communities rely on meteorological information collected at Kennedy Space Center (KSC), located near Cape Canaveral, Florida, to correctly apply the ambient environment to various tasks. The Natural Environments Branch/EV44, located at Marshall Space Flight Center (MSFC) in Huntsville, Alabama, is responsible for providing its NASA customers with meteorological data using various climatological data sources including balloons, surface stations, aircraft, hindcast models, and meteorological towers. Of the many resources available within the KSC region, meteorological towers are preferred for near-surface applications because they record data at regular, frequent intervals over an extensive period of record at a single location. This paper discusses the uses of data measured at several different meteorological towers for a common period of record and how the data can be applied to various engineering decisions for the new Constellation Program Ares and Orion space vehicles.

Altino, Karen M.↗