Results for “Reserves” — Page 42

NASA NTRSDate not supplied

CFD and Thermodynamic Model Predictions of No-Vent Tank Filling in Microgravity

Cryogenic Fluid Management (CFM) of propellant fluids under low gravity conditions present an important challenge in the pursuit of NASA’s future long-duration space missions. Success of these missions relies to a large extent on the ability to store and transfer the cryogenic liquid fuels efficiently and safely. To this end, the Zero-Boil-Off Tank Filling and Transfer Experiment (ZBOT-FT) undertaken by University of Bremen in collaboration with Case Western Reserve University aims at studying two phase flow tank-to-tank transfer operations in microgravity using a simulant fluid, Perfluoro-n-pentane (PNP). As part of this effort, in this work, we present a system level homogeneous thermodynamic model and a two-phase Computational Fluid Dynamics (CFD) model of a no-vent cylindrical tank filling in microgravity. Both 2D axisymmetric and 3D parametric simulations case studies are performed to show the detailed physics of the filling process and the evolution of tank pressure for filling flow rates between 1.0 and 1.65 mL/s. A zero-dimensional homogeneous thermodynamic model is also developed to predict the final steady state tank pressure analytically. Comparison of the 2D and 3D CFD predictions of final pressures to the steady state thermodynamic results displays very good agreement. However, it is shown that the 2D axisymmetric model predicts a maxima in tank pressure before reaching steady state. It is indicted that this pressure maxima is caused by the constraints inherent in the axisymmetric model and is absent in the 3D and thermodynamic pressure predictions.

Two Phase Flow↗

NASA NTRSDate not supplied

FloodPlanet: High-Resolution Commercial Imagery for Training and Validation of Deep Learning-Based Models of Inundation Extent

Flooding events are becoming increasingly frequent worldwide and are known to cause extensive damage. Public optical and radar satellite imagery can be used to detect large areas of inundation in rural areas, however, long revisit times and coarse spatial resolution limit applications for short-lived events and urban areas. Commercial constellations such as those operated by Planet offer increased spatial and temporal resolution and can supplement mapping efforts to provide more information to disaster response, relief, and mitigation efforts. Deep learning requires high quality labeled data for training across coincident sensors. The FloodPlanet dataset presented here contains labeled surface water for 18 events across the world based on Planetscope imagery with coincident Harmonized Landsat Sentinel-2 ( HLS) or Sentinel-1 and builds upon the previously existing Sen1Floods11, xBD, and NASA Sentinel-1 datasets. Sen1Floods11 includes 4,831 512x512 pixel overlapping tiles of coincident Sentinel-1 and Sentinel-2 data observing 11 flood events across the world from 2017-2019. The dataset contains a combination of automated and hand-labeled surface water for use in training and validation of inundation modeling efforts. The xBD dataset identifies flood-damaged buildings and indicates the scale of damage to each (none, minor, moderate, and major) from four flood events which occurred in the United States, India, Nepal, and Bangladesh from the same time period. The NASA dataset contains hand-labeled water bodies observed in Sentinel-1 imagery during five flood events within the 2017-2019 period. The effort presented here utilizes observations from these previously investigated flood events to generate labels of surface water at the 3-5m spatial resolution provided by Planetscope and facilitate the comparison between public and commercial data. A data pipeline was built which uses clustering algorithms to pick the most suitable overlapping chips between the public data and PlanetScope data for manual labeling. Labels were created manually using NASA’s ImageLabeler tool and include areas of high- and low-confidence water. The high confidence designation is reserved for areas of open, unobstructed water while low confidence is used for areas of suspected water beneath vegetation, clouds, or cloud shadows. Expected to be released in late 2022, the FloodPlanet dataset will include tiled imagery with a unique ID for each 1024x1024 pixel tile, 7 bands of HLS data, and high- and low-confidence flood labels in both shapefile and tiff formats. The authors will follow Spatial Temporal Access Catalog (STAC) guidelines to release FloodPlanet on the Radiant Earth ML hub, which hosts public datasets for machine learning.

Alexander Melancon↗

NASA NTRSDate not supplied

New Topographic Products for the Lunar South Pole from the Lunar Orbiter Laser Altimeter (LOLA)

Due to the Moon’s low obliquity and highly cratered topography, its polar environments host regions of extreme illumination conditions. Multiple lines of evidence indicate the presence of geologically stable ice and other volatiles on or below the surface within polar permanently shadowed regions (PSRs). These regions are valuable targets for science and exploration as they hold clues to the history of water in the inner solar system and may hold sufficient reserves for in-situ resource utilization. Standing high above the PSRs, peaks of long-duration sunlight are oases where landed assets can easily generate power and communicate. For these reasons, the lunar south pole is the target of many upcoming missions including NASA’s Artemis program. The Lunar Orbiter Laser Altimeter (LOLA) onboard the Lunar Reconnaissance Orbiter (LRO) provides the global geodetic framework with which past, present, and future missions can accurately geolocate their data. Laser altimeters like LOLA are unaffected by shadows and, therefore, provide the most direct measurements of topography within PSRs. Here we present the latest work to improve LOLA’s polar topographic maps and resulting derived products, such as PSR maps, which are crucial tools for science and exploration of the polar regions

Michael K Barker↗

NASA NTRSDate not supplied

A Survey of ISS and Visiting Vehicle Returned Surfaces for Environmental Characterization and Computer Model Development

The Orbital Debris Engineering Model (ORDEM) developed by the NASA Orbital Debris Program Office (ODPO) is a data-driven model — extensive radar, optical, laboratory, and in situ measurement data sets have been used to build the model since its earliest versions. A salient aspect of professional software development is the verification and validation (V&V) process. Verification answers the question “Is the model built correctly?” while validation addresses the question “Did we build the correct model?” Less extensive, reserved, or independent data sets serve the validation requirement. Due to the dynamic nature of the orbital debris environment, it is critical to use contemporaneous data sources that represent the current environment to support ORDEM development and validation. ORDEM has utilized in situ data collected from Space Shuttle and Hubble Space Telescope surface inspections, now over a decade old. This historical dataset is fundamental for providing baseline in situ measurement data for sizes between 10 to 300 microns, but new data sources are being evaluated using returned surfaces from or near the International Space Station (ISS). This paper reviews a general microscopic survey of ISS soft goods, the Pressurized Mating Adapter 2 (PMA-2) blanket, and a limited-scope feasibility study conducted on the Space Exploration Technologies Corporation (SpaceX) Dragon capsule’s Thermal Protection System (TPS) material. The PMA-2 blanket, exposed to the space environment between 09 July 2013 and 25 February 2015, is an approximately 3.7 m2-area blanket composed of a betacloth outer layer and multiple ballistic fabric inner layers. The SpaceX Cargo Dragon capsule regularly visited the ISS from 2012 through 2020 and potentially provides a timely and well-characterized source of data for modeling purposes. The capsule’s lateral surfaces use SpaceX Proprietary Ablative Material (SPAM) TPS material, a syntactic foam, for thermal management during all mission phases. Seven SPAM extracted samples have been analyzed to date. This paper will provide an overview of the characterization completed for impact features by size, depth, impactor diameter, and the impactor residues chemical analyses, allowing a differentiation between micrometeoroids and orbital debris and a categorization by mass density and density class. Impactor diameter is estimated using damage equations generated from ground-based hypervelocity impact testing. The orbital debris impactors are compared to the current ORDEM 3.2 model of the environment at ISS altitudes. We briefly discuss the meteoroid impactors, including constituents and mass densities, in the general context of current models.

Phillip Anz-meador↗

NASA NTRS2023

NDARC - NASA Design and Analysis of Rotorcraft: Input - Appendix 3

The NDARC code performs design and analysis tasks. The design task involves sizing the rotorcraft to satisfy specified design conditions and missions. The analysis tasks can include off-design mission performance analysis, flight performance calculation for point operating conditions, and generation of subsystem or component performance maps. Figure 1-1 illustrates the tasks. The principal tasks (sizing, mission analysis, flight performance analysis) are shown in the figure as boxes with heavy borders. Heavy arrows show control of subordinate tasks. The aircraft description (figure 1-1) consists of all the information, input and derived, that defines the aircraft. The aircraft consists of a set of components, including fuselage, rotors, wings, tails, and propulsion. This information can be the result of the sizing task; can come entirely from input, for a fixed model; or can come from the sizing task in a previous case or previous job. The aircraft description information is available to all tasks and all solutions (indicated by light arrows). The sizing task determines the dimensions, power, and weight of a rotorcraft that can perform a specified set of design conditions and missions. The aircraft size is characterized by parameters such as design gross weight, weight empty, rotor radius, and engine power available. The relations between dimensions, power, and weight generally require an iterative solution. From the design flight conditions and missions, the task can determine the total engine power or the rotor radius (or both power and radius can be fixed), as well as the design gross weight, maximum takeoff weight, drive system torque limit, and fuel tank capacity. For each propulsion group, the engine power or the rotor radius can be sized. Missions are defined for the sizing task, and for the mission performance analysis. A mission consists of a number of mission segments, for which time, distance, and fuel burn are evaluated. For the sizing task, certain missions are designated to be used for design gross weight calculations; for transmission sizing; and for fuel tank sizing. The mission parameters include mission takeoff gross weight and useful load. For specified takeoff fuel weight with adjustable segments, the mission time or distance is adjusted so the fuel required for the mission (burned plus reserve) equals the takeoff fuel weight. The mission iteration is on fuel weight or energy. Flight conditions are specified for the sizing task, and for the flight performance analysis. For the sizing task, certain flight conditions are designated to be used for design gross weight calculations; for transmission sizing; for maximum takeoff weight calculations; and for antitorque or auxiliary thrust rotor sizing. The flight condition parameters include gross weight and useful load. For flight conditions and mission takeoff, the gross weight can be maximized, such that the power required equals the power available. A flight state is defined for each mission segment and each flight condition. The aircraft performance can be analyzed for the specified state, or a maximum effort performance can be identified. The maximum effort is specified in terms of a quantity such as best endurance or best range, and a variable such as speed, rate of climb, or altitude. The aircraft must be trimmed, by solving for the controls and motion that produce equilibrium in the specified flight state. Different trim solution definitions are required for various flight states. Evaluating the rotor hub forces may require solution of the blade flap equations of motion.

NDARC↗

NASA NTRSDate not supplied

Aerial Vehicle Routing and Scheduling for UAS Traffic Management: A Monte Carlo Tree Search Approach

Numerous unmanned aircraft systems operating at low altitudes to deliver goods and services may one day become ubiquitous in our cities. In the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) framework, such a concept is envisioned, where aerial vehicles operate beyond visual line of sight (BVLOS) within specifically reserved and time stamped “corridors” in the airspace. For example, these corridors or operational intent volumes can connect an aerial vehicle’s origin site to its destination site for package delivery operations. There may also be more than one corridor available for an aerial vehicle to choose from and often different corridors may intersect with one another. Thus, it is imperative to ensure flight trajectories belonging to different aerial vehicles are not in conflict. Per the UTM CONOPs, we assume that a vehicle almost always stays inside its corridor or operational volume. This work provides a framework for strategic deconfliction of UTM or package delivery drones, where we schedule the departure time of all vehicles subject to various temporal constraints (including the corridor deconfliction at the intersections). We present the “multi-route weighted package delivery problem” which serves as an exemplifying model for strategic deconfliction in UTM. In the multi-route weighted package delivery problem, a graph network is given which consists of a set of depots (source) and drop-off (destination) nodes, with multiple routes (defined as a sequence of waypoints) connecting the depots to drop-off nodes. In addition, routes are weighted by the associated ground risk and total travel distance for package delivery. The goal is for a known set of aerial vehicles to depart from the depots, choose a route and take off time, while avoiding conflicts with other aerial vehicles, and minimizing both risk and distance traveled. We provide a mixed integer linear programming (MILP) formulation of the problem, as well as a heuristic solution based on Monte Carlo Tree Search (MCTS) – a method used in game theory and artificial intelligence – to overcome limitations inherent to optimal solvers. Computational results show the advantages of using MCTS over the MILP formulation; the former can provide a sub-optimal solution quickly, and may sometimes even reach an optimal solution, whereas the latter may not even produce a solution in reasonable time. Furthermore, results from both the MILP formulation and MCTS methods were validated using a preliminary agent-based simulator implementing the UTM concept of operations. Thus, the MCTS method can be seen as a scalable solution to the complex multi-route weighted package delivery problem and may possibly be extended to similar complex optimization problems.

Kenny Chour↗

NASA NTRS2022

A Systems Margin Metric for Capability Above Basic Estimates

A systems margin metric is described that indicates the total system performance capability above the basic estimates (CABE) for mass, power, energy, propellant, V, ISP, computer performance, and any other design parameter with a defined margin. This concept normalizes all terms for contingency, margin, growth allowance, and reserves to the amount that the basic mass of the vehicle could grow if each of those “room to grow” terms were released. The CABE metric is intended to provide a uniform indication of the total performance margin of a system at a given point in the project life cycle. The CABE metric can also provide a normalized means to trade the “room to grow” terms between various resources in order to solve system performance issues as they arise. For example, the CABE percentage for ISP margin could be equally traded with the CABE percentage for power margin.

Lepsch, Roger↗

NASA NTRSDate not supplied

Results from Two Full-Scale Hawker 4000 Fuselage Drop Tests

In the spring of 2022, NASA Langley Research Center (LaRC) conducted two full-scale drop tests on a Hawker 4000 partial fuselage structure for the evaluation of composite material response under dynamic impact loading conditions. The specific objectives of the tests were to induce dynamic composite damage initiation and progression and in primary structure and to understand the nature of the failure modes. The tests were conducted via dividing the fuselage hardware into three smaller sections. Dynamic drop tests were conducted on the forward and aft portions of the fuselage under different dynamic loading conditions, while the middle section was reserved for materials testing. Both tests were conducted by dropping the test articles from a height of 14 feet with significant amounts of pitch, which was added to initiate damage in specific regions of the structure. The test articles were ballasted then instrumented with accelerometers at various locations and speckle-coated on both exterior sides with a black and white stochastic pattern for use with three-dimensional digital image correlation (3D-DIC). In addition, the interior belly portion of the forward section test article was also speckle-coated, which was intended to capture deformations on the belly of the test article from the interior at impact. The interior of the aft section test article was not speckle-coated. Prior to testing, bow-tie markers were applied at various locations for use in marker tracking, to measure impact conditions such as vertical impact velocity and pitch. In this report, a partial summary of the data collected from the tests is presented. The data was primarily in the form of accelerometers to measure impact acceleration and 3D-DIC to measure deformation and failure onset and propagation. Post-test inspections revealed the primary damage mechanism was fiber cracking and delamination primarily in and around the area that contacted the impact surface. Acceleration pulse shapesin the ballast locations were trapezoidal in nature and ranged between 10 g and 40 g, depending on the location and test.

full scale testing↗

NASA NTRS2023

Northeast Alaska Climate: Using Earth Observations to Evaluate Snow Variability through a Climatological Analysis to Support Ecological Monitoring in Northeast Alaska

Alaska is experiencing climate change at an unprecedented rate, with temperatures increasing twice as fast as the national average. The resulting changes to the landscape and ecosystems are significant, including shorter winters, declining snow depth, thawing permafrost, and rapidly receding glaciers. These changes are not only exacerbating the negative impacts of oil exploration but also affecting the food security of indigenous communities that rely on hunting as a subsistence food source. With the US Fish and Wildlife Service managing a potential tundra travel season for the first time in its history, adequate data on historic snow variables is essential to protect the unique habitat of the area. This project used NASA satellite and assimilation system data to inform and improve the current understanding of snow patterns in the Arctic National Wildlife Refuge and the National Petroleum Reserve – Alaska. The DEVELOP team used MODIS Normalized Difference Snow Index data to determine snow season duration, snow change frequency, and the first and last days of snow. The team also utilized the 2.1 Global Land Data Assimilation System and Daymet V4 products to study climatological trends in snow depth and snow water equivalent, respectively, across the study areas. The results of this study give users the capacity to visualize maps of multiple snow variables to monitor changes in snow conditions and proactively prepare for the ecological, cultural, and landscape impacts that changes in snow variability will cause in the future.

Remote Sensing↗

NASA NTRSDate not supplied

Guiding Integration of Formal Verification in Assurance Cases

Assurance cases are being increasingly acknowledged as away to build trust in complex systems with autonomous capabilities. An assurance case is a comprehensive, defensible, and valid justification that a system will function as intended for a specific mission and operating environment. Formal verification is often reserved for the most critical components of such systems. However, formal verification tools are often complex, and their usage is subject to many constraints and contextual dependencies. This can raise challenges both for performing the verification as well as reflecting the verification results appropriately in the assurance case, especially for non-expert users of the verification tool. To address these challenges, we present a tool-supported methodology for integrating formal verification results in an assurance case by capturing key verification method information in a rigorously constructed assurance case. In particular, we capture the tool specification in terms of its inputs, outputs, and assurance constraints as assumptions over inputs and guarantees provided over its outputs. The tool specification is parametrized over the inputs and outputs to both guide the intended application of the tool, as well as to check that the tool has been applied following the stated assumptions and that the guarantees hold. We define a generic tool assurance argument pattern that enables integration of the verification results in the assurance case by allowing custom refinement and automated instantiation for each tool use. We demonstrate our methodology on two formal verification tools and their applications to the verification of neural network properties for the aircraft domain.

Assurance Cases↗

NASA NTRSDate not supplied

Results from Two Full-Scale Hawker 4000 Fuselage Drop Tests

In the spring of 2022, NASA Langley Research Center (LaRC) conducted two full-scale drop tests on a Hawker 4000 partial fuselage structure for the evaluation of composite material response under dynamic impact loading conditions. The specific objectives of the tests were to induce dynamic composite damage initiation and progression and in primary structure and to understand the nature of the failure modes. The tests were conducted via dividing the fuselage hardware into three smaller sections. Dynamic drop tests were conducted on the forward and aft portions of the fuselage under different dynamic loading conditions, while the middle section was reserved for materials testing. Both tests were conducted by dropping the test articles from a height of 14 feet with significant amounts of pitch, which was added to initiate damage in specific regions of the structure. The test articles were ballasted then instrumented with accelerometers at various locations and speckle-coated on both exterior sides with a black and white stochastic pattern for use with three-dimensional digital image correlation (3D-DIC). In addition, the interior belly portion of the forward section test article was also speckle-coated, which was intended to capture deformations on the belly of the test article from the interior at impact. The interior of the aft section test article was not speckle-coated. Prior to testing, bow-tie markers were applied at various locations for use in marker tracking, to measure impact conditions such as vertical impact velocity and pitch. In this report, a partial summary of the data collected from the tests is presented. The data was primarily in the form of accelerometers to measure impact acceleration and 3D-DIC to measure deformation and failure onset and propagation. Post-test inspections revealed the primary damage mechanism was fiber cracking and delamination primarily in and around the area that contacted the impact surface. Acceleration pulse shapesin the ballast locations were trapezoidal in nature and ranged between 10 g and 40 g, depending on the location and test.

full scale testing↗

NASA NTRS2023

South Slough Water Resources: Monitoring Changes in Water Quality to Identify Stressors in Eelgrass Extent Throughout the Coos Estuary

The Coos estuary in Southern Oregon supports a variety of habitats, including eelgrass (Zostera marina) meadows. Eelgrass meadows provide shelter and sustenance to local and migratory wildlife, including commercially important fishes, and cultural resources to local communities. These ecosystem services establish eelgrass as an ecologically, economically, and culturally important resource. However, the extent and density of eelgrass meadows within this estuary have declined substantially since 2005, threatening the ecosystem services they provide. NASA DEVELOP partnered with the South Slough National Estuarine Research Reserve and the Confederated Tribes of the Coos, Lower Umpqua, and Siuslaw Indians’ Department of Natural Resources to generate time-series maps of the water quality conditions (chlorophyll-a, turbidity) and eelgrass extent in the Coos estuary from 2016 to 2023 to better understand the conditions driving eelgrass decline. The DEVELOP team used NASA Earth observations including Landsat 8 Operational Land Imager (OLI), Landsat 9 OLI-2, and the European Space Agency’s Sentinel-2 Multispectral Instrument (MSI) to generate these time-series maps. The team faced limitations in the feasibility of detecting eelgrass within the Coos Estuary, including spectral resolution, tidal phase, and turbidity. These limitations indicate additional in situ data collection will be necessary for accurate eelgrass assessment. Meanwhile, the team determined it is feasible to assess turbidity and chlorophyll-a within the Coos Estuary using remote satellite data. These tools enabled the research partners to assess water quality characteristics within the Coos Estuary at a greater spatial scale and may provide a method of inexpensive preliminary investigation of eelgrass meadow locations.

eelgrass↗

NASA NTRS2023

Exploration of Design Drivers for the RVLT Lift+Cruise Reference Aircraft

A trade study was performed on an all-electric version of the Lift+Cruise urban air mobility reference aircraft developed under the Revolutionary Vertical Lift Technology Project. The trade study varied the input parameters of mission range, takeoff altitude, mission reserve time, cell specific energy, disk loading, and payload weight. A step-by-step incremental change in the input parameters was also examined. Analyses were performed using the NASA Design and Analysis of Rotorcraft tool and a Rapid Sizing Tool for electric vertical takeoff and landing aircraft. A comparison of the analysis results between the two tools shows good agreement in trends with differences in slope. Overall, the analysis shows that the Lift+Cruise configuration performs poorly on a NASA-defined, energy-dominated mission due to poor aerodynamic efficiency in forward flight. This poor cruise-flight efficiency results in a large energy requirement, which increases the required battery sizing and corresponding aircraft gross weight. As such, continued conceptual design and refinement of the Lift+Cruise reference aircraft by the RVLT Concepts Team will likely be limited.

UAM↗

NASA NTRS2023

Vertiport Dynamic Density

Advanced Air Mobility (AAM) is envisioned to be another spoke in a region’s transportation system, supplementing ground-based travel with air-based travel. Eventually, air taxis will be pervasive, convenient, and affordable. As demand and operations tempo increase, congestion may arise. Several characteristics of AAM reduce the applicability of techniques used in today’s National Airspace System (NAS) to manage congestion. AAM will include both scheduled and non-scheduled, on-demand operations, challenging strategic deconfliction algorithms. Flights will be fairly short, traversing an urban area, not hundreds or thousands of miles, with corresponding low energy reserves, prohibiting excessive delays. Flight operators will need flexibility in operations, scheduling a flight only minutes before departure, or diverting to an alternate vertiport if it becomes advantageous, further adding to trajectory uncertainty that would challenge strategic deconfliction. Finally, operators will desire privacy to protect sensitive information or preserve competitive advantage, limiting early access to intent information. In this paper, we present an approach for managing congestion at vertiports by providing insight into the traffic situation to support operationally-advantageous and safe land or divert decisions. We propose a metric designed with usefulness and usability in AAM operations in mind. The metric uses the sociology concept of dynamic density (DD) that takes into consideration not only number of flights, but also the interaction of those flights with the vertiport’s limited resources, namely the landing pads and the parking spots. DD supports a Pilot in Command (PIC) with decisions about whether to proceed, expedite, delay, or divert; and supports air traffic control (ATC) and vertiport operators in airspace management and vertiport usage. We demonstrate the metric on a notional vertiport scenario. We also show that DD provides better insight into congestion and resulting flight delays than an aircraft count metric used in traditional air traffic management.

Lilly Spirkovska↗

NASA NTRSDate not supplied

A Survey of ISS and Visiting Vehicle Returned Surfaces for Environmental Characterization and Computer Model Development

The Orbital Debris Engineering Model (ORDEM) developed by the NASA Orbital Debris Program Office (ODPO) is a data-driven model — extensive radar, optical, laboratory, and in situ measurement data sets have been used to build the model since its earliest versions. A salient aspect of professional software development is the verification and validation (V&V) process. Verification answers the question “Is the model built correctly?” while validation addresses the question “Did we build the correct model?” Less extensive, reserved, or independent data sets serve the validation requirement. Due to the dynamic nature of the orbital debris environment, it is critical to use contemporaneous data sources that represent the current environment to support ORDEM development and validation. ORDEM has utilized in situ data collected from Space Shuttle and Hubble Space Telescope surface inspections, now over a decade old. This historical dataset is fundamental for providing baseline in situ measurement data for sizes between 10 to 300 microns, but new data sources are being evaluated using returned surfaces from or near the International Space Station (ISS). This paper reviews a general microscopic survey of ISS soft goods, the Pressurized Mating Adapter 2 (PMA-2) blanket, and a limited-scope feasibility study conducted on the Space Exploration Technologies Corporation (SpaceX) Dragon capsule’s Thermal Protection System (TPS) material. The PMA-2 blanket, exposed to the space environment between 09 July 2013 and 25 February 2015, is an approximately 3.7 m2-area blanket composed of a betacloth outer layer and multiple ballistic fabric inner layers. The SpaceX Cargo Dragon capsule regularly visited the ISS from 2012 through 2020 and potentially provides a timely and well-characterized source of data for modeling purposes. The capsule’s lateral surfaces use SpaceX Proprietary Ablative Material (SPAM) TPS material, a syntactic foam, for thermal management during all mission phases. Seven SPAM extracted samples have been analyzed to date. This paper will provide an overview of the characterization completed for impact features by size, depth, impactor diameter, and the impactor residues chemical analyses, allowing a differentiation between micrometeoroids and orbital debris and a categorization by mass density and density class. Impactor diameter is estimated using damage equations generated from ground-based hypervelocity impact testing. The orbital debris impactors are compared to the current ORDEM 3.2 model of the environment at ISS altitudes. We briefly discuss the meteoroid impactors, including constituents and mass densities, in the general context of current models.

Phillip Anz-Meador↗

NASA NTRSDate not supplied

Recent Ground-based Test Results for the Closed Loop Two-Phase Flow Chilldown Test Module for Future Integration with the Flow Boiling and Condensation Experiment onboard International Space Station

Effective cryogenic fluid management is important to the success of future manned and unmanned NASA missions, and the transfer line chilldown process is recognized as a key technological challenge. To address this challenge, the Two-phase Flow and Thermal Management Lab (TFTML) at Case Western Reserve University is collaborating with NASA Glenn Research Center to develop, test and deploy a closed loop two-phase flow chilldown test section onboard the International Space Station to be integrated with the current Flow Boiling and Condensation Experiment (FBCE) module. The final results of this project will create the first chilldown process database under pure and sustained microgravity conditions and will significantly advance the understanding of the chilldown process for cryogenic fluid management. In the present work, the ground-based test results of the closed loop SS-316 chilldown test section in a horizontal configuration at varying inlet mass flow rates (3-40 g/s) and inlet liquid subcooling (5-35 0C) are presented. The working fluid is PF-5060 and the chilldown curves captured the entire quenching curve involving the different boiling regimes and temperature transition points viz., film boiling, Leidenfrost/re-wetting point, transition boiling, critical heat flux (CHF), nucleate boiling, onset of nucleate boiling (ONB) and single-phase liquid flow regions, when the test section is pre-heated to around 250 0C. The major findings of the present study are, (i) the bottom part of the test section sees a lower re-wetting temperature due to the horizontal flow configuration, (ii) the re-wetting temperatures are relatively unaffected by inlet liquid mass flow rates though the increase in mass flow rates reduce the chilldown time, and (iii) the re-wetting temperatures decrease with decrease in inlet liquid subcooling. Further, preliminary flow visualization studies in a transparent pyrex test section will also be presented to understand the intricate flow boiling regime transitions during the chilldown process.

ISS↗

NASA NTRSDate not supplied

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗

NASA NTRSDate not supplied

CFD Modeling & Validation of Single Bubble Growth During Cavitation in Zero Gravity

Cryogenic Fluid Management (CFM) in zero gravity represents a vital area of research as NASA pursues future long-duration space missions. Success of these missions relies heavily on the ability to store and transfer the cryogenic liquid fuels efficiently and safely. In the Zero Boil-Off Tank (ZBOT-1) Experiment in 2018, unexpected and intense phase change occurred at the screened Liquid Acquisition Device (LAD) during tank pressure control that was later attributed to cavitation. The cavitation ultimately resulted in pump failure. Since cryogen cavitation during storage and transfer of propellants in microgravity has been a source of serious concern for the aerospace community, a series of drop tower experiments with liquid methane were also performed at the University of Bremen to carefully study cavitation from a single bubble nucleus in an engineered micron-sized wall cavity. As the test cell is depressurized during the 10-second drop, cavitation causes bubble growth from the nucleus. Here, in a collaboration between the Case Western Reserve University and University of Bremen, we present a two-phase Computational Fluid Dynamics (CFD) model of bubble growth from a single wall cavity during depressurization in microgravity. Both 2D axisymmetric and 3D parametric simulation case studies are performed to show the evolution of the bubble growth. The model uses a Volume of Fluid (VOF) approach with and without adaptive mesh refinement and adaptive time stepping to capture the growth dynamics using an explicit formulation with algebraic reconstruction of the interface. CFD results show good agreement with corresponding experiment. Based on the velocity, temperature, and volume fraction fields generated by the validation case studies, detailed discussion and analysis of fluid flow and heat transfer during bubble growth process are presented to delineate the intricacies of the cavitation process. Strengths and shortcomings of the present numerical approach in capturing this phenomenon are also highlighted.

Two Phase Flow↗