Development of an Accepted Medical Condition List for Mars Transit Medical Capability Scoping
No abstract available
Nusbaum, Derek M.↗
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Results for “scope”
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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NASA Rotating Detonation Rocket Engine Concept Development Status & Scope
This presentation is a program update for the members of the JANNAF RDRE Panel. There is no technical content. A list of NASA sponsored projects is provided, along with the motivation for NASA’s involvement in the development of this type of propulsion system, and key technology gaps that need to be addressed.
Propulsion↗
Development of an Accepted Medical Condition List for Exploration Medical Capability Scoping
Future NASA human spaceflight programs are on the verge of moving beyond Low Earth Orbit (LEO) to implement missions in lunar space and ultimately Mars. The mission constraints for these types of missions are expected to be progressively challenging for integration of Human Systems requirements into the vehicle and mission architectures. Mass and volume allocations are expected to become increasingly restrictive at the same time that mission realities will drive an increasing need for crew self-sufficiency in the maintenance and repair of both vehicle systems and human systems. To meet these challenges, a systematic, traceable, and repeatable approach to identifying, defining, and prioritizing medical capabilities is required. To provide a systematic and repeatable approach to defining and prioritizing clinical capabilities for spaceflight medicine, a clear process is required for delivering a list of prioritized medical capabilities to the Systems Engineering process that will delineate the mass, power, volume, and similar needs and the trade space analysis for a given space vehicle and mission architecture.
Rebecca Blue↗
UV-SCOPE: Ultraviolet Spectroscopic Characterization of Planets and Their Environments
No abstract provided
Swain, Mark↗
A Machine Learning Approach to Improve Air Traffic Management Initiatives
Collaborating closely with commercial air carriers and related organizations, the Federal Aviation Administration(FAA) regulates air traffic and ensures the safety and efficiency of air operations. Air traffic controllers make strategic decisions, such as delaying, rerouting, or canceling flights, partly based on guidance provided by the FAA’s Air TrafficControl System Command Center (ATCSCC). The guidance includes, among other things, control measures known asTraffic Management Initiatives (TMIs) designed to enhance safety and improve operational efficiency. TMIs play a crucial role in managing the demand and capacity within the U.S. National Airspace System (NAS). Two major TMIs that are routinely used (primarily to mitigate the adverse effects of bad weather) are Ground Delay Programs (GDPs) andGround Stops (GSs). In a GDP, flights destined for airports facing thunderstorm activity experience delays at their origin airports. This proactive approach minimizes the risk of routing aircraft through hazardous weather conditions and also replaces (fuel burning) airborne delays with ground delays. In a GS, a temporary restriction is imposed on the departure or arrival of aircraft at a specific airport or within a designated airspace. Although other TMIs (e.g., miles-in-trail) are also implemented as part of (air) traffic flow management in the NAS, the focus of this work is on GDPs and GSs. Since TMIs, by design, lead to flight delays or cancellations, it is crucial to put in place the right set of parameters(e.g., scope and duration of the GDP). For example, when the end time of a GDP extends beyond what is necessary, it imposes unnecessary delays on departing flights. This situation could occur as a result of inaccurate prediction of the(required) duration of the GDP based on the weather forecast. On the other hand, if a GDP ends prematurely before the underlying capacity constraints are resolved at the destination airport, it may result in airborne holding. The delicate balance lies in matching the termination of the GDP precisely with the resolution of capacity constraints, avoiding both the imposition of unnecessary ground delays and the need for airborne holding due to premature program termination.Failing to specify the right parameters for TMIs also leads to flight delays, creating a significant obstacle in managing the increasing traffic volumes causing increased work load for the controllers. To address this issue, we propose the integration of Machine Learning (ML) models in the traffic flow management(TFM) pipeline. In current operations, decisions are made by human experts based on extensive training, historical patterns, available traffic and weather data. Since we have an abundance of data from past events that tell us the likely impact of various TMIs, by ingesting historical data, properly trained ML models can offer valuable insights and aid human decision-making. With the FAA increasingly exploring advanced analytics, ML emerges as a focal point for enhancing TFM within the National Airspace System (NAS). As a first step, this study aims to provide traffic controllers with decision-making support for the issuance and adjustment of TMIs. Data analytics and machine learning have been previously employed to address some of the challenges associated with TMIs. Numerous studies have concentrated on various facets of TMI issuance, exploring factors influencing TMI parameters, including arrival rate, airport capacity, and delay prediction. For example, using weather forecasts, several statistical methods were used to produce probabilistic capacity profiles which in conjunction with deterministic models provided insights into the GDP planning process [1–4]. The downside of using deterministic models is that they rely on fixed inputs and predetermined rules, which lack the ability to account for the inherent uncertainty and variability present in real-world scenarios. In a separate series of studies, researchers aimed to predict the occurrences of GDPs and GSs. The majority of these studies utilized various supervised learning methods, including Decision Trees, Naive Bayes, Support VectorMachines, and Random Forests to analyze the influence of weather conditions and arrival demand on TMI incidents[5–8]. However, these studies primarily focused on predicting the incidence of TMIs without explicitly addressing the scope of TMIs, including their duration and their geographical coverage. Furthermore, the emphasis of these studies was largely on GDPs, given their higher frequency and longer duration when compared to GSs. A limited number of studies focused on predicting the parameters of TMIs, specifically addressing their duration and extent. In one such study focusing on optimizing the TMI parameters at San Francisco International Airport (SFO),the authors utilized a probabilistic forecast of fog [9]. They simulated various capacity scenarios based on the (fog)burn-off forecasts, selecting GDP parameters that minimized airborne and overall ground delays. However, this approach exclusively emphasizes stratus (fog) burn-off as the primary determinant of GDP and GS, neglecting other influential factors like severe weather events, runway closures, lower capacity than traffic demand, and other important variables. Given the complexity of predicting the TMI and determining its scope, we seek a more holistic approach. We aim to consider all significant factors that could impact TMIs and their parameters. What sets this research apart is the fusion of all data sources relevant to the issuance and adjustment of TMIs and it represents the first comprehensive attempt to optimize TMIs in this manner. Since this comprehensive solution involves various aspects, we break down the problem into smaller components and input all parameters into a unified model called the “TMI Adjuster”. Figure 1 shows the overall framework and the list of datasets used in each model. The objective of the TMI Adjuster module is to deliver reliable, consistent and expedited recommendations for the progression, adjustment, and termination of TMIs. The ML solution entails developing a pipeline capable of predicting the necessity of a TMI (e.g., GS or GDP) along with its various parameters. For example, in the case of a GS, this includes the scope of the GS either in terms of distance from the destination airport or based on pre-defined airspace sectors. Here, scope refers to those regions and departing airports that are subject to the GS. In this paper, we concentrate on the issuance of GSs in the three major airports in the New York area — LaGuardia(LGA), John F. Kennedy International (JFK), and Newark Liberty International (EWR). We fuse traffic, weather and other relevant aviation data from years 2017 to 2019 to train and validate the ML models. In particular, we use the following datasets: •Terminal Aerodrome Forecast (TAF): meteorological forecasts specific to each airport, issued four times a day, covering predefined time periods. •TMI data: includes all GSs and GDPs along with their respective parameters. •Aviation System Performance Metrics (ASPM): includes traffic related data such as aircraft delays, arrival, and departure rates. •Notices to Airmen (NOTAMs): utilized to extract runway closure data and manage interdependencies between terminals in close proximity. •Flight cancellation data •Airspace Flow Programs (AFP): includes information on flight airborne holdings caused by TMIs. The data preprocessing entails transforming ASPM, TMI, AFP, NOTAMs, and weather data into an hourly format and consolidating all datasets by merging them based on date and time as the primary key. The TMI Adjuster framework comprises two parallel models: one dedicated to GS and a second model focused on GDP. As previously mentioned, our specific focus is on the GS model as a multi-classification problem. In this framework, each data point of the GS model input summarizes ten hours of data. Specifically, the data loader for the GS model generates the input and output of the model as follows: at a given time step, the input includes the actual traffic, weather, and TMI data from the two-hour window before the time step, alongside the weather forecast and scheduled traffic for the next 8 hours starting from the time step. Based on this information, the output of the GS model for each time interval consists of three dimensions. The first dimension represents a binary decision on whether there should be a GS in place for the next hour or not. The second dimension is related to the scope of the GS in the United States, and the third dimension is related to the scope of the GS in Canada (i.e., to determine if the GS impacts airports in Canada).One of the challenges with TMI modeling is the sparsity of TMI events, particularly regarding its scope. To address this challenge in the scope of the GS model output, we implement grouping. The GS scope for the US region is defined based on a list of centers that should be included when the GS is in place. With 20 centers in the US, we utilized historical data to group them into 4 categories. In particular, we summarized our historical data in a graph format where nodes represent centers, and link weights are defined based on the co-occurrence of centers in the scope parameter ofTMIs. By identified strongly connected components in this graph, we were able to partition the centers into four groups. We consider two model structures for the GS Model. Firstly, a hierarchical classification model [10], where the human decision-making for a GS is of hierarchical nature. The decision-maker first decides whether there is a need fora GS, and if the answer is yes, determines the scope. A hierarchical classification model organizes the problem into a class hierarchy, typically a tree or a Directed Acyclic Graph (DAG) structure, and considers the dependency of the decision in the previous step to the next component [10]. Here, we employ the local classifier per level approach, which involves training one multi-class classifier for each level of the class hierarchy. The second structure is the independent structure. In this setting, as the name suggests, we do not consider the dependency of the decisions in the different dimensions of the output of the model. Instead, for each dimension, we train a multi-class classifier independently. Table 1 summarizes GS model statistics for training, validation and testing. The table documents the effect of limiting data to the time steps when there was actually a TMI in place or when a TMI had just terminated. This resulted in a more balanced distribution of the GS class(GS positive class)versus “No GS”(GS negative class), which might help the training process. While JFK and LGA follow very similar distributions, with 40% and 42% GS positive class respectively, EWR has proportionally fewer GS incidents at 28%. Our subsequent phase involves evaluating the performance of both hierarchical structure and independent structure using different state-of-the-art multi-class classifier models such as Random Forest, Decision Trees, K-nearest Neighbors, and Logistic Regression and forecast the duration and scope of the GSs.
Farzan Masrour Shalmani↗
Analysis and effects of the advanced solid rocket motor on the Space Shuttle elements
This interim final report summarizes the activities and major accomplishments under NASA/MSFC Contract NAS8-39235 for the initial two year contractual effort beginning October 1, 1991, and ending September 30, 1993. It is not intended as an exhaustive treatment or review of any particular technical issue which was addressed during this two-year period, but does provide an overview of the many induced environment studies and test support activities undertaken by REMTECH and its subcontractors during this two-year period. The initial scope of work has been pursued vigorously through this two-year period by separating the effort into five functional areas. The five functional areas are ascent plume radiation, ascent plume convection, ascent aerodynamic heating, launch stand environments, and reentry heating. A work breakdown structure (WBS) for each functional area was devised to permit nearly autonomous activities within each functional responsibility while maintaining commonality in reporting procedures and cost accounting. The contract was modified 11 times to provide incremental funding and to expand the scope of work. Additional work breakdown structure was incorporated to meet the expanded scope. A contract chronology is provided in Section 2. The initial scope of work, expanded scope, and corresponding WBS designation are described in Section 3. A summary review of major accomplishments is also provided in Section 4 to show the breadth of activities and extent of reporting. Reports, meetings, test activities, and other accomplishments within each functional area are also summarized by WBS heading in the appendices.
Source record↗
The Solar Connections Observatory for Planetary Environments
The NASA Sun-Earth Connection theme roadmap calls for comparative study of how the planets, comets, and local interstellar medium (LISM) interact with the Sun and respond to solar variability. Through such a study we advance our understanding of basic physical plasma and gas dynamic processes, thus increasing our predictive capabilities for the terrestrial, planetary, and interplanetary environments where future remote and human exploration will occur. Because the other planets have lacked study initiatives comparable to the terrestrial ITM, LWS, and EOS programs, our understanding of the upper atmospheres and near space environments on these worlds is far less detailed than our knowledge of the Earth. To close this gap we propose a mission to study {\it all) of the solar interacting bodies in our planetary system out to the heliopause with a single remote sensing space observatory, the Solar Connections Observatory for Planetary Environments (SCOPE). SCOPE consists of a binocular EUV/FUV telescope operating from a remote, driftaway orbit that provides sub-arcsecond imaging and broadband medium resolution spectro-imaging over the 55-290 nm bandpass, and high (R>10$^{5}$ resolution H Ly-$\alpha$ emission line profile measurements of small scale planetary and wide field diffuse solar system structures. A key to the SCOPE approach is to include Earth as a primary science target. From its remote vantage point SCOPE will be able to observe auroral emission to and beyond the rotational pole. The other planets and comets will be monitored in long duration campaigns centered when possible on solar opposition when interleaved terrestrial-planet observations can be used to directly compare the response of both worlds to the same solar wind stream and UV radiation field. Using a combination of observations and MHD models, SCOPE will isolate the different controlling parameters in each planet system and gain insight into the underlying physical processes that define the solar connection.
Oliversen, Ronald J.↗
Visit from JAXA to NASA MSFC: The Engines Element & Ideas for Collaboration
System Design, Development, and Fabrication: Design, develop, and fabricate or procure MB-60 component hardware compliant with the imposed technical requirements and in sufficient quantities to fulfill the overall MB-60 development effort. System Development, Assembly, and Test: Manage the scope of the development, assembly, and test-related activities for MB-60 development. This scope includes engine-level development planning, engine assembly and disassembly, test planning, engine testing, inspection, anomaly resolution, and development of necessary ground support equipment and special test equipment. System Integration: Provide coordinated integration in the realms of engineering, safety, quality, and manufacturing disciplines across the scope of the MB-60 design and associated products development Safety and Mission Assurance, structural design, fracture control, materials and processes, thermal analysis. Systems Engineering and Analysis: Manage and perform Systems Engineering and Analysis to provide rigor and structure to the overall design and development effort for the MB-60. Milestone reviews, requirements management, system analysis, program management support Program Management: Manage, plan, and coordinate the activities across all portions of the MB-60 work scope by providing direction for program administration, business management, and supplier management.
Greene, William D.↗
Agile Approach to Assuring the Safety-Critical Embedded Software for NASA's Orion Spacecraft
Human-rated missions like those in NASA's Orion Program continue to grow in complexity. The role of software in achieving ambitious mission objectives has expanded dramatically in the last few decades. Assuring the safety and performance of the embedded flight software is quickly growing beyond the reach of traditional methods and resource levels. The methods used to build these software-dominant systems evolve in an on-going attempt to keep pace with the scope of our ambitions. Agile software development is now commonplace. The long timelines and large batches of work associated with traditional methods are being replaced by rapid delivery of small increments _ as system capabilities are realized in waves. Assurance of these critical software capabilities must therefore conquer an ever-expanding frontier of challenges, and do so with an approach matched to the evolving development methods. This paper recounts the journey of the Orion Independent Verification and Validation (IV&V) team as we addressed this dynamic environment. Widening our aperture to encompass a dramatically larger mission scope, while adjusting our cadence to synchronize with the rapid pace of agile software development, a new approach to IV&V is emerging. This approach is characterized by a sharper focus on mission capabilities, matched with a method to dynamically _follow the risk' as the IV&V team delivers more compelling assurance data in waves. Traditional methods prevalent in IV&V tend to scope the work using artifacts of the development process as they evolve from preliminary to final versions, and the pace of delivery was synchronized with the development timelines prevalent in the waterfall lifecycle. That more static approach is out of phase with the demands of the new environment. Scoping work according to the critical capabilities of the system (rather than artifacts of development) and synchronizing with the rapid pace of agile development, we are moving toward more effective parity with the demands of the environment. We explain the concrete steps we took, the principles that motivated our choices, and the results we have achieved to date.
Capability based assurance↗
An emulator for minimizing finite element analysis implementation resources
A finite element analysis emulator providing a basis for efficiently establishing an optimum computer implementation strategy when many calculations are involved is described. The SCOPE emulator determines computer resources required as a function of the structural model, structural load-deflection equation characteristics, the storage allocation plan, and computer hardware capabilities. Thereby, it provides data for trading analysis implementation options to arrive at a best strategy. The models contained in SCOPE lead to micro-operation computer counts of each finite element operation as well as overall computer resource cost estimates. Application of SCOPE to the Memphis-Arkansas bridge analysis provides measures of the accuracy of resource assessments. Data indicate that predictions are within 17.3 percent for calculation times and within 3.2 percent for peripheral storage resources for the ELAS code.
Melosh, R. J.↗
Lunar landing and launch facilities and operations
A preliminary definition of a lunar landing and launch facility (LLLF or Complex 391) has been formulated. A Phase 3 lunar base is considered. Without specifying specific lunar base scenarios, three traffic levels are envisioned: 6, 12, and 24 landings/launches per year. A single, multipurpose vehicle for the lunar module is assumed. The design and specification of the vehicle and of the lunar base are outside the scope of this study. However, these two items will impact those items considered within the scope of this study because of interactions at the system boundaries. The scope of this study is graphically portrayed with the systems diagram. Based upon this diagram, nine major design items or areas are considered. These items are: (1) landing/launch site considerations; (2) structure, shelter, safety, and environmental needs; (3) landing/launch guidance, communications, and computing needs; (4) lunar module surface transport system; (5) heavy cargo unloading/loading systems; (6) personnel unloading/loading systems; (7) propellant unloading/loading systems; (8) vehicle storage; and (9) maintenance, repair, test and check-out requirements. Initially, a general, conceptual description of each of these items is given. Then, preliminary sizes, capacities, and other relevant design data for some of these items are identified. The Earth-Moon transportation infrastructure and the baseline lunar module design are summarized.
Source record↗
Dynamic Constraint Satisfaction with Reasonable Global Constraints
Previously studied theoretical frameworks for dynamic constraint satisfaction problems (DCSPs) employ a small set of primitive operators to modify a problem instance. They do not address the desire to model problems using sophisticated global constraints, and do not address efficiency questions related to incremental constraint enforcement. In this paper, we extend a DCSP framework to incorporate global constraints with flexible scope. A simple approach to incremental propagation after scope modification can be inefficient under some circumstances. We characterize the cases when this inefficiency can occur, and discuss two ways to alleviate this problem: adding rejection variables to the scope of flexible constraints, and adding new features to constraints that permit increased control over incremental propagation.
Frank, Jeremy↗
New Direction of NASA Exploration Life Support
NASA's activities in life support Research and Technology Development (R&TD) have changed in both focus and scope following implementation of recommendations from the Exploration System Architecture Study (ESAS). The limited resources available and the compressed schedule to conduct life support R&TD have required that future efforts address the needs of the Crew Exploration Vehicle (CEV), the Lunar Surface Access Module (LSAM) and Lunar Outpost (LO). Advanced Life Support (ALS) efforts related to long duration planetary bases have been deferred or canceled. This paper describes the scope of the new Exploration Life Support (ELS) project; how it differs from ALS, and how it supports critical needs for the CEV, LSAM and LO. In addition, this paper provides rationale for changes in the scope and focus of technical content within ongoing life support R&TD activities.
Chambliss, Joe↗
Modelling the Future Hydroclimatology of the Lower Fraser River and its Impacts on the Spawning Migration Survival of Sockeye Salmon
Short episodic high temperature events can be lethal for migrating adult Pacific salmon (Oncorhynchus spp.). We downscaled temperatures for the Fraser River, British Columbia to evaluate the impact of climate warming on the frequency of exceeding thermal thresholds associated with salmon migratory success. Alarmingly, a modest 1.0 C increase in average summer water temperature over 100 years (1981-2000 to 2081-2100) tripled the number of days per year exceeding critical salmonid thermal thresholds (i.e. 19.0 C). Refined thresholds for two populations (Gates Creek and Weaver Creek) of sockeye salmon (Oncorhynchus nerka) were defined using physiological constraint models based on aerobic scope. While extreme temperatures leading to complete aerobic collapse remained unlikely under our warming scenario, both populations were increasingly forced to migrate upriver at reduced levels of aerobic performance (e.g. in 80% of future simulations, => 90% of salmon encountered temperatures exceeding population specific thermal optima for maximum aerobic scope; T(sub opt)) = 16.3 C for Gates Creek and T(sub sopt)=14.5 C for Weaver Creek). Assuming recent changes to river entry timing persist, we also predicted dramatic increases in the probability of freshwater mortality for Weaver Creek salmon due to reductions in aerobic, and general physiological, performance (e.g. in 42% of future simulations =>50% of Weaver Creek fish exceeded temperature thresholds associated with 0 - 60% of maximum aerobic scope). Potential for adaptation via directional selection on run-timing was more evident for the Weaver Creek population. Early entry Weaver Creek fish experienced 25% (range: 15 - 31%) more suboptimal temperatures than late entrants, compared with an 8% difference (range: 0 - 17%) between early and late Gates Creek fish. Our results emphasize the need to consider daily temperature variability in association with population-specific differences in behaviour and physiological constraints when forecasting impacts of climate change on migratory survival of aquatic species.
Hague, M. J.↗
Multi Modality Brain Mapping System (MBMS) Using Artificial Intelligence and Pattern Recognition
A Multimodality Brain Mapping System (MBMS), comprising one or more scopes (e.g., microscopes or endoscopes) coupled to one or more processors, wherein the one or more processors obtain training data from one or more first images and/or first data, wherein one or more abnormal regions and one or more normal regions are identified; receive a second image captured by one or more of the scopes at a later time than the one or more first images and/or first data and/or captured using a different imaging technique; and generate, using machine learning trained using the training data, one or more viewable indicators identifying one or abnormalities in the second image, wherein the one or more viewable indicators are generated in real time as the second image is formed. One or more of the scopes display the one or more viewable indicators on the second image.
Kateb, Babak↗