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All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Mass and Volume Optimization of Space Flight Medical Kits

Resource allocation is a critical aspect of space mission planning. All resources, including medical resources, are subject to a number of mission constraints such a maximum mass and volume. However, unlike many resources, there is often limited understanding in how to optimize medical resources for a mission. The Integrated Medical Model (IMM) is a probabilistic model that estimates medical event occurrences and mission outcomes for different mission profiles. IMM simulates outcomes and describes the impact of medical events in terms of lost crew time, medical resource usage, and the potential for medically required evacuation. Previously published work describes an approach that uses the IMM to generate optimized medical kits that maximize benefit to the crew subject to mass and volume constraints. We improve upon the results obtained previously and extend our approach to minimize mass and volume while meeting some benefit threshold. METHODS We frame the medical kit optimization problem as a modified knapsack problem and implement an algorithm utilizing dynamic programming. Using this algorithm, optimized medical kits were generated for 3 mission scenarios with the goal of minimizing the medical kit mass and volume for a specified likelihood of evacuation or Crew Health Index (CHI) threshold. The algorithm was expanded to generate medical kits that maximize likelihood of evacuation or CHI subject to mass and volume constraints. RESULTS AND CONCLUSIONS In maximizing benefit to crew health subject to certain constraints, our algorithm generates medical kits that more closely resemble the unlimited-resource scenario than previous approaches which leverage medical risk information generated by the IMM. Our work here demonstrates that this algorithm provides an efficient and effective means to objectively allocate medical resources for spaceflight missions and provides an effective means of addressing tradeoffs in medical resource allocations and crew mission success parameters.

Monte Carlo Method↗
NASA NTRS2012

Climate Change: A New Metric to Measure Changes in the Frequency of Extreme Temperatures using Record Data

Consensus on global warming is the result of multiple and varying lines of evidence, and one key ramification is the increase in frequency of extreme climate events including record high temperatures. Here we develop a metric- called "record equivalent draws" (RED)-based on record high (low) temperature observations, and show that changes in RED approximate changes in the likelihood of extreme high (low) temperatures. Since we also show that this metric is independent of the specifics of the underlying temperature distributions, RED estimates can be aggregated across different climates to provide a genuinely global assessment of climate change. Using data on monthly average temperatures across the global landmass we find that the frequency of extreme high temperatures increased 10-fold between the first three decades of the last century (1900-1929) and the most recent decade (1999-2008). A more disaggregated analysis shows that the increase in frequency of extreme high temperatures is greater in the tropics than in higher latitudes, a pattern that is not indicated by changes in mean temperature. Our RED estimates also suggest concurrent increases in the frequency of both extreme high and extreme low temperatures during 2002-2008, a period when we observe a plateauing of global mean temperature. Using daily extreme temperature observations, we find that the frequency of extreme high temperatures is greater in the daily minimum temperature time-series compared to the daily maximum temperature time-series. There is no such observable difference in the frequency of extreme low temperatures between the daily minimum and daily maximum.

Munasinghe, L.↗
NASA NTRS2023

Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy Data Fusion to Map Global Surface Ozone Concentration and Associated Uncertainty

Estimates of ground-level ozone concentrations have been improved through data fusion of observations and atmospheric chemistry models. Our previous global ozone estimates for the Global Burden of Disease study corrected for bias uniformly across continents and then corrected near monitoring stations using the Bayesian Maximum Entropy (BME) framework for data fusion. Here, we use the Regionalized Air Quality Model Performance (RAMP) framework to correct model bias over a much larger spatial range than BME can, accounting for the spatial inhomogeneity of bias and nonlinearity as a function of modeled ozone. RAMP bias correction is applied to a composite of 9 global chemistry-climate models, based on the nearest set of monitors. These estimates are then fused with observations using BME, which matches observations at measurement stations, with the influence of observations declining with distance in space and time. We create global ozone maps for each year from 1990 to 2017 at fine spatial resolution. RAMP is shown to create unrealistic discontinuities due to the spatial clustering of ozone monitors, which we overcome by applying a weighting for RAMP based on the number of monitors nearby. Incorporating RAMP before BME has little effect on model performance near stations, but strongly increases R 2 by 0.15 at locations farther from stations, shown through a checkerboard cross-validation. Corrections to estimates differ based on location in space and time, confirming heterogeneity. We quantify the likelihood of exceeding selected ozone levels, finding that parts of the Middle East, India, and China are most likely to exceed 55 parts per billion (ppb) in 2017. About 96% of the global population was exposed to ozone levels above the World Health Organization guideline of 60 µg m −3 (30 ppb) in 2017. Our annual fine-resolution ozone estimates may be useful for several applications including epidemiology and assessments of impacts on health, agriculture, and ecosystems.

Ozone↗
NASA NTRSDate not supplied

The Impact of Delayed Communication on NASA’s Human-Systems Operations: Preliminary Results of a Systematic Review

Throughout the history of human spaceflight, NASA has relied on a team of ground-based experts on Earth to manage its missions, vehicles, and crews to ensure crew safety and mission success. However, as missions progress beyond low-Earth orbit (LEO), this paradigm of dependence on ground must evolve. Beyond LEO, in missions to the moon and Mars, crews will confront new challenges: limited evacuation options, reduced resupply capabilities, and significant communication delays that impede real-time support from experts on the ground. This reduction in ground support amplifies the likelihood that crews will be unable to adequately respond to unanticipated, safety-critical events. Understanding the scope of these risks and identifying effective countermeasures hinges on understanding the impact of communication delays on complex operations, especially in urgent, unforeseen events. Real-time communication currently provides the crew with continuous access to a large, extensively resourced ground team skilled in anomaly resolution. However, as communication delays grow, the need to transfer some responsibilities from ground experts to onboard crew becomes evident. NASA has been exploring this shift in operational responsibilities and its effectiveness in managing complex operations for decades. Nevertheless, a comprehensive understanding of the specific challenges posed by communication delays and the necessary countermeasures to mitigate them remains a gap. In this paper, we present an update on our systematic review of the literature on communication delays, the first in-depth review since 2013 (Rader et al.). We introduce a coding taxonomy to capture key constructs from papers of interest and discuss preliminary findings. These preliminary results suggest two significant research gaps: limited studies have been conducted 1) with lunar-like latencies and 2) on problem-solving strategies for the maximum latencies expected in Mars missions. We outline plans and propose recommendations to address these gaps through ongoing and future research.

human-systems integration↗
NASA NTRSDate not supplied

The Impact of Delayed Communications on NASA's Human-Systems Operations: Preliminary Results of a Systematic Review

Throughout the history of human spaceflight, NASA has relied on a team of ground-based experts on Earth to manage its missions, vehicles, and crews to ensure crew safety and mission success. However, as missions progress beyond low-Earth orbit (LEO), this paradigm of dependence on ground must evolve. Beyond LEO, in missions to the moon and Mars, crews will confront new challenges: limited evacuation options, reduced resupply capabilities, and significant communication delays that impede real-time support from experts on the ground. This reduction in ground support amplifies the likelihood that crews will be unable to adequately respond to unanticipated, safety-critical events. Understanding the scope of these risks and identifying effective countermeasures hinges on understanding the impact of communication delays on complex operations, especially in urgent, unforeseen events. Real-time communication currently provides the crew with continuous access to a large, extensively resourced ground team skilled in anomaly resolution. However, as communication delays grow, the need to transfer some responsibilities from ground experts to onboard crew becomes evident. NASA has been exploring this shift in operational responsibilities and its effectiveness in managing complex operations for decades. Nevertheless, a comprehensive understanding of the specific challenges posed by communication delays and the necessary countermeasures to mitigate them remains a gap. In this paper, we present an update on our systematic review of the literature on communication delays, the first in-depth review since 2013 (Rader et al.). We introduce a coding taxonomy to capture key constructs from papers of interest and discuss preliminary findings. These preliminary results suggest two significant research gaps: limited studies have been conducted 1) with lunar-like latencies and 2) on problem-solving strategies for the maximum latencies expected in Mars missions. We outline plans and propose recommendations to address these gaps through ongoing and future research.

earth-independence↗
NASA NTRS2020

MSL Telecom Automated Anomaly Detection

The Mars Science Laboratory (MSL) Telecom Operations Team at the Jet Propulsion Laboratory (JPL) has implemented a machine learning system in order to automate the anomaly detection process as a part of daily operations. Machine learning enables reliable detection of anomalies in Telecom-related telemetry and automated reporting of Telecom subsystem status, resulting in an 90% reduction in team workload and improved anomaly detection reliability. At present, machine learning methods are used to detect: 1. Anomalous long-term trends in telemetry data 2. Anomalous time-domain evolution of telemetry values Both types of anomalies pose their own unique challenges that are addressed in different ways. In the first case, long term trending of daily minima, maximum, and mean telemetry values in temperatures, currents, voltages, and radio frequency (RF) power levels is used in addition to hard threshold safety checks to look for changes in long-term equipment health and performance. Long-term trending methods allow for ordinary seasonal variations in these quantities caused by temperature changes over the course of the Martian year while allowing operators to determine whether current performance remains in line with historical values from previous years. Changes in long-term trends can provide important insights into the health and status of the rover's on-board systems as well as valuable early warning if subtle degradation begins to take hold. But while trending of daily statistics is valuable, it does not detect anomalies in the short-term time evolution of data over the course of minutes or hours during a day, and this task is handled with short-term shape analysis. Principal components analysis (PCA) has been found to provide robust detection of short-term anomalies, and several examples of the use of PCA to detect actual anomalous events will be provided here. In using PCA, we use both the percentage of explained variance and also a log likelihood test on the PCA expansion coefficients to flag telemetry data for human review. Previous work in the field of spacecraft anomaly detection includes [1] for MSL and [2] for some other JPL missions.

Mukai, Ryan↗
NASA NTRS1996

Early Energetic Particle Irradiation of the HED Parent Body Regolith

Previous studies have shown that many individual grains within the dark phase of the Kapoeta howardite were irradiated with energetic particles while residing on the surface of the early HED regolith. Particle tracks in these grains vary in density by more than an order of magnitude and undoubtedly were formed by energetic heavy (Fe) ions associated with early solar flares. Early Irradiation of HED Regolith: Concentrations of excess Ne alone are not sufficient to decide between competing galactic and solar irradiation models. However, from recent studies of depth samples of oriented lunar rocks, we have shown that the cosmogenic 21-Ne/22-Ne ratio produced in feldspar differs substantially between Galactic Cosmic Radiation (GCR) and solar protons, and that this difference is exactly that predicted from cross-section data. Using Ne literature data and new isotopic data we obtained on acid-etched, separated feldspar from both the light and dark phases of Kapoeta, we derive 21-Ne/22-Ne = 0.80 for the recent GCR irradiation and 21-Ne/22-Ne = 0.68 for the early regolith irradiation. This derived ratio indicates that the early Ne production in the regolith occurred by both galactic and solar protons. If we adopt a likely one-component regolith model in which all grains were exposed to galactic protons but individual grains had variable exposure to solar protons, we estimate that this early GCR irradiation lasted for about 3-6 m.y. More complex two-component regolith models involving separate solar and galactic irradiation would permit this GCR age to be longer. Higher-energy solar protons would permit the GCR to be longer. Higher-energy solar protons would permit the GCR age to be shorter. Further, cosmogenic 126(Xe) in Kapoeta dark is no more than a factor of about 2 higher than that observed in Kapoeta light. Because 126(Xe) can only be formed by galactic protons and not solar protons, these data support a short GCR irradiation for the HED regolith. This would also be the maximum time peRiod for the solar irradiation. Various asteroidal regolith models, based on Monte Carlo modeling of impact rates as a function of size and on irradiation features of meteorites, predict surface exposure times of about 0.1 to 10 m.y., and depend on such factors as gravity, rock mechanical properties, and micrometeoroid flux. Because the depth at which solar Fe tracks are produced (is much less than 1 micrometer) is much less than the depth at which Solar Cosmic Rays (SCR) Ne is produced (about 1 cm), for a reasonably well-stirred HED regolith the "surface exposure time" for SCR 21-Ne production should be significantly longer than that for solar tracks and some other surface irradiation features. Enhanced Solar Proton Irradiation: For bulk samples of Kapoeta dark feldspar and a one-component regolith model, the derived ratio of 21-Ne/22-Ne = 0.68 implies that the early production ratio of SCR 21-Ne to GCR 21-Ne was about 0.5-1.5. This ratio is independent of any assumptions about the fraction of dark grains that are irradiated or of the variability in the degree of solar irradiation among grains. The 21-Ne SCR/GCR ratio indirectly derived from bulk Kapoeta pyroxene is somewhat larger, as is the ratio derived for simple two-component regolith models. Individual feldspar grains that were extensively solar irradiated would require even larger 21-Ne SCR/GCR production ratios. In contrast, the theoretical SCR/GCR production ratio for lunar feldspar with 0 g/CM2 shield ing is is less than or equal to 2, and the lowest ratio observed in near-surface samples of lunar anorthosites is less than or equal to 1. Considering the greater solar distance of Vesta (compared to the Moon), the likelihood that SCR 21-Ne was acquired under some shielding where production rates are lower, and the likelihood that the exposure time to galactic protons exceeded the exposure time to solar protons because of their very different penetration depths, the 21-Ne SCR/GCR production ratio on the HED parent body was probably < 0.1. The relatively large difference between the derived 21-Ne SCR/GCR ratio in Kapoeta dark feldspar and the estimated production ratio strongly indicates that the early solar irradiation involved a flux -20-50x the recent solar flux. This enhanced proton flux was probably associated with an overall greater solar activity in the first approximately 10(exp 7) to 10(exp 8) years of solar history.

Bogard, D. D.↗
NASA NTRSDate not supplied

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Drone usage has been on the rise in recent years with applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. Once regulations and safety policies are put in place to allow for the widespread use of unmanned drones, the number of aircraft in the National Airspace System (NAS) is expected to skyrocket to millions, potentially congesting the airspace which increases the likelihood of separation violations and possibly incidents. Currently, flight infrastructure can only support a few thousand aircraft flying over the United States National Airspace System (NAS) at any given time. A delay at one airport can send ripple effects throughout the system, causing more delays and missed connections. In air traffic control, separation is the concept of keeping an “ownship” aircraft outside a minimum distance from “intruder” aircraft to reduce the risk of the aircraft colliding, as well as preventing accidents due to secondary factors, such as wake turbulence. Maintaining proper separation is often a safety critical property for fixed-wing drones in the airspace. This paper addresses drone separation in time and distance for high volume corridors (en route) and lanes (on ground), merging as well as crossing intersections of multiple corridors/lanes. In this paper, the term drone is applied to both Unmanned Aerial Vehicle (UAV) and small Unmanned Aircraft System (UAS) vehicles operating autonomously. There exists a gamut of approaches to the merging and crossing problems. At one end of the extreme are the conservative yet low cost and verifiable solutions of today that deal with two drones at a time. At the other end are complex Machine Learning-based solutions with high computing requirements for fully autonomous drones of the future that are expected to handle all contentions. This paper presents a feasible and verifiable strategic approach to these problems that is based on distributed cooperation between the drones and the infrastructure. Three phases of the strategic approach (Prepare, Adjust, Commit) are presented. Simulation results are presented that show the proposed approach is stable and resilient to induced perturbations and guarantees a set of fixed-wing drones to merge and cross intersections by adjusting their speed based on their distance to the aircraft in front of them while remaining in the equilibrium state. The equilibrium state is defined as the state when a set of n aircraft move at a relatively constant speed and uniform spacing from each other in a congested system. A congested system is defined as the state when at least one aircraft cannot move at its maximum allowed speed. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Simulation results are presented that assess the feasibility of the approach.

Distributed↗
NASA NTRSDate not supplied

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Drone usage has been on the rise in recent years with applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. Once regulations and safety policies are put in place to allow for the widespread use of unmanned drones, the number of aircraft in the National Airspace System (NAS) is expected to skyrocket to millions, potentially congesting the airspace which increases the likelihood of separation violations and possibly incidents. Currently, flight infrastructure can only support a few thousand aircraft flying over the United States National Airspace System (NAS) at any given time. A delay at one airport can send ripple effects throughout the system, causing more delays and missed connections. In air traffic control, separation is the concept of keeping an “ownship” aircraft outside a minimum distance from “intruder” aircraft to reduce the risk of the aircraft colliding, as well as preventing accidents due to secondary factors, such as wake turbulence. Maintaining proper separation is often a safety critical property for fixed-wing drones in the airspace. This paper addresses drone separation in time and distance for high volume corridors (en route) and lanes (on ground), merging as well as crossing intersections of multiple corridors/lanes. In this paper, the term drone is applied to both Unmanned Aerial Vehicle (UAV) and small Unmanned Aircraft System (UAS) vehicles operating autonomously. There exists a gamut of approaches to the merging and crossing problems. At one end of the extreme are the conservative yet low cost and verifiable solutions of today that deal with two drones at a time. At the other end are complex Machine Learning-based solutions with high computing requirements for fully autonomous drones of the future that are expected to handle all contentions. This paper presents a feasible and verifiable strategic approach to these problems that is based on distributed cooperation between the drones and the infrastructure. Three phases of the strategic approach (Prepare, Adjust, Commit) are presented. Simulation results are presented that show the proposed approach is stable and resilient to induced perturbations and guarantees a set of fixed-wing drones to merge and cross intersections by adjusting their speed based on their distance to the aircraft in front of them while remaining in the equilibrium state. The equilibrium state is defined as the state when a set of n aircraft move at a relatively constant speed and uniform spacing from each other in a congested system. A congested system is defined as the state when at least one aircraft cannot move at its maximum allowed speed. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Simulation results are presented that assess the feasibility of the approach.

Distributed↗