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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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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Separator Reconnection at the Magnetopause for Predominantly Northward and Southward IMF: Techniques and Results

In this work, we demonstrate how to track magnetic separators in three-dimensional simulated magnetic fields with or without magnetic nulls, apply these techniques to enhance our understanding of reconnection at the magnetopause. We present three methods for locating magnetic separators and apply them to 3-D resistive MHD simulations of the Earth's magnetosphere using the Block-Adaptive-Tree Solar-wind Roe-type Upwind Scheme code. The techniques for finding separators and determining the reconnection rate are insensitive to interplanetary magnetic field (IMF) clock angle and can in principle be applied to any magnetospheric model. Moreover, the techniques have a number of advantages over prior separator finding techniques applied to the magnetosphere. The present work examines cases of high and low resistivity for two clock angles. We go beyond previous work examine the separator during Flux Transfer Events (FTEs). Our analysis of reconnection on the magnetopause yields a number of interesting conclusions: Reconnection occurs all along the separator even during predominately northward IMF cases. Multiple separators form in low-resistivity conditions, and in the region of an FTE the separator splits into distinct branches. Moreover, the local contribution to the reconnection rate, as determined by the local parallel electric field, drops in the vicinity of the FTE with respect to the value when there are none.

modeling↗
NASA NTRS2021

Data-driven landslide nowcasting at the global scale

Landslides affect nearly every country in the world each year. To better understand this global hazard, the Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed previously. LHASA version 1 combines satellite precipitation estimates with a global landslide susceptibility map to produce a gridded map of potentially hazardous areas from 60° North-South every 3 h. LHASA version 1 categorizes the world’s land surface into three ratings: high, moderate, and low hazard with a single decision tree that first determines if the last seven days of rainfall were intense, then evaluates landslide susceptibility. LHASA version 2 has been developed with a data-driven approach. The global susceptibility map was replaced with a collection of explanatory variables, and two new dynamically varying quantities were added: snow and soil moisture. Along with antecedent rainfall, these variables modulated the response to current daily rainfall. In addition, the Global Landslide Catalog (GLC) was supplemented with several inventories of rainfall-triggered landslide events. These factors were incorporated into the machine-learning framework XGBoost, which was trained to predict the presence or absence of landslides over the period 2015–2018, with the years 2019–2020 reserved for model evaluation. As a result of these improvements, the new global landslide nowcast was twice as likely to predict the occurrence of historical landslides as LHASA version 1, given the same global false positive rate. Furthermore, the shift to probabilistic outputs allows users to directly manage the trade-off between false negatives and false positives, which should make the nowcast useful for a greater variety of geographic settings and applications. In a retrospective analysis, the trained model ran over a global domain for 5 years, and results for LHASA version 1 and version 2 were compared. Due to the importance of rainfall and faults in LHASA version 2, nowcasts would be issued more frequently in some tropical countries, such as Colombia and Papua New Guinea; at the same time, the new version placed less emphasis on arid regions and areas far from the Pacific Rim. LHASA version 2 provides a nearly real-time view of global landslide hazard for a variety of stakeholders.

XGBoos↗
NASA NTRSDate not supplied

CropManage Application for Vineyard Irrigation Decision-Support

CropManage is a free web-application developed by U.C. Cooperative Extension to support evapotranspiration based irrigation scheduling and nutrient management for major specialty crops. Prescribed phenology curves are used to develop daily estimates of canopy cover within a given field, based on days since planting (annual crops) or budbreak (trees, vines). These curves are modulated by a MaxCan parameter representing seasonal maximum canopy cover. Crop development observations can be used to adjust for such factors as weather anomalies or non-standard agronomic practice, as needed. Canopy cover is converted to crop coefficient and combined with reference evapotranspiration to derive daily water consumption. Guidance on crop water requirement is then conveyed to users in terms of system runtime issued on-demand for a given date, largely based on total evapotranspiration since last irrigation event. In this study, CropManage was adapted to vineyards by adding modules accounting for early-season soil moisture depletion and cover crop presence. A crop stress parameter was added to accommodate deficit irrigation practice, allowing the user to specify percentage departure from full water requirement along with start/stop dates. An initial verification exercise was performed on three winegrape vineyards located in California’s Central Coast (2020), North Coast (2020) and Central Valley (2019). Daily crop evapotranspiration was monitored by eddy-covariance fluxtowers. MaxCan was measured by ground and satellite observation. Stress regime was specified by grower practice where available, otherwise stress levels were inferred from applied water records. Mean absolute error and mean bias error of modeled cumulative evapotranspiration were computed with respect to the eddy covariance measurements collected throughout the growing season. Results indicate the modified CropManage water management module performs reasonably well for winegrape. Additional effort is planned to modify the nutrient module for vineyard use.

CropManage↗
NASA NTRSDate not supplied

The Effects of Experimentally Induced Zircon Annealing on Melt Inclusion-Zircon Geochemistry: A Case Study of Archean Zircon From the Barberton Complex

Crystalline melt inclusions (MIs) in Archean zircons can potentially constrain the evolution of magmatic processes. However, to obtain a homogeneous glassy MI representative of the parent melt, devitrified MIs must be heated at temperatures above the liquidus. Although, it is well known that radiation damaged domains in MI-host zircon undergo temperature-time dependent annealing, the effects of annealing on zircon stable isotope and trace element compositions has received less attention. In this study, we heated multiple aliquots of low-magnetism zircons with relatively low levels of radiation damage (Deff<9.6x1014 α-events/mg) from a 3.304 Ga tonalite gneiss xenolith from the Barberton greenstone belt (sample B87-18; Kamo and Davis, 1994) in an internally-heated pressure vessel at 0.4GPa and 1000–1200°C. Zircons were imaged by SEM (CL, BSE) to determine the zoning patterns around the MIs. The radiation damage preserved in the heated and unheated zircons was determined by measuring broadening of the 3(SiO4) Raman stretching band of zircon (FWHM). Oxygen isotope ratios (δ18O), OH/O ratios, and trace and rare earth element (TREE) concentrations were measured in the zircons and MIs using SIMS. The FWHM and Raman shift of the zircon 3(SiO4) band show a systematic increase of zircon crystallinity with increasing experimental temperature. Despite increasing crystallinity, the 3(SiO4) FWHM is not correlated with δ18O(Zrn) values in the heated or unheated zircons, suggesting that δ18O was not altered by laboratory heating in these low initial radiation damage zircons. FWHM and OH/O are positively correlated in both heated and unheated zircons. The unheated zircons have the largest FWHM and OH/O (OH/OMAX=3.5×10-4) suggesting that H2O was lost during experimental heating due to the progressive annealing of radiation-damaged domains in the zircons. Zircon TREE concentrations and FWHM values are not correlated despite the presence of TREE altered domains in both the heated and unheated zircons. Our results demonstrate that pre-experimental zircon TREE concentrations and δ18O(Zrn) remain intact during laboratory heating of crystalline MIs in zircon, while secondary H2O hosted in radiation damaged domains is partially lost. Thus, MIs in low initial radiation damage Archean zircon are a potentially viable tool for constraining melt compositions.

Joseph P. Gonzalez↗
NASA NTRSDate not supplied

Adaptive Stress Testing of Trajectory Predictions in Flight Management Systems

To find failure events and their likelihoods in flight-critical systems, we investigate the use of an advanced black-box stress testing approach called adaptive stress testing. We analyze a trajectory predictor from a developmental commercial flight management system which takes as input a collection of lateral waypoints and en-route environmental conditions. Our aim is to search for failure events relating to inconsistencies in the predicted lateral trajectories. The intention of this work is to find likely failures and report them back to the developers so they can address and potentially resolve shortcomings of the system before deployment. To improve search performance, this work extends the adaptive stress testing formulation to be applied more generally to sequential decision-making problems with episodic reward by collecting the state transitions during the search and evaluating at the end of the simulated rollout. We use a modified Monte Carlo tree search algorithm with progressive widening as our adversarial reinforcement learner. The performance is compared to direct Monte Carlo simulations and to the cross-entropy method as an alternative importance sampling baseline. The goal is to find potential problems otherwise not found by traditional requirements-based testing. Results indicate that our adaptive stress testing approach finds more failures and finds failures with higher likelihood relative to the baseline approaches.

adaptive stress testing↗
NASA NTRS2005

The Calibration and Characterization of Earth Remote Sensing and Environmental Monitoring Instruments

The use of remote sensing instruments on orbiting satellite platforms in the study of Earth Science and environmental monitoring was officially inaugurated with the April 1, 1960 launch of the Television Infrared Observation Satellite (TIROS) [1]. The first TIROS accommodated two television cameras and operated for only 78 days. However, the TIROS program, in providing in excess of 22,000 pictures of the Earth, achieved its primary goal of providing Earth images from a satellite platform to aid in identifying and monitoring meteorological processes. This marked the beginning of what is now over four decades of Earth observations from satellite platforms. reflected and emitted radiation from the Earth using instruments on satellite platforms. These measurements are input to climate models, and the model results are analyzed in an effort to detect short and long-term changes and trends in the Earth's climate and environment, to identify the cause of those changes, and to predict or influence future changes. Examples of short-term climate change events include the periodic appearance of the El Nino-Southern Oscillation (ENSO) in the tropical Pacific Ocean [2] and the spectacular eruption of Mount Pinatubo on the Philippine island of Luzon in 1991. Examples of long term climate change events, which are more subtle to detect, include the destruction of coral reefs, the disappearance of glaciers, and global warming. Climatic variability can be both large and small scale and can be caused by natural or anthropogenic processes. The periodic El Nino event is an example of a natural process which induces significant climatic variability over a wide range of the Earth. A classic example of a large scale anthropogenic influence on climate is the well-documented rapid increase of atmospheric carbon dioxide occurring since the beginning of the Industrial Revolution [3]. An example of the study of a small-scale anthropogenic influence in climate variability is the Atlanta Land-use Analysis Temperature and Air-quality (ATLANTA) project [4]. This project has found that the replacement of trees and vegetation with concrete and asphalt in Atlanta, Georgia, and its environs has created a microclimate capable of producing wind and thunderstorms. A key objective of climate research is to be able to distinguish the natural versus human roles in climate change and to clearly communicate those findings to those who shape and direct environmental policy.

Butler, James J.↗
NASA NTRS2000

Condition Monitoring for Helicopter Data

In this paper the classical "Westland" set of empirical accelerometer helicopter data is analyzed with the aim of condition monitoring for diagnostic purposes. The goal is to determine features for failure events from these data, via a proprietary signal processing toolbox, and to weigh these according to a variety of classification algorithms. As regards signal processing, it appears that the autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; it has also been found that augmentation of these by harmonic and other parameters can improve classification significantly. As regards classification, several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior on training data and is thus able to quantify probability of error in an exact manner, such that features may be discarded or coarsened appropriately.

Wen, Fang↗
NASA NTRS2011

Feature Acquisition with Imbalanced Training Data

This work considers cost-sensitive feature acquisition that attempts to classify a candidate datapoint from incomplete information. In this task, an agent acquires features of the datapoint using one or more costly diagnostic tests, and eventually ascribes a classification label. A cost function describes both the penalties for feature acquisition, as well as misclassification errors. A common solution is a Cost Sensitive Decision Tree (CSDT), a branching sequence of tests with features acquired at interior decision points and class assignment at the leaves. CSDT's can incorporate a wide range of diagnostic tests and can reflect arbitrary cost structures. They are particularly useful for online applications due to their low computational overhead. In this innovation, CSDT's are applied to cost-sensitive feature acquisition where the goal is to recognize very rare or unique phenomena in real time. Example applications from this domain include four areas. In stream processing, one seeks unique events in a real time data stream that is too large to store. In fault protection, a system must adapt quickly to react to anticipated errors by triggering repair activities or follow- up diagnostics. With real-time sensor networks, one seeks to classify unique, new events as they occur. With observational sciences, a new generation of instrumentation seeks unique events through online analysis of large observational datasets. This work presents a solution based on transfer learning principles that permits principled CSDT learning while exploiting any prior knowledge of the designer to correct both between-class and withinclass imbalance. Training examples are adaptively reweighted based on a decomposition of the data attributes. The result is a new, nonparametric representation that matches the anticipated attribute distribution for the target events.

Thompson, David R.↗
NASA NTRS2020

Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning

Finding the most likely path to a set of failure states is important to the analysis of safety-critical systems that operate over a sequence of time steps, such as aircraft collision avoidance systems and autonomous cars. In many applications such as autonomous driving, failures cannot be completely eliminated due to the complex stochastic environment in which the system operates.As a result, safety validation is not only concerned about whether a failure can occur, but also discovering which failures are most likely to occur. This article presents adaptive stress testing (AST), a framework for finding the most likely path to a failure event in simulation. We consider a general black box setting for partially observable and continuous-valued systems operating in an environment with stochastic disturbances. We formulate the problem as a Markov decision process and use reinforcement learning to optimize it. The approach is simulation-based and does not require internal knowledge of the system, making it suitable for black-box testing of large systems. We present different formulations depending on whether the state is fully observable or partially observable. In the latter case, we present a modified Monte Carlo tree search algorithm that only requires access to the pseudorandom number generator of the simulator to overcome partial observability. We also present an extension of the framework, called differential adaptive stress testing (DAST), that can find failures that occur in one system but not in another. This type of differential analysis is useful in applications such as regression testing, where we are concerned with finding areas of relative weakness compared to a baseline. We demonstrate the effectiveness of the approach on an aircraft collision avoidance application, where a prototype aircraft collision avoidance system is stress tested to find the most likely scenarios of near mid-air collision.

Verification and Validation↗
NASA NTRS2021

System Validation on the Europa Clipper mission in Early Implementation Phase

NASA’s next flag-ship mission - Europa Clipper, will embark on a journey to Jupiter’s icy moon Europa in 2024 to assess its environment and habitability with a highly capable spacecraft. Post Jupiter-Orbit-Insertion, the spacecraft will be commanded to perform intricate, yet meticulously planned Europa flybys to perform science investigations using a suite of instruments, while withstanding Jupiter’s harsh radiation environment. The success of this mission is dependent on a well-coordinated project and its elements such as the flight hardware and software, the ground support and mission operations teams, procedures and other cross-cutting elements. The Europa Clipper project needs to ensure that these elements are realized at a reasonable confidence level prior to launch and other mission critical events. System Validation test and analysis activities exercise and confirm the integrity of the system of all project elements in the expected flight environment with reasonable stressing conditions. These activities go beyond system design requirements verification and are driven by validation objectives that describe the end-to-end functional and operational capabilities required during nominal and off-nominal flight-like scenarios and critical events. The challenges associated with validating that the Europa Clipper project as a whole can function and perform correctly to meet the intended mission objectives with the as-delivered capabilities of all of its elements are daunting. This paper discusses the systematic methodology established in the early implementation phase of the Europa Clipper project for developing System Validation activities and their validation objectives, and addressing any validation-related challenges on the project. Approaches include decomposition of mission objectives using activity timelines in the Mission Design plan for developing nominal scenarios, use of fault trees for exploring off-nominal cases and system boundaries, and use of Model-based Systems Engineering (MBSE) tools for planning and prioritizing these activities.

Wang, Xu↗
NASA NTRS2020

A Quantitative Hydroclimatic Context for the European Great Famine of 1315–1317

The European Great Famine of 1315–1317 triggered one of the worst population collapses in European history and ranks as the single worst European famine in mortality as a proportion of population. Historical records point to torrential rainfall, land saturation, crop failure, and prolonged flooding as important causes of the famine. Here we use the tree-ring based Old World Drought Atlas (OWDA) to show that the average of each growing season preceding the Great Famine years (1314–1316) was the fifth wettest over Europe from 1300 to 2012 C.E. The spatial and temporal characteristics of our OWDA-estimated anomalies are in excellent agreement with available historical accounts. We also characterize a mode of European hydroclimate variability that is associated with the Great Famine, which we term the “Great Famine mode.” This mode emerges as the leading mode of European hydroclimate variability from 1300–2012 and is strongly associated with extreme wet and dry events in Europe over the last millennium.

Environmental impact↗
NASA NTRS2018

Variables Influencing RNAV STAR Adherence

In this study we investigated how variables in the aviation domain impact adherence levels of aircraft flying area navigation arrivals with optimized profile descents (RNAV OPDs) (RNAV STARs: aRea NAVigation Standard Terminal Arrival Routes). Variable categories were: weather, aircraft, procedure, and traffic. Non-adherence events analyzed were: miss above, miss below, skip before merge, and skip after merge. Miss below and miss above describe when a flight does not comply vertically with a procedure. Skips refer to a flight leaving a procedure, then returning. Findings of this work reveal that vertical events are most impacted by altitude restriction size, steepness of flight paths, and merging routes. Lateral events were impacted by merging flight conflicts, number of speed restrictions, and the flow rate of the arrival traffic. This study helps increase understanding of how the system is functioning and identifies where procedures are not flexible enough to handle the variability in normal operations.major airports, procedure design, and recommendations for future work.

RNAV OPD↗
NASA NTRS2016

Terral De Vicuna, a Foehnlike Wind in Semiarid Northern Chile: Meteorological Aspects and Implications for the Fulfillment of Chill Requirements in Deciduous Fruit Trees

The terral de Vicuña is a warm and dry wind that flows down the Elqui Valley in north-central Chile typically at dawn and early morning. Given that most terral episodes occur in austral winter when chill accumulation by deciduous fruit trees proceeds, negative effects on agriculture may be expected. During 11 (2004-14) winters a meteorological characterization of terral winds and the assessment of their impact on chill accumulation, by the modified Utah Model and the Dynamic Model, were performed. Within this period, 67 terral days (TD) were identified as those in which nighttime to early morning wind direction and speed, air temperature, and relative humidity reached defined thresholds on an hourly basis (terral hours). Most frequent TD featured 6-9 consecutive terral hours; duration is considered here as a proxy for their intensity. Synoptic-scale meteorological analysis shows that 65% of moderate and strong terral events develop as a cold, migratory anticyclone drifts poleward of the study area, coinciding with the onset of a midtropospheric ridge over central Chile, bringing southwest winds on top of the Andes (approximately 500-hPa level). The remaining 35% are either associated with 500-hPa easterlies (foehn like), with prefrontal conditions ahead of a trough driving northwest 500-hPa winds, or with transitional 500-hPa westerlies.Assessments of chill accumulation during TD show that, although present average and cold winter conditions do not represent a major TD hazard to local agriculture, lower chill accumulation associated with anomalously high nocturnal temperatures could be significantly more important during present and future warmer winters.

agriculture↗
NASA NTRS1977

Until the sun dies

This book gives a popular account of the forces that have shaped human beings into their present form and created the power of human intelligence, and considers the prospects for intelligent life on other planets in the solar system and elsewhere in the universe. The chain of events leading from the big bang to the origin of life on earth is reviewed together with the observations that established the expansion of the universe. Philosophical difficulties with the concept of a universe that has both a beginning and an end are pondered, steady-state cosmology is briefly explained, and the discovery of the relict microwave background is discussed. The formation of the solar system is recounted along with the scientific view of the origin of terrestrial life. Attention is given to the origin of cells and the evolution of oxygen-breathing life, multicelled creatures, armored animals, fishes, amphibians, early reptiles, dinosaurs, and mammals. The development of mammalian intelligence is traced from the early tree dwellers through monkeys, apes, ape men, humanoid tool makers, and primitive members of the genus Homo, to Homo sapiens. Possible evidence for the existence of life on Mars is evaluated together with prospects for communication or other contact with extraterrestrial intelligence.

Jastrow, R.↗
NASA NTRS2005

Surface Soil Changes Following Selective Logging in an Eastern Amazon Forest

In the Brazilian Amazon, selective logging is second only to forest conversion in its extent. Conversion to pasture or agriculture tends to reduce soil nutrients and site productivity over time unless fertilizers are added. Logging removes nutrients in bole wood, enough that repeated logging could deplete essential nutrients over time. After a single logging event, nutrient losses are likely to be too small to observe in the large soil nutrient pools, but disturbances associated with logging also alter soil properties. Selective logging, particularly reduced-impact logging, results in consistent patterns of disturbance that may be associated with particular changes in soil properties. Soil bulk density, pH, carbon (C), nitrogen (N), phosphorus (P), calcium (Ca), magnesium (Mg), potassium (K), iron (Fe), aluminum (Al), delta(sup 3)C, delta(sup 15)N, and P fractionations were measured on the soils of four different types of loggingrelated disturbances: roads, decks, skids, and treefall gaps. Litter biomass and percent bare ground were also determined in these areas. To evaluate the importance of fresh foliage inputs from downed tree crowns in treefall gaps, foliar nutrients for mature forest trees were also determined and compared to that of fresh litterfall. The immediate impacts of logging on soil properties and how these might link to the longer-term estimated nutrient losses and the observed changes in soils were studied.

Olander, Lydia P.↗
NASA NTRSDate not supplied

Game-Theoretic Modeling of Vegetation Composition, Structure, and Dynamics: Physical Constraints, Fundamental Processes, and Emergent Properties

Vegetation structural and compositional dynamics emerge from plant physiological and demographic processes, individual-based competition, vegetation-soil feedbacks, and environmental variations and disturbance events. Predicting long-term changes in vegetation requires scaling plant individual behavior to large scale ecosystem processes. In this presentation, we summarize our studies in the modeling of vegetation demographic processes, competitively dominant plant traits, plant hydraulic processes, and stochastic disturbance effects on ecosystems, and illustrate the roles of the underlying ecological processes and eco-evolutionary optimization in vegetation modeling. With the case studies of evolutionarily stable strategy of allocation, leaf traits, and plant hydraulic processes, we show how the ecosystem processes and vegetation dynamics are determined by the individual-based plant competition and variations of soil and climate conditions. The predictions of ecosystem carbon dynamics can be greatly different with those from the traditional “single-tree” models. We also discuss the tradeoffs of plant traits and evolutionarily optimal strategies in the modeling of terrestrial ecosystem dynamics in an Earth system model.

vegetation models↗
NASA NTRS2016

An Integrated Gate Turnaround Management Concept Leveraging Big Data Analytics for NAS Performance Improvements

"Gate Turnaround" plays a key role in the National Air Space (NAS) gate-to-gate performance by receiving aircraft when they reach their destination airport, and delivering aircraft into the NAS upon departing from the gate and subsequent takeoff. The time spent at the gate in meeting the planned departure time is influenced by many factors and often with considerable uncertainties. Uncertainties such as weather, early or late arrivals, disembarking and boarding passengers, unloading/reloading cargo, aircraft logistics/maintenance services and ground handling, traffic in ramp and movement areas for taxi-in and taxi-out, and departure queue management for takeoff are likely encountered on the daily basis. The Integrated Gate Turnaround Management (IGTM) concept is leveraging relevant historical data to support optimization of the gate operations, which include arrival, at the gate, departure based on constraints (e.g., available gates at the arrival, ground crew and equipment for the gate turnaround, and over capacity demand upon departure), and collaborative decision-making. The IGTM concept provides effective information services and decision tools to the stakeholders, such as airline dispatchers, gate agents, airport operators, ramp controllers, and air traffic control (ATC) traffic managers and ground controllers to mitigate uncertainties arising from both nominal and off-nominal airport gate operations. IGTM will provide NAS stakeholders customized decision making tools through a User Interface (UI) by leveraging historical data (Big Data), net-enabled Air Traffic Management (ATM) live data, and analytics according to dependencies among NAS parameters for the stakeholders to manage and optimize the NAS performance in the gate turnaround domain. The application will give stakeholders predictable results based on the past and current NAS performance according to selected decision trees through the UI. The predictable results are generated based on analysis of the unique airport attributes (e.g., runway, taxiway, terminal, and gate configurations and tenants), and combined statistics from past data and live data based on a specific set of ATM concept-of-operations (ConOps) and operational parameters via systems analysis using an analytic network learning model. The IGTM tool will then bound the uncertainties that arise from nominal and off-nominal operational conditions with direct assessment of the gate turnaround status and the impact of a certain operational decision on the NAS performance, and provide a set of recommended actions to optimize the NAS performance by allowing stakeholders to take mitigation actions to reduce uncertainty and time deviation of planned operational events. An IGTM prototype was developed at NASA Ames Simulation Laboratories (SimLabs) to demonstrate the benefits and applicability of the concept. A data network, using the System Wide Information Management (SWIM)-like messaging application using the ActiveMQ message service, was connected to the simulated data warehouse, scheduled flight plans, a fast-time airport simulator, and a graphic UI. A fast-time simulation was integrated with the data warehouse or Big Data/Analytics (BAI), scheduled flight plans from Aeronautical Operational Control AOC, IGTM Controller, and a UI via a SWIM-like data messaging network using the ActiveMQ message service, illustrated in Figure 1, to demonstrate selected use-cases showing the benefits of the IGTM concept on the NAS performance.

Efficent ATM systems↗
NASA NTRSDate not supplied

MEDPRAT Treatment Clusters: Improving Representation of Mission Medical Risk

INTRODUCTION The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) implements a computational model that aims to quantify spaceflight medical risk by utilizing probabilistic techniques to simulate critical event incidence and outcomes over thousands of simulated mission trials. The goal of MEDPRAT is to characterize mission medical risk and provide insight into medical resource utilization. In order to analyze the medical resource space, treatment must be mapped from each simulated condition, and resources consumed as a result of this treatment must be tracked throughout the course of the mission. A new MEDPRAT feature, ‘treatment clusters’, provide a more sophisticated method of defining the structure and interaction between resources, more closely mimicking the way treatment is carried out clinically. METHODS Treatment clusters expand on the two existing treatment groupings (combination and alternate) adding a new grouping: bundled treatment. Treatment clusters may be combined to any depth, giving users the ability to specify complex treatment trees whose behavior is governed by several user-specified parameters. This approach emphasizes reusability, as treatment clusters, once defined, can be used to create more complex treatment trees or applied to many conditions. By configuring parameters for contribution, efficacy, necessity, primacy, and equivalence, resource relationships and dependencies can be more accurately represented, thereby allowing users to build capabilities with desired treatment properties, for example an intravenous capability for conditions such as anaphylaxis, acute radiation syndrome, etc. MEDPRAT v1.0 remains backward compatible with existing treatment structures, giving users the ability to define new treatment clusters as evidence becomes available, without having to recode their existing treatment databases. In addition to facilitating the representation of more complex treatment options, by pairing treatment clusters with the internal optimization routine, the MEDPRAT set selector, medical resources can be identified as organized in bundles, where appropriate, so that optimized resource sets include groups of highly-dependent resources only when all resources of the group are together. For example, it would be wasteful to include ultrasound gel but not an ultrasound machine, since the gel provides no benefit as a treatment without the ultrasound machine. With treatment clusters, the user may require that both resources are available to provide any benefit as treatment, so that if one resource is optimized out of the set, the other resource will be optimized out as well. RESULTS AND CONCLUSIONS We will report on MEDPRAT treatment clusters used in a bundling study under the IMPACT project of the ExMC element. We will discuss an example of a complex treatment tree. Through the implementation of this feature MEDPRAT enables treatment to be defined and applied in a way that is more representative of the real world, providing more accurate insight into mission medical risk and the medical resource space.

Lawrence Leinweber↗