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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Linking Individual Variation in Movement Behavior to Infection Dynamics in Highly Mobile Species

Animal movements are linked to infectious disease dynamics in wildlife through multiple mechanisms across various spatial and temporal scales. For example, infections can impede the movement of infected individuals, but infected hosts can also disperse infectious agents as they move. Individuals inevitably vary in their responses to infections and their roles in determining infection dynamics, which means that considering variation among individuals – whether across species, across time, or within populations – is important for understanding movement-disease relationships and their implications for epidemiology. Here, I draw on studies from multiple systems to examine how variation in movement behavior links to infectious disease epidemiology. For example, a comparative study of habitat selection across multiple waterfowl species highlights that diel behavioral patterns, rather than individual or seasonal variation, is the most important driver of the risk of overlap between waterfowl and poultry farms, and therefore for the risk of avian influenza spillover. Together, these studies show that individual traits interact with the environment to influence the roles of individuals for infectious disease ecology.

Claire Stewart Teitelbaum↗
NASA NTRS1998

Network Coronal Bright Points: Coronal Heating Concentrations Found in the Solar Magnetic Network

We examine the magnetic origins of coronal heating in quiet regions by combining SOHO/EIT Fe xii coronal images and Kitt Peak magnetograms. Spatial filtering of the coronal images shows a network of enhanced structures on the scale of the magnetic network in quiet regions. Superposition of the filtered coronal images on maps of the magnetic network extracted from the magnetograms shows that the coronal network does indeed trace and stem from the magnetic network. Network coronal bright points, the brightest features in the network lanes, are found to have a highly significant coincidence with polarity dividing lines (neutral lines) in the network and are often at the feet of enhanced coronal structures that stem from the network and reach out over the cell interiors. These results indicate that, similar to the close linkage of neutral-line core fields with coronal heating in active regions (shown in previous work), low-lying core fields encasing neutral lines in the magnetic network often drive noticeable coronal heating both within themselves (the network coronal bright points) and on more extended field lines rooted around them. This behavior favors the possibility that active core fields in the network are the main drivers of the heating of the bulk of the quiet corona, on scales much larger than the network lanes and cells.

Falconer, D. A.↗
NASA NTRS1997

Network Coronal Bright Points: Coronal Heating Concentrations Found in the Solar Magnetic Network

We examine the magnetic origins of coronal heating in quiet regions by combining SOHO/EIT Fe XII coronal images and Kitt Peak magnetograms. Spatial filtering of the coronal images shows a network of enhanced structures on the scale of the magnetic network in quiet regions. Superposition of the filtered coronal images on maps of the magnetic network extracted from the magnetograms shows that the coronal network does indeed trace and stem from the magnetic network. Network coronal bright points, the brightest features in the network lanes, are found to have a highly significant (8 sigma above random chance) coincidence with polarity dividing lines (neutral lines) in the network, and are often at the feet of enhanced coronal structures that stem from the network and reach out over the cell interiors. These results indicate that, similar to the close linkage of neutral-line core fields with coronal heating in active regions, low-lying core fields encasing neutral lines in the magnetic network often drive noticeable coronal heating both within themselves (the network coronal bright points) and on more extended fields lines rooted around them. This behavior favors the possibility that active core fields in the network are the main drivers of the heating of the bulk of the quiet corona, on scales much larger than the network lanes and cells.

Falconer, D. A.↗
NASA NTRS2018

Weak Hydrological Sensitivity to Temperature Change over Land, Independent of Climate Forcing

We present the global and regional hydrological sensitivity (HS) to surface temperature changes, for perturbations to CO2, CH4, sulfate and black carbon concentrations, and solar irradiance. Based on results from ten climate models, we show how modeled global mean precipitation increases by 2-3% per kelvin of global mean surface warming, independent of driver, when the effects of rapid adjustments are removed. Previously reported differences in response between drivers are therefore mainly ascribable to rapid atmospheric adjustment processes. All models show a sharp contrast in behavior over land and over ocean, with a strong surface temperature-driven (slow) ocean HS of 3-5%/K, while the slow land HS is only 0-2%/K. Separating the response into convective and large-scale cloud processes, we find larger inter-model differences, in particular over land regions. Large-scale precipitation changes are most relevant at high latitudes, while the equatorial HS is dominated by convective precipitation changes. Black carbon stands out as the driver with the largest inter-model slow HS variability, and also the strongest contrast between a weak land and strong sea response. We identify a particular need for model investigations and observational constraints on convective precipitation in the Arctic, and large-scale precipitation around the Equator.

Samset, B. H.↗
NASA NTRS1969

Shock tube with implosive jet driver.

Shock tube with cylindrical explosive to implode glass wall and produce high velocity glass particle jet, studying jet velocity and behavior and test gas pressure effects

Gill, S. P.↗
NASA NTRS1990

Evidence for chaotic fault interactions in the seismicity of the San Andreas fault and Nankai trough

The dynamical behavior introduced by fault interactions is examined here using a simple spring-loaded, slider-block model with velocity-weakening friction. The model consists of two slider blocks coupled to each other and to a constant-velocity driver by elastic springs. For an asymmetric system in which the frictional forces on the two blocks are not equal, the solutions exhibit chaotic behavior. The system's behavior over a range of parameter values seems to be generally analogous to that of weakly coupled segments of an active fault. Similarities between the model simulations and observed patterns of seismicity on the south central San Andreas fault in California and in the Nankai trough along the coast of southwestern Japan.

Huang, Jie↗
NASA NTRS2017

Improved Propulsion Modeling for Low-Thrust Trajectory Optimization

Low-thrust trajectory design is tightly coupled with spacecraft systems design. In particular, the propulsion and power characteristics of a low-thrust spacecraft are major drivers in the design of the optimal trajectory. Accurate modeling of the power and propulsion behavior is essential for meaningful low-thrust trajectory optimization. In this work, we discuss new techniques to improve the accuracy of propulsion modeling in low-thrust trajectory optimization while maintaining the smooth derivatives that are necessary for a gradient-based optimizer. The resulting model is significantly more realistic than the industry standard and performs well inside an optimizer. A variety of deep-space trajectory examples are presented.

trajectory↗
NASA NTRS1992

Reducing software mass through behavior control

Attention is given to the tradeoff between communication and computation as regards a planetary rover (both these subsystems are very power-intensive, and both can be the major driver of the rover's power subsystem, and therefore the minimum mass and size of the rover). Software techniques that can be used to reduce the requirements on both communciation and computation, allowing the overall robot mass to be greatly reduced, are discussed. Novel approaches to autonomous control, called behavior control, employ an entirely different approach, and for many tasks will yield a similar or superior level of autonomy to traditional control techniques, while greatly reducing the computational demand. Traditional systems have several expensive processes that operate serially, while behavior techniques employ robot capabilities that run in parallel. Traditional systems make extensive world models, while behavior control systems use minimal world models or none at all.

Miller, David P.↗
NASA NTRSDate not supplied

State of Wildfires

Fire is an essential component of ecosystems and acts as key driver of biogeochemical cycling with impacts on vegetation structure and composition, soil conditions, and climate feedbacks. Fire behavior and effects vary based on the types of fuel, fuel dryness, and frequency of ignition. Due to changing climate and land use patterns, fire danger is increasing in many regions globally, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Recurrent fires help to determine the distribution of trees and grasses, and overall fuel load, which inform the behavior of future fires. Process-based models can be used to capture multi-scale impacts at the forest stand-level up to the landscape level, and across minutes to centuries, but must capture variation in fire behavior and intensity as a function of the fuel and climate, from high intensity forest crown fires within boreal regions to low intensity rapid grass fires of the tropics. Fire model development demonstrates our ability to capture large scale fire influenced biogeography and vegetation distribution at the earth system scale through vegetation traits and fire feedbacks. At the individual stand scale the importance of interactions and feedbacks between above and below ground process is essential to capturing the vegetation dynamics across boreal forest systems. Improving the mechanics of including plant physiology and specifically live fuel moisture content is the next step to advancing the capability of process based models to inform fire research. Advances in the testing and creation of a mechanistic live fuel moisture model demonstrate the foundation for future live fuel dynamics research that can be informed by field and remote sensing information. Improved remote sensing, technological and modeling capabilities support a more comprehensive and cohesive fire response that will be better equipped to overcome current barriers and anticipate the new reality of fires in a warming world.

wildfires↗
NASA NTRS1993

Driving a car with custom-designed fuzzy inferencing VLSI chips and boards

Vehicle control in a-priori unknown, unpredictable, and dynamic environments requires many calculational and reasoning schemes to operate on the basis of very imprecise, incomplete, or unreliable data. For such systems, in which all the uncertainties can not be engineered away, approximate reasoning may provide an alternative to the complexity and computational requirements of conventional uncertainty analysis and propagation techniques. Two types of computer boards including custom-designed VLSI chips were developed to add a fuzzy inferencing capability to real-time control systems. All inferencing rules on a chip are processed in parallel, allowing execution of the entire rule base in about 30 microseconds, and therefore, making control of 'reflex-type' of motions envisionable. The use of these boards and the approach using superposition of elemental sensor-based behaviors for the development of qualitative reasoning schemes emulating human-like navigation in a-priori unknown environments are first discussed. Then how the human-like navigation scheme implemented on one of the qualitative inferencing boards was installed on a test-bed platform to investigate two control modes for driving a car in a-priori unknown environments on the basis of sparse and imprecise sensor data is described. In the first mode, the car navigates fully autonomously, while in the second mode, the system acts as a driver's aid providing the driver with linguistic (fuzzy) commands to turn left or right and speed up or slow down depending on the obstacles perceived by the sensors. Experiments with both modes of control are described in which the system uses only three acoustic range (sonar) sensor channels to perceive the environment. Simulation results as well as indoors and outdoors experiments are presented and discussed to illustrate the feasibility and robustness of autonomous navigation and/or safety enhancing driver's aid using the new fuzzy inferencing hardware system and some human-like reasoning schemes which may include as little as six elemental behaviors embodied in fourteen qualitative rules.

Pin, Francois G.↗
NASA NTRS2006

Agent Reward Shaping for Alleviating Traffic Congestion

Traffic congestion problems provide a unique environment to study how multi-agent systems promote desired system level behavior. What is particularly interesting in this class of problems is that no individual action is intrinsically "bad" for the system but that combinations of actions among agents lead to undesirable outcomes, As a consequence, agents need to learn how to coordinate their actions with those of other agents, rather than learn a particular set of "good" actions. This problem is ubiquitous in various traffic problems, including selecting departure times for commuters, routes for airlines, and paths for data routers. In this paper we present a multi-agent approach to two traffic problems, where far each driver, an agent selects the most suitable action using reinforcement learning. The agent rewards are based on concepts from collectives and aim to provide the agents with rewards that are both easy to learn and that if learned, lead to good system level behavior. In the first problem, we study how agents learn the best departure times of drivers in a daily commuting environment and how following those departure times alleviates congestion. In the second problem, we study how agents learn to select desirable routes to improve traffic flow and minimize delays for. all drivers.. In both sets of experiments,. agents using collective-based rewards produced near optimal performance (93-96% of optimal) whereas agents using system rewards (63-68%) barely outperformed random action selection (62-64%) and agents using local rewards (48-72%) performed worse than random in some instances.

Tumer, Kagan↗
NASA NTRS2009

Impulsive Reconnection in the Sun's Atmosphere

Recent high-resolution observations from the Hinode mission show dramatically that the Sun's atmosphere is filled with explosive activity ranging from chromospheric explosions that reach heights of Mm, to coronal jets that can extend to solar radii, to giant coronal mass ejections (CME) that reach the edge of the heliosphere. The driver for all this activity is believed to be 3D magnetic reconnection. From the large variation observed in the temporal behavior of solar activity, it is clear that reconnection in the corona must take on a variety of distinct forms. The explosive nature of jets and CMEs requires that the reconnection be impulsive in that it stays off until a substantial store of free energy has been accumulated, but then turns on abruptly and stays on until much of this free energy is released. The key question, therefore, is what determines whether the reconnection is impulsive or not. We present some of the latest observations and numerical models of explosive and non-explosive solar activity. We argue that, in order for the reconnection to be impulsive, it must be driven by a quasi-ideal instability. We discuss the generality of our results for understanding 31) reconnection in other contexts.

Antiochos, Spiro K.↗
NASA NTRS2006

Architecture for Control of the K9 Rover

Software featuring a multilevel architecture is used to control the hardware on the K9 Rover, which is a mobile robot used in research on robots for scientific exploration and autonomous operation in general. The software consists of five types of modules: Device Drivers - These modules, at the lowest level of the architecture, directly control motors, cameras, data buses, and other hardware devices. Resource Managers - Each of these modules controls several device drivers. Resource managers can be commanded by either a remote operator or the pilot or conditional-executive modules described below. Behaviors and Data Processors - These modules perform computations for such functions as planning paths, avoiding obstacles, visual tracking, and stereoscopy. These modules can be commanded only by the pilot. Pilot - The pilot receives a possibly complex command from the remote operator or the conditional executive, then decomposes the command into (1) more-specific commands to the resource managers and (2) requests for information from the behaviors and data processors. Conditional Executive - This highest-level module interprets a command plan sent by the remote operator, determines whether resources required for execution of the plan are available, monitors execution, and, if necessary, selects an alternate branch of the plan.

Bresina, John L.↗
NASA NTRS2023

Resilient Strategies in Commercial Aviation

When we fly and nothing scary happens, is the system’s design affording this success? Not always — sometimes humans are the cause of success. This resilient performance is often overlooked. To capture this, we explore two types of strategies: countermeasures and modifications . First, countermeasures are behaviors triggered by variables anticipated to be challenging or problematic ( pressures ). To capture this, we look at examples of how a problem was avoided. For example, a country road may have a hairpin turn where accidents occur. With this pressure identified, we look at successful drivers for insights. Modifications are changes that are created to fill a gap between work-as-imagined and work-as-done. This strategy is from the design of systems. In aviation, work-as-imagined is often explicit, so it can be compared to behaviors using data. These two resilient potentials aim to better understand how systems function, as well as how people contribute to unrecognized successes.

Human factors↗
NASA NTRS2005

Autonomous Medical Care for Exploration

The goal of Autonomous Medical Care (AMC) is to ensure a healthy, well-performing crew which is a primary need for exploration. The end result of this effort will be the requirements and design for medical systems for the CEV, lunar operations, and Martian operations as well as a ground-based crew health optimization plan. Without such systems, we increase the risk of medical events occurring during a mission and we risk being unable to deal with contingencies of illness and injury, potentially threatening mission success. AMC has two major components: 1) pre-flight crew health optimization and 2) in-flight medical care. The goal of pre-flight crew health optimization is to reduce the risk of illness occurring during a mission by primary prevention and prophylactic measures. In-flight autonomous medical care is the capability to provide medical care during a mission with little or no real-time support from Earth. Crew medical officers or other crew members provide routine medical care as well as medical care to ill or injured crew members using resources available in their location. Ground support becomes telemedical consultation on-board systems/people collect relevant data for ground support to review. The AMC system provides capabilities to incorporate new procedures and training and advice as required. The on-board resources in an autonomous system should be as intelligent and integrated as is feasible, but autonomous does not mean that no human will be involved. The medical field is changing rapidly, and so a challenge is to determine which items to pursue now, which to leverage other efforts (e.g. military), and which to wait for commercial forces to mature. Given that what is used for the CEV or the Moon will likely be updated before going to Mars, a critical piece of the system design will be an architecture that provides for easy incorporation of new technologies into the system. Another challenge is to determine the level of care to provide for each mission type. The level of care refers to the amount and type of care one will render based on perceived need and ability. This is in contrast to the standard of care which is the benchmark by which that care is provided. There are certainly some devices and procedures that have unique microgravity or partial gravity requirements such that terrestrial methods will not work. For example, performing CPR on Mars cannot be done in exactly the same way as on Earth because the reduced gravity causes too large a reduction in the forces available for effective compression of the chest. Likewise, fluid behavior in microgravity may require a specialized water filtration and mixing system for the creation of intravenous fluids. This paper will outline the drivers for the design of the medical care systems, prioritization and planning techniques, key system components, and long term goals.

Johnson-Throop, Kathy A.↗
NASA NTRS2019

SSIM: NASA Mars rover robotics flight software simulation

The focus of SSim on MSL was the robotic flight software, including rover mobility and navigation, robotic arm manipulation, and sample acquisition, processing, and delivery. It can execute behaviors in simulation a thousand times faster than they execute in real time on the flight compute element. SSim is used by rover drivers to develop and validate command sequences throughout the planning cycle. SSim has been used to plan all of the Curiosity robotic operations since landing and is expected to continue to be used for the remaining life of the rover.

Leger, Chris↗
NASA NTRS2009

Probabilistic Risk Assessment for Decision Making During Spacecraft Operations

Decisions made during the operational phase of a space mission often have significant and immediate consequences. Without the explicit consideration of the risks involved and their representation in a solid model, it is very likely that these risks are not considered systematically in trade studies. Wrong decisions during the operational phase of a space mission can lead to immediate system failure whereas correct decisions can help recover the system even from faulty conditions. A problem of special interest is the determination of the system fault protection strategies upon the occurrence of faults within the system. Decisions regarding the fault protection strategy also heavily rely on a correct understanding of the state of the system and an integrated risk model that represents the various possible scenarios and their respective likelihoods. Probabilistic Risk Assessment (PRA) modeling is applicable to the full lifecycle of a space mission project, from concept development to preliminary design, detailed design, development and operations. The benefits and utilities of the model, however, depend on the phase of the mission for which it is used. This is because of the difference in the key strategic decisions that support each mission phase. The focus of this paper is on describing the particular methods used for PRA modeling during the operational phase of a spacecraft by gleaning insight from recently conducted case studies on two operational Mars orbiters. During operations, the key decisions relate to the commands sent to the spacecraft for any kind of diagnostics, anomaly resolution, trajectory changes, or planning. Often, faults and failures occur in the parts of the spacecraft but are contained or mitigated before they can cause serious damage. The failure behavior of the system during operations provides valuable data for updating and adjusting the related PRA models that are built primarily based on historical failure data. The PRA models, in turn, provide insight into the effect of various faults or failures on the risk and failure drivers of the system and the likelihood of possible end case scenarios, thereby facilitating the decision making process during operations. This paper describes the process of adjusting PRA models based on observed spacecraft data, on one hand, and utilizing the models for insight into the future system behavior on the other hand. While PRA models are typically used as a decision aid during the design phase of a space mission, we advocate adjusting them based on the observed behavior of the spacecraft and utilizing them for decision support during the operations phase.

dynamic fault trees↗
NASA NTRS2000

Development and Testing of a High Level Axial Array Duct Sound Source for the NASA Flow Impedance Test Facility

In this report both a frequency domain method for creating high level harmonic excitation and a time domain inverse method for creating large pulses in a duct are developed. To create controllable, high level sound an axial array of six JBL-2485 compression drivers was used. The pressure downstream is considered as input voltages to the sources filtered by the natural dynamics of the sources and the duct. It is shown that this dynamic behavior can be compensated for by filtering the inputs such that both time delays and phase changes are taken into account. The methods developed maximize the sound output while (i) keeping within the power constraints of the sources and (ii) maintaining a suitable level of reproduction accuracy. Harmonic excitation pressure levels of over 155dB were created experimentally over a wide frequency range (1000-4000Hz). For pulse excitation there is a tradeoff between accuracy of reproduction and sound level achieved. However, the accurate reproduction of a pulse with a maximum pressure level over 6500Pa was achieved experimentally. It was also shown that the throat connecting the driver to the duct makes it difficult to inject sound just below the cut-on of each acoustic mode (pre cut-on loading effect).

Johnson, Marty E.↗