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At least 487 records · Page 27

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NASA NTRS2020

Linkage among Ice Crystal Microphysics, Mesoscale Dynamics and Cloud and Precipitation Structures Revealed By Collocated Microwave Radiometer and Multi-Frequency Radar Observations

Ice clouds and falling snow are ubiquitous globally and play important roles in the Earth’s radiation budget and precipitation processes. Ice particle microphysical properties (e.g., size, habit and orientation) are not only influenced by ambient environment’s dynamic and thermodynamic conditions, but also intimately connect to the cloud radiative effects and particle fall speeds, which therefore impact up to the future climate projection and down to the details of the surface precipitation (e.g., onset-time, location, type and strength). Our previous work revealed that high-frequency Polarimetric radiance Difference (PD) from passive microwave sensors is a good indicator of the bulk aspect ratio of horizontally oriented ice particles that are often occur inside anvil clouds and/or stratiform precipitations. In this current work, we further investigate the dynamic/thermodynamic mechanisms and cloud/precipitation structures associated with ice-phase microphysics corresponding to different PD signals. In order to do so, collocated CloudSat radar (W-band) and Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM-DPR, Ku/Ka bands) observations as well as European Centre for Medium-Range Weather Forecasts (ECMWF) atmosphere background profiles are grouped according to the magnitude of PD for only stratiform precipitation and/or anvil cloud scenes. We found that horizontally-oriented snow aggregates or large snow particles are likely the major contributor to the high-PD signals at 166 GHz, while low-PD magnitudes can be attributed to small cloud ice, randomly oriented snow aggregates, riming snow or super-cooled water. Further, high (low) PD scenes are found to be associated with stronger (weaker) wind shear and higher (lower) ambient humidity, both of which help promote (prohibit) the growth of frozen particles and the organization of convective systems. An ensemble of squall line cases is studied at the end to demonstrate that the PD asymmetry in the leading and trailing edges of the deep convection line is closely tied to the anvil cloud and stratiform precipitation layers respectively, suggesting the potential usefulness of PD as a proxy of stratiform/convective precipitation flag, as well as a proxy of convection life stage.

Ice crystal microphysics↗

NASA NTRS2017

An Overview of the Orbital Debris and Meteoroid Environments, Their Effects on Spacecraft, and What Can We Do About It?

Because of the high speeds needed for orbital space flight, hypervelocity impacts with objects in space are a constant risk to spacecraft. This includes natural debris - meteoroids - and the debris remnants of our own activities in space. A number of space surveillance assets are used to measure and track spacecraft, used upper stages, and breakup debris. However, much of the debris and meteoroids encountered by spacecraft in Earth orbit is not easily measured or tracked. For every man-made object that we can track, there are hundreds of small debris that are too small to be tracked but still large enough to damage spacecraft. In addition, even if we knew today's environment with perfect knowledge, the debris environment is dynamic and would change tomorrow. This means that much of the risk from both meteoroids and anthropogenic debris is statistical in nature. NASA uses and maintains a number of instruments to statistically monitor the meteoroid and orbital debris environments, and uses this information to compute statistical models for use by spacecraft designers and operators. Because orbital debris is a result of human activities, NASA has led the US government in formulating national and international strategies that space users can employ to limit the growth of debris in the future. This talk will summarize the history and current state of meteoroid and space debris measurements and modeling, how the environment influences spacecraft design and operations, how we are designing the experiments of tomorrow to improve our knowledge, and how we are working internationally to preserve the space environment for the future.

Matney, Mark↗

NASA NTRS2013

Real-Time and High-Fidelity Simulation Environment for Autonomous Ground Vehicle Dynamics

This paper reports on a collaborative project between U.S. Army TARDEC and Jet Propulsion Laboratory (JPL) to develop a unmanned ground vehicle (UGV) simulation model using the ROAMS vehicle modeling framework. Besides modeling the physical suspension of the vehicle, the sensing and navigation of the HMMWV vehicle are simulated. Using models of urban and off-road environments, the HMMWV simulation was tested in several ways, including navigation in an urban environment with obstacle avoidance and the performance of a lane change maneuver.

vehicle sensing↗

NASA NTRS2013

Real-Time and High-Fidelity Simulation Environment for Autonomous Ground Vehicle Dynamics

unmanned ground vehicle (UGV)↗

DOE OSTI2024

OPEN-Augmented Reality GUI for Bioenergy Crop Phenotyping and Precision Agriculture (Donald Danforth Plant Science Center Final Scientific Technical Report)

The project led by the Donald Danforth Plant Science Center, in collaboration with Arizona State University, George Washington University, and Saint Louis University, has made significant strides in advancing the phenotypic analysis of bioenergy crops through the development of an innovative AI processing pipeline. This initiative was primarily funded by ARPA-E, with additional cost-sharing provided by the participating institutions. The project successfully utilized a variety of sensors—3D scanners, thermal, RGB, and hyperspectral—to refine algorithms for data-driven trait signature identification and improve the classification and visualization of plant traits. The developed AI processing pipeline is capable of handling the complex, multidimensional data characteristic of dynamic agricultural environments. 1) Contributions to understanding: The research has advanced the field of plant phenomics by showcasing the synergistic use of various sensor data to enhance the precision of trait analysis in bioenergy crops. Through the integration of 3D scanners, thermal, RGB, and hyperspectral sensors, the project has developed robust data-driven trait signature algorithms and visualization techniques. These innovations have facilitated detailed monitoring and management of plant traits, providing vital insights into plant growth dynamics and stress responses. Further, the project has broadened our understanding of how machine learning can be effectively applied in multi-sensor environments to refine trait analysis. By leveraging diverse datasets, the research has not only improved the accuracy of phenotypic assessments but also established a versatile methodological framework that can be extended beyond agriculture to other fields requiring detailed phenotypic analysis. 2) Technical effectiveness and economic feasibility: The AI processing pipeline developed in this project demonstrated significant technical effectiveness, achieving high throughput analysis of extensive phenotypic data and meeting targeted accuracies. This system exemplified the capability of advanced machine learning technologies to efficiently manage and analyze large, complex datasets. Economically, the implementation of the project-developed pipelines may offer substantial cost savings across multiple sectors. It enhances data analysis processes and significantly reduces the need for manual data interpretation, thereby decreasing both the time and resources required. 3) Public benefit: The project has significantly broadened the scope of agricultural methodologies to enhance phenotypic analysis, with potential applications in various sectors beyond agriculture. Additionally, the initiative fostered an enriching educational and collaborative environment, significantly enhancing the technical skills of participants. It also made substantial contributions to the scientific community by providing open-access data sets and tools, encouraging ongoing research and development across various disciplines. Overall, the project not only met its scientific goals but also showcased the extensive utility of integrating advanced machine learning and sensor data analysis technologies. These advancements have proven instrumental in driving forward both theoretical research and practical applications, setting a strong foundation for future explorations and innovations in data-driven science.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2013

Orbital Debris Engineering Model (ORDEM) v.3

A model of the manmade orbital debris environment is required by spacecraft designers, mission planners, and others in order to understand and mitigate the effects of the environment on their spacecraft or systems. A manmade environment is dynamic, and can be altered significantly by intent (e.g., the Chinese anti-satellite weapon test of January 2007) or accident (e.g., the collision of Iridium 33 and Cosmos 2251 spacecraft in February 2009). Engineering models are used to portray the manmade debris environment in Earth orbit. The availability of new sensor and in situ data, the re-analysis of older data, and the development of new analytical and statistical techniques has enabled the construction of this more comprehensive and sophisticated model. The primary output of this model is the flux [#debris/area/time] as a function of debris size and year. ORDEM may be operated in spacecraft mode or telescope mode. In the former case, an analyst defines an orbit for a spacecraft and "flies" the spacecraft through the orbital debris environment. In the latter case, an analyst defines a ground-based sensor (telescope or radar) in terms of latitude, azimuth, and elevation, and the model provides the number of orbital debris traversing the sensor's field of view. An upgraded graphical user interface (GUI) is integrated with the software. This upgraded GUI uses project-oriented organization and provides the user with graphical representations of numerous output data products. These range from the conventional flux as a function of debris size for chosen analysis orbits (or views), for example, to the more complex color-contoured two-dimensional (2D) directional flux diagrams in local spacecraft elevation and azimuth.

Matney, Mark↗

NASA NTRS2015

Correlation of Amine Swingbed On-Orbit CO2 Performance with a Hardware Independent Predictive Model

The Amine Swingbed Payload is an experimental system deployed on the International Space Station (ISS) that includes a two-bed, vacuum regenerated, amine-based carbon dioxide (CO2) removal subsystem as the principal item under investigation. The aminebased subsystem, also described previously in various publications as CAMRAS 3, was originally designed, fabricated and tested by Hamilton Sundstrand Space Systems International, Inc. (HSSSI) and delivered to NASA in November 2008. The CAMRAS 3 unit was subsequently designed into a flight payload experiment in 2010 and 2011, with flight test integration activities accomplished on-orbit between January 2012 and March 2013. Payload activation was accomplished in May 2013 followed by a 1000 hour experimental period. The experimental nature of the Payload and the interaction with the dynamic ISS environment present unique scientific and engineering challenges, in particular to the verification and validation of the expected Payload CO2 removal performance. A modeling and simulation approach that incorporates principles of chemical reaction engineering has been developed for the amine-based system to predict the dynamic cabin CO2 partial pressure with given inputs of sorbent bed size, process air flow, operating temperature, half-cycle time, CO2 generation rate, cabin volume and the magnitude of vacuum available. Simulation runs using the model to predict ambient CO2 concentrations show good correlation to on-orbit performance measurements and ISS dynamic concentrations for the assumed operating conditions. The dynamic predictive modelling could benefit operational planning to help ensure ISS CO2 concentrations are maintained below prescribed limits and for the Orion vehicle to simulate various operating conditions, scenarios and transients.

Papale, William↗

NASA NTRS1977

Optimization of MLS receivers for multipath environments

Optimal design studies of MLS angle-receivers and a theoretical design-study of MLS DME-receivers are reported. The angle-receiver results include an integration of the scan data processor and tracking filter components of the optimal receiver into a unified structure. An extensive simulation study comparing the performance of the optimal and threshold receivers in a wide variety of representative dynamical interference environments was made. The optimal receiver was generally superior. A simulation of the performance of the threshold and delay-and-compare receivers in various signal environments was performed. An analysis of combined errors due to lateral reflections from vertical structures with small differential path delays, specular ground reflections with neglible differential path delays, and thermal noise in the receivers is provided.

Mcalpine, G. A.↗

DOE OSTI2025

Data for Stetten et al. (2025), "Biogeochemical controls on iron speciation and cycling across upland to shoreline gradients in freshwater and estuarine coastal soils (Lake Erie and Chesapeake Bay, United States)"

Coastal environments are dynamic interfaces that mediate carbon and nutrient exchanges between terrestrial landscapes and open waters, but it is unclear how biogeochemical reactions, in particular iron (Fe) redox transformations, affect the understanding and prediction of coastal ecosystem functions. This dataset includes measurements from two freshwater sites in the Western and Central basins of Lake Erie (Ohio, United States) and two estuarine sites in the Chesapeake Bay (Maryland, United States); the analytical results were reported by Stetten et al. (2025) in Science of the Total Environment. It was produced as part of the COMPASS-FME project, which seeks to advance a scalable, predictive understanding of the fundamental biogeochemical processes, ecological structure, and ecosystem dynamics that distinguish coastal terrestrial-aquatic interfaces from the purely terrestrial or aquatic systems to which they are coupled. The sites were sampled in November 2022 (CRC), December 2022 (MSM), February 2023 (GCW), and March 2023 (OWC); site codes follow those used by Pennington et al. (2025).The dataset consists of the following soil data:- Solid data (Fe concentration, etc.)- Porewater data (sulfate, sulfide, etc.)- Linear combination fitting results of X-ray absorption near edge structure (XANES) spectra; i.e., quantitative results of the oxidation state of Fe, indicated as a proportion of pure Fe(III) and Fe(II) model compounds- Linear combination fitting results of EXAFS (extended X-ray absorption fine structure) spectra, indicated as proportion of of Fe-model compounds (illite, smectite, etc.)Each data type has a single file in comma-separated value (CSV) format. No special software is required to read it.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2015

A Triboelectric Sensor Array for Electrostatic Studies on the Lunar Surface

The moons electrostatic environment requires careful consideration in the development of future lunar landers. Electrostatically charged dust was well documented during the Apollo missions to cause thermal control, mechanical, and visibility issues. The fine dust particles that make up the surface are electrostatically charged as a result of numerous charging mechanisms. The relatively dry conditions on the moon creates a prime tribocharging environment during surface operations. The photoelectric effect is dominant for lunar day static charging, while plasma electrons are the main contributor for lunar night electrostatic effects. Electrostatic charging is also dependent on solar intensity, Earth-moon relative positions, and cosmic ray flux. This leads to a very complex and dynamic electrostatic environment that must be studied for the success of long term lunar missions.In order to better understand the electrostatic environment of planetary bodies, Kennedy Space Center, in previous collaboration with the Jet Propulsion Laboratory, has developed an electrostatic sensor suite. One of the instruments included in this package is the triboelectric sensor array. It is comprised of strategically selected materials that span the triboelectric series and that also have previous spaceflight history. In this presentation, we discuss detailed testing with the triboelectric sensor array performed at Kennedy Space Center. We will discuss potential benefits and use cases of this low mass, low cost sensor package, both for science and for mission success.

Charging↗

NASA NTRS2019

A Comparison of ARTEMIS Data with the Lunar Plasma Design Environment for NASA Crewed Missions

NASA’s Gateway will provide the capability for sustaining a human presence in cis-lunar space. Operations of the Gateway will include spacecraft dockings, extra vehicular activities (EVA), and high-power solar arrays. NASA’s experience with the International Space Station highlighted the importance of evaluating spacecraft charging effects for such operations. For crewed spacecraft, which tend to employ the use of dielectric surfaces in this dynamic plasma environment, reliance on spacecraft charging simulation packages, such as the NASA/Air Force Spacecraft Charging Analyzer Program (Nascap-2k) [Mandell et al., 2006] and Spacecraft Plasma Interaction System (SPIS) [Roussel et al., 2008], is required to understand the risks to hardware and humans. The variability in the lunar plasma environment as the Moon revolves around the Earth, lunar wake effects, and a strong dependency on photoemission and secondary electron emission creates challenges for spacecraft charging analysis. The Design Specification for Natural Environments (DSNE) [NASA, MSFC] is the primary resource for space environments affecting NASA’s crewed missions, and the DSNE provides plasma environments in a standard form for input into simulation packages. NASA developed the existing lunar plasma environment using data from Geotail [Nishida, 1994] along with published lunar plasma wake models [Halekas et al., 2005] based on Lunar Prospector. Since 2011, NASA’s twin Acceleration Reconnection Turbulence & Electrodynamics of Moon’s Interaction with the Sun (ARTEMIS) satellites [Angelopoulos, 2010] have been collecting high resolution plasma and fields observations within the lunar plasma environment providing a much larger dataset of the plasma properties in cislunar space. This research compares the existing lunar plasma environment definition with ARTEMIS data and makes recommendations on the refinement of the environment definition for future lunar missions.

ARTEMIS mission↗

NASA NTRS2013

Investigation of Gearbox Vibration Transmission Paths on Gear Condition Indicator Performance

Helicopter health monitoring systems use vibration signatures generated from damaged components to identify transmission faults. For damaged gears, these signatures relate to changes in dynamics due to the meshing of the damaged tooth. These signatures, referred to as condition indicators (CI), can perform differently when measured on different systems, such as a component test rig, or a full-scale transmission test stand, or an aircraft. These differences can result from dissimilarities in systems design and environment under dynamic operating conditions. The static structure can also filter the response between the vibration source and the accelerometer, when the accelerometer is installed on the housing. To assess the utility of static vibration transfer paths for predicting gear CI performance, measurements were taken on the NASA Glenn Spiral Bevel Gear Fatigue Test Rig. The vibration measurements were taken to determine the effect of torque, accelerometer location and gearbox design on accelerometer response. Measurements were taken at the housing and compared while impacting the gear set near mesh. These impacts were made at gear mesh to simulate gear meshing dynamics. Data measured on a helicopter gearbox installed in a static fixture were also compared to the test rig. The behavior of the structure under static conditions was also compared to CI values calculated under dynamic conditions. Results indicate that static vibration transfer path measurements can provide some insight into spiral bevel gear CI performance by identifying structural characteristics unique to each system that can affect specific CI response.

Dempsey, Paula J.↗

NASA NTRS1988

A dynamic case-based planning system for space station application

We are currently investigating the use of a case-based reasoning approach to develop a dynamic planning system. The dynamic planning system (DPS) is designed to perform resource management, i.e., to efficiently schedule tasks both with and without failed components. This approach deviates from related work on scheduling and on planning in AI in several aspects. In particular, an attempt is made to equip the planner with an ability to cope with a changing environment by dynamic replanning, to handle resource constraints and feedback, and to achieve some robustness and autonomy through plan learning by dynamic memory techniques. We briefly describe the proposed architecture of DPS and its four major components: the PLANNER, the plan EXECUTOR, the dynamic REPLANNER, and the plan EVALUATOR. The planner, which is implemented in Smalltalk, is being evaluated for use in connection with the Space Station Mobile Service System (MSS).

Oppacher, F.↗

NASA NTRSDate not supplied

Adaptive Optimization for System Performance and Combined Bernstein Polynomial, Optimal Reciprocal Collision Avoidance, Differential Dynamic Programming for Trajectory Replanning and Collision Avoidance for UAM Vehicles

The emerging urban air mobility (UAM) sector in aerospace is driving development of unconventional multi-modal vehicle configurations and autonomous flight. The combination of multi-modal vehicle dynamics, complex environment, requirements to deal with flight contingencies in an efficient and safe manner, as well as necessity for precise trajectory following and performance, are the driving influence behind adaptive optimization for system performance. We are interested in trajectory optimization algorithm that would system parameter estimation and identifying the optimal switching time between modes of hybrid dynamical systems. This presentation discusses a parameterized optimal control trajectory optimization algorithm that is an extended and generalized version of Differential Dynamic Programming (DDP), titled Parameterized Differential Dynamic Programming (PDDP). DDP is an efficient trajectory optimization algorithm relying on second order approximations of a system’s dynamics and cost function and has recently been applied to optimize systems with time invariant parameters. Experiments are presented applying PDDP to solve model predictive control (MPC) and moving horizon estimation (MHE) tasks simultaneously. In particular, PDDP is used to determine the optimal transition point between flight regimes of a complex urban air mobility (UAM) class vehicle exhibiting multiple phases of flight and to identify and compensate for actuation faults.

optimization↗

NASA NTRS1984

Boundary layers of the earth's outer magnetosphere

The magnetospheric boundary layer and the plasma-sheet boundary layer are the primary boundary layers of the earth's outer magnetosphere. Recent satellite observations indicate that they provide for more than 50 percent of the plasma and energy transport in the outer magnetosphere although they constitute less than 5 percent by volume. Relative to the energy density in the source regions, plasma in the magnetospheric boundary layer is predominantly deenergized whereas plasma in the plasma-sheet boundary layer has been accelerated. The reconnection hypothesis continues to provide a useful framework for comparing data sampled in the highly dynamic magnetospheric environment. Observations of 'flux transfer events' and other detailed features near the boundaries have been recently interpreted in terms of nonsteady-state reconnection. Alternative hypotheses are also being investigated. More work needs to be done, both in theory and observation, to determine whether reconnection actually occurs in the magnetosphere and, if so, whether it is important for overall magnetospheric dynamics.

Eastman, T. E.↗

NASA NTRS2001

A Framework for Control and Observation in Distributed Environments

As organizations begin to deploy large computational grids, it has become apparent that systems for observation and control of the resources, services, and applications that make up such grids are needed. Administrators must observe the operation of resources and services to ensure that they are operating correctly and they must control the resources and services to ensure that their operation meets the needs of users. Further, users need to observe the performance of their applications so that this performance can be improved and control how their applications execute in a dynamic grid environment. In this paper we describe our software framework for control and observation of resources, services, and applications that supports such uses and we provide examples of how our framework can be used.

Smith, Warren↗

NASA NTRS2013

Lab-On-Chip Clinorotation System for Live-Cell Microscopy Under Simulated Microgravity

Cells in microgravity are subject to mechanical unloading and changes to the surrounding chemical environment. How these factors jointly influence cellular function is not well understood. We can investigate their role using ground-based analogues to spaceflight, where mechanical unloading is simulated through the time-averaged nullification of gravity. The prevailing method for cellular microgravity simulation is to use fluid-filled containers called clinostats. However, conventional clinostats are not designed for temporally tracking cell response, nor are they able to establish dynamic fluid environments. To address these needs, we developed a Clinorotation Time-lapse Microscopy (CTM) system that accommodates lab-on- chip cell culture devices for visualizing time-dependent alterations to cellular behavior. For the purpose of demonstrating CTM, we present preliminary results showing time-dependent differences in cell area between human mesenchymal stem cells (hMSCs) under modeled microgravity and normal gravity.

Yew, Alvin G.↗

NASA NTRS1980

Lab-On-Chip Clinorotation System for Live-Cell Microscopy Under Simulated Microgravity

Yew, Alvin G.↗