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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Health Monitoring and Prognostics for More Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining flying time/distance (for aircraft). This information is important, particularly in the case of unmanned vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing remaining flying time is also safety-critical, since an aircraft that runs out of battery charge while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem.For electric aircraft, propulsion is based on power generated from batteries. Thus, it is critical to monitor battery state charge and to estimate the ability of the battery to support mission activities as it is being discharged during flight operation. The ability of the vehicle to complete its given mission very much depends on the charge left in the batteries based on its operational route, maneuvering, weather conditions along with aging health of the batteries. Hence, for the purpose this discussion, consider the scenario of an unmanned electric aircraft that has some planned sequence of waypoints to reach throughout its mission. In such a scenario, for this particular aircraft, and within the region it is flown, at most two minutes are required to safely land the aircraft. Thus, it is desired to predict at which point in time the aircraft must begin to head to the runway and land.

Goebel, Kai↗
NASA NTRS2019

Long and Short Term Geomagnetic Prediction (Chapter 19)

Prediction of geomagnetic variability depends on the accuracy of geomagnetic field modeling, dynamical modeling of source regions that contribute to geomagnetic signals, and advanced assimilation algorithms that combine effectively the results of geomagnetic field and dynamic models to make accurate estimates of the dynamic states of the sources and, therefore, accurate forecast of geomagnetic variations. In this chapter we provide an overview of recent research efforts in these three research areas, focusing primarily on geomagnetic variations from the dynamic outer core and from solar and lunar tidal effects, but also including a review of relevant research results and developments. Prediction of weak but periodic tidal phenomena, and of strong but chaotic secular variation (SV) showcases two very important new developments which will lead to new opportunities in geomagnetic research and application.

Weijia Kuang↗
NASA NTRS2020

Building a standardized Observing System Simulation Experiment (OSSE) framework for Mars

We advocate that the Decadal Survey recommends the NASA Science Mission Directorate to develop a rigorous Observing System Simulation Experiment (OSSE) framework for Mars, to optimize future atmospheric observations. Atmospheric conditions on Mars are a potential hazard source for landing missions. Errors in the estimates of atmospheric density profiles, inadequate knowledge of wind vertical structure and dust concentration as a function of height are likely causes of uncertainty at the landing site on the order of kilometers. An operational real-time weather forecasting capability for Mars would reduce such uncertainties, carrying enormous benefits to future robotic missions, and would be an invaluable prerequisite for human missions.A real-time forecasting capability relies upon three fundamental components: a critical mass of observing systems, a data assimilation system (DAS), and a global forecast model. The DAS allows the model to ingest the data effectively, optimizing the observational information content,and transforming them into a gridded representation of the atmosphere at a given time, called an ‘analysis’. The analysis is the best estimate of the atmospheric state for that time, and also represents a set of ‘initial conditions’ from which a global model can be initialized, to predict a future state of the atmosphere. The connection between analysis and forecast represents the foundation of modern weather forecasting. However, from the point of view of a forecast system,not all observations are equally impactful, partially because of the problem of “observational error correlation”, one important research topic in data assimilation development. For the Earth, partly due to the spontaneous and deregulated development of observations and forecast capabilities worldwide for more than half a century,the use of observations in contemporary operational forecast systems is suboptimal, with many potentially useful data being underutilized. On the contrary, Mars atmospheric scientists are in the unique situation of designing the next-generation observing systems by learning from the experience gathered on the Earth, so as to assure that the future instruments are specifically optimized to give the maximum benefit to a future weather forecast capability.An immensely powerful tool that has been firmly established by atmospheric scientists on the Earth is represented by a properly designed OSSE framework. A realistic OSSE framework cannot only quantify the benefit of future data types, be them surface based or space borne, but can also help design and optimize an entire observational network. Furthermore, OSSEs can provide deep insights into an atmosphere’s behavior, by addressing conceptual problems of its intrinsic predictability and delineating the regions or features of the atmosphere which are more sensitive to additional data and would benefit from a denser sampling. The difficulties posed by OSSEs are fundamentally different for Earth and Mars. For Earth, the enormous data volume imposes a tremendous constraint on any innovation in the observing systems: it is very hard for a single sensor to impact the skill. For Mars, the problem is the opposite: almost any additional instrument will exert some impact. However, OSSEs can help to evaluate the cost/benefit for every sensor and suggest optimal data configuration and density.The purpose of this white paper is to provide an introduction to a rigorously designed OSSE framework, explain the underlying problems and challenges, and engage the Mars community to collaborate with Earth Atmospheric scientists in order to develop a joint-OSSE framework for Mars with the largest consensual basis possible. An OSSE infrastructure would increase the understanding of the Martian atmosphere, would help NASA to optimize instrument specifications and orbit choice, providing the maximium benefit for a given expenditure of resources, and could even help establishing a roadmap for a future real-time weather forecasting capability.

Oreste Reale↗
NASA NTRSDate not supplied

NASA’s NextGen Remote Sensing Instruments Have Arrived: Data Products For Studying Disease Vectors

Remote sensing can be used to measure, evaluate or estimate both the environment (state functions) and interfaces (processfunctions) defining vector habitats. The products of remote sensing can be integrated directly into the epidemiological equationsto significantly enhance our understanding of disease vector’s life cycles and habitats. The next generation of NASA’s remotesensing instruments which have become recently operational will provide a significant enhancement in our ability to studydisease vector’s life cycles and habitats. These instruments are on the International Space Station (ISS) and includeECOSTRESS, DESIS, and GEDI. ECOSTRESS is a 5 channel, thermal IR instrument with 70 m resolution and approximately1-5 day repeat cycle of day/night pairs. DESIS jointly developed by German Aerospace Center and Teledyne Brown Engineeringis a hyperspectral sensor system of 235 channels and 30 m resolution. DESIS data is only being acquired on demand. GEDI is ahigh-resolution laser ranger used for observing Earth’s forests and topography. NASA’s current ISS instrument configuration provides measurements of the critical environmental measures of environmentalstate functions important to vector & disease life cycles. Remote sensing data provide a spatial context and time series oflandscape scale process functions represented by land use mapping and measurements of ecological functions. Global publichealth is entering a new information age through the use of spatial models of disease vector/host ecologies driven by the use ofremotely sensed data to measure environmental and structural factors critical in determining disease vector habitats. In 2018, NASA initiated a new study for the Surface Biology and Geology (SBG) Designated Observable, identified in the 2018National Academies’ Decadal Survey entitled, “Thriving on Our Changing Planet: A Decadal Strategy for Earth Observationfrom Space.” (https://www.nap.edu/catalog/24938/thriving-on-our-changing-planet-a-decadal-strategy-for-earth) . The SBG isplanned to collect global remote sensing measurements using a hyperspectral spectrometer and multispectral thermal data. Thesedata sets will provide a significant enhancement in our ability to study disease vector’s life cycles and habitats globally. The 3sensors on the ISS provide precursor data to prepare the community for the application of future SBG data toward diseasestudies.

Disease Vectors↗
NASA NTRS2020

Southern Colorado Disasters: Using NASA Observations to Map Aspen Extent and Recovery Due to Wildfire

Quaking aspen (Populus tremuloides) is an important species for wildlife, watershed health, and ecosystem resilience across its range. Heavy ungulate browsing and factors influenced by a changing climate including seasonal temperature changes and moisture deficit have led to reduced post-fire aspen regeneration rates in southern Colorado. This project partnered with Trinchera Ranch and the Colorado State Forest Service to estimate aspen recovery after the Spring Creek Fire, which ignited in June of 2018. The Southern Colorado Disasters team utilized field measurements and satellite imagery from Landsat Operational Land Imager (OLI), Sentinel-2 MultiSpectral Instrument (MSI), and the Shuttle Radar Topography Mission (SRTM) to train and run several random forest models that detect pre- and post-fire aspen extent. Ocular sampling of over 500 points on high-resolution pre-fire and post-fire images identified percentage aspen cover in 30 x 30-meter grid cells. This process provided training data for regression models, which were able to detect aspen across the landscape for both time periods using multiple remote sensing vegetation health indices. In addition, landscape suitability for aspen regeneration was modeled to provide a guide for managers on where to monitor for aspen regeneration post-fire.

DEVELOP Project Summary↗
NASA NTRS2020

Southern Colorado Disasters: Using NASA Earth Observations to Map Aspen Extent and Recovery Due to Wildfire

Quaking aspen (Populus tremuloides) is an important species for wildlife, watershed health, and ecosystem resilience across its range. Heavy ungulate browsing and factors influenced by a changing climate including seasonal temperature changes and moisture deficit have led to reduced post-fire aspen regeneration rates in southern Colorado. This project partnered with Trinchera Ranch and the Colorado State Forest Service to estimate aspen recovery after the Spring Creek Fire, which ignited in June of 2018. The Southern Colorado Disasters team utilized field measurements and satellite imagery from Landsat Operational Land Imager (OLI), Sentinel-2 MultiSpectral Instrument (MSI), and the Shuttle Radar Topography Mission (SRTM) to train and run several random forest models that detect pre- and post-fire aspen extent. Ocular sampling of over 500 points on high-resolution pre-fire and post-fire images identified percentage aspen cover in 30 x 30-meter grid cells. This process provided training data for regression models, which were able to detect aspen across the landscape for both time periods using multiple remote sensing vegetation health indices. In addition, landscape suitability for aspen regeneration was modeled to provide a guide for managers on where to monitor for aspen regeneration post-fire.

DEVELOP Technical Paper↗
NASA NTRSDate not supplied

Estimation With Range Depended Sensor Model

This paper focuses on the improvement of object detection accuracy taking into account the sensor’s reading degradation as the relative range increases. The approach is based on the assumption that range measurement error depends on the actual range. Specifically, we model the measurement error as a proportional to the actual range term plus a zero-mean, Gaussian distributed, and uncorrelated process. The tracking problem is considered in a mixed continuous-discrete time domain, where the target dynamics is in continuous-time and the measurements are in discrete-time, which is an optimal choice in many tracking and navigation applications. We adopt a commonly used continuous time coordinate-uncoupled white-noise acceleration model for a point object to describe the target motion, and use Extended Kalman Filter (EKF) framework to estimate the target's state and the unknown proportionality coefficient.

Vahram Stepanyan↗
NASA NTRSDate not supplied

ACS3 - Flight Dynamics for A Solar Sail Technology Demonstration Mission

The NASA's Advanced Composite Solar Sail System (ACS3) mission consist of a spacecraft that will deploy an 80 m 2 solar sail in a 1000 km sun-synchronous orbit. The main objective of the mission is to demonstrate that the solar wind can impulse the spacecraft to change the semimajor-axis and obtain a different orbit altitude. The sail will be composed of a combination of composite materials with distinct properties, and it will be deployed with lightweight booms from a 12U CubeSat bus, developed by Nanoavionics. The spacecraft will be launched aboard an Electron launch vehicle from Rocket LAB Launch Complex in New Zealand no earlier than July 2023. This paper covers the orbital mechanics and navigation developments to support the mission, from the solar sail trajectory model to the actual flight dynamics system to provide the orbit determination analysis prior to flight. First, we introduce a description of our high-fidelity propagation that accounts for the solar radiation pressure to produce predictive ephemeris of the solar sail performance with several spacecraft attitude modes. As part of our results, we present plots of the expected altitudes achieved by the spacecraft once the solar sail is deployed under various assumptions of the solar weather. In addition to that, we present a full description of our orbit determination process which relies in GPS state vectors to accurately estimate the position and velocity uncertainties at a frequent cadence during the mission. The outcome of this process will be critical to achieving the objective of determining effective altitude change produced by the solar sail.

Solar Sail↗
NASA NTRS2022

Real-World Testing of LiDAR-Inertial Based Navigation and Mapping for Precision Landing

The fusion of LiDAR and inertial measurements duringspacecraft descent and landing can be used to estimate alander’s navigation state and map the terrain below. Together,these data products can be used to enable safe and preciselanding on celestial bodies for which a priori orbital reconnaissanceis insufficient for hazard detection and avoidance.Unlike camera images used in visual terrain relative navigation,LiDAR scans are insensitive to changes in illumination; as aresult, the technique can be used to land in poorly lit areas,or at times of day when the lighting conditions are incongruentwith existing orbital imagery. In this paper, we extend previouswork in which we introduced a factor graph based smoothingapproach for LiDAR-inertial navigation and mapping. Whereasthe algorithms were previously tested on simulated data, thispaper presents testing on real-world data. Data from theAutonomous Landing Hazard Avoidance Technology (ALHAT)airplane flight tests in the Yucca Flats and Death Valley in2009 (FT3), the Morpheus vertical take off and landing flighttests at Kennedy Space Center in 2014 (FT6), and the landingof Perseverance and Ingenuity on Mars in 2021 (M2020) wereused to evaluate algorithm performance. In this paper, weextend our LiDAR-inertial technique to work with a variety ofranging technologies: single point laser altimetry (FT3), denseflash LiDAR (FT6), and six-beam radar (M2020). A thoroughperformance analysis for all three datasets is presented. Datasetpreparation, improvements in algorithm robustness, and outlierrejection, which were necessitated by the transition to realworlddata, are discussed.

Trawny, Nikolas↗
NASA NTRSDate not supplied

ACS3: Flight Dynamics

The NASA's Advanced Composite Solar Sail System (ACS3) mission consist of a spacecraft that will deploy an 80 m2 solar sail in Low Earth Orbit (LEO). The main objective of the mission is to demonstrate that the solar wind can impulse the spacecraft to change the semimajor-axis and obtain a different orbit altitude. The sail will be composed of a combination of composite materials with distinct properties, and it will be deployed with lightweight booms from a 12U CubeSat bus, developed by Nanoavionics. The spacecraft will be launched aboard an Electron launch vehicle from Rocket LAB Launch Complex in New Zealand. This paper covers the orbital mechanics and navigation developments to support the mission, from the solar sail trajectory model to the actual flight dynamics system to provide the orbit determination analysis prior to flight. First, we introduce a description of our high-fidelity propagation that accounts for the solar radiation pressure to produce predictive ephemeris of the solar sail performance with several spacecraft attitude modes. As part of our results, we present plots of the expected altitudes achieved by the spacecraft once the solar sail is deployed under various assumptions of the solar weather. In addition to that, we present a full description of our orbit determination process which relies in GPS state vectors to accurately estimate the position and velocity uncertainties at a frequent cadence during the mission. The outcome of this process will be critical to achieving the objective of determining effective altitude change produced by the solar sail.

Solar sail↗
NASA NTRS2024

Real-Time Temperature Field State Observer and Digital Replica to Support Cyber-Physical Testing

Cyber-physical testing is a class of experimental methods in which a system is partitioned into physical and numerical components to study its overall behavior. Transfer systems are typically needed to capture relevant interactions by enforcing appropriate interface conditions between the physical and numerical portions. While cyber-physical testing is often performed using lumped-parameter systems, thermomechanical cyber-physical testing requires the use of thermal actuators as transfer systems to apply a thermal condition over a spatially distributed region or surface. Thus, the interactions between the numerical and physical components are of a distributed nature, i.e., a field rather than point values. In this paper, we develop and experimentally validate a state observer for temperature fields to enable thermomechanical cyber-physical testing. The state observer provides continuous estimates of temperature over a surface of the thermal actuator using only temperature measurements at discrete locations. The estimated temperature field is then leveraged to account for localized mechanical defects in the interface condition. The method consists of building a digital replica of one portion of the control plant. By imposing defects in the digital replica it is used to generate and output the response of a thermal load pattern that represents that of a physical system with mechanical defects.

Manuel Salmeron↗
NASA NTRS1993

Estimating risk for earth-satellite attenuation prediction

Annual cumulative distributions of attenuation measurements and of rain-rate measurements were obtained from several locations in Europe and the United States. They were analyzed to estimate the year-to-year variability to be associated with a prediction of the expected cumulative distribution. Two models were constructed to estimate variability, an ad hoc model that summarized a number of observations when compared to model predictions and a probabilistic model that applied the ideas of order statistics to the prediction problem when the number of independent attenuation or rain-rate events in a sample year could be estimated. Based on these models, the statistical uncertainty in a model prediction may be estimated. The estimation procedure also provides an answer to the question of the number of years of observation needed to provide an estimate of the empirical distribution with a specified statistical uncertainty.

Crane, Robert K.↗
NASA NTRS2011

Towards Improved Snow Water Equivalent Estimation via GRACE Assimilation

Passive microwave (e.g. AMSR-E) and visible spectrum (e.g. MODIS) measurements of snow states have been used in conjunction with land surface models to better characterize snow pack states, most notably snow water equivalent (SWE). However, both types of measurements have limitations. AMSR-E, for example, suffers a loss of information in deep/wet snow packs. Similarly, MODIS suffers a loss of temporal correlation information beyond the initial accumulation and final ablation phases of the snow season. Gravimetric measurements, on the other hand, do not suffer from these limitations. In this study, gravimetric measurements from the Gravity Recovery and Climate Experiment (GRACE) mission are used in a land surface model data assimilation (DA) framework to better characterize SWE in the Mackenzie River basin located in northern Canada. Comparisons are made against independent, ground-based SWE observations, state-of-the-art modeled SWE estimates, and independent, ground-based river discharge observations. Preliminary results suggest improved SWE estimates, including improved timing of the subsequent ablation and runoff of the snow pack. Additionally, use of the DA procedure can add vertical and horizontal resolution to the coarse-scale GRACE measurements as well as effectively downscale the measurements in time. Such findings offer the potential for better understanding of the hydrologic cycle in snow-dominated basins located in remote regions of the globe where ground-based observation collection if difficult, if not impossible. This information could ultimately lead to improved freshwater resource management in communities dependent on snow melt as well as a reduction in the uncertainty of river discharge into the Arctic Ocean.

Forman, Bart↗
NASA NTRS1976

Large Area Crop Inventory Experiment (LACIE). Phase 1: Evaluation report

It appears that the Large Area Crop Inventory Experiment over the Great Plains, can with a reasonable expectation, be a satisfactory component of a 90/90 production estimator. The area estimator produced more accurate area estimates for the total winter wheat region than for the mixed spring and winter wheat region of the northern Great Plains. The accuracy does appear to degrade somewhat in regions of marginal agriculture where there are small fields and abundant confusion crops. However, it would appear that these regions tend also to be marginal with respect to wheat production and thus increased area estimation errors do not greatly influence the overall production estimation accuracy in the United States. The loss of segments resulting from cloud cover appears to be a random phenomenon that introduces no significant bias into the estimates. This loss does increase the variance of the estimates.

Source record↗
NASA NTRS1967

A Study to Analyze the Permeation of High Density Gases and Propellant Vapors Through Single Layer Teflon or Teflon Structure Materials and Laminations

This report contains the results of a fifteen month analytical and experimental study of the leakage rate of the pressurant gases (N2, He) and the propellant vapors (N2O4,N2H4) through bladder structures consisting of two layers of Teflon separated by a metallic foil diffusion barrier containing microscopic or larger holes. Results were obtained for the steady state leakage rate through circular holes and long rectangular openings in the barrier for arbitrary thicknesses of the two Teflon layers. The effect of hole shape and relative hole position on the leakage rate were studied. The transient problem was analyzed and it was shown that steady state calculations are adequate for estimating the leakage rate. A computer program entitled "Diffusion Analyzer Program" was developed to calculate the leakage rate, both transient and steady state. Finally, the analytical results were compared to experimentally determined values of the leakage rate through a model laminated bladder structure. The results of the analysis are in good agreement with experiment. The experimental effort (Part II of the Bladder Permeation Program) measured the solubility, diffusion coefficient and permeability of helium, nitrogen and nitrogen tetroxide vapor through Teflon TFE and FEP membranes. Data were obtained in the temperature range of 25 to 100 C at pressures ranging from near vacuum to about 20 atmospheres. Results of the experimental effort were compared with the limited data previously reported. As a verification to the applicability of results to actual bladder systems, counter diffusion tests were performed with a laminated sample containing aluminum foil with a selected group of holes.

Johnson, R. L.↗
NASA NTRS1983

Decoupling and observation theory applied to control of a long flexible beam in orbit

Techniques which use decoupling theory and state variable feedback to control the pitch attitude and the flexible mode amplitudes of a long, thin beam are discussed. An observer based on the steady state Kalman filter was incorporated into the control design procedure in order to estimate the values of the modal state variables required for the feedback control law.

Hamer, H. A.↗
NASA NTRS2003

Search-based optimization

The problem of determining the minimum cost hypothetical ancestral sequences for a given cladogram is known to be NP-complete (Wang and Jiang, 1994). Traditionally, point estimations of hypothetical ancestral sequences have been used to gain heuristic, upper bounds on cladogram cost. These include procedures with such diverse approaches as non-additive optimization of multiple sequence alignment, direct optimization (Wheeler, 1996), and fixed-state character optimization (Wheeler, 1999). A method is proposed here which, by extending fixed-state character optimization, replaces the estimation process with a search. This form of optimization examines a diversity of potential state solutions for cost-efficient hypothetical ancestral sequences and can result in greatly more parsimonious cladograms. Additionally, such an approach can be applied to other NP-complete phylogenetic optimization problems such as genomic break-point analysis. c2003 The Willi Hennig Society. Published by Elsevier Science (USA). All rights reserved.

Non-NASA Center↗
NASA NTRSDate not supplied

Evaluating Retrieval Algorithm Climate Stability: Estimating 3D Optical Thickness Bias Distributions by Cloud Type

Detecting climate trends on large spatiotemporal scales requires accurate, stable measurements and stable retrieval algorithms. We strive to estimate how time-variant retrieval algorithm biases may impact trend detection. Here we focus on the 3D cloud optical thickness (τc) bias, which is among the largest in passive cloud retrieval algorithms. If this bias is time dependent, a possibility with potential decadal changes in cloud morphology, it may obscure genuine trends in τc. Although previous studies have evaluated the cloud- and sun-view geometry-dependent 3D τc bias on small spatial scales, before our current study none have evaluated the stability of this well-known bias on climate-relevant large spatiotemporal scales. These studies must estimate large scale distributions of the 3D τc bias by cloud type and estimate how cloud type amount may change between two climate states. We employ a novel approach to estimate large scale distributions of 3D τc using a proxy of the bias that quantifies the departure of clouds from satisfying the 1D radiative transfer assumption used in passive τc retrievals. This existing globally-distributed proxy is an angular consistency metric that was developed using fused Moderate-Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectroradiometer (MISR) measurements. Calculating the 3D τc bias and the proxy, for known cloud fields enables us to establish statistical relationships between these two quantities, which can be used to calculate large-scale distributions of the 3D τc bias. This approach limits the number of 3D radiative transfer simulations required to only those needed to estimate a statistical relationship between the 3D τc bias for known cloud fields and a proxy of the bias. It is likely that future studies will be needed to evaluate retrieval algorithm bias stability for other geophysical variables as the community develops climate data records from satellite observations and their retrievals. This must be done in addition to monitoring and correcting measurement errors and uncertainties and understanding their impact on retrieved essential climate variables.

Yolanda Shea↗