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At least 271 records · Page 15

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NASA NTRS2005

Adaptive Control for Microgravity Vibration Isolation System

Most active vibration isolation systems that try to a provide quiescent acceleration environment for space science experiments have utilized linear design methods. In this paper, we address adaptive control augmentation of an existing classical controller that employs a high-gain acceleration feedback together with a low-gain position feedback to center the isolated platform. The control design feature includes parametric and dynamic uncertainties because the hardware of the isolation system is built as a payload-level isolator, and the acceleration Sensor exhibits a significant bias. A neural network is incorporated to adaptively compensate for the system uncertainties, and a high-pass filter is introduced to mitigate the effect of the measurement bias. Simulations show that the adaptive control improves the performance of the existing acceleration controller and keep the level of the isolated platform deviation to that of the existing control system.

Yang, Bong-Jun↗

NASA NTRS2008

Integrated System Health Management (ISHM) for Test Stand and J-2X Engine: Core Implementation

ISHM capability enables a system to detect anomalies, determine causes and effects, predict future anomalies, and provides an integrated awareness of the health of the system to users (operators, customers, management, etc.). NASA Stennis Space Center, NASA Ames Research Center, and Pratt & Whitney Rocketdyne have implemented a core ISHM capability that encompasses the A1 Test Stand and the J-2X Engine. The implementation incorporates all aspects of ISHM; from anomaly detection (e.g. leaks) to root-cause-analysis based on failure mode and effects analysis (FMEA), to a user interface for an integrated visualization of the health of the system (Test Stand and Engine). The implementation provides a low functional capability level (FCL) in that it is populated with few algorithms and approaches for anomaly detection, and root-cause trees from a limited FMEA effort. However, it is a demonstration of a credible ISHM capability, and it is inherently designed for continuous and systematic augmentation of the capability. The ISHM capability is grounded on an integrating software environment used to create an ISHM model of the system. The ISHM model follows an object-oriented approach: includes all elements of the system (from schematics) and provides for compartmentalized storage of information associated with each element. For instance, a sensor object contains a transducer electronic data sheet (TEDS) with information that might be used by algorithms and approaches for anomaly detection, diagnostics, etc. Similarly, a component, such as a tank, contains a Component Electronic Data Sheet (CEDS). Each element also includes a Health Electronic Data Sheet (HEDS) that contains health-related information such as anomalies and health state. Some practical aspects of the implementation include: (1) near real-time data flow from the test stand data acquisition system through the ISHM model, for near real-time detection of anomalies and diagnostics, (2) insertion of the J-2X predictive model providing predicted sensor values for comparison with measured values and use in anomaly detection and diagnostics, and (3) insertion of third-party anomaly detection algorithms into the integrated ISHM model.

Figueroa, Jorge F.↗

NASA NTRS1973

Separate surfaces for automatic flight controls.

The purpose of this paper is to describe an investigation of separate surface stability augmentation systems for general aviation aircraft. The program objective were twofold: first, a wind tunnel program to determine control effectiveness of separate surfaces in the presence of main surfaces, and hinge moment feedback from separate surfaces via the main surfaces to the pilot; second, a theoretical study to determine the minimum performance of actuators and sensors that can be tolerated, the best slaving gains to be used with separate surfaces, and control authority needed for proper operation under direct pilot control, under autopilot control, and in failure situations. On the basis of the results obtained, it has been concluded that separate surface systems are feasible and advantageous for use in general aviation aircraft.

Roskam, J.↗

NASA NTRS2016

A Natural Interaction Interface for UAVs Using Intuitive Gesture Recognition

The popularity of unmanned aerial vehicles (UAVs) is increasing as technological advancements boost their favorability for a broad range of applications. One application is science data collection. In fields like Earth and atmospheric science, researchers are seeking to use UAVs to augment their current portfolio of platforms and increase their accessibility to geographic areas of interest. By increasing the number of data collection platforms UAVs will significantly improve system robustness and allow for more sophisticated studies. Scientists would like be able to deploy an available fleet of UAVs to fly a desired flight path and collect sensor data without needing to understand the complex low-level controls required to describe and coordinate such a mission. A natural interaction interface for a Ground Control System (GCS) using gesture recognition is developed to allow non-expert users (e.g., scientists) to define a complex flight path for a UAV using intuitive hand gesture inputs from the constructed gesture library. The GCS calculates the combined trajectory on-line, verifies the trajectory with the user, and sends it to the UAV controller to be flown.

Chandarana, Meghan↗

NASA NTRS2022

Using Desis and EO-1 Hyperion Reflectance Time Series for the Assessment of Vegetation Traits and Gross Primary Production (GPP)

This study evaluates the potential of the DLR Earth Sensing Imaging Spectrometer (DESIS) visible through near-infrared (VNIR) surface reflectance to augment the EO-1 Hyperion full spectrum (400-2400 nm) reflectance collection over vegetated flux sites to extend the reflectance time series up to the present. We compared DESIS and Hyperion surface reflectance magnitude and variability at a pseudo-invariant site (PICS) and a vegetated flux site (VFS). VNIR reflectance magnitudes between the two sensors did not significantly differ at the PICS. However, DESIS variability was higher, likely due to differences in the data acquisition time and observation geometry. Using empirical and biophysical models, both DESIS and Hyperion datasets captured the seasonal variations in gross primary production (GPP) and canopy bio-physical parameters such as chlorophyll content, leaf area index (LAI), and senescent material at the VFS. Differences in the magnitudes of the bio-physical parameters were observed, likely due to the differences in the sensors spectral range and resolution. Using VNIR reflectance from EO-1 Hyperion with DESIS convolved to Hyperion spectral resolution to estimate canopy chlorophyll and GPP, we demonstrate that combining historic and current space-based reflectance data in a common multi-sensor approach is feasible. This is of importance for extending the reflectance record established with EO-1 Hyperion to provide continuity with the current orbital instruments (e.g., DESIS/ISS, PRISMA/ASI) and the forthcoming NASA Surface Biology and Geology (SBG), ESA CHIME and DLR EnMAP satellite missions, which is of key importance for comparisons of current and past trends in the seasonal dynamics of vegetation traits and photosynthetic function.

DESIS↗

DOE OSTI2021

Structural Health Monitoring of Microreactor Safety Systems Using Convolutional Neural Networks

Microreactors, a class of modular reactors with net power output of less than 20 MWth, have innovative applications in nuclear and nonnuclear industries due to their portability, reliability, resilience, and high capacity factors. In order to operate microreactors on a wider scale, it is essential to bring down maintenance life-cycle costs while ensuring the integrity of operating such systems. Autonomous operations in microreactors using augmented digital-twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. Structural health monitoring (SHM) is a key component of nuclear DT frameworks. Artificial neural networks can be beneficial to detect degradation in the nuclear safety systems, such as piping equipment systems, by monitoring the sensor data obtained from the plant and its corresponding structures, systems and components. In this report, an SHM methodology is presented which uses convolutional neural networks to determine degraded locations and their corresponding degradation-severity levels at various locations of nuclear piping equipment systems. A simple pipe system, subjected to seismic loads, is selected to design the post-hazard SHM framework. The effectiveness of the proposed SHM methodology is demonstrated by obtaining high accuracy in detecting degraded locations as well as the severity levels.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Optimizing Individualized Treatment Planning for Parkinson’s Disease Using Deep Reinforcement Learning

More than one million people currently live with Parkinson's Disease (PD) in the U.S. alone. Medications, such as levodopa, can help manage PD symptoms. However, medication treatment planning is generally based on patient history and limited interaction between physicians and patients during office visits. This limits the extent of benefit that may be derived from the treatment as disease/patient characteristics are generally non-stationary. Wearable sensors that provide continuous monitoring of various symptoms, such as bradykinesia and dyskinesia, can enhance symptom management. However, using such data to overhaul the current static medication treatment planning approach and prescribe personalized medication timing and dosage that accounts for patient/care-giver/physician feedback/preferences remains an open question. We develop a model to prescribe timing and dosage of medications, given the motor fluctuation data collected using wearable sensors in real-time. We solve the resulting model using deep reinforcement learning (DRL). The prescribed policy determines the optimal treatment plan that minimizes patient's symptoms. Our results show that the model-prescribed policy outperforms the static a priori treatment plan in improving patients' symptoms, providing a proof-of-concept that DRL can augment medical decision making for treatment planning of chronic disease patients.

Watts, Jeremy↗

NASA NTRS1993

Sea ice motions in the central arctic ice central arctic pack ice as inferred from AVHRR imagery

Synoptic observations of ice motion in the Arctic Basin are currently limited to those acquired by drifting buoys and, more recently, radar data from ERS-1. Buoys are not uniformly distributed throughout the Arctic, and SAR coverage is limited regionally and temporally due to the data volume, swath width, processing requirements, and power needs of the SAR. Additional ice-motion observations that can map ice responses simultaneously over large portions of the Arctic on daily to weekly time intervals are thus needed to augment the SAR and buoy data and to provide an intermediate-scale measure of ice drift suitable for climatological analyses and ice modeling. Merging the remotely-sensed ice motions with SAR, AVHRR, OLS, and SSM/I imagery permits studies of ice processes over a range of space and time scales. A medium-resolution passive microwave sensor might be ideal for this purpose, but no such satellite will be available in the near future. Combinations of existing data types are thus likely to be our best bet for generating motion fields with a coverage and resolution best suited to climate studies. Principal objectives of this project are to: (1) demonstrate whether sufficient ice features and ice motion existed within the consolidated ice pack to permit motion tracking using AVHRR imagery; (2) determine the limits imposed on AVHRR mapping by cloud cover; and (3) test the applicability of AVHRR-derived motions in studies of ice-atmosphere interactions.

Emery, William↗

NASA NTRS2011

Global Assimilation of Multi-Sensor Snow Observations for Improved Characterization of Snow Processes

Snow conditions on the land surface are recognized to be key components of the global hydrological cycle as they play a critical role in the determination of local and regional climate. In many mid-latitude and high-latitude regions, the seasonal water storage and associated spring snowmelt dominate the local hydrology. The contribution to the runoff and moisture conditions from snow is vital in supporting agriculture and in determining water resources management practices. Consequently, accurate characterization of snow properties becomes important for both end-use applications and weather and climate research. Recently a joint effort between the u.S. Air Force and NASA has enabled a blended, multi-sensor snow product known as the AFWA NASA Snow Algorithm (ANSA). This global snow dataset has been generated by utilizing the Earth Observation System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) datasets. ANSA product includes estimates of snow cover extent, snow water equivalent (SWE) and SWE-derived snow depth fields. The MODIS-based products enable snow cover mappings under cloud-free conditions whereas the passive microwave data from AMSR-E provides measurements under cloudy conditions. These remotely-sensed snow observations are further augmented with the information from ground-based snow measurements through data fusion techniques. The resulting ANSA products are employed in the NASA Land Information System (LIS) data assimilation framework, which provides a comprehensive environment for integrating community land surface models, ground and satellite-based observations, and ensemble-based data assimilation tools. LIS incorporates the multisensor ANSA snow retrievals with the land surface model estimates to generate spatially and temporally continuous estimates of snow states, through data assimilation. A suite of experiments to assimilate ANSA snow cover, SWE and snow depth estimates with different land surface models in LIS are conducted and the resulting estimates of snow conditions are evaluated against a number of in-situ observational datasets, over several regions of the world. These evaluations are used to compare and contrast the advantages and disadvantages of these multi-sensor snow observations.

Kumar, Sujay↗

NASA NTRS1989

Parallel Architectures for Planetary Exploration Requirements (PAPER)

The Parallel Architectures for Planetary Exploration Requirements (PAPER) project is essentially research oriented towards technology insertion issues for NASA's unmanned planetary probes. It was initiated to complement and augment the long-term efforts for space exploration with particular reference to NASA/LaRC's (NASA Langley Research Center) research needs for planetary exploration missions of the mid and late 1990s. The requirements for space missions as given in the somewhat dated Advanced Information Processing Systems (AIPS) requirements document are contrasted with the new requirements from JPL/Caltech involving sensor data capture and scene analysis. It is shown that more stringent requirements have arisen as a result of technological advancements. Two possible architectures, the AIPS Proof of Concept (POC) configuration and the MAX Fault-tolerant dataflow multiprocessor, were evaluated. The main observation was that the AIPS design is biased towards fault tolerance and may not be an ideal architecture for planetary and deep space probes due to high cost and complexity. The MAX concepts appears to be a promising candidate, except that more detailed information is required. The feasibility for adding neural computation capability to this architecture needs to be studied. Key impact issues for architectural design of computing systems meant for planetary missions were also identified.

Cezzar, Ruknet↗

NASA NTRS1995

Performance Thresholds for Application of MEMS Inertial Sensors in Space

We review types of inertial sensors available and current usage of inertial sensors in space and the performance requirements for these applications. We then assess the performance available from micro-electro-mechanical systems (MEMS) devices, both in the near and far term. Opportunities for the application of these devices are identified. A key point is that although the performance available from MEMS inertial sensors is significantly lower than that achieved by existing macroscopic devices (at least in the near term), the low cost, low size, and power of the MEMS devices opens up a number of applications. In particular, we show that there are substantial benefits to using MEMS devices to provide vibration, and for some missions, attitude sensing. In addition, augmentation for global positioning system (GPS) navigation systems holds much promise.

Smit, Geoffrey N.↗

NASA NTRS2000

Monthly Oceanic Rainfall Derived from Space-Borne Microwave Data

A technique for estimating monthly oceanic rainfall rate using multi-channel microwave measurements has been developed. This technique is applied to the Special Sensor Microwave Imager (SSM/I) data since 1987 to infer monthly rainfall for the Global Precipitation Climatology Project (GPCP). A modified version of this algorithm is now being applied to the TRMM Microwave Imager (TMI) data. There are three prominent features of this algorithm. First, the knowledge of the form of the rainfall intensity probability density function used to augment the measurements. Second, utilizing a linear combination of the 19.35 and 22.235 GHz channels to deemphasize the effect of water vapor. Third, an objective technique has been developed to estimate the rain layer thickness from the 19.35 and 22.23 5 GHz brightness temperature histograms, TMI data with better spatial resolution and 24 hour sampling (vs. sun-synchronized sampling, which is limited to two narrow intervals of local solar time for DMSP satellites) prompt us to study the similarity and difference between these two rainfall estimates. One year of rainfall data (January to December 1998) are used in this study. The mean differences of these two rain estimates are typically less than 15%. Statistical tests are administrated to evaluate the differences between rainfall estimates from SSM/I and TMI data. Differences between TMI and SSM/I rain estimates are discussed in the context of radiative transfer calculations and rainfall estimate algorithm.

Chang, Alfred T. C.↗

DOE OSTI2026

Large Language Model for Validation, Optical Calibration, and Learning (VOCAL) Distributed Temperature Sensing Interface

Distributed temperature sensing (DTS) using fiber optic sensors (FOS) offers a promising method for temperature measurements in advanced reactors, such as sodium fast reactors and molten salt cooled reactors. To support the calibration and validation of DTS measurements, Argonne National Laboratory developed the Validation, Optical Calibration, and Learning (VOCAL) software package. This report describes the integration of a local large language model (LLM) with a retrieval-augmented generation (RAG) system into the VOCAL interface to serve as an interactive user assistant. The LLM framework enhances the VOCAL platform’s accessibility to users by explaining interface components, clarifying inputs and outputs, and answering user queries dynamically in real-time. The accuracy of the LLM assistant performance was evaluated with 20 queries regarding the interface and its parameters using experimental data from the Thermal Hydraulic Experimental Test Article (THETA) facility. Results demonstrate that the LLM achieved a 95% accuracy rate, with a BERTScore of 0.8816 and SBERT value of 0.7417. Furthermore, validation of the RAG system within the LLM framework showed optimal accuracy with k-values between 1 and 2 using the k-refinement convergence test. The prompt perturbation analysis demonstrated good initial consistency for the RAG system, exhibiting the highest accuracy under punctuation variations and the greatest sensitivity under query reordering. Notably, the model’s errors were limited to data retrieval failures rather than factual hallucinations, reinforcing its baseline reliability. The integration of LLM provides a highly accurate, userfriendly enhancement to the VOCAL platform without disrupting its core computational capabilities for FOS calibration and validation.

Hong, Evan↗

NASA NTRS1987

A new approach for vibration control in large space structures

An approach for augmenting vibration damping characteristics in space structures with large panels is presented. It is based on generation of bending moments rather than forces. The moments are generated using bimetallic strips, suitably mounted at selected stations on both sides of the large panels, under the influence of differential solar heating, giving rise to thermal gradients and stresses. The collocated angular velocity sensors are utilized in conjunction with mini-servos to regulate the control moments by flipping the bimetallic strips. A simple computation of the rate of dissipation of vibrational energy is undertaken to assess the effectiveness of the proposed approach.

Kumar, K.↗

NASA NTRS1994

Update on TAO moored ORG array

During the Coupled Ocean Atmosphere Response Experiment (COARE) six TAO moorings were equipped with optical rain gauges (ORG's). In late 1993 moorings deployed on the equator at 154E and 157.5E were recovered and not redeployed as they were augmentations to the TAO array for COARE only. In December 1993, four TAO moorings were equipped with ORG's: one each at 2N, 156E and 2S, 156E and ORG doublets on the equator at 0, 156E and 0, 165E. The 2N, 156E mooring has been lost. By the end of April all sites will have been serviced and six refurbished sensors will again be deployed in the same locations.

Freitag, H. Paul↗

NASA NTRS2007

A Satellite-based Assessment of Trans-Pacific Transport of Pollution Aerosol

It has been well documented that pollution aerosol and dust from East Asia can transport across the North Pacific basin, reaching North America and beyond. Such intercontinental transport extends the impact of aerosols for climate change, air quality, atmospheric chemistry, and ocean biology from local and regional scales to hemispheric and global scales. Long term, measurement-based studies are necessary to adequately assess the implications of these wider impacts. A satellite-based assessment can augment intensive field campaigns by expanding temporal and spatial scales and also serve as constraints for model simulations. Satellite imagers have been providing a wealth of evidence for the intercontinental transport of aerosols for more than two decades. Quantitative assessments, however, became feasible only recently as a result of the much improved measurement accuracy and enhanced new capabilities of satellite sensors. In this study, we generated a 4-year (2002 to 2005) climatology of optical depth for pollution aerosol (defined as a mixture of aerosols from urbanlindustrial pollution and biomass burning in this study) over the North Pacific from MODerate resolution Imaging Spectro-radiometer (MODIS) observations of fine- and coarse-mode aerosol optical depths. The pollution aerosol mass loading and fluxes were then calculated using measurements of the dependence of aerosol mass extinction efficiency on relative humidity and of aerosol vertical distributions from field campaigns and available satellite observations in the region. We estimated that about 18 Tg/year pollution aerosol is exported from East Asia to the northwestern Pacific Ocean, of which about 25% reaches the west coast of North America. The pollution fluxes are largest in spring and smallest in summer. For the period we have examined the strongest export and import of pollution particulates occurred in 2003, due largely to record intense Eurasia wildfires in spring and summer. The overall uncertainty of pollution fluxes is estimated at about 80%. A reduction of uncertainty can be achieved with a better characterization of pollution aerosol through integrating emerging A-Train measurements. Simulations by the Goddard Chemistry Aerosol Radiation and Transport (GOCART) and Global Modeling Initiative (GMI) models agree quite well with the satellite-based estimates of annual and latitudeintegrated fluxes, with larger model-satellite differences in latitudinal variations of fluxes.

Yu, Hongbin↗

NASA NTRS2010

Using Integer Clocks to Verify the Timing-Sync Sensor Network Protocol

We use the UPPAAL model checker for Timed Automata to verify the Timing-Sync time-synchronization protocol for sensor networks (TPSN). The TPSN protocol seeks to provide network-wide synchronization of the distributed clocks in a sensor network. Clock-synchronization algorithms for sensor networks such as TPSN must be able to perform arithmetic on clock values to calculate clock drift and network propagation delays. They must be able to read the value of a local clock and assign it to another local clock. Such operations are not directly supported by the theory of Timed Automata. To overcome this formal-modeling obstacle, we augment the UPPAAL specification language with the integer clock derived type. Integer clocks, which are essentially integer variables that are periodically incremented by a global pulse generator, greatly facilitate the encoding of the operations required to synchronize clocks as in the TPSN protocol. With this integer-clock-based model of TPSN in hand, we use UPPAAL to verify that the protocol achieves network-wide time synchronization and is devoid of deadlock. We also use the UPPAAL Tracer tool to illustrate how integer clocks can be used to capture clock drift and resynchronization during protocol execution

Huang, Xiaowan↗

NASA NTRSDate not supplied

Multispectral Imagery Research and Applications

The NASA Short-term Prediction Research and Transition (SPoRT) Center developed techniques to improve the quality and interpretation of multispectral imagery derived from NASA/NOAA geostationary satellites using statistical and machine learning approaches. A physically-based machine learning approach, DustTracker-AI, was developed to overcome the problem of night-time dust detection and to augment dust analysis with satellite products such as the Dust RGB. Additionally, an updated limb-correction and intercalibration methodology for short-wave, near-infrared, thermal infrared, and water vapor bands was developed for the purpose of developing a suite of high-quality RGB imagery that can be used at high viewing angles and across the constellation of geostationary sensors. This presentation will briefly highlight the techniques developed to detect dust in difficult night-time scenes and improve the quality and interpretation of multispectral imagery.

Emily Berndt↗