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At least 649 records · Page 36

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NASA NTRS1999

Space Acceleration Measurement System for Free Flyers

Experimenters from the fluids, combustion, materials, and life science disciplines all use the microgravity environment of space to enhance their understanding of fundamental physical phenomena caused by disturbances from events such as spacecraft maneuvers, equipment operations, atmospheric drag, and (for manned flights) crew movement. Space conditions reduce gravity but do not eliminate it. To quantify the level of these disturbances, NASA developed the Space Acceleration Measurement System (SAMS) series to collect data characterizing the acceleration environment on the space shuttles. This information is provided to investigators so that they can evaluate how the microgravity environment affects their experiments. Knowledge of the microgravity environment also helps investigators to plan future experiments. The original SAMS system flew 20 missions on the shuttle as well as on the Russian space station Mir. Presently, Lewis is developing SAMS-II for the International Space Station; it will be a distributed system using digital output sensor heads. The latest operational version of SAMS, SAMS-FF, was originally designed for free flyer spacecraft and unmanned areas. SAMS-FF is a flexible, modular system, housed in a lightweight package, and it uses advances in technology to improve performance. The hardware package consists of a control and data acquisition module, three different types of sensors, data storage devices, and ground support equipment interfaces. Three different types of sensors are incorporated to measure both high- and low-frequency accelerations and the roll rate velocity. Small, low-power triaxial sensor heads (TSH's) offer high resolution and selectable bandwidth, and a special low-frequency accelerometer is available for high-resolution, low-frequency applications. A state-of-the-art, triaxial fiberoptic gyroscope that measures extremely low roll rates is housed in a compact package. The versatility of the SAMS-FF system is shown in the three different types of missions SAMS-FF has supported.

Kacpura, Thomas J.↗

NASA NTRS2015

Control of a Glove-Based Grasp Assist Device

A grasp assist system includes a glove and sleeve. The glove includes a digit, i.e., a finger or thumb, and a force sensor. The sensor measures a grasping force applied to an object by an operator wearing the glove. The glove contains a tendon connected at a first end to the digit. The sleeve has an actuator assembly connected to a second end of the tendon and a controller in communication with the sensor. The controller includes a configuration module having selectable operating modes and a processor that calculates a tensile force to apply to the tendon for each of the selectable operating modes to assist the grasping force in a manner that differs for each of the operating modes. A method includes measuring the grasping force, selecting the mode, calculating the tensile force, and applying the tensile force to the tendon using the actuator assembly.

Bergelin, Bryan J↗

NASA NTRS2021

Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC)

With the rapid growth in the capabilities of smaller satellites, satellite architectures that replace a single, extremely capable spacecraft with multiple, cheaper ones are gaining in popularity. Unfortunately, the orbit design process for constellations can be significantly more involved, especiallywhen the relative placement of the individual spacecraft within the constellation is not constrained by mission and/or science objectives. Optimizing a satellite constellation in the presence of multiple, competing objectives is a highly complex problem to which many traditional mathematical optimization methods cannot be applied and few tools exist to help mission designers search for promising candidate mission designs. The Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC) has been created to search for near-optimal constellation design options. GenETOC combines a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA II) with STK Components libraries (a 3rdparty .NET package created by Analytical Graphics Inc.) to create a framework that enables a mission designer to generate a simulation that models the design problem and obtain a family of potential, near-optimal solutions that can be investigated more in detail. GenETOC was developed in C# using the .NET framework with Windows Presentation Foundation (WPF) serving as the framework from which to create the graphical user interface (GUI). GenETOC user inputs can be categorized into three major data components: definition of the problem (areas of interest, satellite decision parameters, and sensor configurations), definition of performance objectives, and specification of the genetic algorithm (GA) parameters. In the problem definition component, the user is prompted to define the areas of interest against which the performance metrics will be computed, define the sensor parameters and attach them to specific spacecraft, select which satellite orbital parameters will be added to the decision space of the GA, and specify the range of desired values for each optimization parameter. For performance objectives, the user is presented with a list of available coverage and revisit performance based calculation options from which two metrics are chosen to serve as the objective functions that the GA will use to evaluate solutions during the optimization process. Finally, the definition of the GA parameters provides user control over the number of generations (number of optimization iterations), the population size (number of candidate constellations created in each generation), and the adaptive mutation and crossover threshold values (control parameters for how frequently each process occurs during the optimization). GenETOC has been extensively tested to verify the individual components of the optimization process. The GA has been tested against a suite of GA test problems to confirm convergence to the known two and three-dimensional Pareto fronts. The coverage and revisit performance metrics obtained in GenETOC are compared with STK desktop scenarios, confirming the constellations are being appropriately modeled within GenETOC simulations. A walkthrough of a simple, example problem is provided to illustrate the workings of GenETOC and to demonstrate the output available to the mission designer.

mission design↗

NASA NTRS2017

Precision Projector Laboratory: Detector Characterization with an Astronomical Emulation Testbed

As astronomical observations benefit from improved sensitivity, the effectiveness of scientific programs is becoming limited by systematics that often originate in poorly understood image sensor behavior. Traditional, bottom-up detector characterization methods provide one way to model underlying detector physics, and generate ever more faithful numerical simulations, but this approach is vulnerable to preconceptions and over-simplification. The alternative top-down approach is laboratory emulation, which enables observation, calibration, and analysis scenarios to be tested without relying on a complete understanding of the underlying physics. This complements detector characterization and simulation efforts by testing their validity. We describe a laboratory facility and experimental testbed that supports the emulation of a wide range of mission concepts such as gravitational weak lensing measurements by WFIRST and high precision spectrophotometry of transiting exoplanets by JWST. An Offner relay projects readily customizable "scenes" (e.g. stars, galaxies, spectra) with very low optical aberration over the full area of a typical optical or near infrared image sensor. f/8 and slower focal ratios may be selected, spanning those of most proposed space missions and approximating the point spread function (PSF) size of seeing limited ground based surveys. Diffraction limited PSFs are projected over a wide field of view and wavelength range to deliver highly predictable image patterns down to sub-pixel scales with stable intensity and fine motion control. The testbed enables realistic validation of detector performance on science-like images, which aids mission design and survey strategy, as well as targeted investigations of various detector effects.

Seshadri, Suresh↗

NASA NTRS2021

Normalization Method and Application for MODIS TEB Assessments using Earth Scene Measurements

Selected Earth targets are commonly used for satellite sensor calibration assessments (e.g. sensor stability and inter-comparisons). Moreover, typical scenes used for the calibration assessment of the thermal emissive bands (TEBs) includeDome Concordia (Dome-C), ocean, desert, and deep convective clouds (DCC). Reference data used for these calibration assessments can come from another band, another instrument, or ground measurements. The Dome-C site, covered with uniformly-distributed permanent snow, is normally used for the assessment of the TEBsat cold temperatures. Furthermore, ocean and desert measurements prove useful for scenes with higher temperatures. The DCC, one of the most consistent and coldest targets, can be used for the TEBs calibration and product stability assessments. MODIS band 31 (~ 11 m) can be used as a reference for these scenes. However, measurements over these scenes have seasonal variations, and the DCC brightness temperatures (BTs) have asymmetrical distributions. These features can introduce additional uncertainty to the stability assessments. A normalization method is applied by using an empirical model to derive reference-dependent BTs. Using the developed empirical model, measurements can be normalized to a reference BT in order to enhance the calibration assessment’s accuracy. This method is evaluated using all four scene types (i.e. ocean, desert, snow (Dome-C), and DCC) and applied to all the Terra and Aqua MODIS TEBs. Stability assessments over the instruments’ entire data records are presented and discussed. The technique can be applied in future efforts to support MODIS TEBs calibration assessments.

MODIS TEB↗

DOE OSTI2023

Surface acoustic wave sensor for refrigerant leakage detection

A SAW sensor is optimized for detection of refrigerant leakage in a refrigerant system or other gases, vapors, explosives or chemicals of interest. The SAW sensor includes a piezoelectric substrate; an interdigitated transducer deposited on the piezoelectric substrate, the interdigitated transducer having an input portion that receives input surface acoustic waves and an output portion that emits output surface acoustic waves; and a refrigerant sensor film located between the input portion and the output portion of the interdigitated transducer, the refrigerant sensor film including a sorbent material that is selected for preferential adsorption of a target refrigerant over atmospheric gases. Adsorption of the target refrigerant by the sorbent material results in a frequency shift of a frequency of the output surface acoustic waves relative to a frequency of the input surface acoustic waves. The sorbent material may be a metal organic framework (MOF) material, a covalent organic framework (COF) material, a porous organic cage or organic macrocyles such as calix [n] arene and its related derivatives.

Kunapuli, Raghuit Prasad↗

DOE OSTI2020

Consistency-Enhanced Evolution for Variable Selection Can Identify Key Chemical Information from Spectroscopic Data

In the last few decades, spectroscopic techniques such as near-infrared (NIR) spectroscopy have gained wide applications in several industries, such as the pharmaceutical, agricultural, oil, and gas industries. As a result, various soft sensors have been developed to predict sample properties from spectroscopic readings. Because the spectroscopic readings at different wavelengths, especially at the adjacent wavelengths, are highly correlated, it has been shown that variable selection could significantly improve a soft sensor’s prediction performance while reducing the model complexity. To improve the prediction performance, most variable selection methods focus on identifying the variables (i.e., wavelengths or wavelength segments) that are strongly correlated with the dependent variable. Although many successful applications have been reported, these variable selection methods do have their limitations. Specifically, the selected wavelengths sometimes show little connection to the chemical bounds or functional groups presenting in the sample. In addition, the selected variables can be quite sensitive to the choice of the training samples. In this work, we address these limitations from a different perspective: if a variable selection algorithm can identify the truly relevant input variables, it should consistently identify the same subset of variables regardless of the choice of the training samples. Therefore, we propose a variable selection method that aims to improve the consistency of variable selection resulting from different training samples. Furthermore, the new algorithm is termed consistency-enhanced evolution for variable selection (CEEVS). To demonstrate the performance and robustness of CEEVS, we compare the proposed method with three representative variable selection methods using five published NIR data sets. These case studies clearly demonstrate that by improving the variable selection consistency, we can not only achieve improved prediction performance, but also identify key chemical information from spectroscopic data.

42 ENGINEERING↗

NASA NTRS2015

Non-Intrusive, Distributed Gas Sensing Technology for Advanced Spacesuits

Chemical sensors for monitoring gas composition, including oxygen, humidity, carbon dioxide, and trace contaminants, are needed to characterize and validate spacesuit design and operating parameters. This paper reports on the first prototypes of a non-intrusive gas sensing technology based on flexible sensitive patches positioned inside spacesuit prototypes and interrogated via optical fibers routed outside the suit, taking advantage of the transparent materials of the suit prototypes. The sensitive patches are based on luminescent materials whose emission parameters vary with the partial pressure of a specific gas. Patches sensitive to carbon dioxide, humidity, and temperature have been developed, and their preliminary laboratory characterization in Mark III-like helmet parts is described. The first prototype system consists of a four-channel fiber optic luminescent detector that can be used to monitor any of the selected target gases at four locations. To switch from one gas to another we replace the (disposable) sensor patches and adjust the system settings. Repeatability among sensitive patches and of sensor performance from location to location has been confirmed, assuring that suit engineers will have flexibility in selecting multiple sensing points, fitting the sensor elements into the spacesuit, and easily repositioning the sensor elements as desired. The evaluation of the first prototype for monitoring carbon dioxide during washout studies in a spacesuit prototype is presented.

Delgado, Jesus↗

NASA NTRS2015

Non-Intrusive, Distributed Gas Sensing Technology for Advanced Spacesuits

Chemical sensors for monitoring gas composition, including oxygen, humidity, carbon dioxide, and trace contaminants are needed to characterize and validate spacesuit design and operating parameters. This paper reports on the first prototypes of a non-intrusive gas sensing technology based on flexible sensitive patches positioned inside spacesuit prototypes and interrogated by optical fibers routed outside the suit, taking advantage of the transparent materials of the suit prototypes. The sensitive patches are based on luminescent materials whose emission parameters vary with the partial pressure of a specific gas. Patches sensitive to carbon dioxide, humidity, oxygen, and ammonia have been developed, and their preliminary characterization in the laboratory using Mark III-like helmet parts is described. The first prototype system consists of a four-channel fiber optic luminescent detector that can be used to monitor any of the selected target gases at four locations. To switch from one gas to another we replace the (disposable) sensor patches and adjust the system settings. Repeatability among sensitive patches and of sensor performance from location to location has been confirmed, assuring that suit engineers will have flexibility in selecting multiple sensing points, fitting the sensor elements into the spacesuit, and easily repositioning the sensor elements as desired. The evaluation of the first prototype for monitoring carbon dioxide during washout studies in a space suit prototype is presented.

Delgado, Jesus↗

DOE OSTI2019

Robust Predictive Design of Field Measurements for Evapotranspiration Barriers Using Universal Multiple linear Regression

Surface barriers are normally installed to reduce downward water movement into contaminated zones. Specifically, evapotranspiration (ET) barriers are used to store water and release it, via ET, before it can percolate into an underlying waste zone. To assess the effectiveness of a surface barrier, we used an existing data set, model-simulated data, and a dimensionality reduction approach called universal multiple linear regression (uMLR) to optimize the required number of sensors in a 2-m thick surface barrier. To understand the usefulness of implementing predictive uMLR to accommodate multiple monitoring objectives, we compare several network designs, selected based on down-sampling of existing data, with a recommended sensor design based on model simulations performed without consideration of existing data. We further added consideration of “fuzzy” design, which allows more practical guidelines for field implementation of uMLR. We discovered that uMLR, combined with robust decision-making, provides a simple, flexible, and high-quality network design for monitoring the total water stored in a surface barrier across multiple uncertain conditions.

58 GEOSCIENCES↗

DOE OSTI2020

Machine Learning-Based Prediction of Distribution Network Voltage and Sensor Allocation

Increasing penetration levels of fast-varying energy resources might negatively affect power system operation. At the same time, sensor deployment throughout distribution networks improves system awareness and enables the development of new and advanced voltage control solutions. Such control techniques rely on accurate prediction in anticipation of voltage violation scenarios. This paper analyzes various approaches to voltage prediction in a distribution system, and it is shown that combining multiple techniques into a single regressor improves its predictive power. Moreover, a two-step regressor is proposed in which initial predictions based on a global regressor are refined by local regressors; in this case, prediction errors decrease significantly. Additionally, a clustering approach is employed to perform sensor allocation so that only the most influential buses are selected for monitoring without diminishing prediction accuracy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Post-hazard condition assessment of nuclear piping-equipment systems: Novel approach to feature extraction and deep learning

Over the past decade, the use of artificial intelligence techniques in the field of health-monitoring has gained significant interest, especially for structures such as building and bridges. However, applications to industrial systems such as equipment-piping systems in nuclear plants have not been explored. Here, in this paper, it is shown that the existing techniques developed for buildings and bridges cannot be extended directly to equipment-piping systems as the response of such systems is governed by multiple localized modes unlike that in buildings and bridges. This paper proposes a new approach that consists of three key aspects: (i) a novel vector of degradation-sensitive features extracted from measured data, (ii) using a deep Artificial Neural Network (ANN) for diagnosis of degradation location and degradation severity, and (iii) consideration of uncertainty in degradation severity when training the ANN. Degradation in piping-equipment systems can occur due to flow-accelerated erosion and corrosion. These locations can potentially exhibit damage such as localized yielding or initiation of cracking due to an external event such as an earthquake. Moreover, such locations can at times go undetected by current inspection techniques. Therefore, a robust framework is needed for detection of degradation after a seismic event. This manuscript proposes a proof-of-concept framework, which utilizes data collected from sensors to generate a deep ANN database for predicting degraded locations and severity in a piping-equipment system. Degradation severity is classified as minor, moderate, and severe. In the suggested methodology, a novel vector of degradation-sensitive features is extracted from the sensor data to train the ANN. A simple piping-equipment system is selected to demonstrate feature extraction as a means to simplify pattern recognition, explore the design and parameters of an ANN, and develop a sensor placement strategy. The effectiveness of the proposed framework is demonstrated on a realistic primary safety system of a two-loop nuclear reactor. It is shown that the proposed post-hazard condition assessment framework is able to detect degraded locations along with the severity levels, including minor degradation, with considerably higher accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2019

Stretchable sensors for environmental monitoring

The development of flexible and stretchable sensors has been receiving increasing attention in recent years. In particular, stretchable, skin-like, wearable sensors are desirable for a variety of potential applications such as personalized health monitoring, human-machine interfaces, and environmental sensing. In this paper, we review recent advancements in the development of mechanically flexible and stretchable sensors and systems that can be used to quantitatively assess environmental parameters including light, temperature, humidity, gas, and pH. We discuss innovations in the device structure, material selection, and fabrication methods which explain the stretchability characteristics of these environmental sensors and provide a detailed and comparative study of their sensing mechanisms, sensor characteristics, mechanical performance, and limitations. Finally, we provide a summary of current challenges and an outlook on opportunities for possible future research directions for this emerging field.

47 OTHER INSTRUMENTATION↗

NASA NTRS1992

Sensor-model prediction, monitoring and in-situ control of liquid RTM advanced fiber architecture composite processing

In-situ frequency dependent electromagnetic sensors (FDEMS) and the Loos resin transfer model have been used to select and control the processing properties of an epoxy resin during liquid pressure RTM impregnation and cure. Once correlated with viscosity and degree of cure the FDEMS sensor monitors and the RTM processing model predicts the reaction advancement of the resin, viscosity and the impregnation of the fabric. This provides a direct means for predicting, monitoring, and controlling the liquid RTM process in-situ in the mold throughout the fabrication process and the effects of time, temperature, vacuum and pressure. Most importantly, the FDEMS-sensor model system has been developed to make intelligent decisions, thereby automating the liquid RTM process and removing the need for operator direction.

Kranbuehl, D.↗

DOE OSTI2020

Machine Learning-Based Prediction of Distribution Network Voltage and Sensors Allocation: Preprint

Increasing penetration of fast-varying energy resources may negatively affect power systems' operation. At the same time, sensor deployment throughout distribution networks improves system awareness and enables the development of new and advanced voltage control solutions. Such control techniques rely on accurate prediction in anticipation to voltage violation scenarios. This paper analyzes various approaches for voltage prediction in a distribution system; it is shown that combining multiple techniques into a single regressor improves its predictive power. Moreover, a two-step regressor is proposed, where initial predictions based on a global regressor are refined by local regressors; in this case, prediction errors decrease significantly. Additionally, a clustering approach is employed for performing sensor allocation, so that only the most influential buses are selected for monitoring without diminishing prediction accuracy.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Machine Learning-Based Prediction of Distribution Network Voltage and Sensor Allocation

61 RADIATION PROTECTION AND DOSIMETRY↗

NASA NTRSDate not supplied

Flight Test Design and Implementation for Airspace Independent Surveillance Through a Distributed Ground Based Sensor Network

The paper presents a system architecture for distributed sensing, networking and computing, its hardware implementation, and execution of initial flight experiments to validate theoretical findings. It induces development of distributed sensing requirements, framework, and architecture, development of distributed ground node hardware prototypes, integration of all nodes and testing of baseline functionalities, integration of in-house developed perception, migration and tracking software packages, establishing flight scenario and flyable path for a selected UAS, flying the air vehicle along the path, recording sensors measurements, pre-processing them and transferring the resulting data to an optimal computing center. It also addresses the challenges related to pre-flight hardware calibration, clock synchronization, sensor registration and establishing a communication network. Sensors data processing results demonstrate the functionality of the presented distributed architecture and satisfactory performance of the applied technologies.

Target tracking↗

NASA NTRS1973

Flight calibration tests of a nose-boom-mounted fixed hemispherical flow-direction sensor

Flight calibrations of a fixed hemispherical flow angle-of-attack and angle-of-sideslip sensor were made from Mach numbers of 0.5 to 1.8. Maneuvers were performed by an F-104 airplane at selected altitudes to compare the measurement of flow angle of attack from the fixed hemispherical sensor with that from a standard angle-of-attack vane. The hemispherical flow-direction sensor measured differential pressure at two angle-of-attack ports and two angle-of-sideslip ports in diametrically opposed positions. Stagnation pressure was measured at a center port. The results of these tests showed that the calibration curves for the hemispherical flow-direction sensor were linear for angles of attack up to 13 deg. The overall uncertainty in determining angle of attack from these curves was plus or minus 0.35 deg or less. A Mach number position error calibration curve was also obtained for the hemispherical flow-direction sensor. The hemispherical flow-direction sensor exhibited a much larger position error than a standard uncompensated pitot-static probe.

Armistead, K. H.↗