New sensors for ultrasound - Measuring temperature profiles
Ultrasonic measurement of temperature distribution in solid, liquid and gaseous media, using reflection and transmission techniques
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Ultrasonic measurement of temperature distribution in solid, liquid and gaseous media, using reflection and transmission techniques
Laboratory work dealing with the frequency characteristic of the plasma impedance of spherical and cylindrical electrode systems is reported. The influence of the ion sheath on various features of the impedance characteristic is emphasized. Those features are the series and parallel resonance as well as additional resonances due to the excitation of electroacoustic and cyclotron harmonic waves. The dependence of the series and parallel resonances on dc biasing leads to a method of determining the ion sheath capacity for a cylindrical electrode system. The obtained values agree fairly well with those obtained from a theoretical model for the density and potential distribution in the sheath of a cylindrical sensor aligned with a supersonic plasma flow. The amplitude of resonances due to excitation of longitudinal plasma waves (electroacoustic and cyclotron harmonic) is reduced or even vanishes for sufficiently negative dc bias. Positive bias first leads to an increased amplitude up to a certain dc bias value above which, however, the amplitude decreases again due to electron absorption at the sensor surface.
Dexterous manipulation of a robot hand requires the use of multiple sensors integrated into the mechanical hand under distributed microcomputer control. Where space applications such as construction, assembly, servicing and repair tasks are desired of smart robot arms and robot hands, several critical drives influence the design, engineering and integration of such an electromechanical hand. This paper describes a smart robot hand developed at the Jet Propulsion Laboratory for experimental use and evaluation with the Protoflight Manipulator Arm (PFMA) at the Marshall Space Flight Center (MSFC).
The KSC has implemented an integrated system that coordinates state-of-the-art robotic subsystems. It is a sensor based real-time robotic control system performing operations beyond the capability of an off-the-shelf robot. The integrated system provides real-time closed loop adaptive path control of position and orientation of all six axes of a large robot; enables the implementation of a highly configurable, expandable testbed for sensor system development; and makes several smart distributed control subsystems (robot arm controller, process controller, graphics display, and vision tracking) appear as intelligent peripherals to a supervisory computer coordinating the overall systems.
The system comprises: (1) two PUMA 560 robot arms, each equipped with the latest JPL developed smart hands which contain 3-D force/moment and grasp force sensors; (2) two general purpose force reflecting hand controllers; (3) a NS32016 microprocessors based distributed computing system together with JPL developed universal motor controllers; (4) graphics display of sensor data; (5) capabilities for time delay experiments; and (6) automatic data recording capabilities. Several different types of control modes are implemented on this system using different feedback control techniques. Some of the control modes and the related feedback control techniques are described, and the achievable control performance for tracking position and force trajectories are reported. The interaction between position and force trajectory tracking is illustrated. The best performance is obtained by using a novel, task space error feedback technique.
The objective was to examine state-of-the-art optical sensing and processing technology applied to control the motion of flexible spacecraft. Proposed large flexible space systems, such an optical telescopes and antennas, will require control over vast surfaces. Most likely distributed control will be necessary involving many sensors to accurately measure the surface. A similarly large number of actuators must act upon the system. The used technical approach included reviewing proposed NASA missions to assess system needs and requirements. A candidate mission was chosen as a baseline study spacecraft for comparison of conventional and optical control components. Control system requirements of the baseline system were used for designing both a control system containing current off-the-shelf components and a system utilizing electro-optical devices for sensing and processing. State-of-the-art surveys of conventional sensor, actuator, and processor technologies were performed. A technology development plan is presented that presents a logical, effective way to develop and integrate advancing technologies.
A problem of current interest in computational aerodynamics is the prediction of unsteady vortex flows over aircraft at high angles of attack. A six-month experimental effort was conducted at the John H. Harper Wind Tunnel to acquire qualitative and quantitative information on the unsteady vortex flow over a generic wing-body configuration at high angles of attack. A double-delta flat-plate wing with beveled edges was combined with a slender sharp-nosed body-of-revolution fuselage to form the generic configuration. This configuration produces a strong attached leading edge vortex on the wing, as well as sharply-peaked flow velocity spectra above the wing. While it thus produces flows with several well-defined features of current interest, the model was designed for efficiency of representation in computational codes. A moderate number of surface pressure ports and two unsteady pressure sensors were used to study the pressure distribution over the wing and body surface at high angles of attack; the unsteady pressure sensing did not succeed because of inadequate signal-to-noise ratio. A pulsed copper vapor laser sheet was used to visualize the vortex flow over the model, and vortex trajectories, burst locations, mutual induction of vortex systems from the forebody, strake, and wing, were quantified. Laser Doppler velocimetry was used to quantify all 3 components of the time-average velocity in 3 data planes perpendicular to the freestream direction. Statistics of the instantaneous velocity were used to study intermittency and fluctuation intensity. Hot-film anemometry was used to study the fluctuation energy content in the velocity field, and the spectra of these fluctuations. In addition, a successful attempt was made to measure velocity spectra, component by component, using laser velocimetry, and these were compared with spectra measured by hot-film anemometry at several locations.
The measurement of the temporal distribution of fuel in gas turbine combustors is important in considering pollution, combustion efficiency and combustor dynamics and acoustics. Much of the previous work in measuring fuel distributions in gas turbine combustors has focused on the spatial aspect of the distribution. The temporal aspect however, has often been overlooked, even though it is just as important. In part, this is due to the challenges of applying real-time diagnostic techniques in a high pressure and high temperature environment. A simple and low-cost instrument that non-intrusively measures the real-time fuel-to-air ratio (FAR) in a gas turbine combustor has been developed. The device uses a dual wavelength laser absorption technique to measure the concentration of most hydrocarbon fuels such as jet fuel, methane, propane, etc. The device can be configured to use fiber optics to measure the local FAR inside a high pressure test rig without the need for windows. Alternatively, the device can readily be used in test rigs that have existing windows without modifications. An initial application of this instrument was to obtain time-resolved measurements of the FAR in the premixer of a lean premixed prevaporized (LPP) combustor at inlet air pressures and temperatures as high as 17 atm at 800 K, with liquid JP-8 as the fuel. Results will be presented that quantitatively show the transient nature of the local FAR inside a LPP gas turbine combustor at actual operating conditions. The high speed (kHz) time resolution of this device, combined with a rugged fiber optic delivery system, should enable the realization of a flight capable active-feedback and control system for the abatement of noise and pollutant emissions in the future. Other applications that require an in-situ and time-resolved measurement of fuel vapor concentrations should also find this device to be of use.
The Severe Thunderstorm Observations, Research, and Monitoring network (STORMnet) became operational in 2001 as a test bed to infuse new science and technologies into the severe and hazardous weather forecasting and warning process. STORMnet is collaboration among NASA scientists, National Weather Service (NWS) forecasters, emergency managers and other partners. STORMnet integrates total lightning observations from a ten-station 3-D VHF regional lightning mapping array, the National Lightning Detection Network (NLDN), real-time regional NEXRAD Doppler radar, satellite visible and infrared imagers, and a mobile atmospheric profiling system to characterize storms and their evolution. The storm characteristics and life-cycle trending are accomplished in real-time through the second generation Lightning Imaging Sensor Demonstration and Display (LISDAD II), a distributed processing system with a JAVA-based display application that allows anyone, anywhere to track individual storm histories within the Tennessee Valley region of north Alabama and Tennessee, a region of the southeastern U.S. well known for abundant severe weather.
The magnitude of shortwave and longwave dative fluxes are critical to surface energy balance variations over the Greenland ice sheet, affecting many aspects of its climate, including melt rates, the nature of low-level temperature inversions, the katabatic wind regime and buoyant stability of the atmosphere. Nevertheless, reliable measurements of the radiative fluxes over the ice sheet are few in number, and have been of limited duration and areal distribution (e.g. Ambach, 1960; 1963, Konzelmann et al., 1994, Harding et al., 1995, Van den Broeke, 1996). Hourly GC-Net radiation flux measurements spanning 1995-2001 period have been used to produce a monthly dataset of surface radiation balance components. The measurements are distributed widely across Greenland and incorporate multiple sensors
This work presents a computationally-ecient approach for damage determination that quanti es uncertainty in the provided diagnosis. Given strain sensor data that are polluted with measurement errors, Bayesian inference is used to estimate the location, size, and orientation of damage. This approach uses Bayes' Theorem to combine any prior knowledge an analyst may have about the nature of the damage with information provided implicitly by the strain sensor data to form a posterior probability distribution over possible damage states. The unknown damage parameters are then estimated based on samples drawn numerically from this distribution using a Markov Chain Monte Carlo (MCMC) sampling algorithm. Several modi cations are made to the traditional Bayesian inference approach to provide signi cant computational speedup. First, an ecient surrogate model is constructed using sparse grid interpolation to replace a costly nite element model that must otherwise be evaluated for each sample drawn with MCMC. Next, the standard Bayesian posterior distribution is modi ed using a weighted likelihood formulation, which is shown to improve the convergence of the sampling process. Finally, a robust MCMC algorithm, Delayed Rejection Adaptive Metropolis (DRAM), is adopted to sample the probability distribution more eciently. Numerical examples demonstrate that the proposed framework e ectively provides damage estimates with uncertainty quanti cation and can yield orders of magnitude speedup over standard Bayesian approaches.
The Advanced ACTPol (AdvACT) upgrade to the Atacama Cosmology Telescope features large arrays of multichroic pixels consisting of two orthogonal-polarization pairs of superconducting bolometers at two observing frequency bands. We present measurements of the detector properties and noise data in a subset of a fielded multichroic array of AlMn transition-edge sensor (TES) detectors. In this array, the distribution of critical temperature T(sub c) across detectors appears uniform at the percent level. The measured noise-equivalent power (NEP) distributions over approximately 1200 detectors are consistent with expectations. We find median NEPs of 4.0×10(exp −17) W/ √ Hz for low-band detectors and 6.2×10(exp −17) W/ √ Hz for high-band detectors under covered-window telescope test conditions with optical loading comparable to observing with precipitable water vapor approximately 0.5 mm. Lastly, we show the estimated detector optical efficiency, and demonstrate the ability to perform optical characterization over hundreds of detectors at once using a cryogenic blackbody source.
Numerous large river basins of the world have few and irregular observations of the components of the terrestrial hydrological cycle with the exception of stream gauges at a few locations and at the outlet along with sparsely distributed rain gauges. Using observations from satellite sensors and output from global land surface models, it is possible to study these under-observed river basins. With populations greater than a billion people, some of these rivers (e.g., the Ganga-Brahmaputra, the Yangtze, the Nile and the Mekong) are the economic engines of the countries they transect, yet thorough assessment of their flow dynamics and variability in regard to water resource management is still lacking. In this paper, we use soil moisture (0-2m) and surface runoff from the NASA Global Land Data Assimilation System (GLDAS), evapotranspiration, and Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and rainfall from the Tropical Rainfall Measuring Mission (TRMM) and total water storage anomaly from the Gravity Recovery and Climate Experiment (GRACE) to examine variability of individual water balance components. To this end, understanding the inter-annual and intra-seasonal variability and the spatial variability of the water balance components in the major river basins of the world will help to plan for improved management of water resources for the future.
A capability for mapping meter-level resolution soil moisture with frequent temporal sampling over large regions is essential for quantifying local-scale environmental heterogeneity and eco-hydrologic behavior. However, available surface soil moisture (SSM) products generally involve much coarser grain sizes ranging from 30 m to several 10s of kilometers. Hence a new method is proposed to estimate 3-m resolution SSM using a combination of multi-sensor fusion, machine- learning (ML) and Cumulative Distribution Function (CDF) matching approaches. This method established favorable SSM correspondence between 3-m pixels and overlying 9-km grid cells from overlapping Planet SuperDove (PSD) observations and NASA Soil Moisture Active-Passive (SMAP) mission products. The resulting 3-m SSM predictions showed improved accuracy by reducing ab- solute bias and RMSE by ~0.01 cm3/cm3 over the original SMAP data in relation to in-situ soil moisture measurements for the Australian Yanco region, while preserving the high sampling frequency (1-3 day global revisit) and sensitivity to surface wetness (R 0.865) from SMAP. Heterogeneous soil moisture distributions varying with vegetation biomass gradients and irrigation regimes were generally captured within a selected study area. Further algorithm refinement and implementation for regional applications will allow for improvement in water resources management, precision agriculture, and disaster forecasts and responses.
Radar data can usually detect hail; however, it is difficult for present day radar to accurately discriminate between hail and rain. Local ground-based hail sensors are much better at detecting hail against a rain background, and when incorporated with radar data, provide a much better local picture of a severe rain or hail event. The previous disdrometer interpolation/ extrapolation algorithm described a method to interpolate horizontally between multiple ground sensors (a minimum of three) and extrapolate vertically. This work is a modification to that approach that generates a purely extrapolated 3D spatial distribution when using a single sensor.
An array of effectors and sensors has been designed, tested and implemented on a Blended Wing Body Uninhabited Aerial Vehicle (UAV). The UAV is modified to serve as a flying, controls research, testbed. This effector/sensor array provides for the dynamic vehicle testing of controller designs and the study of decentralized control techniques. Each wing of the UAV is equipped with 12 distributed effectors that comprise a segmented array of independently actuated, contoured control surfaces. A single pressure sensor is installed near the base of each effector to provide a measure of deflections of the effectors. The UAV wings were tested in the North Carolina State University Subsonic Wind Tunnel and the pressure distribution that result from the deflections of the effectors are characterized. The results of the experiments are used to develop a simple, but accurate, prediction method, such that for any arrangement of the effector array the corresponding pressure distribution can be determined. Numerical analysis using the panel code CMARC verifies this prediction method.
Sensors and sensor systems are central to providing improved awareness of an individual's health state and surroundings. This presentation describes efforts to develop Smart Sensor Systems for a range of health and safety applications moving towards distributed intelligence. This includes efforts to develop a human health breath monitoring system using this microsensor Smart Sensor System approach. The same core technology can be used for a range of applications related to improved human health and environmental monitoring. Other examples of human health relevant technology development will also be given.
A heterogeneous set of system components monitored by a varied suite of sensors and a particle-filtering (PF) framework, with the power and the flexibility to adapt to the different diagnostic and prognostic needs, has been developed. Both the diagnostic and prognostic tasks are formulated as a particle-filtering problem in order to explicitly represent and manage uncertainties in state estimation and remaining life estimation. Current state-of-the-art prognostic health management (PHM) systems are mostly centralized in nature, where all the processing is reliant on a single processor. This can lead to a loss in functionality in case of a crash of the central processor or monitor. Furthermore, with increases in the volume of sensor data as well as the complexity of algorithms, traditional centralized systems become for a number of reasons somewhat ungainly for successful deployment, and efficient distributed architectures can be more beneficial. The distributed health management architecture is comprised of a network of smart sensor devices. These devices monitor the health of various subsystems or modules. They perform diagnostics operations and trigger prognostics operations based on user-defined thresholds and rules. The sensor devices, called computing elements (CEs), consist of a sensor, or set of sensors, and a communication device (i.e., a wireless transceiver beside an embedded processing element). The CE runs in either a diagnostic or prognostic operating mode. The diagnostic mode is the default mode where a CE monitors a given subsystem or component through a low-weight diagnostic algorithm. If a CE detects a critical condition during monitoring, it raises a flag. Depending on availability of resources, a networked local cluster of CEs is formed that then carries out prognostics and fault mitigation by efficient distribution of the tasks. It should be noted that the CEs are expected not to suspend their previous tasks in the prognostic mode. When the prognostics task is over, and after appropriate actions have been taken, all CEs return to their original default configuration. Wireless technology-based implementation would ensure more flexibility in terms of sensor placement. It would also allow more sensors to be deployed because the overhead related to weights of wired systems is not present. Distributed architectures are furthermore generally robust with regard to recovery from node failures.