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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Five techniques for sensing forces with a manipulator are compared analytically and experimentally. The techniques compared are: a six-axis wrist force/torque sensor, joint torque sensors, link strain gauges, motor current sensors, and flexibility modeling. The accuracy and repeatability fo each technique is quantified and compared.
Abstract We autonomously directed a small quadcopter package delivery Uncrewed Aerial Vehicle (UAV) or “drone” to take off, fly a specified route, and land for a total of 209 flights while varying a set of operational parameters. The vehicle was equipped with onboard sensors, including GPS, IMU, voltage and current sensors, and an ultrasonic anemometer, to collect high-resolution data on the inertial states, wind speed, and power consumption. Operational parameters, such as commanded ground speed, payload, and cruise altitude, were varied for each flight. This large data set has a total flight time of 10 hours and 45 minutes and was collected from April to October of 2019 covering a total distance of approximately 65 kilometers. The data collected were validated by comparing flights with similar operational parameters. We believe these data will be of great interest to the research and industrial communities, who can use the data to improve UAV designs, safety, and energy efficiency, as well as advance the physical understanding of in-flight operations for package delivery drones.
Algorithm developed for use in automatic detection of dc series arcs in power cables, cable terminations, and junction boxes. (Sometimes detects arcs inside electrical loads.) System in which algorithm utilized is one receiving power from microprocessor-based remote power controller that includes load-current sensor. Algorithm analyzes digitized load-current readings for fluctuations characteristic of dc series arcs.
The primary focus of the SIMPLE project was to develop, commission, test and introduce intelligent voltage and current sensor platform with enhanced characteristics (accuracy, bandwidth and harmonic range) and high measurement granularity for medium voltage distribution system monitoring, protection and control. The enhancements incorporated existing (commercially available) sensors and made them suitable for applications such as voltage regulation, frequency support, fault detection and location, distribution system state estimation, power quality measurements and electrical distribution network topology processing. The development of the intelligent sensor platform was driven by the need for high-fidelity monitoring and control of medium voltage distribution feeder to enable higher penetration of renewable resources and microgrid operation.
Fast, precise and robust sensing of currents and motor shaft angle is essential for the excellent performance of electric and hybrid vehicles (EV/HEV). Multiplexing techniques are commonly applied in data acquisition systems (DAQs) to digitize the signals sensed in EV/HEV drives. Frequency-division multiplexing (FDM) applied to get the signals from current sensors and resolver angular position sensor has advantages over conventional multiplexing approaches. However, problems such as aging and mechanical imperfections distort the outputs of those sensors, producing measurement errors of the angular position and currents. Conventional techniques designed to compensate for those errors cannot be applied in signals multiplexed in frequency. This paper proposes online techniques to detect and compensate for the distortions in the resolver sensor and current sensors. The demultiplexing process was adjusted to allow distortion detection and compensation. An auto-associative neural network (ANN) compensates for the current measurement error, while an energy-based technique is applied to compensate for the distortions in the resolver outputs. The obtained results show that the distortions were compensated, allowing a more accurate estimation of stator currents and angular position when FDM is applied in EV/HEV DAQs.
A system characterizes and identifies one of a plurality of different operating modes of a number of electric loads. The system includes a processor; a voltage sensor providing a voltage signal for one of the electric loads to the processor; a current sensor providing a current signal for the one electric load to the processor; and a routine executed by the processor and structured to characterize the different operating modes using steady state and voltage-current trajectory features determined from the voltage and current signals, and to identify a particular one of the different operating modes based on a plurality of operating mode membership functions of the steady state and voltage-current trajectory features.
This manuscript details the development of a reduced volume attitude sensor developed at NASA. This new sensor uses MEMs based accelerometers to reduce the volume when compared to current sensors used in wind-tunnel models. Our team has designed, fabricated, and evaluated the Mini/MEMs Tri-Axis Sensor System (MTASS). The current MTASS design accommodates three tactical-grade accelerometers into the volume of a single axis accelerometer that is currently used. This arrangement allows for the estimation of model pitch and roll angles. Static testing of the MTASS demonstrated angle accuracies within ±0.01° for both pitch and roll.
This manuscript details the development of a reduced volume attitude sensor developed at NASA. This new sensor uses MEMs based accelerometers to reduce the volume when compared to current sensors used in wind-tunnel models. Our team has designed, fabricated, and evaluated the Mini/MEMs Tri-Axis Sensor System (MTASS). The current MTASS design accommodates three tactical-grade accelerometers into the volume of a single axis accelerometer that is currently used. This arrangement allows for the estimation of model pitch and roll angles. Static testing of the MTASS demonstrated angle accuracies within ±0.01° for both pitch and roll.
Fast overcurrent tripping circuit designed for incorporation into power metal oxide/semiconductor field-effect transistor (MOSFET) switching circuit. Serves as fast electronic circuit breaker by sensing voltage across MOSFET's during conduction and switching MOSFET's off within 1 microsecond after voltage exceeds reference value corresponding to tripping current. Acts more quickly than Hall-effect current sensor and, in comparison with shunt current-measuring circuits, smaller and consumes less power. Also ignores initial transient overcurrents during first 5 microseconds of switching cycle.
In the design of new aerospace materials, developmental testing is conducted to characterize the behavior of the material under severe environmental conditions of high stress, temperature, and vibration. But to test these materials under extreme conditions requires sensors that can perform in harsh environments. Current sensors can only monitor high temperature test samples using long throw instrumentation, but this is inherently less accurate than a surface mounted sensor, and provides no means for fabrication process monitoring. A promising alternative is the use of sapphire optical fiber sensors. Sapphire is an incredibly rugged material, being extremely hard (9 mhos), chemically inert, and having a melting temperature (over 2000 C). Additionally, there is a extensive background of optical fiber sensors upon which to draw for sapphire sensor configurations.
The chapter starts with a brief overview of the state-of-the-art of the sensing technologies. Principles of detection and the stage of integration in conjunction with the need are discussed. Based on the projected sensor requirements to accomplish propulsion systems with distributed active control, the existent gaps and selected types of sensor technologies are addressed, which are foreseen to deliver the required sensitivity, the resolution, the range, and the bandwidth in classes of sensors and which will allow operation under harsh environment conditions. By changing the packaging and/or design of the current sensors, operation environments of 750°C may be possible, which would meet requirements for sensors located towards the engine intake, compressor and in some cases low-pressure turbine. However, the majority of sensors for locations close to the engine combustion chamber or afterburner (with operation temperatures up to 1700°C) do not exist. There is also a need for smart sensors, which would enable future distributed control architecture. In addition a number of sensors, which are of interest for more intelligent gas turbine engines, are being explored or do not yet exist, for example turbine emission species sensors, burning patter factor sensors, fuel property sensors, and exhaust gas composition sensors. Meanwhile, rapid emerging technologies were made available during the three years duration of the Task Group. These include tip clearance measurement technologies such as eddy current, and microwave methods. To meet future sensor requirements, new fabrication and material technologies (MEMS, other), advanced sensing principles (spectroscopy, other), and their potential applications to new sensor types need to be explored. The status of current R&D for new sensors and future expectations are summarized in a comprehensive table. Sensors and associated electronics that have to operate in high temperature are foreseen to be largely available by 2015 (SiC) and 2020 (SiCN). Both of these technologies come with the potential of embedding such high temperature sensors within the structure. The information presented in the chapter represents the state-of-the-art of the sensing technology of the years 2005-2007 to the best knowledge of the authors. The authors are also fully aware of the fact that there may be many ongoing research programs as well as individual research that are not publicly supported and public information is not available. Therefore, the report might miss that section of knowledge. Also, progress in research and development is made every day such that often, the recent information may be fast outdated. The reader should bear in mind these facts.
The development of the LEO (EOS) missions has led the way to new technologies and new science discoveries. However, LEO measurements alone cannot cost effectively produce high time resolution measurements needed to move the science to the next level. Both GEO and the Lagrange points, L1 and L2, provide vantage points that will allow higher time resolution measurements. GEO is currently being exploited by weather satellites, but the sensors currently operating at GEO do not provide the spatial or spectral resolution needed for atmospheric trace gas, ocean or land surface measurements. It is also may be possible to place active sensors in geostationary orbit. It seems clear, that the next era in earth observation and discovery will be opened by sensor systems operating beyond near earth orbit.
Thermocouples are generally used to provide real-time temperature indications in instrumented tests performed at material and test reactors. Passive temperature monitors, such as Silicon Carbide (SiC) and melt wires, may be included in such tests as an independent technique of detecting peak temperatures experienced during irradiation. In less expensive static (drop-in) capsule tests, which have no leads attached for real-time data transmission, melt wires, and SiC temperature monitors (TMs) are essentially the only possibility for peak temperature indication. A melt wire involves placing materials (wires) of a known composition and melting temperature in a test. An inventory is maintained at Material Science Laboratory (MSL) for melt wires ranging in temperatures from 30°C to 1500°C. Unfortunately, melt wires are limited in that it can only detect whether a single temperature is or is not exceeded (melt wire melted or not). SiC TMs, which can also be used to detect peak irradiation temperatures, are advantageous because a single monitor can allow to determine the peak temperature reached within a relatively broad range (100 – 1200°C) resulting in accuracies within ±20°C. Irradiation temperature is determined by measuring a property change after isochronal annealing or during a continuously monitored annealing process using specialized equipment at MSL. Recent research has produced a passive monitor known as sublime temperature monitor. This passive sensor has the capability of recording temperature gradients and pinpointing exactly where a temperature is located along that gradient. Long measurement lengths are achieved with very high accuracy in the location of desired temperature measurements (±2 mm over a 1 m span); however, this sensor has not been deployed in a nuclear reactor. This article will focus only on passive temperature sensors currently being researched and implemented under the Advanced Sensors and Instrumentation (ASI) program at Idaho National Laboratory (INL).
Design and performance of a digital signal processing platform for enhancing off-the-shelf voltage and current sensors are presented. The platform was developed for enabling advanced distribution grid applications with existing low-cost sensors. This digital sensor system leverages a variety of off-the-shelf voltage and current sensing technologies, analog-to-digital conversion, and sensor correction algorithms to yield more accurate and reliable digital measurements that support a multitude of applications and use cases. Sample performance and accuracy results are provided, including measurements at power frequency and higher harmonics. The system extends the native performance of the medium voltage sensors used in the system, for example, broadening measurement bandwidth beyond a few kHz, appropriate for measuring switching surges and harmonics on medium voltage distribution systems. The digital sensor system is installed and characterized in the field with a high-bandwidth optical calibration system to determine site-dependent calibration and performance issues.
Continuous monitoring of areas nearby the electric grid is critical for preventing and early detection of devastating wildfires. Existing wildfire monitoring systems are intermittent and oblivious to local ambient risk factors, resulting in poor wildfire awareness. Ambient sensor suites deployed near the gridlines can increase the monitoring granularity and detection accuracy. However, these sensors must address two challenging and competing objectives at the same time. First, they must remain powered for years without manual maintenance due to their remote locations. Second, they must provide and transmit reliable information if and when a wildfire starts. The first objective requires aggressive energy savings and ambient energy harvesting, while the second requires continuous operation of a range of sensors. To the best of our knowledge, this paper presents the first self-sustained cyber-physical system that dynamically co-optimizes the wildfire detection accuracy and active time of sensors. The proposed approach employs reinforcement learning to train a policy that controls the sensor operations as a function of the environment (i.e., current sensor readings), harvested energy, and battery level. Here, the proposed cyber-physical system is evaluated extensively using real-life temperature, wind, and solar energy harvesting datasets and an open-source wildfire simulator. In long-term (5 years) evaluations, the proposed framework achieves 89% uptime, which is 46% higher than a carefully tuned heuristic approach. At the same time, it averages a 2-minute initial response time, which is at least 2.5× faster than the same heuristic approach. Furthermore, the policy network consumes 0.6 mJ per day on the TI CC2652R microcontroller using TensorFlow Lite for Micro, which is negligible compared to the daily sensor suite energy consumption.
The potential of space systems and technology for detecting and monitoring ocean oil spills and waste pollution was assessed as well as the impact of this application on communication and data handling systems. Agencies charged with responsibilities in this area were identified and their measurement requirements were ascertained in order to determine the spatial resolution needed to characterize operational and accidental discharges. Microwave and optical sensors and sensing techniques were evaluated as candidate system elements. Capabilities are described for the following: synthetic aperture radar, microwave scatterometer, passive microwave radiometer, microwave altimeter, electro-optical sensors currently used in airborne detection, existing space-based optical sensors, the thematic mapper, and the pointable optical linear array.
An experimental pushbroom scan sensor, the Multispectral Resource Sampler (MRS), being developed by NASA for a future earth orbiting flight is presented. This sensor will provide new earth survey capabilities beyond those of current sensor systems, with a ground resolution of 15 m over a swath width of 15 km in four bands. The four arrays are aligned on a common focal surface requiring no beamsplitters, thus causing a spatial separation on the ground which requires computer processing to register the bands. Along track pointing permits stereo coverage at variable base/height ratios and atmospheric correction experiments, while across track pointing will provide repeat coverage, from a Landsat-type orbit, of every 1 to 3 days. The MRS can be used for experiments in crop discrimination and status, rock discrimination, land use classification, and forestry.
Robot systems in critical applications, such as those in space and nuclear environments, must be able to operate during component failure to complete important tasks. One failure mode that has received little attention is the failure of joint position sensors. Current fault tolerant designs require the addition of directly redundant position sensors which can affect joint design. A new method is proposed that utilizes analytical redundancy to allow for continued operation during joint position sensor failure. Joint torque sensors are used with a virtual passive torque controller to make the robot joint stable without position feedback and improve position tracking performance in the presence of unknown link dynamics and end-effector loading. Two Cartesian accelerometer based methods are proposed to determine the position of the joint. The joint specific position determination method utilizes two triaxial accelerometers attached to the link driven by the joint with the failed position sensor. The joint specific method is not computationally complex and the position error is bounded. The system wide position determination method utilizes accelerometers distributed on different robot links and the end-effector to determine the position of sets of multiple joints. The system wide method requires fewer accelerometers than the joint specific method to make all joint position sensors fault tolerant but is more computationally complex and has lower convergence properties. Experiments were conducted on a laboratory manipulator. Both position determination methods were shown to track the actual position satisfactorily. A controller using the position determination methods and the virtual passive torque controller was able to servo the joints to a desired position during position sensor failure.