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The NASA test airplane, B-52B-008, was a carrier for drop tests of the shuttle booster recovery parachute system. The purpose of the test support by Boeing was to monitor the vertical loads on the pylon hooks. The hooks hold the Drop Test Vehicle to the B-52 pylon during drop test missions. The loads were monitored to assure the successful completion of the flight and the safety of the crew.
Most helicopter data trends cannot be extrapolated to tiltrotors because blade geometry and aerodynamic behavior, as well as rotor and fuselage interactions, are significantly different for tiltrotors. A tiltrotor model has been developed to investigate the aeromechanics of tiltrotors, to develop a comprehensive database for validating tiltrotor analyses, and to provide a research platform for supporting future tiltrotor designs. The Full-Span Tiltrotor Aeroacoustic Model (FS TRAM) is a dual-rotor, powered aircraft model with extensive instrumentation for measurement of structural and aerodynamic loads. This paper will present the Full-Span TRAM test capabilities and the first set of data obtained during a 40- by 80-Foot Wind Tunnel test conducted in late 2000 at NASA Ames Research Center. The Full-Span TRAM is a quarter-scale representation of the V-22 Osprey aircraft, and a heavily instrumented NASA and U.S. Army wind tunnel test stand. Rotor structural loads are monitored and recorded for safety-of-flight and for information on blade loads and dynamics. Left and right rotor balance and fuselage balance loads are monitored for safety-of-flight and for measurement of vehicle and rotor aerodynamic performance. Static pressure taps on the left wing are used to determine rotor/wing interactional effects and rotor blade dynamic pressures measure blade airloads. All of these measurement capabilities make the FS TRAM test stand a unique and valuable asset for validation of computational codes and to aid in future tiltrotor designs. The Full-Span TRAM was tested in the NASA Ames Research Center 40- by 80-Foot Wind Tunnel from October through December 2000. Rotor and vehicle performance measurements were acquired in addition to wing pressures, rotor acoustics, and Laser Light Sheet (LLS) flow visualization data. Hover, forward flight, and airframe (rotors off) aerodynamic runs were performed. Helicopter-mode data were acquired during angle of attack and thrust sweeps for a variety of tunnel speeds. Wake geometry images were acquired using LLS photographs and suggest dual tip vortex formation at low thrust conditions. The full paper will include comparisons to isolated-rotor TRAM data acquired at the Duits-Nederlandse Windtunnel (DNW) in 1998. The FS TRAM has been established as a valuable national asset for tiltrotor research. Data reduction and analysis of the 40- by 80-Foot Wind Tunnel test results are underway. Follow-on testing of the FS TRAM is currently being planned for the NASA Ames 80- by 120-Foot Wind Tunnel in late 2001.
Load-transfer unit connects airborne computer to standby power supply in case primary supply fails. Concept adaptable to systems in which power interruptions cannot be tolerated; for example, computers with volatile memories, safety equipment, and precise timers. Load-transfer unit monitors voltages and load current. Microprocessor controls transistor switches that connect load to whichever power supply has highest priority and correct voltage.
An experimental wireless, in-vessel neutron monitor was developed to measure the reactivity of an advanced breeder reactor as the core is loaded for the first time to preclude an accidental critically incident. The environment is liquid sodium at a temperature of approx. 220 C, with negligible gamma or neutron radiation. With ultrasonic transmission of neutron data, no fundamental limitation was observed after tests at 230 C for 2000 h. The neutron sensitivity was approx. 1 count/s-nv, and the potential data transmission rate was approx. 10,000 counts/s.
Criteria and recommended practices are presented for the prediction and verification of transportation and handling loads for the space vehicle structure and for monitoring these loads during transportation and handling of the vehicle or major vehicle segments. Elements of the transportation and handling systems, and the forcing functions and associated loads are described. The forcing functions for common carriers and typical handling devices are assessed, and emphasis is given to the assessment of loads at the points where the space vehicle is supported during transportation and handling. Factors which must be considered when predicting the loads include the transportation and handling medium; type of handling fixture; transport vehicle speed; types of terrain; weather (changes in pressure of temperature, wind, etc.); and dynamics of the transportation modes or handling devices (acceleration, deceleration, and rotations of the transporter or handling device).
The topic of stress tunable laser crystals is addressed in this study with the purpose of determining the piezo-optic coefficients of a new laser material. This data was collected using a quadruple pass birefringence technique because of its high degree of sensitivity relative to the other methods examined including fringe shift analysis using a Mach-Zender interferometer. A green He-Ne laser was passed through a light chopper and Glan-Thompson prism before entering a crystal of Erbium doped Yttrium Aluminum Garnet (Er:YAG) (used in order to validate the experimental technique). The Er:YAG crystal is mounted in a press mechanism and the laser is quadruple passed through test specimen before being returned through the prism and the orthogonally polarized portion of the beam measured with a optical sensor. At a later stage, the Er:YAG crystal was replaced with a new crystal in order to determine the piezo-optic coefficients of this uncharacterized material. The applied load was monitored with the use of a 50 lb. load cell placed in line with the press. Light transmission readings were taken using a lock-in amplifier while load cell measurements were taken with a voltmeter from a 5 volt, 0.5 amp power supply. Despite the fact that an effective crystal press damping system was developed, size limitations precluded the use of the complete system. For this reason, data points were taken only once per full turn so as to minimize the effect of non uniform load application on the collected data. Good correlation was found in the transmission data between the experimentally determined Er:YAG and the previously known peizo-optic constants of non-doped crystal with which it was compared. The variation which was found between the two could be accounted for by the aforementioned presence of Erbium in the experimental sample (for which exact empirical data was not known). The same test procedure was then carried out on a Yttrium Gallium Aluminum garnet (YGAG) for the purpose of establishing values of its unknown piezo-optic constant tensor using experimentally collected transmission data. Significant variation between the piezo-optic constants of YAG and YGAG crystals was found however, the excellent data correlation of separate experimental runs carried out on the YGAG sample demonstrates the validity of these results. The data collected during the stressing of the YGAG was of high quality, however the amount of data collected was somewhat limited by a fracture of YGAG specimen which undoubted altered the crystalline lattice structure and hence precluded any further testing.
A method of indirectly monitoring component loads through common flight variables is proposed which requires an accurate model of the underlying nonlinear relationships. An artificial neural network (ANN) model learns relationships through exposure to a database of flight variable records and corresponding load histories from an instrumented military helicopter undergoing standard maneuvers. The ANN model, utilizing eight standard flight variables as inputs, is trained to predict normalized time-varying mean and oscillatory loads on two critical components over a range of seven maneuvers. Both interpolative and extrapolative capabilities are demonstrated with agreement between predicted and measured loads on the order of 90 percent to 95 percent. This work justifies pursuing the ANN method of predicting loads from flight variables.
The tensile fracture behavior of 15 center-notched hybrid laminates was studied. Three basic laminate groups were tested: (1) a baseline group with graphite/epoxy plies, (2) a group with the same stacking sequence but where the zero-deg plies were one or two plies of S-glass or Kevlar, and (3) a group with graphite plies but where the zero-deg plies were sandwiched between layers of perforated Mylar. Specimens were loaded linearly with time; load, far field strain, and crack opening displacement (COD) were monitored. The loading was stopped periodically and the notched region was radiographed to reveal the extent and type of damage (failure progression). Results of the tests showed that the hybrid laminates had higher fracture toughnesses than comparable all-graphite laminates. The higher fracture toughness was due primarily to the larger damage region at the ends of the slit; delamination and splitting lowered the stress concentration in the primary load-carrying plies. A linear elastic fracture analysis, which ignored delamination and splitting, underestimated the fracture toughness. For almost all of the laminates, the tests showed that the fracture toughness increased with crack length. The size of the damage region at the ends of the slit and COD measurements also increased with crack length.
1. Kennedy Space Center (KSC) is developing a mobile launching system with autonomous propellant loading capabilities for liquid-fueled rockets. An autonomous system will be responsible for monitoring and controlling the storage, loading and transferring of cryogenic propellants. The Physics Simulation Software will reproduce the sensor data seen during the delivery of cryogenic fluids including valve positions, pressures, temperatures and flow rates. The simulator will provide insight into the functionality of the propellant systems and demonstrate the effects of potential faults. This will provide verification of the communications protocols and the autonomous system control. 2. The High Pressure Gas Facility (HPGF) stores and distributes hydrogen, nitrogen, helium and high pressure air. The hydrogen and nitrogen are stored in cryogenic liquid state. The cryogenic fluids pose several hazards to operators and the storage and transfer equipment. Constant monitoring of pressures, temperatures and flow rates are required in order to maintain the safety of personnel and equipment during the handling and storage of these commodities. The Gas House Autonomous System Monitoring software will be responsible for constantly observing and recording sensor data, identifying and predicting faults and relaying hazard and operational information to the operators.
A distributed data acquisition and control system has been developed for the NASA Flight Loads Research Facility. The DACS II system is composed of seven computer systems and four array processors configured as a main computer system, three satellite computer systems, and 13 analog input/output systems interconnected through three independent data networks. Up to three independent heating and loading tests can be run concurrently on different test articles or the entire system can be used on a single large test such as a full scale hypersonic aircraft. Thermal tests can include up to 512 independent adaptive closed loop control channels. The control system can apply up to 20 MW of heating to a test specimen while simultaneously applying independent mechanical loads. Each thermal control loop is capable of heating a structure at rates of up to 150 F per second over a temperature range of -300 to +2500 F. Up to 64 independent mechanical load profiles can be commanded along with thermal control. Up to 1280 analog inputs monitor temperature, load, displacement and strain on the test specimens with real time data displayed on up to 15 terminals as color plots and tabular data displays. System setup and operation is accomplished with interactive menu-driver displays with extensive facilities to assist the users in all phases of system operation.
A circuit topology has been designed to guarantee the output of intrinsically safe power for the operation of electrical devices in a hazardous environment. This design uses a MOSFET (metal oxide semiconductor field-effect transistor) as a switch to connect and disconnect power to a load. A test current is provided through a separate path to the load for monitoring by a comparator against a preset threshold level. The circuit is configured so that the test current will detect a fault in the load and open the switch before the main current can respond. The main current passes through the switch and then an inductor. When a fault occurs in the load, the current through the inductor cannot change immediately, but the voltage drops immediately to safe levels. The comparator detects this drop and opens the switch before the current in the inductor has a chance to respond. This circuit protects both the current and voltage from exceeding safe levels. Typically, this type of protection is accomplished by a fuse or a circuit breaker, but in order for a fuse or a circuit breaker to blow or trip, the current must exceed the safe levels momentarily, which may be just enough time to ignite anything in a hazardous environment. To prevent this from happening, a fuse is typically current-limited by the addition of the resistor to keep the current within safe levels while the fuse reacts. The use of a resistor is acceptable for non-battery applications where the wasted energy and voltage drop across the resistor can be tolerated. The use of the switch and inductor minimizes the wasted energy. For example, a circuit runs from a 3.6-V battery that must be current-limited to 200 mA. If the circuit normally draws 10 mA, then an 18-ohm resistor would drop 180 mV during normal operation, while a typical switch (0.02 ohm) and inductor (0.97 ohm) would only drop 9.9 mV. From a power standpoint, the current-limiting resistor protection circuit wastes about 18 times more power than the switch and the inductor configuration. In the fault condition, both the resistor and the inductor react immediately. The resistor reacts by allowing more current to flow and dropping the voltage. Initially, the inductor reacts by dropping the voltage, and then by not allowing the current to change. When the comparator detects the drop in voltage, it opens the switch, thus preventing any further current flow. The inductor alone is not sufficient protection, because after the voltage drop has settled, the inductor would then allow the current to change, in this example, the current would be 3.7 A. In the fault condition, the resistor is flowing 200 mA until the fuse blows (anywhere from 1 ms to 100 s), while the switch and inductor combination is flowing about 2 A test current while monitoring for the fault to be corrected. Finally, as an additional safety feature, the circuit can be configured to hold the switch opened until both the load and source are disconnected.
The use of structural health monitoring systems on spacecraft structures can play a crucial role in ensuring the safety, reliability, and longevity of the structure by gathering and analyzing onboard sensor data. Of specific importance is monitoring for excessive loading at critical interfaces as any off-nominal structural excitations experienced by spacecraft structures can cause early unpredicted high structural life consumption or damage. The availability and cost of flight-certified sensors along with the size of spacecraft structures and allowable payload mass drives the need for a method to estimate loads using sparsely-located sensors. Numerous approaches such as physics-based, statistical learning, and physics-enhanced statistical learning algorithms have gained popularity among structural prognostics applications. However, developing noise-robust prediction models to assess loads and structural life predictions from a sparse multi-sensor data acquisition system can be a challenging task. This paper discusses the evaluation of physics-based versus machine-learning algorithms for predicting loads and structural life at mission critical locations on the spacecraft structure using a finite element loads analysis with the application of simulated noise and noise reduction techniques. To estimate the loads from accelerations, the physics-based algorithm leverages a loads transformation matrix from a Craig-Bampton reduced finite element model. A System Equivalent Reduction Expansion Process (SEREP) and a pseudo-inverse approach are considered to expand from the onboard sensor degrees of freedom to the Craig-Bampton model degrees of freedom. The machine learning algorithm provides a data driven solution/mapping of the sensor accelerations to the loads at the mission critical locations using a high dimensionality analysis. Although these strategies produce comparable loads prediction without noise, the limitations of these strategies with incorporating simulated noise and noise reduction techniques with low signal to noise ratio signals are evaluated. The study demonstrates the immense potential of statistical learning algorithms for sparse structural prognostic models and enhancing signal denoising techniques. These findings also highlight the need for noise-resilient prognostic models and low-noise data acquisition systems onboard spacecraft structures.
There is a mounting need for low-cost monitoring with online sensing technologies to maintain grid reliability and uptime. Here we introduce an innovative low-cost, embedded optical sensing technology initially focused on transformers that was developed and demonstrated at a major electric utility, Con Edison. A version of it that can be retrofitted onto existing transformers in the field was also developed. Two new 500 kVA distribution network transformers were built with embedded fiber-optic (FO) sensors and qualified per industry standards. Vibration, temperature, and corrosion were key parameters monitored. The first transformer with embedded FO sensors was installed at a Con Edison facility and monitored at our team’s office. The second one was installed in an urban street-side underground location with online data processing/feature extraction algorithms and monitored through a wireless router. Additionally, an older transformer was also retrofitted. Data analysis was done on these transformers showing promising correlations with their corresponding loading cycles. Furthermore, key events such as the transformer primary-side energizing, and other events were detected. In general, the technology was demonstrated over 6 months across the 3 transformers instrumented with promising results. Thus, it has the potential to enable predictive maintenance for transformers and other grid assets.
A series of tests was conducted to support development of an analytical model for predicting the failure strains of stitched warp-knit carbon/epoxy composite materials with through-thicknesss damage in the form of a crack-like notch. Measurements of strain near notch tips, crack opening displacement (COD), and applied load were monitored in all tests. The out-of-plane displacement at the center of the notch was also measured when the specimen was subjected to bending. Three types of loading were applied: pure bending, pure tension, and combined bending and tension.
A recent study of plug load devices in Army buildings has identified opportunities to save over 83 million kWh of electricity valued at over $5 million per year. These savings may be conservative and were identified by inventorying and monitoring plug load and miscellaneous electric load (MEL) equipment within representative Army buildings. The objective was to better understand the energy consumed by these devices and identify approaches for reducing it while enhancing resilience. This summary highlights the findings, lessons learned, and recommendations identified by the study.
Ibis Networks is a full-stack cleantech company that provides plug-level energy monitoring and control to solve energy and asset management problems for the enterprise. During DATES – DATES, an NREL research team worked with the Ibis team to validate their product, the InteliSocket. The Ibis InteliSocket is a pass-through plug-load energy monitor and controller that is designed to reduce energy wasted by common 120 V plug-in devices in commercial office buildings, such as computer peripherals, conference room AV equipment, and break-room appliances. The system can shut off supply power to these end uses via remote control, manual switches, pre-set schedules, or automated control algorithms. The scope of this IN2 project was the development and refinement of “smart” learning behavior algorithms (LBAs), which could help installation processes and dramatically expand the sockets’ capabilities and energy-saving potential by suggesting suitable control schedules that are based on monitored use patterns. While Ibis has the analytical and software expertise for algorithm development, the lack of test data, both in a controlled laboratory setting and in real-life deployment scenarios, represented a key barrier toward commercialization of the product. Assistance through the IN2 program provided an opportunity to conduct the needed “trial and error” algorithm development. The project included baseline field-data collection, laboratory testing, and field validation components, all of which were conducted at the NREL campus between April 2017 and July 2019.
The dataset presents atmospheric mass loadings of black carbon (BC) and brown carbon (BrC), during the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign that took place from 2021 – 2023 in Gothic, CO. A Magee Scientific aethalometer (model AE33) was deployed at the Rocky Mountain Biological Laboratory, 200 m north of AMF2 to monitor mass loadings of optically absorbing aerosols. The instrument inlet was equipped with a BGI SCC 1.829 ambient cyclone with a particle cutoff of 2.5 μm and placed 5 m above ground level. The dataset reports hour-resolved BC mass loadings at 370, 470, 520, 590, 660, 880, and 950 (BC370, BC470, BC 520, BC590, BC660, BC880, BC950 in ng/m3) reported by the instrument built-in algorithm, which calculates mass loadings from the rate of change of the attenuation of light transmitted through the aerosol-laden filter. BC880 is the operationally defined reference of the BC concentration. Hour-resolved absorbance at each wavelength were calculated from their respective mass absorption cross-section in inverse megameters (Mm-1). The absorption Angstrom exponent (AAE) for BrC and BC were calculated for each hour. BrC AAE (AAE_BrC) was calculated from the slope of Log(wavelength) versus Log(absorbance) based on the measurements at 370, 470, and 520 nm. BC AAE (AAE_BrC) was calculated from the slope of Log(wavelength) versus Log(absorbance) based on the measurements at 590, 660, 880, and 950 nm. The aethalometer record spans time periods of 4/5/2022 – 10/3/2022 and 12/8/2022 – 10/9/2023. The ambient CO2 concentration recorded by a Vaisala CARBOCAP carbon dioxide probe GMP343 is also reported in ppm.
In this paper, we propose OctoKV, an innovative network-based key-value storage system. OctoKV addresses the repetitive address translation overhead associated with traditional key-value stores running on file systems on the client side. To mitigate this overhead, we implemented the key-value store on the server side using NVMe-oF and a user-level NVMe driver. In particular, we employed fine-grained resource monitoring and load balancing based on heuristics to optimize I/O performance. OctoKV is deployed on a Linux cluster with Intel SPDK. The extensive evaluation shows that OctoKV achieves lower I/O response times in comparison to traditional approaches where key-value stores run on the client side. Also, the proposed load balancing strategies efficiently enhance I/O response times by equally distributing the workload from overloaded cores to other cores.