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Method for identifying aircraft that exceed a specified noise condition in a defined geographical area
The invention is a method for identifying aircraft, flying at supersonic speeds, that exceed a specified noise condition in a defined geographical area that may be employed once restrictions to supersonic aircraft flights over the United States are lifted.
Acoustic Shock Formation in Noise Propagation During Military Aircraft Ground Run-Up Operations
Not provided.
Near-field acoustical holography and acoustic power analysis of a simulated, highly heated supersonic jet
Although near-field acoustical holography (NAH) and acoustic intensity analysis have previously been used to investigate the apparent jet noise sources produced by military aircraft, explicit connections to supersonic jet characteristics cannot be made due to a lack of information about the exhaust plume. Here, to begin to bridge this gap and better understand the source information yielded by NAH, the current study instead applies NAH to a virtual measurement of the near-field pressures of a highly heated laboratory-scale supersonic jet generated by large-eddy simulation (LES). The holographic reconstructions of the pressure, particle velocity, and acoustic intensity are found to match the LES-generated acoustic field well and are used to calculate the acoustic power of the jet. The jet's calculated overall acoustic power is compared to the free-stream mechanical power, resulting in an acoustic efficiency of 1.5%. Ray-tracing of the acoustic intensity to the jet centerline generates an axial distribution of the acoustic power origin, showing that almost all the power originates from the supersonic portion of the flow and with the distribution peak upstream of the potential core tip. Holographic reconstruction of the pressures along the nozzle lipline captures the general spectral shape of the LES-generated pressures, though it underestimates the amplitude.
Applying novel analytical tools for analyzing multidimensional secondary organic aerosol measurements
In the atmosphere, secondary organic aerosols (SOA) are often the major components of fine particulate matter and interact with clouds and radiation. SOA comprises a mixture of thousands of organic compounds. There is tremendous complexity and uncertainty in understanding SOA formation, since it is formed by oxidation and gas to particle conversion of a variety of sources: natural biogenic, anthropogenic (vehicles, cooking coal combustion) and biomass burning. The Aerosol Mass Spectrometer (AMS) produces multidimensional chemical information about SOA but analyzing this data to understand SOA sources relies on time consuming analyses (~months to years) such as the positive matrix factorization (PMF). PMF also becomes difficult for aircraft data where signal to noise ratio is weaker. There is a critical need to develop fast machine learning techniques that can analytically provide information about SOA sources using AMS data on the same timescales as the data is being collected (~minutes). We apply a machine learning supervised classification approach: the multinomial logistic regression to rapidly classify AMS data obtained from aircraft measurements.
Electrification of Aircraft: Challenges, Barriers, and Potential Impacts
Small-scale aircraft are being electrified with small e-aircraft already certified to fly and with test flights underway for retrofits of existing aircraft focused on near-term deployment. Internationally, from 2018 to 2020, the number of electric aircraft projects underway increased 50%. These technologies are projected to increase operational efficiency and reduce emissions and noise from a growing aviation sector. This emerging interest in aviation electrification includes aircraft, energy supply equipment, and battery storage manufacturers. Federal agencies are currently funding a variety of efforts including technology research as well as forecasting demand and environmental impacts and state governments have also begun pushing for further investigation in this area. This paper provides an overview of the current state and potential future development of aviation electrification. To understand the types of research questions around current and future challenges for this emerging sector, we consider a case study of the potential deployment of small commercial electric aircraft for existing, under-served markets and consider operational requirements and technical challenges as well as the range of questions that will need to be addressed around charging infrastructure, grid integration, policy and regulatory requirements, and emissions impacts. This case study also outlines potential nearer-term solutions and research areas of interest that will need to be addressed in the longer term at much larger scales and highlights NREL capabilities for addressing these challenges.
Real-time Data Analytics for Condition Monitoring of Complex Industrial Systems
Modern industrial systems are now fitted with several sensors for condition monitoring. This is advantageous because these sensors can provide mass amounts of data that have the potential for aiding in tasks such as fault detection, diagnosis, and prognostics. However, the information valuable for performing these tasks is often clouded in noise and must be mined from high-dimensional data structures. Therefore, this dissertation presents a data analytics framework for performing these condition monitoring tasks using high-dimensional data. Demonstrations of this framework are detailed for challenges related to power generation systems in automobiles, power plants, and aircraft engines. These implementations leverage data collected from state-of-the-art, industry class test-rigs. Results indicate the ability of this framework to develop effective methodologies for condition monitoring of complex systems.
Spread Spectrum Time Domain Reflectometry (SSTDR) and Frequency Domain Reflectometry (FDR) for Detection of Cable Anomalies Using Machine Learning
Cables are initially qualified for nuclear power plant use for 40 years. As plants extend their operating license to 60 and 80 years, continued use of these cables must shift to a performance-based approach since it is cost prohibitive to completely replace cables that are likely still capable of performing their design function. A variety of cable tests are available and are commonly applied during outages when the cables can be taken out of service. Frequency domain reflectometry (FDR) is one of these test methods that is being more broadly accepted and used because it not only detects anomalies along the cable with a low-voltage signal that does not stress the cable insulation, but the technique also locates the anomalies. This supports follow-up local inspection and local repair or partial replacement of a damaged cable segment. Currently, FDR testing is only applied to cables that are taken out of service since the test instrument would be damaged by operational voltages. A related technology that has found some acceptance in the aircraft and rail industry is spread spectrum time domain reflectometry (SSTDR). This technology has been implemented with a custom commercial instrument by LiveWire Innovation Inc. that is designed to operate on live cables up to 1000 volts. One of the main conclusions of a previous effort was that cable reflectometry plots can be difficult for humans to analyze due to baseline noise, low or noisy anomaly response peaks, or large responses from cable ends. Detection of cable anomalies for many of these frequencies and test conditions was challenging for manual analysis. This presented an ideal opportunity for ML analysis to distinguish undamaged cable indications from anomalous cable indications. This research discusses application of machine learning (ML) to reflectometry cable test methods. The goal was to assess feasibility to distinguish undamaged cable reflectometry responses from damaged or anomalous cable reflectometry responses. The assessment considered the 3 instruments, multiple frequency bandwidths from each instrument, multiple cable anomalies and test conditions, and both supervised and unsupervised ML approaches. Although approaches and analysis methods were not identical or directly comparable, both outputs were encouraging. The unsupervised prediction weighted accuracy was assessed by instrument and by frequency. It performed better at high frequencies with the highest prediction accuracy of 0.84 for the higher frequency FDR, 0.79 for the 48-MHz LiveWire SSTDR, and 0.77 for 300-MHz PNNL SSTDR. The initial weighted accuracy average across all frequencies for using supervised ML was 0.56 to 0.68. The supervised analysis was repeated with noisier training data removed resulting in weighted accuracies of 0.69 to 0.87. These weighted accuracies are not directly comparable due to differences in the supervised and unsupervised analysis details but do indicate an encouraging trend. Even with limited and unbalanced data, strong prediction accuracies seem encouraging for further work including more data under a wider range of conditions.
Extended Bandwidth Spread Spectrum Time Domain Reflectometry Cable Test for Thermal Aging, Low Resistance Fault, and Water Detection
In 2022, researchers at Pacific Northwest National Laboratory (PNNL) used the Accelerated and Real-Time Environmental Nodal Assessment (ARENA) cable and motor test bed to characterize spread spectrum time domain reflectometry (SSTDR) and compare the responses of an SSTDR instrument to those of a frequency domain reflectometry (FDR) instrument. Results showed both techniques could detect and locate cable anomalies such as phase-to-phase low resistance and shorts, thermal insulation damage, mechanical insulation damage, and the presence or absence of water in some conditions. The SSTDR tests used a commercial instrument provided by LiveWire Innovations Inc. This commercial instrument performed tests at 6, 12, 24, and 48 MHz bandwidth. The results of these tests were compared to FDR tests where bandwidths could be extended up to 1.3 GHz, although the best responses for cable tests were from 100 to 500 MHz. Lower bandwidth signals can propagate better along the cable while higher bandwidths have higher resolution for impedance change reflections allowing more precise indication of location and separation of anomalies. The 2022 research found that FDR responses were clearer than SSTDR and speculated that a higher bandwidth SSTDR could more successfully detect and locate cable anomalies. One advantage of the SSTDR system investigated was that it was designed for energized online use up to 1,000 volts, which may be a significant advantage for nuclear power plant use. The LiveWire SSTDR instrument is an established product in the rail and aircraft industry and updating the SSTDR hardware parameters is difficult to justify without more conclusive testing. Therefore, a software adjustable laboratory SSTDR instrument was developed by PNNL and was used to test extended bandwidth SSTDR cable tests. Within the ARENA test bed, 42 cable conditions were tested with the PNNL SSTDR, FDR, and the LiveWire SSTDR—each operating at four different bandwidths. Observations and conclusions regarding the relative performance of the three instruments over different bandwidths are note below. Responses of the PNNL SSTDR (at 50 MHz) and the LiveWire SSTDR (at 48 MHz) were similar. The PNNL SSTDR higher frequency bandwidths behaved as expected showing sharper peaks and higher noise. This validated the PNNL SSTDR as a reasonable implementation of the SSTDR technology. Lower bandwidth SSTDR responses (particularly 6 and 12 MHz) may have increased value for use within longer cables but were not particularly effective at identifying anomalous cable behavior in the 100 ft cables tested here. The higher bandwidths of the PNNL SSTDR (50, 100, 200, and 400 MHz) did not provide substantially clearer cable reflectometry responses, but having the higher frequency responses available did add to the cable test evaluation. Strong responses to shorts and low impedance faults between phases were particularly evident in the higher bandwidth PNNL SSTDR and the FDR data. Measurements were repeatable, with similar responses obtained from a thermally aged cable for tests taken a month apart. Signal noise was affected in the unshielded cable by the local in-tray cable arrangement including proximity to metal edges and rungs of the cable tray. Foam isolation of the cable from the tray metal reduced in both FDR and SSTDR responses. Cable condition monitoring in nuclear power plants will likely benefit from both more informative off-line testing methods and from the development of on-line methods for continuous monitoring of cables in use. The LWRS-funded ARENA test bed was a valuable resource for this development and direct comparison of nuclear electrical cable condition monitoring technologies. Test results are targeted to guide industry advancement of testing and monitoring tools for cable aging management.
Acquisition and Processing Considerations for Infrared Images of Rotating Turbine Blades
Abstract As designers aim to increase efficiency in gas turbines for aircraft propulsion and power generation, spatially resolved experimental measurements are needed to validate computational models and compare improvement gains of new cooling designs. Infrared (IR) thermography is one such method for obtaining spatially resolved temperature measurements. As technological advances in thermal detectors enable faster integration times, surface temperature measurements of rotating turbine blades become possible to capture including the smallest features. This paper outlines opportunities enabled by the latest IR detector technologies for capturing spatially resolved rotating blade temperatures, while also addressing some of the challenges of implementing IR for turbine rigs such as the one in the Steady Thermal Aero Research Turbine (START) Laboratory. This paper documents critical steps in achieving accurate measurements including calibration, integration times, spatial noise, and motion blur. From these results, recommendations are provided for achieving accurate IR measurements collected in a rotating turbine facility to study film cooling.
Stratospheric temperature measurements from nanosatellite stellar occultation observations of refractive bending
Abstract. Stellar occultation observations from space can probe the stratosphere and mesosphere at a fine vertical scale around the globe. Unlike other measurement techniques like radiosondes and aircraft, stellar occultation has the potential to observe the atmosphere above 30 km, and unlike radio occultation, stellar occultation probes fine-scale phenomena with potential to observe atmospheric turbulence. We imaged the refractive bending angle of a star centroid for a series of occultations by the atmosphere. Atmospheric refractivity, density, and then temperature are retrieved from the bending observations with the Abel transformation and Edlén's law, the hydrostatic equation, and the ideal gas law. The retrieval technique is applied to data collected by two nanosatellites operated by Terran Orbital. Measurements were primarily taken by the GEOStare SV2 mission, with a dedicated imaging telescope, supplemented with images captured by spacecraft bus sensors, namely the star trackers on other Terran Orbital missions. The bending angle noise floor is 10 and 30 arcsec for the star tracker and GEOStare SV2 data, respectively. The most significant sources of uncertainty are due to centroiding errors due to the fairly low-resolution stellar images and telescope pointing knowledge derived from noisy satellite attitude sensors. The former mainly affects the star tracker data, while the latter limits the GEOStare SV2 accuracy, with both providing low vertical resolution. This translates to a temperature profile retrieval up to roughly 20 km for both star tracker and GEOStare SV2 datasets. In preparation of an upcoming 2023 mission designed to correct these deficiencies, SOHIP, we simulated bending angle measurements with varying magnitudes of error. The expected maximum altitude of retrieved temperature is 41 km on average for these simulated measurements with a noise floor of 0.39 arcsec. Our work highlights the capabilities of stellar occultation observations from nanosatellites for atmospheric sounding. Future work will investigate high-frequency observations of atmospheric gravity waves and turbulence, mitigating the major uncertainties observed in these datasets.