Turning Noise into Data: Characterization of the Van Allen Radiation Belt Using SDO Spikes Data
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SMD↗
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Turning Noise into Data: Characterization of the Van Allen Radiation Belt Using SDO Spikes Data
Explore the source record for details and available documents.
SMD↗
Parameter estimation for dynamical systems under continuous and discontinuous Gaussian noise using data assimilation techniques.
Abstract not provided.
Najera-Flores, David Aaron↗
Perceived Noise Analysis for Offset Jets Applied to Commercial Supersonic Aircraft
A systems analysis was performed with experimental jet noise data, engine/aircraft performance codes and aircraft noise prediction codes to assess takeoff noise levels and mission range for conceptual supersonic commercial aircraft. A parametric study was done to identify viable engine cycles that meet NASA's N+2 goals for noise and performance. Model scale data from offset jets were used as input to the aircraft noise prediction code to determine the expected sound levels for the lateral certification point where jet noise dominates over all other noise sources. The noise predictions were used to determine the optimal orientation of the offset nozzles to minimize the noise at the lateral microphone location. An alternative takeoff procedure called "programmed lapse rate" was evaluated for noise reduction benefits. Results show there are two types of engines that provide acceptable mission range performance; one is a conventional mixed-flow turbofan and the other is a three-stream variable-cycle engine. Separate flow offset nozzles reduce the noise directed toward the thicker side of the outer flow stream, but have less benefit as the core nozzle pressure ratio is reduced. At the systems level for a three-engine N+2 aircraft with full throttle takeoff, there is a 1.4 EPNdB margin to Chapter 3 noise regulations predicted for the lateral certification point (assuming jet noise dominates). With a 10% reduction in thrust just after clearing the runway, the margin increases to 5.5 EPNdB. Margins to Chapter 4 and Chapter 14 levels will depend on the cumulative split between the three certification points, but it appears that low specific thrust engines with a 10% reduction in thrust (programmed lapse rate) can come close to meeting Chapter 14 noise levels. Further noise reduction is possible with engine oversizing and derated takeoff, but more detailed mission studies are needed to investigate the range impacts as well as the practical limits for safety and takeoff regulations.
Turbofans↗
Perceived Noise Analysis for Offset Jets Applied to Commercial Supersonic Aircraft
A systems analysis was performed with experimental jet noise data, engine/aircraft performance codes and aircraft noise prediction codes to assess takeoff noise levels and mission range for conceptual supersonic commercial aircraft. A parametric study was done to identify viable engine cycles that meet NASAs N+2 goals for noise and performance. Model scale data from offset jets was used as input to the aircraft noise prediction code to determine the expected sound levels for the lateral certification point where jet noise dominates over all other noise sources. The noise predictions were used to determine the optimal orientation of the offset nozzles to minimize the noise at the lateral microphone location. An alternative takeoff procedure called programmed lapse rate was evaluated for noise reduction benefits. Results show there are two types of engines that provide acceptable range performance; one is a standard mixed-flow turbofan with a single-stage fan, and the other is a three-stream variable-cycle engine with a multi-stage fan. The engine with a single-stage fan has a lower specific thrust and is 8 to 10 EPNdB quieter for takeoff. Offset nozzles reduce the noise directed toward the thicker side of the outer flow stream, but have less benefit as the core nozzle pressure ratio is reduced and the bypass-to-core area ratio increases. At the systems level for a three-engine N+2 aircraft with full throttle takeoff, there is a 1.4 EPNdB margin to Chapter 3 noise regulations predicted for the lateral certification point (assuming jet noise dominates). With a 10 reduction in thrust just after takeoff rotation, the margin increases to 5.5 EPNdB. Margins to Chapter 4 and Chapter 14 levels will depend on the cumulative split between the three certification points, but it appears that low specific thrust engines with a 10 reduction in thrust (programmed lapse rate) can come close to meeting Chapter 14 noise levels. Further noise reduction is possible with additional reduction in takeoff thrust using programmed lapse rate, but studies are needed to investigate the practical limits for safety and takeoff regulations.
Turbofans↗
Perceived Noise Analysis for Offset Jets Applied to Commercial Supersonic Aircraft
A systems analysis was performed with experimental jet noise data, engineaircraft performance codes and aircraft noise prediction codes to assess takeoff noise levels and mission range for conceptual supersonic commercial aircraft. A parametric study was done to identify viable engine cycles that meet NASAs N+2 goals for noise and performance. Model scale data from offset jets was used as input to the aircraft noise prediction code to determine the expected sound levels for the lateral certification point where jet noise dominates over all other noise sources. The noise predictions were used to determine the optimal orientation of the offset nozzles to minimize the noise at the lateral microphone location. An alternative takeoff procedure called programmed lapse rate was evaluated for noise reduction benefits. Results show there are two types of engines that provide acceptable range performance; one is a standard mixed-flow turbofan with a single-stage fan, and the other is a three-stream variable-cycle engine with a multi-stage fan. The engine with a single-stage fan has a lower specific thrust and is 8 to 10 EPNdB quieter for takeoff. Offset nozzles reduce the noise directed toward the thicker side of the outer flow stream, but have less benefit as the core nozzle pressure ratio is reduced and the bypass-to-core area ratio increases. At the systems level for a three-engine N+2 aircraft with full throttle takeoff, there is a 1.4 EPNdB margin to Chapter 3 noise regulations predicted for the lateral certification point (assuming jet noise dominates). With a 10 reduction in thrust just after takeoff rotation, the margin increases to 5.5 EPNdB. Margins to Chapter 4 and Chapter 14 levels will depend on the cumulative split between the three certification points, but it appears that low specific thrust engines with a 10 reduction in thrust (programmed lapse rate) can come close to meeting Chapter 14 noise levels. Further noise reduction is possible with additional reduction in takeoff thrust using programmed lapse rate, but studies are needed to investigate the practical limits for safety and takeoff regulations.
Jet Noise↗
Effects of forward velocity and acoustic treatment on inlet fan noise
Flyover and static noise data from several engines are presented that show inlet fan noise measured in flight can be lower than that projected from static tests for some engines. The differences between flight and static measurements appear greatest when the fan fundamental tone due to rotor-stator interaction or to the rotor alone field is below cutoff. Data from engine and fan tests involving inlet treatment on the walls only are presented that show the attenuation from this treatment is substantially larger than expected from previous theories or flow duct experience. Data showing noise shielding effects due to the location of the engine on the airplane are also presented. These observations suggest that multiringed inlets may not be necessary to achieve the desired noise reduction in many applications.
Feiler, C. E.↗
Effects of forward velocity and acoustic treatment on inlet fan noise
Flyover and static noise data from several engines are presented that show inlet fan noise measured in flight can be lower than that projected from static tests for some engines. The differences between flight and static measurements appear greatest when the fan fundamental tone due to rotor-stator interaction or to the rotor-alone field is below cutoff. Data from engine and fan tests involving inlet treatment on the walls only are presented that show the attenuation from this treatment is substantially larger than expected from previous theories or flow duct experience. Data showing noise shielding effects due to the location of the engine on the airplane are also presented. These observations suggest that multiringed inlets may not be necessary to achieve the desired noise reduction in many applications.
Feiler, C. E.↗
Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification
Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE
denoising autoencoder↗
A comparison between an existing propeller noise theory and wind tunnel data
The noise of three supersonic helical tip speed propellers was compared with the noise predicted by an existing noise theory. Comparisons of the peak blade passage tones showed fairly good agreement beween theory and experiment at the lowest helical tip Mach numbers tested, 0.86 and 1.00, while at higher numbers, the theory predicted higher noise levels than measured. When the differences among the propellers were considered the theory and measurement showed fairly good agreement. Directivity measurements in general showed that the measured blade passage tone data peaked further downstream than the theory predicted. At the cruise design condition the harmonics appeared to fall off faster in the data than the theory indicated.
Dittmar, J. H.↗
Enhanced Core Noise Modeling for Turbofan Engines
This report describes work performed by MTC Technologies (MTCT) for NASA Glenn Research Center (GRC) under Contract NAS3-00178, Task Order No. 15. MTCT previously developed a first-generation empirical model that correlates the core/combustion noise of four GE engines, the CF6, CF34, CFM56, and GE90 for General Electric (GE) under Contract No. 200-1X-14W53048, in support of GRC Contract NAS3-01135. MTCT has demonstrated in earlier noise modeling efforts that the improvement of predictive modeling is greatly enhanced by an iterative approach, so in support of NASA's Quiet Aircraft Technology Project, GRC sponsored this effort to improve the model. Since the noise data available for correlation are total engine noise spectra, it is total engine noise that must be predicted. Since the scope of this effort was not sufficient to explore fan and turbine noise, the most meaningful comparisons must be restricted to frequencies below the blade passage frequency. Below the blade passage frequency and at relatively high power settings jet noise is expected to be the dominant source, and comparisons are shown that demonstrate the accuracy of the jet noise model recently developed by MTCT for NASA under Contract NAS3-00178, Task Order No. 10. At lower power settings the core noise became most apparent, and these data corrected for the contribution of jet noise were then used to establish the characteristics of core noise. There is clearly more than one spectral range where core noise is evident, so the spectral approach developed by von Glahn and Krejsa in 1982 wherein four spectral regions overlap, was used in the GE effort. Further analysis indicates that the two higher frequency components, which are often somewhat masked by turbomachinery noise, can be treated as one component, and it is on that basis that the current model is formulated. The frequency scaling relationships are improved and are now based on combustor and core nozzle geometries. In conjunction with the Task Order No. 10 jet noise model, this core noise model is shown to provide statistical accuracy comparable to the jet noise model for frequencies below blade passage. This model is incorporated in the NASA FOOTPR code and a user s guide is provided.
Stone, James R.↗
Selected methods for quantification of community exposure to aircraft noise
A review of the state-of-the-art for the quantification of community exposure to aircraft noise is presented. Physical aspects, people response considerations, and practicalities of useful application of scales of measure are included. Historical background up through the current technology is briefly presented. The developments of both single-event and multiple-event scales are covered. Selective choice is made of scales currently in the forefront of interest and recommended methodology is presented for use in computer programing to translate aircraft noise data into predictions of community noise exposure. Brief consideration is given to future programing developments and to supportive research needs.
Edge, P. M., Jr.↗
Validation of aircraft noise prediction program
Predictions made with NASA's aircraft noise prediction program (ANOPP) were compared with flyover noise data. A data base of six flyover noise runs for the DC-10-40/JT9D-59A configuration was used. For all power settings, ANOPP consistently underpredicted the low frequency spectral levels, overpredicted high frequency spectral levels and, consequently, overpredicted the inlet and aft PNLT time histories.
Kapper, C. Y.↗
Large Engine Technology (LET) Task XXXVII Low-Bypass Ratio Mixed Turbofan Engine Subsonic Jet Noise Reduction Program Test Report
NASA Glenn Research Center supported a three year effort to develop the technology for reducing jet noise from low-bypass ratio engines. This effort concentrated on both analytical and experimental approaches using various mixer designs. CFD and MGB predictions are compared with LDV and noise data, respectively. While former predictions matched well with data, experiment shows a need for improving the latter predictions. Data also show that mixing noise can be sensitive to engine hardware upstream of the mixing exit plane.
Hauser, Joseph R.↗
Conflict Detection Performance Analysis for Function Allocation Using Time-Shifted Recorded Traffic Data
The performance of the conflict detection function in a separation assurance system is dependent on the content and quality of the data available to perform that function. Specifically, data quality and data content available to the conflict detection function have a direct impact on the accuracy of the prediction of an aircraft's future state or trajectory, which, in turn, impacts the ability to successfully anticipate potential losses of separation (detect future conflicts). Consequently, other separation assurance functions that rely on the conflict detection function - namely, conflict resolution - are prone to negative performance impacts. The many possible allocations and implementations of the conflict detection function between centralized and distributed systems drive the need to understand the key relationships that impact conflict detection performance, with respect to differences in data available. This paper presents the preliminary results of an analysis technique developed to investigate the impacts of data quality and data content on conflict detection performance. Flight track data recorded from a day of the National Airspace System is time-shifted to create conflicts not present in the un-shifted data. A methodology is used to smooth and filter the recorded data to eliminate sensor fusion noise, data drop-outs and other anomalies in the data. The metrics used to characterize conflict detection performance are presented and a set of preliminary results is discussed.
Guerreiro, Nelson M.↗
Medium-Sized Helicopter Noise Abatement Flight Test Data Report
Helicopter noise has consistently hindered operations within urban areas and surrounding communities. To combat this, an extensive flight test campaign, described herein, was conducted to gather acoustic data to support helicopter noise research (e.g., physics-based modeling, operational planning, etc.). Acoustic measurements were collected from four helicopters in the medium-sized weight class with maximum takeoff gross weights between 7,400 and 14,200 lb: a Bell 205 “Huey”, a Sikorsky S-76D, a Leonardo AW139, and a US Coast Guard MH-65 Dolphin (a variant of the commercial Airbus AS365 Dauphin). Each aircraft per- formed a subset of level flyovers, steady descents, turns with various initial conditions, and noise abatement approaches. A distributed ground microphone array spanning approximately 2,000 by 4,000 ft collected measurements of each flyover event. Additionally, a dedicated semicircular microphone array was used for gathering acoustic measurements of static conditions (e.g., hover). This report provides an overview of the flight test methodology, conditions tested, equipment used, and details of the accompanying distribution dataset. Figures of sample results generated for each test point are included to enable comparison and selection of the data at the discretion of the user. Specifically, source noise hemispheres of the steady flight conditions illustrate directivity of over- all levels. Noise contours over the array provide insight into the ground levels associated with approaches and turning events. These data figures are derived from the original acoustic pressure-time histories and vehicle tracking data that are provided in the distribution dataset.
Rotorcraft↗
Wavelet-Based Interpolation and Representation of Non-Uniformly Sampled Spacecraft Mission Data
A well-documented problem in the analysis of data collected by spacecraft instruments is the need for an accurate, efficient representation of the data set. The data may suffer from several problems, including additive noise, data dropouts, an irregularly-spaced sampling grid, and time-delayed sampling. These data irregularities render most traditional signal processing techniques unusable, and thus the data must be interpolated onto an even grid before scientific analysis techniques can be applied. In addition, the extremely large volume of data collected by scientific instrumentation presents many challenging problems in the area of compression, visualization, and analysis. Therefore, a representation of the data is needed which provides a structure which is conducive to these applications. Wavelet representations of data have already been shown to possess excellent characteristics for compression, data analysis, and imaging. The main goal of this project is to develop a new adaptive filtering algorithm for image restoration and compression. The algorithm should have low computational complexity and a fast convergence rate. This will make the algorithm suitable for real-time applications. The algorithm should be able to remove additive noise and reconstruct lost data samples from images.
Bose, Tamal↗
Towards an optimal inversion method for remote atmospheric sensing
The inference of atmospheric structure from satellite radiometric observations requires an inversion algorithm. A variety of techniques was spawned to meet these demands. One class, the nonlinear inversion methods, copes with the problem of data noise. Unlike linear techniques which require a priori data smoothing, the nonlinear method can be applied directly to raw data. The algorithm discriminates the noise input by resolving the inferences into two types of solution, associating the real roots with atmospheric structure while ascribing the imaginary roots to noise.
King, J. I. F.↗