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At least 109 records · Page 6

Effects of aircraft noise on flight and ground structures

Acoustic loads measured on jet-powered STOL configurations are presented for externally blown and upper surface blown flap models ranging in size from a small laboratory model up to a full-scale aircraft model. The implications of the measured loads for potential acoustic fatigue and cabin noise are discussed. Noise transmission characteristics of light aircraft structures are presented. The relative importance of noise transmission paths, such as fuselage sidewall and primary structure, is estimated. Acceleration responses of a historic building and a residential home are presented for flyover noise from subsonic and supersonic aircraft. Possible effects on occupant comfort are assessed. The results from these three examples show that aircraft noise can induce structural responses that are large enough to require consideration in the design or operation of the aircraft.

Mixson, J. S.↗

Convolutional neural network based non-iterative reconstruction for accelerating neutron tomography *

Abstract Neutron computed tomography (NCT), a 3D non-destructive characterization technique, is carried out at nuclear reactor or spallation neutron source-based user facilities. Because neutrons are not severely attenuated by heavy elements and are sensitive to light elements like hydrogen, neutron radiography and computed tomography offer a complementary contrast to x-ray CT conducted at a synchrotron user facility. However, compared to synchrotron x-ray CT, the acquisition time for an NCT scan can be orders of magnitude higher due to lower source flux, low detector efficiency and the need to collect a large number of projection images for a high-quality reconstruction when using conventional algorithms. As a result of the long scan times for NCT, the number and type of experiments that can be conducted at a user facility is severely restricted. Recently, several deep convolutional neural network (DCNN) based algorithms have been introduced in the context of accelerating CT scans that can enable high quality reconstructions from sparse-view data. In this paper, we introduce DCNN algorithms to obtain high-quality reconstructions from sparse-view and low signal-to-noise ratio NCT data-sets thereby enabling accelerated scans. Our method is based on the supervised learning strategy of training a DCNN to map a low-quality reconstruction from sparse-view data to a higher quality reconstruction. Specifically, we evaluate the performance of two popular DCNN architectures—one based on using patches for training and the other on using the full images for training. We observe that both the DCNN architectures offer improvements in performance over classical multi-layer perceptron as well as conventional CT reconstruction algorithms. Our results illustrate that the DCNN can be a powerful tool to obtain high-quality NCT reconstructions from sparse-view data thereby enabling accelerated NCT scans for increasing user-facility throughput or enabling high-resolution time-resolved NCT scans.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Interior noise and vibration measurements on operational military helicopters and comparisons with various ride quality criteria

The results of physical measurements of the interior noise and vibration obtained within eight operational military helicopters are presented. The data were extensively analyzed and are presented in the following forms: noise and vibration spectra, overall root-mean-square acceleration levels in three linear axes, peak accelerations at dominant blade passage frequencies, acceleration exceedance data, and overall and ""A'' weighted sound pressure levels. Peak acceleration levels were compared to the ISO 1-hr reduced comfort and fatigue decreased proficiency boundaries and the NASA discomfort criteria. The ""A'' weighted noise levels were compared to the NASA annoyance criteria, and the overall noise spectra were compared to MIL-STD-1294 (""Acoustical Noise Limits in Helicopters''). Specific vibration components at blade passage frequencies for several aircraft exceeded both the ISO reduced comfort boundary and the NASA passenger discomfort criteria. The ""A'' weighted noise levels, corrected for SPH-4 helmet attenuation characteristics, exceeded the NASA annoyance threshold for several aircraft.

Clevenson, S. A.↗

Applicability of Loads Estimation Techniques Using Sparse Acceleration Sensor Data to Spacecraft Structural Health Monitoring

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.

Spacecraft Structural Health Monitoring↗

The Effects of Target and Missile Characteristics on Theoretical Minimum Miss Distance for a Beam-Rider Guidance System in the Presence of Noise

A study has been made to determine the relative importance of those factors which place an inherent limitation on the minimum obtainable miss distance for a beam-rider navigation system operating in the presence of glint noise and target evasive maneuver. Target and missile motions are assumed to be coplanar. The factors considered are the missile natural frequencies and damping ratios, missile steady-state acceleration capabilities, target evasive maneuver characteristics, and angular scintillation noise characteristics.

Stewart, Elwood C.↗

DriveSense: A Noise-Resilient Framework for Driving Mode Identification

Accurate drive mode classification is essential for enhancing the reliability and predictive maintenance of heavy-duty electric trucks. This study proposes a novel fuzzy logic-based framework, DriveSense, for real-time drive mode classification, addressing key challenges such as sensor noise, transitional behaviors, and computational efficiency. The proposed approach integrates a two-stage filtering pipeline, combining adaptive outlier removal and a dynamic Kalman filter to enhance data quality. A fuzzy inference system with smoothened trapezoidal membership functions is then applied to classify driving modes into standstill, constant speed, acceleration, and deceleration while mitigating the effects of noise and edge cases. Performance evaluation using real-world and simulated drive cycles demonstrates significant improvements in classification accuracy (up to 97.8%), F1-score (up to 0.97), and robustness against noise, while reducing false positives. Comparative analysis against baseline models, demonstrates DriveSense’s superior accuracy and generalizability across diverse driving patterns. The framework’s lightweight and interpretable fuzzy inference engine operates with low computational latency, ensuring compatibility with real-time embedded systems typical of heavy-duty electric trucks. Moreover, DriveSense models transitional behaviors through overlapping fuzzy sets and adaptive borderline classification logic, enabling smooth identification of subtle shifts such as rolling stops or gradual deceleration. These results highlight DriveSense’s potential to enhance predictive maintenance strategies, reduce downtime, and support scalable, fleet-wide diagnostics.

Kumar, Praveen [Oak Ridge National Laboratory (ORN↗

Detection in fixed and random noise in foveal and parafoveal vision explained by template learning

Foveal and parafoveal contrast detection thresholds for Gabor and checkerboard targets were measured in white noise by means of a two-interval forced-choice paradigm. Two white-noise conditions were used: fixed and twin. In the fixed noise condition a single noise sample was presented in both intervals of all the trials. In the twin noise condition the same noise sample was used in the two intervals of a trial, but a new sample was generated for each trial. Fixed noise conditions usually resulted in lower thresholds than twin noise. Template learning models are presented that attribute this advantage of fixed over twin noise either to fixed memory templates' reducing uncertainty by incorporation of the noise or to the introduction, by the learning process itself, of more variability in the twin noise condition. Quantitative predictions of the template learning process show that it contributes to the accelerating nonlinear increase in performance with signal amplitude at low signal-to-noise ratios.

NASA Center ARC↗

Study of a porous surface microphone sensor in an aerofoil

The porous microphone in an airfoil is described as a directional sensor which rejects flow noise. The airfoil allows the sensor to be rotated in the airflow over a wide range of yaw angles, 0 to 90 degrees, avoiding flow separation over the surface of the sensor and its associated additional flow noise. The microphone is discussed in terms of its acoustic properties, vibration sensitivity, effect of Mach number on the directivity function, and flow noise. Additional information on the acoustic calibration of the microphone, the acceleration sensitivity of the airfoil, stationary source and receiver in a moving gas, acoustic tests in airflow, and flow noise tests of the airfoil porous surface sensor is included.

Noiseux, D. U.↗

Assessment of the impact of advanced air-transport technology

The long term prospects for commercial supersonic transportation appear attractive enough to keep supersonic research active and reasonably healthy. On the other hand, the uncertainties surrounding an advanced supersonic transport, (AST) specifically fuel price, fuel availability and noise, are too significant to warrant an accelerated research and development program until they are better resolved. It is estimated that an AST could capture about $50 billion (1979 dollars) of the potential $150 billion in sales up to the year 2010.

Maxwell, R. L.↗

Dynamical evidence for Planet X

The dynamical evidence for a planet beyond the orbit of Neptune is reviewed. Three years of radio tracking data from Pioneer 10 can be fit to the noise level with no evidence for unmodelled acceleration at a level higher than 5 x 10 to the -14th km/sq s. The evidence does not place severe limits on the Planet X model, but does place a firm limit of five earth masses on a hypothetical comet belt just beyond the orbit of Neptune.

Anderson, John D.↗

A Transportable Gravity Gradiometer Based on Atom Interferometry

A transportable atom interferometer-based gravity gradiometer has been developed at JPL to carry out measurements of Earth's gravity field at ever finer spatial resolutions, and to facilitate high-resolution monitoring of temporal variations in the gravity field from ground- and flight-based platforms. Existing satellite-based gravity missions such as CHAMP and GRACE measure the gravity field via precise monitoring of the motion of the satellites; i.e. the satellites themselves function as test masses. JPL's quantum gravity gradiometer employs a quantum phase measurement technique, similar to that employed in atomic clocks, made possible by recent advances in laser cooling and manipulation of atoms. This measurement technique is based on atomwave interferometry, and individual laser-cooled atoms are used as drag-free test masses. The quantum gravity gradiometer employs two identical atom interferometers as precision accelerometers to measure the difference in gravitational acceleration between two points (Figure 1). By using the same lasers for the manipulation of atoms in both interferometers, the accelerometers have a common reference frame and non-inertial accelerations are effectively rejected as common mode noise in the differential measurement of the gravity gradient. As a result, the dual atom interferometer-based gravity gradiometer allows gravity measurements on a moving platform, while achieving the same long-term stability of the best atomic clocks. In the laboratory-based prototype (Figure 2), the cesium atoms used in each atom interferometer are initially collected and cooled in two separate magneto-optic traps (MOTs). Each MOT, consisting of three orthogonal pairs of counter-propagating laser beams centered on a quadrupole magnetic field, collects up to 10(exp 9) atoms. These atoms are then launched vertically as in an atom fountain by switching off the magnetic field and introducing a slight frequency shift between pairs of lasers to create a moving rest frame for the trapped atoms. While still in this moving-frame molasses, the laser frequencies are further detuned from the atomic resonance (while maintaining this relative frequency shift) to cool the atom cloud's temperature to 2 K or below, corresponding to an rms velocity of less than 2 cm/s. After launch, the cold atoms undergo further state and velocity selection to prepare for atom interferometry. The atom interferometers are then realized using laser-induced stimulated Raman transitions to perform the necessary manipulations of each atom, and the resulting interferometer phase is measured using laser-induced fluorescence for state-normalized detection. More than 20 laser beams with independent controls of frequency, phase, and intensity are required for this measurement sequence. This instrument can facilitate the study of Earth's gravitational field from surface and air vehicles, as well as from space by allowing gravity mapping from a low-cost, single spacecraft mission. In addition, the operation of atom interferometer-based instruments in space offers greater sensitivity than is possible in terrestrial instruments due to the much longer interrogation times available in the microgravity environment. A space-based quantum gravity gradiometer has the potential to achieve sensitivities similar to the GRACE mission at long spatial wavelengths, and will also have resolution similar to GOCE for measurement at shorter length scales.

Yu, Nan↗

Graph Identification of Proteins in Tomograms (GRIP-Tomo) 2.0: Topologically aware classification for proteins

Cryo-electron tomography (cryo-ET) enables structural characterization of biomolecules under near-native conditions. Existing approaches for interpreting the resulting three-dimensional volumes are computationally expensive and have difficulty interpreting density associated with small proteins/complexes. To explore alternate approaches for identifying proteins in cryo-ET data we pursued a Graph Network and topologically invariant approach. Here, we report on a fast algorithm that classifies particles by searching for nuances of evolutionarily conversed motifs and the geometrical characteristics of protein structure. GRIP-Tomo 2.0 is a machine-learning pipeline that extracts interpretable topological features of protein structures within noisy experimental backgrounds. Compared to version 1.0, the new pipeline includes three upgrades that significantly improve performance including synthetic tomogram generation simulating realistic noise, graph-based persistent feature extraction as protein fingerprints, and high-performance computing acceleration. GRIP-Tomo 2.0 achieves over 90% accuracy in classifying between proteins and noise using both real and synthetic datasets which represents a foundational step toward advancing cryo-ET workflows and empowering automated visual proteomics.

Li, Chengxuan↗

MAGIS-100 environmental characterization and noise analysis

We investigate and analyze site specific systematics for the MAGIS-100 atomic interferometry experiment at Fermi National Accelerator Laboratory. As atom interferometers move out of the laboratory environment passive and active mitigation for noise sources must be implemented. To inform the research and development of the experiment design, we measure ambient temperature, humidity, and vibrations of the installation site. We find that temperature fluctuations will necessitate enclosures for critical subsystems and a temperature controlled laser room for the laser system. We also measure and analyze the vibration spectrum above and below ground for the installation site. The seismic vibration effect of gravity gradient noise is also modeled using input from a low-noise seismometer at multiple locations and a mitigation scheme is studied using a stochastic simulation and characterized by a suppression factor.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DeepMC: a deep learning method for efficient Monte Carlo beamlet dose calculation by predictive denoising in magnetic resonance-guided radiotherapy

Abstract Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation quality and speed, creating an unmet need for the acceleration of Monte Carlo (MC) dose calculation. Existing deep learning approaches to denoise the final plan MC dose fail to achieve the accuracy and speed requirements of large-scale beamlet dose calculation in the presence of a strong magnetic field for online adaptive radiotherapy planning. Our deep learning dose calculation method, DeepMC, addresses these needs by predicting low-noise dose from extremely noisy (but fast) MC-simulated dose and anatomical inputs, thus enabling significant acceleration. DeepMC simultaneously reduces MC sampling noise and predicts corrupted dose buildup at tissue-air material interfaces resulting from MR-field induced electron return effects. Here we demonstrate our model’s ability to accelerate dose calculation for daily treatment planning by a factor of 38 over traditional low-noise MC simulation with clinically meaningful accuracy in deliverable dose and treatment delivery parameters. As a post-processing approach, DeepMC provides compounded acceleration of large-scale dose calculation when used alongside established MC acceleration techniques in variance reduction and graphics processing unit-based MC simulation.

Engineering↗

Simulated orbiter lift-off acceleration - A stochastic model

A simple procedure for generating synthetic orbiter liftoff acceleration is described. This stochastic method is based on a filtered white noise model which accounts for the nonstationary time evolution of amplitude and frequency content of orbiter lift-off accelerations. The example of Space Transportation System STS-41 Z-acceleration is carefully examined, and the corresponding synthetic orbiter lift-off accelerations model was developed. It is shown that the resulting simulated accelerations preserve significant properties of the original record. Statistically based acceleration response spectra curves for the simulated orbiter lift-off excitations are also evaluated and the results are compared with those for the actual flight data.

Lee-Glauser, Gina↗

Characterizing performance of ultra-sensitive accelerometers

An overview is given of methodology and test results pertaining to the characterization of ultra sensitive accelerometers. Two issues are of primary concern. The terminology ultra sensitive accelerometer is used to imply instruments whose noise floors and resolution are at the state of the art. Hence, the typical approach of verifying an instrument's performance by measuring it with a yet higher quality instrument (or standard) is not practical. Secondly, it is difficult to find or create an environment with sufficiently low background acceleration. The typical laboratory acceleration levels will be at several orders of magnitude above the noise floor of the most sensitive accelerometers. Furthermore, this background must be treated as unknown since the best instrument available is the one to be tested. A test methodology was developed in which two or more like instruments are subjected to the same but unknown background acceleration. Appropriately selected spectral analysis techniques were used to separate the sensors' output spectra into coherent components and incoherent components. The coherent part corresponds to the background acceleration being measured by the sensors being tested. The incoherent part is attributed to sensor noise and data acquisition and processing noise. The method works well for estimating noise floors that are 40 to 50 dB below the motion applied to the test accelerometers. The accelerometers being tested are intended for use as feedback sensors in a system to actively stabilize an inertial guidance component test platform.

Sebesta, Henry↗

Fluid Physics Under a Stochastic Acceleration Field

The research summarized in this report has involved a combined theoretical and computational study of fluid flow that results from the random acceleration environment present onboard space orbiters, also known as g-jitter. We have focused on a statistical description of the observed g-jitter, on the flows that such an acceleration field can induce in a number of experimental configurations of interest, and on extending previously developed methodology to boundary layer flows. Narrow band noise has been shown to describe many of the features of acceleration data collected during space missions. The scale of baroclinically induced flows when the driving acceleration is random is not given by the Rayleigh number. Spatially uniform g-jitter induces additional hydrodynamic forces among suspended particles in incompressible fluids. Stochastic modulation of the control parameter shifts the location of the onset of an oscillatory instability. Random vibration of solid boundaries leads to separation of boundary layers. Steady streaming ahead of a modulated solid-melt interface enhances solute transport, and modifies the stability boundaries of a planar front.

Vinals, Jorge↗