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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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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Planetary Transmission Diagnostics

This report presents a methodology for detecting and diagnosing gear faults in the planetary stage of a helicopter transmission. This diagnostic technique is based on the constrained adaptive lifting algorithm. The lifting scheme, developed by Wim Sweldens of Bell Labs, is a time domain, prediction-error realization of the wavelet transform that allows for greater flexibility in the construction of wavelet bases. Classic lifting analyzes a given signal using wavelets derived from a single fundamental basis function. A number of researchers have proposed techniques for adding adaptivity to the lifting scheme, allowing the transform to choose from a set of fundamental bases the basis that best fits the signal. This characteristic is desirable for gear diagnostics as it allows the technique to tailor itself to a specific transmission by selecting a set of wavelets that best represent vibration signals obtained while the gearbox is operating under healthy-state conditions. However, constraints on certain basis characteristics are necessary to enhance the detection of local wave-form changes caused by certain types of gear damage. The proposed methodology analyzes individual tooth-mesh waveforms from a healthy-state gearbox vibration signal that was generated using the vibration separation (synchronous signal-averaging) algorithm. Each waveform is separated into analysis domains using zeros of its slope and curvature. The bases selected in each analysis domain are chosen to minimize the prediction error, and constrained to have the same-sign local slope and curvature as the original signal. The resulting set of bases is used to analyze future-state vibration signals and the lifting prediction error is inspected. The constraints allow the transform to effectively adapt to global amplitude changes, yielding small prediction errors. However, local wave-form changes associated with certain types of gear damage are poorly adapted, causing a significant change in the prediction error. The constrained adaptive lifting diagnostic algorithm is validated using data collected from the University of Maryland Transmission Test Rig and the results are discussed.

Lewicki, David G.↗
DOE OSTI2017

Contextual Compression of Large-Scale Wind Turbine Array Simulations

Data sizes are becoming a critical issue particularly for HPC applications. We have developed a user-driven lossy wavelet-based storage model to facilitate the analysis and visualization of large-scale wind turbine array simulations. The model stores data as heterogeneous blocks of wavelet coefficients, providing high-fidelity access to user-defined data regions believed the most salient, while providing lower-fidelity access to less salient regions on a block-by-block basis. In practice, by retaining the wavelet coefficients as a function of feature saliency, we have seen data reductions in excess of 94 percent, while retaining lossless information in the turbine-wake regions most critical to analysis and providing enough (low-fidelity) contextual information in the upper atmosphere to track incoming coherent turbulent structures. Our contextual wavelet compression approach has allowed us to deliver interative visual analysis while providing the user control over where data loss, and thus reduction in accuracy, in the analysis occurs. We argue this reduced but contextualized representation is a valid approach and encourages contextual data management.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Spectral Analysis of LDV Measurements inside of the TAMU Air-Cooled Reactor Cavity Cooling System - 18688

Spectral analysis was applied to the LDV measurements inside of the TAMU air-cooled RCCS facility at two different locations to both evaluate the feasibility of spectral methods for this experiment and gain a first insight into the flow behavior using a Fourier representation. Application of the wavelet transform showed that there are patterns in the flow signified by repeating frequencies and flow structures. Although the sampling rate of 7.92 Hz at the data point centered at the bottom center of riser 4 was lower than the higher frequency sampling rate of 57.92 Hz at the top data point of the upper plenum, the wavelet transforms of the higher frequency data point showed that the dominant frequencies were bounded within 4 Hz. Application of wavelet analysis is limited by the size of the interrogation window but may be modified according to the frequency range of interest by adjusting the size of the time width for the undilated Morlet mother wavelet. Spectral analysis can be applied to future experiments to understand uncharacterized flow behavior such as flow reversal inside of the upper plenum and for benchmarking future CFD simulations. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Quantized Constant-Q Gabor Atoms for Sparse Binary Representations of Cyber-Physical Signatures

Increased data acquisition by uncalibrated, heterogeneous digital sensor systems such as smartphones present new challenges. Binary metrics are proposed for the quantification of cyber-physical signal characteristics and features, and a standardized constant-Q variation of the Gabor atom is developed for use with wavelet transforms. Two different continuous wavelet transform (CWT) reconstruction formulas are presented and tested under different signal to noise ratio (SNR) conditions. A sparse superposition of Nth order Gabor atoms worked well against a synthetic blast transient using the wavelet entropy and an entropy-like parametrization of the SNR as the CWT coefficient-weighting functions. The proposed methods should be well suited for sparse feature extraction and dictionary-based machine learning across multiple sensor modalities.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2020

Coarse-grain cluster analysis of tensors with application to climate biome identification

A tensor provides a concise way to codify the interdependence of complex data. Treating a tensor as a d-way array, each entry records the interaction between the different indices. Clustering provides a way to parse the complexity of the data into more readily understandable information. Clustering methods are heavily dependent on the algorithm of choice, as well as the chosen hyperparameters of the algorithm. However, their sensitivity to data scales is largely unknown. In this work, we apply the discrete wavelet transform to analyze the effects of coarse-graining on clustering tensor data. We are particularly interested in understanding how scale affects clustering of the Earth's climate system. The discrete wavelet transform allows classification of the Earth's climate across a multitude of spatial-temporal scales. The discrete wavelet transform is used to produce an ensemble of classification estimates, as opposed to a single classification. Each element of the ensemble is a clustering at a different spatial-temporal scale. Information theoretic approaches are used to identify important scale lengths in clustering the L15 Climate Datset. We also discover a sub-collection of the ensemble that spans the majority of the variance observed, allowing for efficient consensus clustering techniques that can be used to identify climate biomes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Multiresolution GPC-Structured Control of a Single-Loop Cold-Flow Chemical Looping Testbed

Chemical looping is a near-zero emission process for generating power from coal. It is based on a multi-phase gas-solid flow and has extremely challenging nonlinear, multi-scale dynamics with jumps, producing large dynamic model uncertainty, which renders traditional robust control techniques, such as linear parameter varying H ∞ design, largely inapplicable. This process complexity is addressed in the present work through the temporal and the spatiotemporal multiresolution modeling along with the corresponding model-based control laws. Namely, the nonlinear autoregressive with exogenous input model structure, nonlinear in the wavelet basis, but linear in parameters, is used to identify the dominant temporal chemical looping process dynamics. The control inputs and the wavelet model parameters are calculated by optimizing a quadratic cost function using a gradient descent method. The respective identification and tracking error convergence of the proposed self-tuning identification and control schemes, the latter using the unconstrained generalized predictive control structure, is separately ascertained through the Lyapunov stability theorem. The rate constraint on the control signal in the temporal control law is then imposed and the control topology is augmented by an additional control loop with self-tuning deadbeat controller which uses the spatiotemporal wavelet riser dynamics representation. The novelty of this work is three-fold: (1) developing the self-tuning controller design methodology that consists in embedding the real-time tunable temporal highly nonlinear, but linearly parametrizable, multiresolution system representations into the classical rate-constrained generalized predictive quadratic optimal control structure, (2) augmenting the temporal multiresolution loop by a more complex spatiotemporal multiresolution self-tuning deadbeat control loop, and (3) demonstrating the effectiveness of the proposed methodology in producing fast recursive real-time algorithms for controlling highly uncertain nonlinear multiscale processes. The latter is shown through the data from the implemented temporal and augmented spatiotemporal solutions of a difficult chemical looping cold flow tracking control problem.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Long-term changes in the seasonality of Baltic sea level

The seasonal cycle accounts for about 40% of the total sea level variability in the Baltic Sea. In a climate change context, changes are expected to occur, not only in mean levels but also in the seasonal characteristics of sea level. The present study addresses the quantification of changes in the seasonal cycle of sea level from a set of century-long tide gauge records in the Baltic Sea. In order to obtain robust estimates of the changes in amplitude and phase of the seasonal cycle, we apply different methods, including continuous wavelet filtering, multi-resolution decomposition based on the maximal overlap discrete wavelet transform, auto-regressive-based decomposition, singular spectrum analysis and empirical mode decomposition. The results show that all methods generally trace a similar long-term variability of the annual cycle amplitudes, and we focus on discrete wavelet analysis as the natural counterpart of classical moving Fourier analysis. In contrast to previous studies suggesting the existence of long-term changes in the seasonal cycle, in particular an increase of the annual amplitude, we find alternating periods of high and low amplitudes without any clear indication of systematic long-term trends. The derived seasonal patterns are spatially coherent, discriminating the stations in the Baltic entrance from the remaining stations in the Baltic basin, for which zonal wind accounts for typically more than 40% of the variations in amplitude.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

An intrinsic velocity–curvature–acceleration relationship for weakly unstable gaseous detonations

The shock motion of calculated weakly unstable cellular detonation was analyzed using concepts from velocity-curvature theory to develop new insight into the underlying physical mechanisms driving the cellular instability. The cellular cycle was shown to follow a surface in a three-dimensional coordinate system composed of the local shock velocity, curvature, and acceleration. Wavelets of the detonation shock were found to follow a velocity-curvature trajectory that was characteristic of an initially reactive wave that decays in time to a decoupled blast wave. Near the Mach stem, these trajectories were only modulated by the strength of the Mach stem at the time the wavelet was generated, indicating that the small reaction zone present in this region is the dominant factor driving the flow. Away from the Mach stem, all wavelet trajectories collapsed to a common curve in velocity-curvature space that was consistent with motion of a decaying blast wave. For the mixture studied, the apparent adherence of shock motion to a unique surface in velocity-curvature-acceleration space indicates the possible existence of an intrinsic mixture-specific relationship for cellular gaseous detonation. In conclusion, this relationship and analysis methodology also provides a mechanism for quantification of the shock velocity and shape fluctuations present in cellular detonation, which may provide utility for modeling detonation engineering applications such as rotating detonation engine design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

The effect of emissive biased limiter on the magnetohydrodynamic modes in the IR-T1 tokamak

A moveable emissive biased limiter (EBL) for the investigation of spatial and temporal structure of MHD modes in IR-T1 tokamak, based on mirnov oscillations, was designed and constructed. The biasing has been considered to improve the global confinement by setting up an electric field at the plasma edge. Radial electric field (E{sub r}) modifies edge plasma turbulence, plasma rotation, and transport. Mirnov oscillations using singular value decomposition (SVD) and wavelet techniques were analyzed. SVD algorithm has been employed to analyze the frequency and wavenumber harmonics of the MHD fluctuations. The time-resolved frequency component analysis has been performed using wavelets. The EBL was applied to plasma at 10 ms with negative polarity. The results show that after applying EBL, the m = 2 mode is grown, m = 3 mode is suppressed, and H{sub {alpha}} radiation is decreased. Furthermore, results of the wavelet analysis of mirnov coil in the time range of 8-12 ms indicate that 1.5 ms after applying EBL, the MHD frequency is reduced from 45 kHz to 25 kHz.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Multiscale Flow for robust and optimal cosmological analysis

We propose Multiscale Flow, a generative Normalizing Flow that creates samples and models the field-level likelihood of two-dimensional cosmological data such as weak lensing. Multiscale Flow uses hierarchical decomposition of cosmological fields via a wavelet basis and then models different wavelet components separately as Normalizing Flows. The log-likelihood of the original cosmological field can be recovered by summing over the log-likelihood of each wavelet term. This decomposition allows us to separate the information from different scales and identify distribution shifts in the data such as unknown scale-dependent systematics. The resulting likelihood analysis can not only identify these types of systematics, but can also be made optimal, in the sense that the Multiscale Flow can learn the full likelihood at the field without any dimensionality reduction. We apply Multiscale Flow to weak lensing mock datasets for cosmological inference and show that it significantly outperforms traditional summary statistics such as power spectrum and peak counts, as well as machine learning–based summary statistics such as scattering transform and convolutional neural networks. We further show that Multiscale Flow is able to identify distribution shifts not in the training data such as baryonic effects. Finally, we demonstrate that Multiscale Flow can be used to generate realistic samples of weak lensing data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Revisiting the question: Does high-latitude solar activity lead low-latitude solar activity in time phase?

Cross-correlation analysis and wavelet transform methods are used to investigate whether high-latitude solar activity leads low-latitude solar activity in time phase or not, using the data of the Carte Synoptique solar filaments archive from 1919 March to 1989 December. From the cross-correlation analysis, high-latitude solar filaments have a time lead of 12 Carrington solar rotations with respect to low-latitude ones. Both the cross-wavelet transform and wavelet coherence indicate that high-latitude solar filaments lead low-latitude ones in time phase. Furthermore, low-latitude solar activity is better correlated with high-latitude solar activity of the previous cycle than with that of the following cycle, which is statistically significant. Thus, the present study confirms that high-latitude solar activity in the polar regions is indeed better correlated with the low-latitude solar activity of the following cycle than with that of the previous cycle, namely, leading in time phase.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

WHAT CAUSES THE INTER-SOLAR-CYCLE VARIATION OF TOTAL SOLAR IRRADIANCE?

The Physikalisch Meteorologisches Observatorium Davos total solar irradiance (TSI), Active Cavity Radiometer Irradiance Monitoring TSI, and Royal Meteorological Institute of Belgium TSI are three typical TSI composites. Magnetic Plage Strength Index (MPSI) and Mount Wilson Sunspot Index (MWSI) should indicate the weak and strong magnetic field activity on the solar full disk, respectively. Cross-correlation (CC) analysis of MWSI with three TSI composites shows that TSI should be weakly correlated with MWSI, and not be in phase with MWSI at timescales of solar cycles. The wavelet coherence (WTC) and partial wavelet coherence (PWC) of TSI with MWSI indicate that the inter-solar-cycle variation of TSI is also not related to solar strong magnetic field activity, which is represented by MWSI. However, CC analysis of MPSI with three TSI composites indicates that TSI should be moderately correlated and accurately in phase with MPSI at timescales of solar cycles, and that the statistical significance test indicates that the correlation coefficient of three TSI composites with MPSI is statistically significantly higher than that of three TSI composites with MWSI. Furthermore, the cross wavelet transform (XWT) and WTC of TSI with MPSI show that the TSI is highly related and actually in phase with MPSI at a timescale of a solar cycle as well. Consequently, the CC analysis, XWT, and WTC indicate that the solar weak magnetic activity on the full disk, which is represented by MPSI, dominates the inter-solar-cycle variation of TSI.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Uncovering the intrinsic variability of gamma-ray bursts

We develop a robust technique to determine the minimum variability timescale for gamma-ray burst (GRB) light curves, utilizing Haar wavelets. Our approach averages over the data for a given GRB, providing an aggregate measure of signal variation while also retaining sensitivity to narrow pulses within complicated time series. In contrast to previous studies using wavelets, which simply define the minimum timescale in reference to the measurement noise floor, our approach identifies the signature of temporally smooth features in the wavelet scaleogram and then additionally identifies a break in the scaleogram on longer timescales as a signature of a true, temporally unsmooth light curve feature or features. We apply our technique to the large sample of Swift GRB gamma-ray light curves and for the first time—due to the presence of a large number of GRBs with measured redshift—determine the distribution of minimum variability timescales in the source frame. We find a median minimum timescale for long-duration GRBs in the source frame of Δt {sub min} = 0.5 s, with the shortest timescale found being on the order of 10 ms. This short timescale suggests a compact central engine (3 × 10{sup 3} km). We discuss further implications for the GRB fireball model and present a tantalizing correlation between the minimum timescale and redshift, which may in part be due to cosmological time dilation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Detection of the significant impact of source clustering on higher order statistics with DES Year 3 weak gravitational lensing data

We measure the impact of source galaxy clustering on higher order summary statistics of weak gravitational lensing data. By comparing simulated data with galaxies that either trace or do not trace the underlying density field, we show that this effect can exceed measurement uncertainties for common higher order statistics for certain analysis choices. We evaluate the impact on different weak lensing observables, finding that third moments and wavelet phase harmonics are more affected than peak count statistics. Using Dark Energy Survey (DES) Year 3 (Y3) data, we construct null tests for the source-clustering-free case, finding a p-value of p = 4 × 10 −3 (2.6σ) using third-order map moments and p = 3 × 10 −11 (6.5σ) using wavelet phase harmonics. The impact of source clustering on cosmological inference can be either included in the model or minimized through ad hoc procedures (e.g. scale cuts). We verify that the procedures adopted in existing DES Y3 cosmological analyses were sufficient to render this effect negligible. Failing to account for source clustering can significantly impact cosmological inference from higher order gravitational lensing statistics, e.g. higher order N-point functions, wavelet-moment observables, and deep learning or field-level summary statistics of weak lensing maps.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Poster — Thur Eve — 09: Evaluation of electrical impedance and computed tomography fusion algorithms using an anthropomorphic phantom

Integration of biological conductivity information provided by Electrical Impedance Tomography (EIT) with anatomical information provided by Computed Tomography (CT) imaging could improve the ability to characterize tissues in clinical applications. In this paper, we report results of our study which compared the fusion of EIT with CT using three different image fusion algorithms, namely: weighted averaging, wavelet fusion, and ROI indexing. The ROI indexing method of fusion involves segmenting the regions of interest from the CT image and replacing the pixels with the pixels of the EIT image. The three algorithms were applied to a CT and EIT image of an anthropomorphic phantom, constructed out of five acrylic contrast targets with varying diameter embedded in a base of gelatin bolus. The imaging performance was assessed using Detectability and Structural Similarity Index Measure (SSIM). Wavelet fusion and ROI-indexing resulted in lower Detectability (by 35% and 47%, respectively) yet higher SSIM (by 66% and 73%, respectively) than weighted averaging. Our results suggest that wavelet fusion and ROI-indexing yielded more consistent and optimal fusion performance than weighted averaging.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Using compressive sensing to recover images from PET scanners with partial detector rings

Purpose: Most positron emission tomography/computed tomography (PET/CT) scanners consist of tightly packed discrete detector rings to improve scanner efficiency. The authors’ aim was to use compressive sensing (CS) techniques in PET imaging to investigate the possibility of decreasing the number of detector elements per ring (introducing gaps) while maintaining image quality. Methods: A CS model based on a combination of gradient magnitude and wavelet domains (wavelet-TV) was developed to recover missing observations in PET data acquisition. The model was designed to minimize the total variation (TV) and L1-norm of wavelet coefficients while constrained by the partially observed data. The CS model also incorporated a Poisson noise term that modeled the observed noise while suppressing its contribution by penalizing the Poisson log likelihood function. Three experiments were performed to evaluate the proposed CS recovery algorithm: a simulation study, a phantom study, and six patient studies. The simulation dataset comprised six disks of various sizes in a uniform background with an activity concentration of 5:1. The simulated image was multiplied by the system matrix to obtain the corresponding sinogram and then Poisson noise was added. The resultant sinogram was masked to create the effect of partial detector removal and then the proposed CS algorithm was applied to recover the missing PET data. In addition, different levels of noise were simulated to assess the performance of the proposed algorithm. For the phantom study, an IEC phantom with six internal spheres each filled with F-18 at an activity-to-background ratio of 10:1 was used. The phantom was imaged twice on a RX PET/CT scanner: once with all detectors operational (baseline) and once with four detector blocks (11%) turned off at each of 0 °, 90 °, 180 °, and 270° (partially sampled). The partially acquired sinograms were then recovered using the proposed algorithm. For the third test, PET images from six patient studies were investigated using the same strategy of the phantom study. The recovered images using WTV and TV as well as the partially sampled images from all three experiments were then compared with the fully sampled images (the baseline). Comparisons were done by calculating the mean error (%bias), root mean square error (RMSE), contrast recovery (CR), and SNR of activity concentration in regions of interest drawn in the background as well as the disks, spheres, and lesions. Results: For the simulation study, the mean error, RMSE, and CR for the WTV (TV) recovered images were 0.26% (0.48%), 2.6% (2.9%), 97% (96%), respectively, when compared to baseline. For the partially sampled images, these results were 22.5%, 45.9%, and 64%, respectively. For the simulation study, the average SNR for the baseline was 41.7 while for WTV (TV), recovered image was 44.2 (44.0). The phantom study showed similar trends with 5.4% (18.2%), 15.6% (18.8%), and 78% (60%), respectively, for the WTV (TV) images and 33%, 34.3%, and 69% for the partially sampled images. For the phantom study, the average SNR for the baseline was 14.7 while for WTV (TV) recovered image was 13.7 (11.9). Finally, the average of these values for the six patient studies for the WTV-recovered, TV, and partially sampled images was 1%, 7.2%, 92% and 1.3%, 15.1%, 87%, and 27%, 25.8%, 45%, respectively. Conclusions: CS with WTV is capable of recovering PET images with good quantitative accuracy from partially sampled data. Such an approach can be used to potentially reduce the cost of scanners while maintaining good image quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A Novel Fault Diagnosis Approach for Rolling Bearing Based on CWT and Adaptive Sparse Representation

Extraction and enhancement of weak impulse signature is the key of rolling bearing fault prognostics in which case the features are often weak and covered by noise. Tunable Q-factor wavelet transform (TQWT), as an emerging wavelet construction theory developed in a frequency domain explicitly, has the advantages of matching with the specific oscillation behavior of signal components. In this article, an adaptive sparse representation (ASR) method is proposed, which integrates the sparse code shrinkage (SCS) and parameter optimization into TQWT. However, direct application of ASR is difficult to extract fault signatures at the early stage or low-speed operation due to weak fault symptoms and background noise. A novel fault diagnosis strategy based on continuous wavelet transform (CWT) and ASR is investigated. CWT owns significant advantages on multiscale subdivision and weak signal detection. The results of simulated and experimental vibration signal analyses verify the effectiveness of the proposed method in accurately extracting weak impulse features from the noise environment.

Yuan, Xing↗
DOE OSTI2025

Improving the Transportability of a Deep Learning Denoising Model Using Transfer Learning Techniques

The adoption of machine learning techniques in the seismology community has led to great performance improvements in several areas, including signal processing. Specifically, the development of deep learning–based seismic waveform denoising models has the potential to yield improvements in signal detection capabilities for networks operating in particularly noisy environments. Recent advancements in the design of these deep learning denoising models have included the incorporation of continuous and discrete wavelet transform functions into the network architecture to improve the learning capabilities and efficiency of said models. These wavelet transform–based seismic denoising models have shown improved denoising capabilities in regions where there is good agreement between the data features present in the training and evaluation datasets. However, questions remain about the overall transportability of these models to other monitoring regions. Here, in this study, we will determine the baseline transportability of a newly developed multilevel wavelet‐transform convolutional neural network (MWCNN) seismic denoising model. We accomplish this by taking a version of the MWCNN denoising model trained on data collected from the Utah region and evaluating its denoising performance on datasets collected from the neighboring Nevada region, which differ with regard to monitoring sensor types and event histories. We find that there is a notable variability in denoising performance related to the degree of similarity between the initial and new target datasets. The most notable difference in denoising performance is the ability of the denoising model to preserve accurate amplitude information associated with the signal energy present in the waveform data. Finally, we evaluate the ability of transfer learning techniques to improve the transportability of the MWCNN denoising model. We find that although there is still a performance gap present in the denoising results of the MWCNN model, transfer learning did yield improved results.

Quinones, Louis [Sandia National Laboratories (SNL↗