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DOE OSTI2023

Deep neural operators can predict the real-time response of floating offshore structures under irregular waves

The use of neural operators in a digital twin model of an offshore floating structure holds the potential for a significant shift in the prediction of structural responses and health monitoring, offering valuable real-time control insights. In this work, we investigate the effectiveness of three neural operators, namely the deep operator network (DeepONet), the Fourier neural operator (FNO), and the Wavelet neural operator (WNO), to accurately capture the responses of a floating structure under six different sea state codes (3 − 8) based on the wave characteristics described by the World Meteorological Organization (WMO). To further enhance the accuracy of the vanilla architecture of the neural operators, novel extensions, such as wavelet-DeepONet and self-adaptive WNO, are proposed in this paper. The results demonstrate that these high-precision neural operators can deliver structural responses more efficiently, up to two orders of magnitude faster than a dynamic analysis using conventional numerical solvers. Additionally, compared to gated recurrent units (GRUs), a commonly used recurrent neural network for time-series estimation, neural operators are both more accurate and efficient, especially in situations with limited data availability. Taken together, our study shows that FNO outperforms all other operators for approximating the mapping of one input functional space to the output space as well as for responses that have small bandwidth of the frequency spectrum. Conversely, DeepONet, with historical states, proves most accurate in learning the mapping of multiple input functions to the output space and capturing responses within a broad frequency spectrum.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Projective multiscale time-integration for electrostatic particle-in-cell methods

The simulation of problems in kinetic plasma physics is often challenging due to strongly coupled phenomena across multiple scales. In this work, we propose a wavelet-based coarse-grained numerical scheme, based on the framework of Equation-Free Projective Integration, for a kinetic plasma system modeled by the Vlasov–Poisson equations. A kinetic particle-in-cell (PIC) code is used to simulate the meso scale dynamics for short time intervals. This allows the extrapolation over long time-steps of the behavior of a coarse wavelet-based discretization of the system. To validate the approach and the underlying concepts, we perform two 1D1V numerical experiments: nonlinear propagation and steepening of an ion wave, and the expansion of a plasma slab in vacuum. The direct comparisons to resolved PIC simulations show good agreement. We show that the speedup of the projective integration scheme over the full particle scheme scales linearly with the system size, demonstrating efficiency while taking into account fully kinetic, non-Maxwellian effects. This suggests that the approach is potentially interesting for kinetic plasma problems with a large separation of scales.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Towards automated and real-time multi-object detection of anguilliform fishes from sonar data using YOLOv8 deep learning algorithm

Eels (Anguilla spp.), including American eels (Anguilla rostrata), European eels (Anguilla anguilla), and Japanese eels (Anguilla japonica), are species of critical management and regulatory concern due to their vulnerability to various stressors during downstream migrations. Accurate and efficient detection of migrating eels can improve our understanding of fish behaviors and fish-hydraulic structure interactions, thereby facilitating the design, operation, and optimization of more effective downstream passage facilities from both biological and economic perspectives. However, a real-time, automated framework for detecting migrating eels in real-world applications is currently lacking. Leveraging imaging sonar as a reliable technology for fish passage monitoring, field data are acquired using imaging sonar and then converted to single sonar frames/images for subsequent analysis. In this study, a framework based on the You Only Look Once Version 8 (YOLOv8)-based convolutional neural network is proposed for multi-object detection of eels and non-eel fish using the sonar images after image subtraction and additional wavelet denoising. The results from both training and testing phases demonstrate that the framework's ability can successfully detect both eels and non-eel fish in preprocessed sonar images, achieving F1-scores and mAP@0.50 exceeding 0.84. Additionally, the incorporation of wavelet denoising during preprocessing slightly improve detection performance. Furthermore, the transferability of this framework from eel to lamprey detection is demonstrated to be feasible given the similar morphological characteristics of these two species. Overall, the proposed framework achieves accurate and efficient detection of migrating eels, providing reliable and real-time information that can help conserve vulnerable eel and eel-like populations.

Deep learning↗

DOE OSTI2015

Wave like signatures in aerosol optical depth and associated radiative impacts over the central Himalayan region

Doppler Lidar and Multi-Filter Rotating Shadowband Radiometer (MFRSR) observations are utilized to show wave like signatures in aerosol optical depth (AOD) during daytime boundary layer evolution over the Himalayan region. Fourier analysis depicted 60–80 min periods dominant during afternoon hours, implying that observed modulations could be plausible reason for the AOD forenoon–afternoon asymmetry which was previously reported. Inclusion of wave amplitude in diurnal variation of aerosol radiative forcing estimates showed ~40% additional warming in the atmosphere relative to mean AOD. The present observations emphasize the importance of wave induced variations in AOD and radiation budget over the site.

Aerosol optical depth↗

DOE OSTI2021

Rotor blade imbalance fault detection for variable-speed marine current turbines via generator power signal analysis

Marine hydrokinetic (MHK) turbines extract renewable energy from oceanic environments. However, due to the harsh conditions that these turbines operate in, system performance naturally degrades over time. Thus, ensuring efficient condition-based maintenance is imperative towards guaranteeing reliable operation and reduced costs for marine hydrokinetic power. This work proposes a novel framework aimed at identifying and classifying the severity of rotor blade pitch imbalance faults experienced by marine current turbines (MCTs). In the framework, a Continuous Morlet Wavelet Transform (CMWT) is first utilized to acquire the wavelet coefficients encompassed within the 1P frequency range of the turbine's rotor shaft. From these coefficients, several statistical indices are tabulated into a six-dimensional feature space. Next, Principle Component Analysis (PCA) is employed on the resulting feature space for dimensionality reduction, and then the application of a K-Nearest Neighbor (KNN) machine learning algorithm is utilized for fault detection and severity classification. The effectiveness of the proposed framework is validated using a high-fidelity MCT numerical simulation platform, where results demonstrate that the presence of a pitch imbalance fault can be accurately detected 100% of the time and correctly classified based upon severity more than 97% of the time.

42 ENGINEERING↗

DOE OSTI2017

Airborne measurements of isoprene and monoterpene emissions from southeastern U.S. forests

Isoprene and monoterpene emission rates are essential inputs for atmospheric chemistry models that simulate atmospheric oxidant and particle distributions. Process studies of the biochemical and physiological mechanisms controlling these emissions are advancing our understanding and the accuracy of model predictions but efforts to quantify regional emissions have been limited by a lack of constraints on regional distributions of ecosystem emission capacities. We used an airborne wavelet-based eddy covariance measurement technique to characterize isoprene and monoterpene fluxes with high spatial resolution during the 2013 SAS (Southeast Atmosphere Study) in the southeastern United States. The fluxes measured by direct eddy covariance were comparable to emissions independently estimated using an indirect inverse modeling approach. Isoprene emission factors based on the aircraft wavelet flux estimates for high isoprene chemotypes (e.g., oaks) were similar to the MEGAN2.1 biogenic emission model estimates for landscapes dominated by oaks. Aircraft flux measurement estimates for landscapes with fewer isoprene emitting trees (e.g., pine plantations), were about a factor of two lower than MEGAN2.1 model estimates. The tendency for high isoprene emitters in these landscapes to occur in the shaded understory, where light dependent isoprene emissions are diminished, may explain the lower than expected emissions. This result demonstrates the importance of accurately representing the vertical profile of isoprene emitting biomass in biogenic emission models. Airborne measurement-based emission factors for high monoterpene chemotypes agreed with MEGAN2.1 in landscapes dominated by pine (high monoterpene chemotype) trees but were more than a factor of three higher than model estimates for landscapes dominated by oak (relatively low monoterpene emitting) trees. This results suggests that unaccounted processes, such as floral emissions or light dependent monoterpene emissions, or vegetation other than high monoterpene emitting trees may be an important source of monoterpene emissions in those landscapes and should be identified and included in biogenic emission models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Solid waste bin detection and classification using Dynamic Time Warping and MLP classifier

Highlights: • Solid waste bin level detection using Dynamic Time Warping (DTW). • Gabor wavelet filter is used to extract the solid waste image features. • Multi-Layer Perceptron classifier network is used for bin image classification. • The classification performance evaluated by ROC curve analysis. - Abstract: The increasing requirement for Solid Waste Management (SWM) has become a significant challenge for municipal authorities. A number of integrated systems and methods have introduced to overcome this challenge. Many researchers have aimed to develop an ideal SWM system, including approaches involving software-based routing, Geographic Information Systems (GIS), Radio-frequency Identification (RFID), or sensor intelligent bins. Image processing solutions for the Solid Waste (SW) collection have also been developed; however, during capturing the bin image, it is challenging to position the camera for getting a bin area centralized image. As yet, there is no ideal system which can correctly estimate the amount of SW. This paper briefly discusses an efficient image processing solution to overcome these problems. Dynamic Time Warping (DTW) was used for detecting and cropping the bin area and Gabor wavelet (GW) was introduced for feature extraction of the waste bin image. Image features were used to train the classifier. A Multi-Layer Perceptron (MLP) classifier was used to classify the waste bin level and estimate the amount of waste inside the bin. The area under the Receiver Operating Characteristic (ROC) curves was used to statistically evaluate classifier performance. The results of this developed system are comparable to previous image processing based system. The system demonstration using DTW with GW for feature extraction and an MLP classifier led to promising results with respect to the accuracy of waste level estimation (98.50%). The application can be used to optimize the routing of waste collection based on the estimated bin level.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2011

Characterizing the performance of ecosystem models across time scales: A spectral analysis of the North American Carbon Program site-level synthesis

Ecosystem models are important tools for diagnosing the carbon cycle and projecting its behavior across space and time. Most assessments of model performance occur at individual temporal scales, but ecosystems respond to drivers at multiple time scales. Spectral methods, such as wavelet analyses, present an alternative approach that enables the identification of the dominant time scales contributing to model performance in the frequency domain. In this study we used wavelet analyses to synthesize the performance of twenty-one ecosystem models at nine eddy-covariance towers as part of the North American Carbon Program's site-level inter-comparison. This study expands upon previous single-site and single-model analyses to determine what patterns of model failure are consistent across a diverse range of models and sites.

58 GEOSCIENCES↗

DOE OSTI2021

Quantifying Dam-Induced Fluctuations in Streamflow Frequencies Across the Colorado River Basin

Periodic fluctuations in natural streamflow are a major driver of river ecosystem dynamics and water resource management. However, most U.S. rivers are impacted both by long-term hydroclimatic trends and dams that alter flow variability. Despite these impacts, it remains largely unexplored how dams affect the dominant frequencies of natural streamflow over a highly regulated river network. We investigated the entire Colorado River Basin (CRB) to understand how the annual (10–14 months) and multi-annual (24–60 months) frequencies in natural flow regimes have been progressively altered by dams. Given the significant alteration over the CRB, we captured changes in streamflow frequencies between naturalized and observed monthly flows via wavelet analysis. Based on the similarity of changes in streamflow frequencies (annual and multi-annual) over the last 30 years, sections of the riverine network were classified into four groups. The annual frequency was relatively well preserved downstream of Hoover Dam, while showing a systematic trend of alteration downstream of Glen Canyon Dam until Hoover Dam. Meanwhile, the multi-annual frequency component was highly altered for the entire Lower Colorado main stem (i.e., downstream of Glen Canyon). We also identified dams with significant impacts on streamflow frequency by comparing wavelet coherence estimates. This study advances the notion that dams fundamentally alter river flow regimes across multiple frequencies and with varying amplitudes over time and space, with alteration propagating – or being dampened – by both hydroclimatic fluctuations and water resource management.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A physics‐informed learning technique for fault location of DC microgrids using traveling waves

Abstract Fast and accurate fault location in DC power systems is of particular importance to ensure their reliable operation. One of the approaches for implementing a fast‐tripping protection scheme is to use Traveling waves (TW) initiated by a fault scenario. This paper proposes a physics‐informed machine learning approach that utilizes TWs for fault location in DC microgrids. TWs are extracted by the so‐called multiresolution analysis which identifies the TW's wavelet coefficients for multiple frequency ranges. This paper deploys Parseval's theorem to find the energy of wavelet coefficients as a quantitative metric for describing TWs. The hypothesis of this paper is that once the Parseval energy curves for a specific cable are extracted, they can be utilized to locate faults along with that cable regardless of the DC system in which the cable is deployed. The fault location algorithm uses Parseval energy curves to train a Gaussian Process (GP) estimator. With the Parseval energy values of measured current at the protection device location, the GP estimator is able to estimate fault locations with high accuracy. The effectiveness of the proposed algorithm is verified by simulating a DC microgrid system in PSCAD/EMTDC.

Paruthiyil, Sajay Krishnan↗

DOE OSTI2025

Travelling wave‐based fault detection and location in a real low‐voltage DC microgrid

Abstract This paper discusses a device‐level implementation of a travelling wave (TW) protection device (PD) designed for a real low‐voltage DC microgrid. The TWPD fault detection and location algorithm is executed on a commercial digital signal processor (DSP) board, involving signal sampling at 1 MHz via the DSP board's analog‐to‐digital converter (ADC). The analogue input card measures positive pole, negative pole and pole‐to‐pole voltages at the TWPD location. Upon a successful fault detection using a second‐order high‐pass filter, the voltage data is normalised and multi‐resolution analysis (MRA) is performed on a 128‐sample buffer around the TW arrival time. MRA employs the discrete wavelet transform (DWT) to capture high‐frequency voltage patterns, and then the Parseval's energy theorem quantifies these TW characteristics by computing the energy of reconstructed wavelet coefficients. These energy values per decomposed frequency band are the basis for training a random forest classifier that predicts fault location and type. The TWPD is fully implemented and connected to a real DC microgrid in Albuquerque, NM, USA, for validation, and results are shown for field tests verifying the performance under faults.

Paruthiyil, Sajay Krishnan [Department of Electric↗

DOE OSTI2011

Intracluster light in clusters of galaxies at redshifts $0.4 < z < 0.8$

The study of intracluster light can help us to understand the mechanisms taking place in galaxy clusters, and to place constraints on the cluster formation history and physical properties. However, owing to the intrinsic faintness of ICL emission, most searches and detailed studies of ICL have been limited to redshifts z<0.4.We search for ICL in a subsample of ten clusters detected by the ESO Distant Cluster Survey (EDisCS), at redshifts 0.4<z<0.8, that are also part of our DAFT/FADA Survey. We analyze the ICL by applying the OV WAV package, a wavelet-based technique, to deep HST ACS images in the F814W filter and to V-band VLT/FORS2 images of three clusters. Detection levels are assessed as a function of the diffuse light source surface brightness using simulations. In the F814W filter images, we detect diffuse light sources in all the clusters, with typical sizes of a few tens of kpc (assuming that they are at the cluster redshifts). The ICL detected by stacking the ten F814W images shows an 8sigma detection in the source center extending over a ~50x50kpc2 area, with a total absolute magnitude of -21.6 in the F814W filter, equivalent to about two L* galaxies per cluster. We find a weak correlation between the total F814W absolute magnitude of the ICL and the cluster velocity dispersion and mass. There is no apparent correlation between the cluster mass-to-light ratio (M/L) and the amount of ICL, and no evidence for any preferential orientation in the ICL source distribution. We find no strong variation in the amount of ICL between z=0 and z=0.8. In addition, we find wavelet-detected compact objects (WDCOs) in the three clusters for which data in two bands are available; these objects are probably very faint compact galaxies that in some cases are members of the respective clusters. We have shown that ICL is important in clusters at least up to z=0.8.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2012

Spatiotemporal correlation between microdischarges in concentric ring pattern in dielectric barrier discharge at atmospheric pressure

The spatiotemporal correlation between microdischarges of the concentric ring pattern in a dielectric barrier discharge in argon at atmospheric pressure is studied by the wavelet-correlation technique for the first time. The concentric ring patterns lasting more than 5 min have been obtained under circular boundaries with different sizes by suddenly raising the applied voltage. The average correlation coefficient between microdischarge clusters increases with their discharge region increasing. The wavelet-correlation shows a higher correlation degree between the microdischarge clusters at the edge where (d|U{sub appl}|/dt)<0 than at the edge where (d|U{sub appl}|/dt)>0 in per half-cycle of the applied voltage U{sub appl}.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2013

Accurate complex scaling of three dimensional numerical potentials

The complex scaling method, which consists in continuing spatial coordinates into the complex plane, is a well-established method that allows to compute resonant eigenfunctions of the time-independent Schroedinger operator. Whenever it is desirable to apply the complex scaling to investigate resonances in physical systems defined on numerical discrete grids, the most direct approach relies on the application of a similarity transformation to the original, unscaled Hamiltonian. We show that such an approach can be conveniently implemented in the Daubechies wavelet basis set, featuring a very promising level of generality, high accuracy, and no need for artificial convergence parameters. Complex scaling of three dimensional numerical potentials can be efficiently and accurately performed. By carrying out an illustrative resonant state computation in the case of a one-dimensional model potential, we then show that our wavelet-based approach may disclose new exciting opportunities in the field of computational non-Hermitian quantum mechanics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2013

Fractal dynamics of light scattering intensity fluctuation in disordered dusty plasmas

Dynamic light scattering (DLS) technique is a simple and yet powerful technique for characterizing particle properties and dynamics in complex liquids and gases, including dusty plasmas. Intensity fluctuation in DLS experiments often studied using correlation analysis with assumption that the fluctuation is statistically stationary. In this study, the temporal variation of the nonstationary intensity fluctuation is analyzed directly to show the existence of fractal characteristics by employing wavelet scalogram approach. Wavelet based scale decomposition approach is used to separate non-scaling background noise (without dust) from scaling intensity fluctuation from dusty plasma. The Hurst exponents for light intensity fluctuation in dusty plasma at different neutral gas pressures are determined. At low pressures, weaker damping of dust motions via collisions with neutral gases results in stronger persistent behavior in the fluctuation of DLS time series. The fractal scaling Hurst exponent is demonstrated to be useful for characterizing structural phases in complex disordered dusty plasma, especially when particle configuration or sizes are highly inhomogeneous which makes the standard pair-correlation function difficult to interpret. The results from fractal analysis are compared with alternative interpretation of disorder based on approximate entropy and particle transport using mean square displacement.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2014

Time-frequency analysis of non-stationary fusion plasma signals using an improved Hilbert-Huang transform

An improved Hilbert-Huang transform method is developed to the time-frequency analysis of non-stationary signals in tokamak plasmas. Maximal overlap discrete wavelet packet transform rather than wavelet packet transform is proposed as a preprocessor to decompose a signal into various narrow-band components. Then, a correlation coefficient based selection method is utilized to eliminate the irrelevant intrinsic mode functions obtained from empirical mode decomposition of those narrow-band components. Subsequently, a time varying vector autoregressive moving average model instead of Hilbert spectral analysis is performed to compute the Hilbert spectrum, i.e., a three-dimensional time-frequency distribution of the signal. The feasibility and effectiveness of the improved Hilbert-Huang transform method is demonstrated by analyzing a non-stationary simulated signal and actual experimental signals in fusion plasmas.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

The benefits of using time-frequency analysis with synthetic aperture focusing technique

Improvements in detection and resolution are always desired and needed. There are various instruments available for the inspection of concrete structures that can be used with confidence for detecting different defects. However, more often than not that confidence is heavily dependent on the experience of the operator rather than the clear, objective discernibility of the output of the instrument. The challenge of objective discernment is amplified when the concrete structures contain multiple layers of reinforcement, are of significant thickness, or both, such as concrete structures in nuclear power plants. We seek to improve and extend the usefulness of results produced using the synthetic aperture focusing technique (SAFT) on data collected from thick, complex concrete structures. A secondary goal is to improve existing SAFT results, with regards to repeatedly and objectively identifying defects and/or internal structure of concrete structures. Towards these goals, we are applying the time-frequency technique of wavelet packet decomposition and reconstruction using a mother wavelet that possesses the exact reconstruction property. However, instead of analyzing the coefficients of each decomposition node, we select and reconstruct specific nodes based on the frequency band it contains to produce a frequency band specific time-series representation. SAFT is then applied to these frequency specific reconstructions allowing SAFT to be used to visualize the reflectivity of a frequency band and that band's interaction with the contents of the concrete structure. We apply our technique to data sets collected using a commercial, ultrasonic linear array (MIRA) from two 1.5m × 2m × 25cm concrete test specimens. One specimen contains multiple layers of rebar. The other contains honeycomb, crack, and rebar bonding defect analogs. This approach opens up a multitude of possibilities for improved detection, readability, and overall improved objectivity. We will focus on improved defect/reinforcement isolation in thick and multilayered reinforcement environments. Additionally, the ability to empirically explore the possibility of a frequency-band-defect-type relationship or sensitivity becomes available.

36 MATERIALS SCIENCE↗

DOE OSTI2011

WAVE PROPAGATION AND JET FORMATION IN THE CHROMOSPHERE

We present the results of numerical simulations of wave propagation and jet formation in solar atmosphere models with different magnetic field configurations. The presence in the chromosphere of waves with periods longer than the acoustic cutoff period has been ascribed to either strong inclined magnetic fields, or changes in the radiative relaxation time. Our simulations include a sophisticated treatment of radiative losses, as well as fields with different strengths and inclinations. Using Fourier and wavelet analysis techniques, we investigate the periodicity of the waves that travel through the chromosphere. We find that the velocity signal is dominated by waves with periods around 5 minutes in regions of strong, inclined field, including at the edges of strong flux tubes where the field expands, whereas 3 minute waves dominate in regions of weak or vertically oriented fields. Our results show that the field inclination is very important for long-period wave propagation, whereas variations in the radiative relaxation time have little effect. Furthermore, we find that atmospheric conditions can vary significantly on timescales of a few minutes, meaning that a Fourier analysis of wave propagation can be misleading. Wavelet techniques take variations with time into account and are more suitable analysis tools. Finally, we investigate the properties of jets formed by the propagating waves once they reach the transition region, and find systematic differences between the jets in inclined-field regions and those in vertical field regions, in agreement with observations of dynamic fibrils.

79 ASTRONOMY AND ASTROPHYSICS↗