Harmonic Parameters Research Articles

Harmonics and Interharmonics Detection Based on Synchrosqueezing Adaptive S-Transform

The integration of renewable energy generation and nonlinear power electronic equipment into the grid brings about complex harmonics and interharmonics problems. The amplitude and frequency of harmonics and interharmonics should be detected by high time-frequency (T-F) resolution methods owing to their time-varying transient features. In this paper, a synchrosqueezing adaptive S-transform (SAST) method is proposed to detect the parameters of harmonics. Firstly, the time-frequency spectrum (TFS) of the harmonic signals is acquired by an adaptive S-transform (AST) algorithm. The TFS results are then subjected to synchronous compression, so as to achieve higher time-frequency representation precision. The detection results of the simulation signals show that SAST can achieve a better time-frequency resolution than the S-transform (ST) and synchrosqueezing short-time Fourier transform (SSTFT). In addition, the application of SAST to the analysis of experimental signals also suggests its superiority in the parameter detection of harmonics, especially for the time-varying interharmonics.

A high-precision harmonic distortion measurement algorithm and its system design based on time-domain matrix operations

In this paper, a harmonic distortion measurement algorithm and its system design based on time-domain matrix operations are proposed, which can quickly calculate the harmonic amplitudes, phases and total harmonic distortion of a signal. To realize the calculation of harmonic signal parameters in the time domain, we proposed a method to convert the nonlinear function representing the signal into a linear function, constructed a matrix equation, and solved the equation by the row transformation method, which significantly reduced the complexity of the algorithm. Finally, based on the proposed algorithm, a system design was implemented by the digital signal processing theory, and the measurements showed that the system achieves an amplitude error of nearly 1% and a THD error of less than 0.3% when measuring the generated 10 kHz fundamental with 5-order harmonics. The system can be used to measure and calculate the amplitudes and THD of harmonic distortion signals with high precision.

Research on the Contour Modeling Method of Peripheral Nerve Internal Fascicular Groups During the Non-Splitting/Merging Phase and Distribution Rules of Model Parameters.

ObjectivesTo investigate benchmark data for docking the same functional nerve bundles based on the mathematical contour model of peripheral nerve internal fascicular groups.Materials and MethodsFirst, the discrete points of the original contours of nerve bundles were extracted into a dataset through the image process. Second, two indicators were employed to evaluate the modeling precision. Third, the dataset was modeled by the 3rd-order quasi-uniform B-spline method. Fourth, the dataset was modeled by the Fourier transform method. Fifth, all contours were modeled by the 4th-order Fourier method. Then, the histogram of each parameter from the Fourier model was calculated. Furthermore, the probability density function was fit to each parameter.ResultsFirst, the optimized sampling number of the 3rd-order quasi-uniform B-spline method is 21. The sampling number is the control point number of the 3rd-order quasi-uniform B-spline, which produces more than 63 parameters in the model. Second, when the Fourier transform model is employed to model the contour of nerve bundles, the optimized order number yields a 4th-order Fourier model, which has 16 parameters. Third, when all contours are modeled by the 4th-order Fourier model, the statistical analysis shows that (1) the pitch parameters a1 and d1 obey the mixed Gaussian distribution; (2) the harmonic parameter b3 obeys the normal distribution; and (3) the pitch parameters b1 and c1 and the remaining harmonic parameters obey the t distribution with position and scale.ConclusionThis work paves the way for the exploration of the correlation between model parameters and spatial extension.

Increasing the sensitivity and expanding the functionality of bipolar analysis of rotary machine vibrations

Vibration-acoustic diagnostics of rotary machine defects is the most effective method for non-destructive testing of their technical condition. Practice shows that its successful use largely depends on a set of available methods for analyzing vibration processes. Gears are the most common and heavy-duty components that largely determine the overall vibratory condition of a machine. There is quite a variety of methods for vibration-based diagnostics of gear defects. They include an interesting method of bipolar analysis which consists in separate analysis of the positive and negative parts of a vibration signal with subsequent formation of the diagnostic indicator in the form of differences, ratios, etc. The method is aimed at assessing the quality of gearbox assembly by the position of the tooth contact pattern. Limited area of use and low sensitivity is a disadvantage of this method. The paper shows that the use of broadband vibration maxima in the application of bipolar analysis in vibration-based diagnostics of rotary machine defects significantly increases its efficiency. Using the example of wear of tooth flanks in the sun gear satellite gears pair and the value of the backlash in the differential gearbox of a turboprop engine, it was found to be ensured by increasing the sensitivity of the method and expanding its functionality. In our analysis, we used statistics of gearboxes with different degrees of wear of the tooth flanks and repaired gearboxes with different backlashes. In this case, a wide set of well-known diagnostic indicators can be used: intensity of n-dimensional vectors of informative series, parameters of individual harmonics, amplitude modulation depth, probabilistic characteristics in selected frequency bands, dimensionless discriminants, cepstra, etc. The paper presents some examples of these methods.

Design of Multi-Renewable Energy Storage and Management System Using RL-ICSO Based MPPT Scheme for Electric Vehicles

Nowadays, traditional power systems are being developed as an emergence for the use of smart grids that cover the integration of multi-renewable energy sources with power electronics converters. Efforts were made to design power quality controllers for multi-renewable energy systems (photovoltaic (PV), Fuel Cell and Battery) to meet huge energy demands. Though there have been several techniques employed so far, the power quality issue is a major concern. In this paper, a multi-objective optimal energy management system for electric vehicles (EVs) is proposed using a reinforcement learning mechanism. Furthermore, the maximum power point tracking (MPPT)-based Reinforcement Learning-Iterative cuckoo search optimization algorithm (RL-ICSO) along with the Proportional Integral Derivative (PID) controller is incorporated. For this, a renewable energy source is considered as input for eliminating voltage and current harmonics. Similarly, a DC to AC inverter using a Model Predictive Control (MPC) controller-based pulse generation process was carried out to incorporate the power quality compensation of multi-renewable energy microgrid harmonics in three-phase systems. The generated energy is checked for any liabilities by adding a fault in the transmission line and thereby rectifying the fault by means of the Unified Power Quality Controller (UPQC) device. Thus, the fault-rectified power is stored in the grid, and the transmitting power can be used for EV charging purposes. Thus, the energy storage system is effective in charging and storing the needed power for EVs. The performance estimation is carried out by estimating the simulation outcome on Total Harmonic Distortion (THD) values, parameters, load current and voltage. In addition, the performance estimation is employed, and the outcomes attained are represented. The analysis depicts the effectiveness of the power and energy management ability of the proposed approach.

Improvement of the electric energy quality in underground electric networks in highly productive coal mines

One of the main factors for the effective functioning of the power supply system in highly productive coal mines is the uninterrupted power supply of underground consumers for the entire process cycle at sufficient amount of electric energy at a high level of quality and performance. The analysis of electric energy consumption in highly productive coal mine has shown that about 57 % of electric energy consumers are located in underground workings. Consumers can be divided into the following areas of basic technological process of coal production: production areas (13 %); conveyor transport areas (13 %); preparation areas (8 %), and areas of auxiliary processes of coal production: mine drainage (23 %). The increase in the share of controlled-velocity electric drives in the total power balance of fully-mechanized longwalls leads to factors previously atypical of underground power networks. Such factors include changes to the harmonic composition of the network, arising higher current and voltage harmonics, affecting the supplying network and causing heating of electrical equipment, power and electric energy losses. Therefore, the most pressing issues are to improve electric energy quality in underground electric networks of highly productive coal mines. The study has developed a technique for experimental investigations of quality indicators of electric energy (presented in the form of algorithm) in respect to specific conditions of highly productive coal mines. These include dangerous facilities in terms of sudden gas/dust outbursts. This technique was tested at a number of coal mines of JSC SUEK-Kuzbass. The study also presents the results of experimental investigations to determine the actual level of total harmonic distortion (factor) in underground electric networks of fully-mechanized longwalls of coal mines. Of greater importance is justification of higher harmonic filter parameters. To this end a calculation algorithm based on the developed technique has been proposed. Research has shown that application of forward and inverse Clarke transformations for calculating the harmonic filter parameters is applicable for all voltage levels. The simulation model of power supply system of a coal mine fully-mechanized longwall allows conditions of higher harmonics damping to be studied by means of a device for improvement of electric energy quality. Applying the proposed technical solutions to improve the quality of electric energy based on the simulation modeling allowed the successful damping of higher harmonics to be achieved. For example, the total harmonic components voltage (THD (U)) was reduced from 9.07 to 1.77 %.

A Three-Dimensional Microwave Sparse Imaging Approach Using Higher-Order Basis Functions

This paper presents a novel method for three-dimensional microwave imaging based on sparse processing. To enforce the sparsity of the unknown function, we take advantage of the fact that arbitrary three-dimensional electromagnetic fields can be decomposed into two components with respect to the radial direction: one with transverse-magnetic polarization and the other with transverse-electric polarization. Each component can be further expressed as a sum of spherical harmonics, which provide the dictionary exploited by the sparse processing algorithm. Our measurement model relates the data and the parameters of the spherical harmonics’ sources, which are uniformly distributed on a grid sampling the imaging domain. By relying on the theory of degrees of freedom of electromagnetic fields, it can be shown that only a few harmonics are sufficient to accurately represent the measured scattered field from objects whose diameter is of the order of the wavelength, thus allowing reducing the dimension of the adopted dictionary. We analyze several imaging scenarios to assess the algorithm’s performance, including different object shapes, sensor orientations, and signal-to-noise ratios. Moreover, we compare the obtained results with other state-of-the-art linear imaging techniques. Notably, thanks to the adopted dictionary, the proposed algorithm can yield accurate images of both convex and concave objects.

Computation of Nonlinear Load Harmonic Currents in the Presence of External Distortions

This paper deals with the issues of computation of the nonlinear load harmonic currents in the presence of external distortions based on the real measurements with help of passive harmonic. Such values are necessary when modeling an equivalent nonlinear load as current sources in the presence of external distortions. The passive filter allows to separate external and internal distortions, which is necessary when determining the harmonic currents of a single consumer. The influence of various parameters on the computation of harmonic currents of a nonlinear load, such as the parameters of a passive harmonic filter, the impedance of the power supply transformer, the load parameters of the consumer, taking into account the harmonic generation from the grid side, and from the consumer side, is considered. It is shown that an external source of distortion has practically no effect on the error in estimating the harmonic current magnitudes of a nonlinear load. The obtained simulation results were confirmed experimentally on a laboratory test bench. Recommendations for the selection of passive harmonic filter parameters have been developed to minimize the error in determining the harmonic current magnitudes of a nonlinear load.

Coupled cluster theory for the ground and excited states of two-dimensional quantum dots

We present a study of the two-dimensional circular quantum dot model Hamiltonian using a range of quantum chemical ab initio methods. Ground and excited state energies are computed on different levels of perturbation theories, including the coupled cluster method. We outline a scheme to compute the required Coulomb integrals in real space and utilize a semianalytic solution to the integral over the Coulomb kernel in the vicinity of the singularity. Furthermore, we show that the remaining basis set incompleteness error for two-dimensional quantum dots scales with the inverse number of virtual orbitals, allowing us to extrapolate to the complete basis set limit energy. By varying the harmonic potential parameter we tune the correlation strength and investigate the predicted ground and excited state energies.

On the correlation of temperature harmonic content with energy dissipation in C45 steel samples under fatigue loading

Fatigue damage onset involves a transformation of mechanical energy. This is in part spent into work of plastic deformation and fracture and in part transformed into heat. Therefore, different thermomechanical heat sources are activated during a fatigue loading cycle, resulting in a temperature modulation that can be characterized by its frequency content. Several studies have monitored the temperature on samples undergoing high-cycle fatigue, measuring specific thermomechanical metrics which could serve as indicators of damage evolution. The present work evaluates the harmonic content of temperature, to investigate its correlation with the material dissipation. The harmonic terms investigated are the amplitude and phase of temperature at the loading frequency (First Harmonic) and at twice the loading frequency (Second Harmonic). The paper reviews the mechanisms linking the evolution of heat dissipation to changes in these four parameters. The theoretical analysis is supported by an experimental campaign carried out on C45 steel tensile specimen subject to R=−1 and R=0.1 loading ratios. The harmonic metrics are evaluated analyzing both their spatially averaged value and their spatial dispersion, to check for the formation of localized heat dissipation zones in early fatigue life. The study shows that the phase of the Second Harmonic signal has a peculiar bimodal distribution that can be correlated to either a thermoelastic or dissipative prevailing effect. This behavior evolves towards a unimodal dissipative behavior when the load amplitude is increased, thus proposing the Second Harmonic phase parameter as an effective indicator for monitoring internal intrinsic dissipation in the material.

Laser-induced fluorescence experimental spectroscopy and theoretical calculations of uranium monoxide

As a model molecule of actinide chemistry, UO molecule plays an important role in understanding the electronic structure and chemical bonding of actinide-containing species. We report a study of the laser-induced fluorescence spectra of the U16O and U18O using two-dimensional spectroscopy. Several rotationally resolved excitation spectra were investigated. Accurate molecular rotational constants and equilibrium internuclear distances were reported. Low-lying electronic states information was extracted from high resolution dispersed fluorescence spectra and analyzed by the ligand field theory model. The configuration of the ground state was determined as U2+(5f37s)O2−. The branching ratios, and the vibrational harmonic and anharmonic parameters were also obtained. Radiative lifetimes were determined by recording the time-resolved fluorescence spectroscopy. Transition dipole moments were calculated using the branching ratios and the radiative lifetimes. These findings were elucidated by using quantum-chemical calculations, and the chemical bonding was also analyzed. The findings presented in this work will enrich our understanding of actinide-containing molecules.

Properties of Harmonic and Composite Half-Waves, Determination of the Uniform Time Sampling Interval of Digital Signal Processors

When building autonomous real-time systems (RTS), it is necessary to solve the problem of optimal multitasking loading of a number of parallel functioning digital signal processors. One of the reserves for achieving the desired result is the implementation of samples from the sensor signals of information about the magnitude of the signal most rarely in time. In this case, it is necessary to provide a linear or stepwise approximation of the signal by samples with an acceptable reconstruction error. One of the system tasks of these processors is filtering signals or limiting the spectrum to the cutoff frequency. A distinctive feature of the approach proposed in the article is the fulfillment of the condition: if the measurement of this frequency is difficult (for example, in the electromechanical means of the RTS), then for such signals it is proposed to match the maximum values of the harmonic half-wave parameters: approximation error, speed and acceleration. The study opens up the prospect of applying new approaches to sampling the time of signals in the amplitude-time domain and determining the equivalent cutoff frequency of the signal spectrum for such signals. In this article, the dependences of the value of the unit of system time for input-output of data on the degree of agreement between the maximum values of the signal parameters are obtained. A mathematical model of the extreme behavior of a signal between two adjacent samples is given in the form of a harmonic half-wave. The study is also extended to convex composite harmonic functions, according to which the signal can deviate from the results of a linear or stepwise approximation of the signal for these samples. The comparison of the models by the value of the relative time sampling intervals, depending on the degree of matching of the maximum parameters of the harmonic half-wave, is carried out. When comparing, in addition to these maximum parameters, the relationship of the maximum signal speed with the error of approximating the samples by steps and the relationship of the maximum acceleration of the signal with the maximum error of the linear approximation was taken into account. The results make it possible to determine the duration of the intervals of uniform sampling of the signal time based on the results of the inspection of the control object, substantiate a significant increase in the sampling interval of time or a similar increase in the number of tasks to be solved per unit of system time.

Harmonic detection algorithm based on Nuttall double-window all-phase Fast Fourier Transform trispectral interpolation

In harmonic detection of power grid, spectrum leakage of traditional FFT algorithm affects the accuracy of detection results. In this paper, a Nuttall double-window all-phase FFT trispectral interpolation algorithm is proposed. The phase of the spectral line with the maximum amplitude at the sampling point is taken as the initial phase of the signal, and the position of the main harmonic line is determined by using the amplitudes of the three spectral lines near the harmonic peak frequency point. Simulation results show that this algorithm can reduce the error of harmonic parameter detection more effectively and reduce the influence of white noise.

Nonlinear load modeling for analysis of non-sinusoidal conditions in electrical networks based on measurements of harmonic parameters

Non-sinusoidal conditions in electrical networks need to be calculated for their control and development of technical measures to maintain harmonic parameters according to the requirements of regulatory documents. These calculations are impossible without electrical network and nonlinear load models that adequately reflect them in computational programs. Nonlinear load models have been developed for a long time. Some studies present general modeling principles and models of various nonlinear devices. Others consider some nonlinear devices as equivalent nonlinear loads connected to low and medium voltage networks. A whole host of high-power nonlinear electrical equipment is connected to high voltage networks. Modeling nonlinear loads connected to these networks is a problem. Research of measured parameters of harmonic conditions in electrical networks has shown that they are random values. The probabilistic nature is determined by the network configuration, a range of network components, the number of nonlinear loads, wave and frequency properties of the network, harmonic source phase currents, voltage at terminals of nonlinear electrical equipment, changes in operating conditions and load power, and many other factors. Nonlinear loads can only be modeled based on the measurements of parameters of harmonic conditions due to their unpredictability. The paper presents an overview of existing methods for modeling nonlinear loads, a methodological approach to modeling nonlinear loads based on measured parameters, an algorithm for modeling harmonics of active and reactive currents, a computational program algorithm designed to identify distribution functions of measured current harmonics, and modeling results for current harmonics of railway transformers supplying power to electric locomotives.

Galvanic Vestibular Stimulation Effects on EEG Biomarkers of Motor Vigor in Parkinson's Disease.

Background: Impaired motor vigor (MV) is a critical aspect of Parkinson's disease (PD) pathophysiology. While MV is predominantly encoded in the basal ganglia, deriving (cortical) EEG measures of MV may provide valuable targets for modulation via galvanic vestibular stimulation (GVS).Objective: To find EEG features predictive of MV and examine the effects of high-frequency GVS.Methods: Data were collected from 20 healthy control (HC) and 18 PD adults performing 30 trials total of a squeeze bulb task with sham or multi-sine (50–100 Hz “GVS1” or 100–150 Hz “GVS2”) stimuli. For each trial, we determined the time to reach maximum force after a “Go” signal, defined MV as the inverse of this time, and used the EEG data 1-sec prior to this time for prediction. We utilized 53 standard EEG features, including relative spectral power, harmonic parameters, and amplitude and phase of bispectrum corresponding to standard EEG bands from each of 27 EEG channels. We then used LASSO regression to select a sparse set of features to predict MV. The regression weights were examined, and separate band-specific models were developed by including only band-specific features (Delta, Theta, Alpha-low, Alpha-high, Beta, Gamma). The correlation between MV prediction and measured MV was used to assess model performance.Results: Models utilizing broadband EEG features were capable of accurately predicting MV (controls: 75%, PD: 81% of the variance). In controls, all EEG bands performed roughly equally in predicting MV, while in the PD group, the model using only beta band features did not predict MV well compared to other bands. Despite having minimal effects on the EEG feature values themselves, both GVS stimuli had significant effects on MV and profound effects on MV predictability via the EEG. With the GVS1 stimulus, beta-band activity in PD subjects became more closely associated with MV compared to the sham condition. With GVS2 stimulus, MV could no longer be accurately predicted from the EEG.Conclusions: EEG features can be a proxy for MV. However, GVS stimuli have profound effects on the relationship between EEG and MV, possibly via direct vestibulo-basal ganglia connections not measurable by the EEG.

Dynamic Harmonic Phasor Estimator Considering Frequency Deviation

The fundamental frequency of power system may deviate from the nominal value due to various reasons, such as load variation, subsynchronous resonance and fault occurrence. The maximum frequency deviation considered by the latest IEEE standard is 5 Hz. This paper proposes a dynamic harmonic synchrophasor estimator, which can accurately estimate the dynamic harmonic parameters even if the frequency deviation reaches 5 Hz. Multiple discrete frequencies are generated at a fixed frequency interval on both sides of the nominal frequency, and multiple dynamic harmonic filters are generated offline based on those frequencies. The windowed interpolation discrete Fourier transform is adopted for online monitoring of the fundamental frequency. According to the detected frequency, a harmonic filter, which is generated with the discrete frequency that is closest to the detected frequency, can be selected to analyze the dynamic harmonic synchrophasor, frequency and rate of change of frequency. The performances of the proposed estimator are verified through tests under various frequency deviation conditions, and the test results show that the proposed estimator can accurately estimate the dynamic harmonic parameters when the frequency deviation is within 5 Hz.

Suppressing of Power Line Artifact From Electroencephalogram Measurements Using Sparsity in Frequency Domain.

Electroencephalogram (EEG) plays an important role in brain disease diagnosis and research of brain-computer interface (BCI). However, the measurements of EEG are often exposed to strong interference of power line artifact (PLA). Digital notch filters (DNFs) can be applied to remove the PLA effectively, but it also results in severe signal distortions in the time domain. To address this problem, spectrum correction (SC) based methods can be utilized. These methods estimate harmonic parameters of the PLA such that compensation signals are produced to remove the noise. In order to ensure high accuracy during harmonic parameter estimations, a novel approach is proposed in this paper. This novel approach is based on the combination of sparse representation (SR) and SC. It can deeply mine the information of PLA in the frequency domain. Firstly, a ratio-based spectrum correction (RBSC) using rectangular window is employed to make rough estimation of the harmonic parameters of PLA. Secondly, the two spectral line closest to the estimated frequency are calculated. Thirdly, the two spectral lines with high amplitudes can be utilized as input of RBSC to make finer estimations of the harmonic parameters. Finally, a compensation signal, based on the extracted harmonic parameters, is generated to suppress PLA. Numerical simulations and actual EEG signals with PLA were used to evaluate the effectiveness of the improved approach. It is verified that this approach can effectively suppress the PLA without distorting the time-domain waveform of the EEG signal.

Analysis of Errors in Active Power and Energy Measurements Under Random Harmonic Distortion Conditions

Abstract As harmonic distortion of voltage and current is reality in the power system, the need for accurate measurement of electrical power and energy goes beyond the instruments’ specifications and calibration procedures regarding pure sine wave signals. Several international standards and recommendations provide test signals for examination of electricity meters under non-sinusoidal conditions, however, not all of the test signal parameters’ possible states are faithfully represented in those documents. Because the high order harmonics may possess random amplitudes and phase shifts in relation to components at fundamental frequency, it is important that the meter’s performance is verified with random waveforms as well. The non-linear dependence between the measured power/energy and the phase shifts, both between fundamental and harmonic components, provides additional complexity of such an analysis. Simple test signals, which are in accordance with the standards’ demands and propositions, are used for determination of the measurement error in case of different harmonic distortion parameter change. In order for a general error function for any measurement device to be determined, mathematical modelling, regarding the results from multiple tests, is performed. The mathematical model presents a strong dependence between a single component’s phase shifts and a meter’s error and it provides a systematization of all signal parameters’ influence on the measurement accuracy.

Progress on nonlinear ultrasonic evaluation of damages within the adhesively bonded composites

<p indent="0mm">Adhesively bonded composite structures are widely used in aerospace, shipbuilding and automotive industries because of such advantages as lower structural weight and more uniformly distributed stresses. As an important component of the adhesive joints, properties of the adhesive interface determine the integrity of the whole adhesive structure, making the research on damage detection and evaluation of the adhesive interface an extensively concerned topic in the related fields. The ultrasonic nondestructive testing technique, which has a long history of development since its appearance, is an effective technology for testing, evaluating and characterizing material properties and damages. Because of the advantage of realizing the detection without causing additional destruction to the structure and materials, the ultrasonic nondestructive testing has been widely used in engineering practice. However, the existing traditional ultrasonic nondestructive testing technology mainly concerns the initiation and accumulation of material damages, final failure of the structure on service. It is difficult in characterizing the bonding strength, bonding force, early degradation and micro-damage of the bonding surface directly and quantitatively, which is not capable of realizing the structural health monitoring and life prediction of the adhesive joints. It is promising that the nonlinear ultrasonic technology developed in recent years can effectively solve the problems mentioned above. When nonlinear waves are employed, this technique has higher sensitivity in characterizing the change of micro-structure of materials and micro-damages, whose spatial dimension is much smaller than the wavelength of ultrasonic waves. Considering these advantages, nondestructive testing and evaluation employing nonlinear waves have been widely investigated in recent years; a series of new methods have appeared and many important achievements have been obtained. Detection and evaluation of damages within bonded composite structures and adhesively jointed interfaces are more complicated. There are two types of adhesive failure: Adhesion failure in the interface layer between the adhesive and the adhered, and cohesion failure in the adhesive layer. Due to the existence of adhesive interfaces, complex refraction, reflection and scattering will occur when ultrasonic waves propagate in the adhesive composite structure and interfaces, particularly the existence of the adhesive layer will increase the attenuation of the wave energy. These problems make the investigation of wave propagation in the adhesively bonded structure even more complicated. Based on the nonlinear ultrasonic technique, many investigations have been carried out and many fruitful achievements have been obtained in examining and evaluating the early degradation of the bonded interface. Generally, bulk waves, guided waves and mixing waves are employed in theoretical and experimental research. Bulk waves can be excited and received without much difficulty, and guided waves are effective in examining plate and shell structures. In both cases, the magnitude of high-order harmonic waves and related nonlinear parameters are used in the process of evaluation and characterization. As to the mixing wave method, the amplitude of the mixing resonance waves and the nonlinear parameter based on the mixing resonance waves are employed in evaluating and locating the early damage within the composite structure and the adhesively bonded interface. In this paper, we review the progress of applying the nonlinear ultrasonic waves in evaluating early degradation and micro-damage of the adhesively bonded composite structures and interface damage, including the theoretical and experimental investigations of employing transmitted bulk waves, guided waves and mixed waves. Furthermore, future research related to the characterization of composite structures and adhesive joints is suggested.

Using Artificial Neural Networks for the Estimation of Subsurface Tidal Currents from High-Frequency Radar Surface Current Measurements

An extensive record of current velocities at all levels in the water column is an indispensable requirement for a tidal resource assessment and is fully necessary for accurate determination of available energy throughout the water column as well as estimating likely energy capture for any particular device. Traditional tidal prediction using the least squares method requires a large number of harmonic parameters calculated from lengthy acoustic Doppler current profiler (ADCP) measurements, while long-term in situ ADCPs have the advantage of measuring the real current but are logistically expensive. This study aims to show how these issues can be overcome with the use of a neural network to predict current velocities throughout the water column, using surface currents measured by a high-frequency radar. Various structured neural networks were trained with the aim of finding the network which could best simulate unseen subsurface current velocities, compared to ADCP data. This study shows that a recurrent neural network, trained by the Bayesian regularisation algorithm, produces current velocities highly correlated with measured values: r2 (0.98), mean absolute error (0.05 ms−1), and the Nash–Sutcliffe efficiency (0.98). The method demonstrates its high prediction ability using only 2 weeks of training data to predict subsurface currents up to 6 months in the future, whilst a constant surface current input is available. The resulting current predictions can be used to calculate flow power, with only a 0.4% mean error. The method is shown to be as accurate as harmonic analysis whilst requiring comparatively few input data and outperforms harmonics by identifying non-celestial influences; however, the model remains site specific.