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Extensive assessment about the efficiency and quality of the power production is important to the floating wind turbine (FWT) development. The power performance of a wind turbine depends on turbine dynamics, control strategy, and atmospheric conditions. As for FWTs, the additional floater oscillations should be particularly incorporated. In this paper, extensive simulations are conducted for a Spar‐type FWT using the FAST software. The relative wind velocity introduced by floater oscillations and the corresponding power capture mechanisms are analyzed in detail. A concept of equivalent turbulence intensity is proposed to generally describe the intensity of floater oscillation. It is observed to satisfyingly serve as an independent variable for FWT power production. The mapping relationship between the FWT power generation and the equivalent turbulence intensity is constructed. Results show that the FWT output power increases linearly with the equivalent turbulence intensity square in the below‐rated region, while it is well regulated around the rated power by the bladepitch controller in the above‐rated region. The normalized power fluctuation augments linearly with equivalent turbulence intensity over the whole operating wind speeds except the rated‐nearby region. The findings are potentially helpful for the power forecasting, controller development, economical evaluation, and wind farm optimizations.

Aerodynamic loading is one of the most dominating environmental excitations of Floating Wind Turbines (FWTs) and plays an important role in the FWT dynamics. In this study, we developed a model Spar-type FWT and then constructed a dedicated experiment apparatus to reveal the aerodynamic loading effects. As for the floater motion, the wind loading serves as an external exciting force, as well as potential damping source and equivalent added mass item. To take all these roles into account, we proposed a concept of aerodynamic loading effect. The presence of aerodynamic loading effect is validated by free decay tests and white noise wave tests. Results show that the aerodynamic loading effect alters the natural frequencies and damping ratios of the FWT system. We suggest the FWT designers refer to the altered natural frequencies when designing the floater and the FWT controllers. We experimentally observed that the increased aerodynamic loading tends to suppress the pitch resonance vibration while amplifies the resonance vibration at surge frequency. Besides, the nacelle motions, blade loads, and the tower dynamics, are all significantly impacted by the aerodynamic loading effect. The presented results are potentially helpful for optimizing FWTs and developing advanced FWT controllers.

Radiation-induced oral mucositis is a major adverse event of radiotherapy. Severe oral mucositis may cause unwanted interruption in radiotherapy and reduce long-term survival in cancer patients receiving radiotherapy, but until now, there have been no effective options for preventing radiation-induced oral mucositis. Quercetin is a flavonoid that is widely found in food species and has anti-inflammatory, antioxidant, and anticancer activities. In this study, we investigated a new role of quercetin in preventing radiation-induced oral mucositis. Quercetin exerted preventive effects against radiation-induced oral mucositis induced by single-dose (25 Gy) ionizing radiation or fractionated ionizing radiation ( 8 Gy × 3 ) in C57BL/6 mice and maintained the proliferation ability of basal epithelial cells. Quercetin pretreatment alleviated reactive oxygen species generation, NF-κB pathway activation, and downstream proinflammatory cytokine production and reduced DNA double-strand breaks and cellular senescence induced by ionizing radiation. Quercetin also upregulated BMI-1 expression in oral epithelial cells and promoted ulcer repair. In addition, quercetin exerted similar radioprotective effects in irradiated primary cultured normal human keratinocytes, reduced reactive oxygen species generation and proinflammatory cytokine release, and promoted DNA double-strand break repair and wound healing by upregulating the expression of BMI-1, which is a polycomb group protein. Thus, quercetin can block multiple pathological processes of radiation-induced oral mucositis by targeting BMI-1 and may be a potential treatment option for preventing radiation-induced oral mucositis.

In this study, we designed an integrated fixture system using eddy current sensors and piezoelectric actuators. With this system, active supporting forces are exerted on thin-walled workpieces to adjust their stiffness in a real-time manner and thus to suppress chatter in milling. When modeling this system, we used a mass–stiffness–damping element to characterize the dynamic behavior of the actuator under high-speed responses, and we considered the electromechanical coupling between the mechanical elements and the drive circuit of the actuator. We then proposed an optimal delayed state feedback controller for the closed-loop system. The delayed state is obtained by introducing the time delay in milling dynamics into the regular state feedback, and the optimal gain is worked out through the differential quadrature and gradient descent, which is a much more computationally efficient method than the classic semi-discretization. Among all similar approaches, our strategy leads to the largest stability region for milling of thin-walled parts and requires the least energy input, which are proved by simulations and experiments.

Contact of balls in screw nuts and bearings causes the nonlinearity of a ball screw feed system. The identification of nonlinear stiffness helps to understand and control the feed system. The axial vibration of the ball screw feed system can be descripted by a single-degree-of-freedom (SDOF) system with polynomial nonlinear terms. Since generalized frequency response functions (GFRFs) can be expressed in terms of linear and nonlinear system parameters, one can estimate these parameters based on the measured GFRFs. In this effort, both single-tone and multitone harmonic inputs are employed to measure GFRFs, and a simple search algorithm is proposed to find the suitable frequency set of the multitone input. Parameter identification based on these two methods has been compared by numerical simulation and experiment. Since the response spectrum measured from vibration experiment of a ball screw system has even and odd harmonics, it is suitable to simplify the restoring force with square and cubic nonlinearity form. Static experiment is also conducted to verify the identified parameters, and the results show that the method based on single-tone inputs performs effectively in identifying axial stiffness of a ball screw feed system, but the method based on multitone inputs gets the incorrect results in practical application

Frames, shells, and hybrid structures with early damages, such as early cracks, often behave as extremely weak nonlinear systems, among which the nonlinearity is difficult to be detected, especially if the system response is affected by the noise. To avoid these damages becoming catastrophic failures, developing effective incipient damages detection methods is important. The nonlinear output frequency response functions (NOFRFs) and associated indexes can be considered as one kind of the prospective detection tools, which are usually determined from the established nonlinear autoregressive with exogenous inputs (NARX) model. However, the hyperparameters in the NARX model are difficult to be determined so that the identification accuracy cannot be guaranteed. Therefore, it is important to develop more accurate methods to estimate the NOFRFs and their associated indicators for damage detection. Motivated by the powerful learning abilities of convolutional neural networks (CNN) and long short-term memory (LSTM) networks, a novel method based on NOFRFs and the CNN-LSTM model for detecting the early damages in structures is proposed. By applying the beat excitation, the response of the structure is divided into two components, where the approximately linear component is used to estimate the frequency characteristic of the linear component by the classical linear model and the nonlinear component is used to establish the CNN-LSTM model. By calculating the responses of the two models, the NOFRFs and associated indexes can be accurately estimated, and then the early damage can be detected. Simulation and experimental studies verify the potential and effectiveness of the novel method proposed in this article.

With the increase of the rotor diameter and the deterioration of operating conditions, modern wind turbines suffer from more and more significant time-varying non-torque loads, which increases the burden of turbine structures especially the gearbox. Based on an aeroelastic loose coupling approach and assembly of the finite element method, an integrated drivetrain coupling analysis model including blade module, aerodynamic module, and gearbox module is established in this study. This proposed model is validated by comparing the calculation results with previous literature. Taking National Renewable Energy Laboratory 5-MW wind turbine as the research object, the gearbox vibration responses, gear meshing forces and bearing forces under non-torque loads caused by blade gravity, wind shear (WS), tower shadow (TS) and yawed inflow are studied in detail. Results show that the y-direction displacements of the gearbox, sun gear 1, sun gear 2 and gear are larger than those in x-direction because Fy or Mx generated by blade gravity, WS and TS dominates the non-torque loads. The non-torque loads lead to a non-uniform planet load sharing especially for the planetary gear stage 1. Because of the fluctuations of non-torque loads, not only the rotation frequencies of the corresponding carrier but also the multiple frequencies of the carrier 1 are observed in the frequency spectrums. The non-torque loads are mainly borne by carrier 1 bearings. Except for the blade gravity, the bearing forces caused by other unsteady inflows have obvious fluctuations.

The Froude scaling law is usually utilized in the wave basin model tests of Floating Wind Turbines (FWTs). However, the Froude-Scaled Rotor (FSR) cannot generate desired aerodynamic loads due to the Reynolds-Number Scaling Effect (RNSE). To mitigate the adverse effects of RNSE, two approaches are proposed to design Performance-Scaled Rotors (PSRs) in this paper. Taking DTU 10 MW baseline wind turbine as an example, the SD2030 airfoil is selected to replace the original FFA-W3-xx airfoils. Maximum Lift Tracking (MLT) and Load Distribution Matching (LDM) algorithms are proposed to assign the chord lengths and twist angles. Herein, MLT leads all airfoils to operate at the optimal angle of attack that corresponds to the maximum lift coefficient and afterwards increasing the chord lengths. LDM simultaneously adjusts the chord length and twist angle, aiming to match the span-wise distribution of normal force at the design point. Results show that both approaches can generate desired rotor thrusts in a range of tip speed ratios, which seems to outperform prior PSRs in the existing publications. The blade mass and inertia can be preserved with careful manufacturing procedures. The redesigned PSRs are helpful to improve the accuracy and reliability of FWT model tests in the wave basin.

Modal parameter identification is a mature technology. However, there are some challenges in its practical applications such as the identification of vibration systems involving closely spaced modes and intensive noise contamination. This paper proposes a new time-frequency method based on intrinsic chirp component decomposition (ICCD) to address these issues. In this method, a redundant Fourier model is used to ameliorate border distortions and improve the accuracy of signal reconstruction. The effectiveness and accuracy of the proposed method are illustrated using three examples: a cantilever beam structure with intensive noise contamination or environmental interference, a four-degree-of-freedom structure with two closely spaced modes, and an impact test on a cantilever rectangular plate. By comparison with the identification method based on the empirical wavelet transform (EWT), it is shown that the presented method is effective, even in a high-noise environment, and the dynamic characteristics of closely spaced modes are accurately determined.

Time-varying systems are applied extensively in practical applications, and their related parameter identification techniques are of great significance for structural health monitoring of time-varying systems. To improve the identification accuracy for time-varying systems, this study puts forward a novel parameter identification approach in the time–frequency domain using intrinsic chirp component decomposition (ICCD). ICCD is a powerful tool for signal decomposition and parameter extraction, with good signal reconstruction capability in a high-noise environment. To maintain good identification effects for the time-varying system in a noisy environment, the proposed method introduces a redundant Fourier model for the non-stationary signal, including instantaneous frequency (IF) and instantaneous amplitude (IA). The accuracy and effectiveness of the proposed approach are demonstrated by a single-degree-of-freedom system with three types of time-varying parameters, as well as an example of a multi-degree-of-freedom system. The effects of different levels of measured noise on the identified results are also discussed in detail. Numerical results show that the proposed method is very effective in tracking the smooth, periodical, and non-smooth variations of time-varying systems over the entire identification time period even when the response signal is contaminated by intense noise.

Recently, a powerful signal processing technique called variational mode decomposition (VMD) has drawn a great interest in various applications. The VMD assumes that the signal components are narrowly banded and separated in the frequency domain. Therefore, it cannot analyze wideband nonstationary signals with spectrum-overlapped components. To address this issue, a novel scheme called warped VMD (WVMD) is proposed in this paper. The WVMD includes the following steps. First, the phase function of a certain signal component is accurately estimated by optimizing a spectrum concentration index. Next, the time-warping principle is used to transform the original signal to a warped one in the phase coordinate where the spectrums of the components are well separated. Then, the conventional VMD can be used to effectively decompose the warped signal. Finally, the obtained signal components (of the warped signal) can be taken back to the time coordinate by the inverse warping operation. The WVMD works well in noisy environments and can even decompose signals with very close components. The effectiveness of the WVMD is validated by some simulated signals, a bat echolocation signal, and applications to real-life vibration signals of a rotor test rig and a hydroturbine.

The platform pitch motion of a Floating Wind Turbine (FWT) introduces additional relative wind speed to the rotor. This additional relative wind speed distributes linearly along the vertical altitude, which is similar to the linear wind shear, thus it is addressed as the platform-pitch-induced (PPI) wind shear effect. In this paper, the PPI wind shear is investigated numerically with the Free Vortex Method (FVM). Firstly, the typical wind shear and the PPI wind shear are separately analyzed and then compared with each other. The wind profile of the typical wind shear is stationary. However, the wind profile of the PPI wind shear is time-varying, which complicates the aerodynamics of the FWT. Subsequently, the influencing factors of the PPI wind shear are thoroughly discussed. Results show that the PPI wind shear is related to the FWTstructures, and it is increased with the increases of the platform pitch amplitude, frequency and the tip speed ratio. The PPI wind shear introduces significant fluctuations to the aerodynamic loads, which smear the power quality and result in potential fatigue damages to the FWT structures. To mitigate the adverse effects of the PPI wind shear, advanced control strategies and optimized structure designs should be developed.

Signal decomposition has drawn growing interest in various applications these days. Some recent decomposition methods, like the variational mode decomposition (VMD) and the variational nonlinear chirp mode decomposition (VNCMD), employ a joint-optimization scheme to accurately estimate all the signal modes underlying a signal. Some existing issues for these methods are: requiring prior knowledge of the number of the signal modes, empirically setting the bandwidth parameter to a fixed value, and lacking of an effective initialization scheme for the optimization algorithm. To address these issues, this paper presents a new decomposition approach called adaptive chirp mode pursuit (ACMP). Similar to the matching pursuit method, the ACMP captures signal modes one by one in a recursive framework. In addition, an adaptive bandwidth parameter updating rule and an instantaneous frequency initialization method based on Hilbert transform are incorporated into the ACMP. Several examples including simulated signals as well as real-life applications are provided to show the effectiveness and advantages of the ACMP.

The offshore floating wind turbines (OFWT) inevitably experience yawed flows, which result in fluctuations in the angle of attacks (AOAs) of the airfoils and therefore considerably impact the aerodynamics. In this paper, the AOA of the blade of a horizontal OFWT under static and dynamic yawed conditions are investigated using the Free Vortex Method (FVM). Results show that the AOA fluctuation under yawed conditions could be attributed to three effects: the blade advancing & retreating effect, the non-uniform induction effect and the upwind & downwind yawing effect. At a positive yaw angle, the blade advancing & retreating effect results in a maximum AOA at the azimuth of 0°. This effect is more dominant for the inboard airfoils than the outboard ones. The non-uniform induction effect results in a maximum AOA at 90° for inboard airfoils while at 270° for outboard ones. This effect is more evident for the outboard blade segments. When the OFWT experiences platform yawing motion or during the dynamic yawing process, the upwind & downwind yawing effect occurs. This effect increases the AOA when the blade is yawing upwind and vice versa. It is more dominant for outboard airfoils and it increases as the yawing rate augments.

Signals propagated in dispersive systems usually present multimodal and nonlinear group delay (GD) properties. The characterization of GDs and the signal decomposition are still challenging issues. Existing methods are mainly limited to identify monotonous GDs by estimating monotonous instantaneous frequencies. Bandpass filters are usually utilized to separate each mode, but the inherent defects of digital filters will decrease the reconstruction accuracy inevitably. In this paper, we propose the frequency-domain intrinsic component decomposition method (FICD). On one hand, it can characterize nonlinear and non-monotonic GDs in frequency domain by using different kernel functions. On the other hand, the method itself can act as a time-frequency filter to separate and reconstruct each mode simultaneously. One of the advantages of our method is that it can deal with close or overlapped signals with high precision. Finally, simulated examples are provided to verify the effectiveness and efficiency.

Real-time chatter detection is important in improving the surface quality of workpieces in milling. Since the process from stable cutting to chatter is characterized by the progressive variation of the vibration energy distribution, entropy has been utilized to capture the decreasing randomness of vibration signals when chatter occurs. To make such an index more sensitive to transitions of the cutting state, the entropy can be computed based on signal components obtained through signal decomposition techniques. However, the classic empirical mode decomposition (EMD) is difficult to put into practice due to its weak robustness to noises. The up-to-date variational mode decomposition (VMD) has strict requirements on priori information of the signal and thus is not applicable either. In this paper, a novel method named the iterative Vold-Kalman filter (I-VKF) is proposed under the framework of the greedy algorithm, where the Vold-Kalman filter (VKF), a classic order-tracker for rotating machinery, is improved to recursively extract each signal component. In the meantime, a spectrum concentration index-based technique is developed for the instantaneous chatter frequency estimation to adaptively determine the filter parameter. Numerical examples demonstrate the superiority of the I-VKF over the original VKF, EMD, and VMD, especially in the presence of strong noises. Combined with the energy entropy of extracted components and an automatically calculated threshold, the proposed strategy greatly helps in timely chatter detection, which has been verified by dynamic simulation and experiments.

A series of shear thickening fluids based on the modified silica nanoparticles by silane coupling agent, stearic acid, and titanate suspended in polyethylene glycol (PEG) were prepared, and the effects of particle modification on the shear thickening behaviors of these fluids were investigated. It can be found that the modification obviously decreases the particle agglomeration, improves the particle dispersion property, and increases the maximum particle concentration in fluid but leads to the reduction of the viscosity and shear thickening effect of the fluids due to the decrease of particle interactions. The modified silica fluids exhibit stronger structural recovery ability and shear thickening reversibility even after multiple shear breakage.

Signal denoising is crucial to condition monitoring of mechanical systems as fault characteristics may be hidden by strong noise in some cases. In this paper, we focus on the denoising of nonstationary vibration signals which contain multiple complex components. To this end, we introduce a time-frequency filter called envelope-tracking filter (ETF). The basic idea of the ETF is to characterize the envelope of a signal using Fourier basis functions and estimate the envelope by solving a least-squares problem. We show that the ETF is similar to the Vold–Kalman filter, but it has several promising advantages, i.e., easier to determine the bandwidth expression, more flexible to handle signals at different noise levels, and less sensitive to errors of instantaneous frequency (IF) estimations. Our denoising method includes two steps: 1) extract IFs of each nonstationary component by optimizing a spectrum concentration index, and 2) recover each component by using the ETF. Our method works very well in heavy noise and is effective for signals with overlapped components. Some examples including simulated and real-life vibration signals are offered to show the validity of the method.

The impulse response function (IRF) identification of linear mechanical systems is important in many engineering applications. This paper proposes a novel IRF identification method of linear system...

In this study, a series of shear thickening fluids based on the suspension of silica nanoparticles in polyethylene glycol (PEG) were prepared, and the influences of particle size, concentration, and shear on the shear thickening effect were investigated by rheological measurements. It was observed that with the increase of silica concentration, the viscosity and critical shear rate of the fluid become higher, and the shear thickening phenomenon becomes more obvious. Under the same particle concentration, higher shear thickening effect and lower critical shear thickening concentration appear for smaller nanoparticles but for larger micro particles. As the temperature increases, the enhanced Brownian motion leads to the rising of critical shear rate and the strengthening of shear thickening effect. The rheological properties of these fluids are very sensitive to the shear rate with fast viscosity change for response, and smaller nanoparticles can recover their network structures more easily after shear breakage.

Doppler frequency can reflect the dynamic properties of the radar target and thus provides an effective approach to analyze the target signatures. In this paper, a novel method is proposed to accurately estimate Doppler frequencies of multi-component radar signals. The method employs a more general model, i.e., the redundant Fourier model to represent various Doppler frequency trajectories. In the first stage of the method, a multi-component signal is decomposed into some individual components using a de-chirping method. Then, the parameterized time-frequency transform (PTFT) with a matched Fourier kernel is designed to estimate frequencies of the obtained components. With the estimated frequencies, the complex envelopes of the components can then be estimated through a joint-least-squares method. We show that the phase information of the obtained envelopes can be used for error compensation in the frequency estimation. The proposed method integrating the PTFT with the phase compensation technique can significantly improve the accuracy of Doppler frequency estimation. Both simulated and experimental examples are provided, indicating the potential of the method in analyzing Doppler signals with overlapped components. The future study will focus on the real applications of the method for radar target detection and monitoring, such as human vital sign detection and condition monitoring of industrial equipments (e.g., wind turbines).

As a specific case of chirp component, the short-time chirp component (STCC) only continues a duration in a reference interval with a birth and death. A STCC usually possesses of important information directly related to kinematic or structural characteristics of a target. In this paper, a novel method is developed to decompose a multi-component short-time chirp signal into a series of STCCs. The decomposition method is mainly based on a parameterized mathematical model, which is used to represent the STCC, and a peak tracking approach, which is adopted to extract the instantaneous frequencies (IFs), and corresponding births and deaths for all the STCCs. With the model and the extracted IFs, the decomposition can be conducted by solving a linear system of equations. The effectiveness of the proposed method is demonstrated by two simulated examples and a marine biologic sound.

A challenge in analysing non-stationary multi-component signals is to isolate nonlinearly time-varying signals especially when they are overlapped in time and frequency plane. In this paper, a framework integrating time-frequency analysis-based demodulation and a non-parametric Gaussian latent feature model is proposed to isolate and recover components of such signals. The former aims to remove high-order frequency modulation (FM) such that the latter is able to infer demodulated components while simultaneously discovering the number of the target components. The proposed method is effective in isolating multiple components that have the same FM behavior. In addition, the results show that the proposed method is superior to generalised demodulation with singular-value decomposition-based method, parametric time-frequency analysis with filter-based method and empirical model decomposition base method, in recovering the amplitude and phase of superimposed components.

The power and thrust characteristics of an offshore floating wind turbine (OFWT) not only depend on the tip speed ratio λ and the blade pitch angle θ, but also closely relate to the platform motions. In this paper, the influences of the platform surge motion on the behaviors of the power output and rotor thrust of a typical Spar-type OFWT are investigated using Free Vortex Method (FVM). The influences of the surge frequency and amplitude are analyzed separately at first. Afterwards, a reduced frequency k is proposed to combine the influences of surge frequency and amplitude. The power and thrust curves are derived as functions of the tip speed ratio λ and reduced frequency k with blade pitch angle θ constant at zero. Results show that when the reduced frequency increases, mean power output decreases at low tip speed ratios but increases at high tip speed ratios; mean thrust decreases slightly for all tip speed ratios except λ = 12. Power and thrust variations increase as tip speed ratio or reduced frequency increases. Power and thrust coefficients decline with an increasing reduced frequency.

Considering the flexibility of gear foundations and rotational effects, a geared rotor dynamic model for thin-rimmed helical gears is established. The deformable shafts and gear foundations are modelled by Timoshenko beam elements and Mindlin-Reissner shell elements, respectively. The beam element and the shell element are connected using the ‘interfacial coupling element’. Then the modal superposition method is adopted to improve the computational efficiency. The proposed dynamic model is verified through the comparison between the simulated responses and experimental results in a published literature. Based on the proposed dynamic model, the effects of the web thickness, the rotational effects and the helix angle on the dynamic behaviors are discussed. The results show that the dynamic responses of thin-rimmed gears obtained from the traditional dynamic model and the proposed model are quite different, which emphasizes the significances of the flexible body modeling. Compared to the centrifugal stiffening and the spin softening, the gyroscopic effect has more significant effect on dynamic characteristics. The nodal diameter vibration cannot be excited due to the absence of the axial exciting force when helix angle is zero. The increase of the helix angle will lead to more significant out-of-plane vibration of the gear foundation.

This paper proposes a novel nonlinear dynamical system identification method based on the sparse regression algorithm and the separable least squares method. To effectively avoid solving the second derivative of the displacement signal and reduce the effect of noise, the Duhamel's integral is adopted to represent the dynamic relationship between the system input and output. In the expression form of Duhamel's integral, nonlinear dynamical system identification can be cast as a separable least squares problem. Thus, the separable least squares method is leveraged to separately identify the parameters of the linear subsystem and the coefficients corresponding to nonlinearities among the nonlinear dynamical system. During the identification process of nonlinear restoring forces, one complete set of nonlinear basis functions are used to represent the nonlinear restoring forces. Not all the candidate nonlinear terms are contributing, however, thus the sparse regression algorithm is adopted to select the actual contributing nonlinear components in the candidate nonlinear terms and eliminate the non-contributing nonlinear components, and then the corresponding parameters of contributing nonlinear components are estimated by the unbiased least squares method. Finally, one RKHS (Reproducing Kernel Hilbert Space)-based non-parametric de-noise method is further proposed to reduce the noise in the vibration displacement and obtain the noise-reduced velocity from the displacement signal. The numerical simulation about the identification of the rotating blade-casing system and the dynamic experiment of the HSLDS (high-static-low-dynamic stiffness) isolator system verify the effectiveness of the new identification method for nonlinear dynamical systems proposed in this paper.

Blade pitch control (BPC) is an indispensable part of a variable speed wind turbine, which maintains a stable power output by adjusting the blade pitch angle to adapt to the changing inflow speed introduced by the platform pitching motion (PPM). An integrated aeroelastic-servo transmission system of a floating offshore wind turbine (FOWT) is established and validated. Comparing with existing models, our model considers the effect of the BPC on the FOWT, apart from the dynamic characteristic of the drivetrain and the aeroelastic coupling of the blade. Firstly, dynamic behaviours of the FOWT under different tip speed ratios are compared. Then the performance of FOWT with and without the BPC influence is compared, followed by a dynamic analysis of the FOWT under a varying platform pitching amplitude. Results show that the tip speed ratio slightly alters the FOWT performance under the PPM. Considering the effect of the BPC, the fluctuation of the generator power reduce, but the aerodynamic thrust tends to enlarge the PPM. The non-torque loads induced by the PPM are insensitive to BPC. Fluctuation of the system response increases with increasing platform pitching amplitude.

Variational mode decomposition (VMD), a recently introduced method for adaptive data analysis, has aroused much attention in various fields. However, the VMD is formulated based on the assumption of narrow-band property of the signal model. To analyze wide-band nonlinear chirp signals (NCSs), we present an alternative method called variational nonlinear chirp mode decomposition (VNCMD). The VNCMD is developed from the fact that a wideband NCS can be transformed to a narrow-band signal by using demodulation techniques. Our decomposition problem is, thus, formulated as an optimal demodulation problem, which is efficiently solved by the alternating direction method of multipliers. Our method can be viewed as a time–frequency filter bank, which concurrently extracts all the signal modes. Some simulated and real data examples are provided showing the effectiveness of the VNCMD in analyzing NCSs containing close or even crossed modes.

Utilizing Doppler radar to conduct noncontact vital sign detection has attracted growing interest in recent years. Aiming to extract the vital sign information from the baseband signal effectively and accurately, a novel signal processing method based on parameterized demodulation (PD) is proposed. To effectively characterize the baseband signal whose phase consists of two oscillating components (i.e., the respiration and heartbeat components), the proposed algorithm defines a demodulation operator with sine kernel functions and formulates the phase demodulation as a parameter optimization problem. To increase the computational efficiency of the algorithm, the parameters corresponding to the respiration and heartbeat components are estimated sequentially. Specifically, the respiration component is first estimated and removed from the phase of the baseband signal, and then, the heartbeat component is extracted from the residual signal. Compared with the existing methods, the proposed algorithm is free of the resolution problem in fast Fourier transform-based methods with a limited data length and can obtain accurate rate tracking in a noisy environment. Both simulated and experimental results are provided to demonstrate the advantages and effectiveness of the proposed method for accurate noncontact vital sign detection.

In some applications, it is necessary to analyze multi-component non-stationary signals whose components severely overlap in the time-frequency (T-F) domain. Separating those signal components is desired but very challenging for existing methods. To address this issue, we propose a novel non-parametric algorithm called ridge path regrouping (RPRG) to extract the instantaneous frequencies (IFs) of the overlapped components from a T-F representation (TFR). The RPRG first detects the ridges of a multi-component signal from a TFR and then extracts the desired IFs by regrouping the ridge curves according to their variation rates at the intersections. After the IFs are obtained, component separation is achieved by using the intrinsic chirp component decomposition (ICCD) method. Different from traditional T-F filter-based methods, the ICCD can accurately reconstruct overlapped components by using a joint-estimation scheme. Finally, applications of separating some simulated and experimental micro-Doppler signals are presented to show the effectiveness of the method.

We introduce a general model to characterize multi-component chirp signals.The model represents instantaneous frequencies and instantaneous amplitudes of the intrinsic chirp components as Fourier series.We propose a novel chirp component decomposition method based on the model.The method is able to decompose multi-component chirp signals with intersecting IFs. In this paper, we consider the decomposition problem for multi-component chirp signals (MCCSs). We develop a general model to characterize MCCSs, where instantaneous frequencies (IFs) and instantaneous amplitudes (IAs) of the intrinsic chirp components (ICCs) are modeled as Fourier series. The decomposition problem thus boils down to identifying the developed model. The IF estimation is addressed using the framework of the general parameterized time-frequency transform and then the signal can be easily reconstructed by solving a linear system. For the practical implementation of our method, we present a two-step algorithm, which is initiated by an iterative scheme to achieve preliminary separation of the ICCs, followed by a joint-refinement step to get high-resolution ICC reconstructions. Our method acts as a time-varying band-pass filter and can even separate ICCs that cross in the time-frequency domain. The method is applied to analyze several simulated and real signals, which indicates its usefulness in various applications.

Harmonic axial force resulting from a propeller’s first vibration mode is a major cause of tonal sound radiation of an underwater vehicle. To reduce the harmonic force, we employ a dynamic antiresonant vibration isolator (DAVI) in parallel with thrust bearing of the shafting system to attenuate vibration transmitted to the hull. The methods of transfer matrices and substructure synthesis are used to create a semi-analytical dynamic model of the propeller–shaft–hull system with DAVI. In this model, the elastic properties of the propeller and foundation are taken into consideration. The force transmissibility and power flow are then used to evaluate the isolation performance of the DAVI. For the purpose of comparison, a resonance changer (RC) proposed in the published literature is also used to reduce the axial vibration transmission. It is demonstrated numerically that by using DAVI, the vibration and power flow of the underwater vehicle are greatly attenuated at the designed frequency without obviously changing the axial fundamental resonance frequency of the shafting system, which is superior to the RC isolator.

We propose a novel method, called chirplet path fusion, to analyze nonstationary signals with time-varying frequencies. As opposed to many existing methods that assume the signal persists throughout the whole time, the proposed method can be applied in cases where the signals are only present in short time frames. A demodulation-operator-based method is introduced to estimate the locally matched chirplet atoms which can form a chirplet path in the time–frequency domain. According to the density of the multiple chirplet paths obtained under different scales, the effective time support and the instantaneous frequency (IF) of the target signal can be estimated. Our method is effective in analyzing the nonstationary signal with discontinuous IF curve and works well under heavy noise. Several simulated signals and the vibration signal of a rotor test rig are considered to verify the effectiveness of the proposed method.

This study focuses on integrating an active vibration controller into the finite element model of a piezoelectric laminated plate with the controller–structure interactions considered. A finite element model of a piezoelectric laminated plate is formulated using the third-order shear deformation theory. A state-space model is set up by performing a system identification technique. The state-space model is then used to design an optimal vibration controller. Considering that the finite element model is more appropriate than state-space model for dynamic simulation, the state-space model-based controller is integrated into the finite element model to capture the controller–structure interactions. The results obtained by applying vibration controller in state-space model are also presented to make a comparison. It is numerically demonstrated that the controller–structure interactions occur and cause performance degradation in case that the state-space model-based controller works with the finite element model. There is no prior guarantee that a state-space model-based controller satisfying the control requirements still works well in closed loop with the finite element model. The results of this study can be used to evaluate the controller performance for the piezoelectric smart structures during the preliminary design stage.

Intra-wave modulation effects with oscillating time-varying frequencies have been observed in physical systems including mechanical, ocean, power, and even biological ones. Such phenomena correspond to actual physical processes with which the underlying dynamics of these systems can be understood. In this work, we study intra-wave modulation effects in milling processes with the aid of iterative nonlinear chirp mode decomposition (INCMD), a recently proposed technique for analyzing complex dynamic responses. A nonlinear non-stationary template signal is provided to model milling vibration responses, and an INCMD-based strategy is developed to extract embedded modulation features. Through dynamic simulations and experimental verification, it has been demonstrated that distinct intra-wave modulation frequencies that are exactly equal to theoretical chatter frequencies calculated based on the Floquet theory exist to indicate different cutting states, and milling instability is accompanied by the variation of such a characteristic quantity. Bessel functions can mathematically relate explicit modulation patterns with intricate spectral distributions of milling responses. Moreover, the switch of the modulation pattern, which remains noticeable in the presence of noise, emerges far earlier than visible chatter marks do, indicating the superiority of the chatter detection and even prediction utilizing intra-wave modulation features.

The tower is an essential supporting structure for a Floating Wind Turbine (FWT). Due to the complexities of system structure and the environmental excitations, FWT towers present rather complicated loading characteristics, potentially threat the system reliability. This paper aims to reveal the FWT tower dynamics in wind-wave-current environments with a dedicated experimental apparatus. A new strategy is proposed to design the FWT structure. Then, extensive tests are conducted with different offshore environments and varying rotor operating conditions. Results show that the FWT tower loading presents complicated dynamic characteristics. The frequency response mainly consists of three parts: the low-frequency component associated with floater oscillations and wind excitations, the wave-frequency component induced by the linear wave loads, and the high-frequency component resulted from the rotor-nacelle assembly (RNA) operating and vibrations. The low- and wave-frequency responses increase with the wave height, while the high-frequency response amplifies with the aerodynamic loads. Vibrations transmit between the upper RNA and the lower floating platform through the tower structure, smearing the system stability and increasing the structure fatigue damages. Optimizations should be proposed to improve the FWT tower performance to suppress or isolate these unexpected vibrations.

Barely visible indentation damage after quasi-static indentation in sandwich CF/EP composites was assessed using ultrasonic guided wave signals. Finite element analyses were conducted to investigate the interaction between guided waves and damage, further to assist in the selection process of the Lamb wave sensitive modes for debonding identification. Composite sandwich beams and panels structures were investigated. Using the beam structure, a damage index was defined based on the change in the peak magnitude of the captured wave signals before and after the indentation, and the damage index was correlated with the residual deformation (defined as the depth of the dent), that was further correlated with the amount of crushing within the core. Both A0 and S0 Lamb wave modes showed high sensitivity to the presence of barely visible indentation damage with residual deformation of 0.2 mm. Furthermore, barely visible indentation damage was assessed in composite sandwich panels after indenting to 3 and 5 mm, and the damage index was defined, based on (a) the peak magnitude of the wave signals before and after indentation or (b) the mismatch between the original and reconstructed wave signals based on a time-reversal algorithm, and was subsequently applied to locate the position of indentation.

To analyze the valuable frequency component for time-varying frequency-modulated (FM) signals, component extraction is necessary in most applications. Considering the advantage of parameterized demodulation (PD) in transforming FM signals to be stationary, a novel component extraction method based on PD and singular value decomposition (PD-SVD) for both monocomponent and multicomponent signals is proposed. By extending the idea of PD, the time-varying term of the continuous phase function for the interested FM component can be removed, thus resulting in a highly self-correlated component with constant phase. Then, the extraction of the target component from noise or other components can be realized by SVD. Compared with the existing methods, the proposed algorithm is able to analyze the multicomponent signal with crossed instantaneous frequency trajectories and effectively improve the signal-to-noise ratio of the extracted component. The effectiveness of the proposed method is demonstrated by applying it on several numerical signals and the radial vibration signal of a hydroturbine rotor, indicating the potential of analyzing many practical FM signals.