Traditional Time Domain Research Articles

Time-Domain Load Identification and Dynamic Displacement Inversion of Discharge Sluice in Large Hydropower Stations Based on the Conjugate Gradient Least Squares Iteration Algorithm

The identification of flow-induced vibration loads is the key to solving the vibration problem of hydraulic structures induced by high-speed water flow. In this study, a time-domain load identification method for discharge sluices in large hydropower stations based on the conjugate gradient least squares (CGLS) iteration algorithm is proposed. The main idea of this method is to first construct a mathematical model through the response signal and the structural modal parameters for solving the equivalent load acting on the sluice structure. Then, it uses the iterative regularization method to solve the ill-posed problem in the load identification process of the traditional time-domain method. Numerical simulations show that, in comparison to the traditional Tikhonov regularization algorithm, the CGLS algorithm is capable of reducing the relative error of the identified dynamic loads by approximately 100%, with higher accuracy and noise immunity. In this study, the CGLS algorithm is used to identify the external dynamic loads acting on the sluice structure during the flood discharge process of a large hydropower station in China. Three equivalent dynamic loads acting on the pier and the bottom plate are obtained. The positive analysis of the vibration response of the discharge structure is conducted using the equivalent load. The results show that the error between the measured vibration response signals and the vibration response signals obtained from the positive analysis is relatively minor, with the exception of a few individual measurement points. The displacement response mean square error is less than 10%, thereby substantiating the accuracy and validity of the CGLS load identification method proposed in this study in the actual engineering.

A Quasi Time-Domain Method for Fatigue Analysis of Reactor Pressure Vessels in Floating Nuclear Power Plants in Marine Environments

The reactor pressure vessel (RPV) in onshore nuclear power plants is typically analysed for fatigue life by considering the temperature, internal pressure, and seismic effects using a simplified time-domain fatigue analysis. In contrast, the frequency-domain fatigue analysis method is commonly employed to assess the fatigue life of ship structures. The RPV of a floating nuclear power plant (FNPP) is subjected to a combination of temperature, internal pressure, and wave loads in the marine environment. Consequently, it is essential to effectively integrate the frequency-domain fatigue analysis method used for hull structures with the time-domain fatigue analysis method for RPVs in FNPPs or, alternatively, to develop a suitable method that effectively accounts for the temperature, internal pressure, and wave loads. In this study, a quasi-time-domain method is proposed for the fatigue analysis of RPVs in FNPPs. In this method, secondary components of marine environmental loads are filtered out using principal component analysis. Subsequently, the stress spectrum induced by waves is transformed into a stress time history. Fatigue stress under the combined influence of temperature, internal pressure, and wave loads is then obtained through a stress component superposition method. Finally, the accuracy of the quasi-time-domain method was validated through three numerical examples. The results indicate that the calculated values obtained by the quasi-time-domain method are slightly higher than those obtained by the traditional time-domain method, with a maximum deviation of no more than 24%. Additionally, the computation time of the quasi-time-domain method is reduced by 98.67% compared to the traditional time-domain method.

An RD-Domain Virtual Aperture Extension Method for Shipborne HFSWR

High-frequency surface wave radar (HFSWR) is widely used for detecting sea surface or low-altitude targets due to its all-weather operation and over-the-horizon detection capability. To further enhance the maneuverability and detection range of HFSWR, shipborne HFSWR has been developed. However, compared to shore-based platforms, shipborne platforms face challenges such as a small array aperture and reduced Direction of Arrival (DOA) estimation performance due to their limited size. The traditional time–domain virtual aperture extension method, based on the principle of space-time equivalence, aims to improve the array aperture but has limitations when used for HFSWR background or multiple targets with different speeds. To address these issues, this paper proposes a range-Doppler domain (RD-domain) virtual aperture extension method for the uniform linear array, based on the uniform motion model. The contributions of this work include (1) a continuous motion model for shipborne HFSWR, (2) a virtual aperture processing flowchart for shipborne HFSWR, and (3) an RD-domain aperture extension method suitable for HFSWR background or multiple targets with varying speeds. Through simulation and experimental data, we validate the proposed method and analyze its performance.

Gravitational Wave Ringdown Analysis Using the F -statistic

After the final stage of the merger of two black holes (BHs), the ringdown signal takes an important role in providing information about the gravitational dynamics in strong field. We introduce a novel time-domain (TD) approach, predicated on the F -statistic, for ringdown analysis. This method diverges from traditional TD techniques in that its parameter space remains constant irrespective of the number of modes incorporated. This feature is achieved by reconfiguring the likelihood and analytically maximizing over the extrinsic parameters that encompass the amplitudes and reference phases of all modes. Consequently, when performing the ringdown analysis under the assumption that the ringdown signal is detected by the Einstein Telescope, the parameter estimation computation time is shortened by at most 5 orders of magnitude compared to the traditional TD method. We further establish that traditional TD methods become difficult when including multiple overtone modes due to close oscillation frequencies and damping times across different overtone modes. Encouragingly, this issue is effectively addressed by our new TD technique. The accessibility of this new TD method extends to a broad spectrum of research and offers flexibility for various topics within BH spectroscopy applicable to both current and future gravitational wave detectors.

Three-dimensional numerical simulation and experimental validation of airborne ground-penetrating radar based on CNCS-FDTD method under undulating terrain conditions

Traditional Finite Difference Time Domain (FDTD) approaches face challenges with increased computational demands and errors as terrain complexity and flight altitude rising. This study introduces the Crank-Nicolson-Cycle-Sweep-FDTD (CNCS-FDTD) method, enhancing airborne ground penetrating radar (GPR) simulations over undulating terrains. CNCS-FDTD, as an unconditionally stable implicit algorithm, overcomes these by allowing larger time steps without the constraints of the Courant-Friedrichs-Lewy (CFL) condition. Our research aims to assess how CNCS-FDTD can improve computational efficiency and accuracy in modeling airborne GPR responses across varied terrains. Initial simulations using a three-dimensional graben model indicate that CNCS-FDTD can maintain calculation stability with significantly larger time steps, reducing computational time by 42%. To further verify the reliability of the numerical simulation results, the paper also presents experimental tests of the undulating terrain model. By comparing the numerical and physical simulation results of models under different flight altitudes and terrain conditions, the accuracy of the simulation results is validated.

Research on Multi-Core Curvature Sensing Measurement Based on PPP-BOTDA.

To address the issue of spatial resolution limitations in traditional Brillouin optical time-domain analysis systems due to phonon lifetime constraints, we employed pre-pumped pulse technology. Additionally, to mitigate the double-peak phenomenon observed in pre-pumped Brillouin optical time-domain analysis systems, we implemented a two-sided band interference method to reduce the linewidth of the double-peak fitting. We conducted bending measurements on three eccentric cores and intermediate cores spaced 120° apart. Our results demonstrate that the system described in this paper can achieve a spatial resolution of 30 cm, with bimodal linewidths of 23.1 MHz and 16.0 MHz. Using the parallel transmission frame algorithm, we determined the curvature of a seven-core fiber with a curvature diameter of approximately 10 cm to be 20.67 m-1, with an error margin of 3.2%.

Mobileception-ResNet for transient stability prediction of novel power systems

Power system transient stability prediction (TSP) is particularly important as power systems change and evolve, including the rapid growth of renewable energy, the proliferation of electric vehicles, and the construction of smart grids. Traditional time-domain simulation methods are time-consuming and cannot achieve online prediction. Direct methods are poorly adapted and cannot be applied to complex power systems. Existing machine learning algorithms only classify the transient stability without providing the degree of transient stability of the system. Therefore, a fast and accurate power system TSP method is needed to assist operators in implementing timely measures to improve the stability of the power system running. This study proposes a Mobileception-ResNet network, Mobileception-ResNet is formed by Inception-ResNet-v2, MobileNet-v2, and a fully connected layer. In this study, Mobileception-ResNet and nine comparison models are experimented on two node systems, i.e., the IEEE 10–39 and 69–300 systems. In the IEEE 10–39 system, the root mean square error, mean absolute error, and mean absolute percentage error of Mobileception-ResNet are 44.13 %, 36.74 %, and 39.96 % lower, and the coefficient of determination is 0.04 % higher, respectively, when compared to the comparative model with the best evaluation indicator; in the IEEE 69–300 system, the corresponding values are 2.6 %, 12.83 %, 12.55 %, and 0.01 %, respectively.

Harmonic Sequence Component Model-Based Small-Signal Stability Analysis in Synchronous Machines during Asymmetrical Faults

Power systems are complex and often subject to faults, requiring accurate mathematical models for a thorough analysis. Traditional time-domain models are employed to evaluate the dynamic response of power system elements during transmission system faults. However, only the positive sequence components are considered for unbalanced faults, so the small-signal stability analysis is no longer accurate when assuming balanced conditions for asymmetrical faults. The dynamic phasor approach extends traditional models by representing synchronous machines with harmonic sequence components, making it suitable for an unbalanced condition analysis and revealing dynamic couplings not evident in conventional methods. By modeling electrical and mechanical equations with harmonic sequence components, the study implements an eigenvalue sensitivity analysis and participation factor analysis to identify the variable with significant participation in the critical modes and consequently in the dynamic response of synchronous machines during asymmetric faults, thereby control strategies can be proposed to improve system stability. The article validates the dynamic phasor model through simulations of a single-phase short circuit, demonstrating its accuracy and effectiveness in representing the transient and dynamic behavior of synchronous machines, and correctly identifies the harmonic sequence component with significant participation in the critical modes identified by the eigenvalue sensitivity to the rotor angular velocity and rotor angle.

Time-domain structural model updating following the Bayesian approach in the absence of system input information

The modal-domain structural model updating and damage detection methods own the advantage that they perform the whole computation without the information of system input, when compared to the time-domain. The system input required in the time-domain methods is hardly measured in realistic field tests of civil engineering structures. However, the time-domain methods own the advantage that they carry out model updating based on raw measured dynamic responses but not the processed (and compressed) modal parameters. The modal-domain methods only uses the compressed modal parameters, and some useful system information may be lost in the compressing process. Thus, higher errors and uncertainties are induced in the model updating results from the modal-domain methods. To take advantages and avoid disadvantages from the traditional time-domain and modal-domain model updating methods, a novel time-domain MCMC-based Bayesian model updating method without the information of the system input is developed in this paper by incorporating a newly developed dynamic response reconstruction technique and extending the formulation of the posterior probability density function (PDF) and the corresponding bridge PDF in the MCMC sampling process. A new modal decomposition algorithm is proposed in this paper for accurate modal response decomposition even in case of dense or sparse modes, and filters are not required for higher computation accuracy. Experimental case studies on a two-story steel frame were conducted to illustrate the proposed methodology in both structural model updating and damage detection. According to the analysis results, it is concluded that the proposed methodology outperforms the modal-domain methods especially in the cases with small number of measured DOFs.

Application of spatial filtering high order FDTD method in the multi-physics field coupled equations

The traditional finite-difference time-domain method (FDTD(2, 2)) is widely employed to solve the coupled equations of quantum-electromagnetic multi-physics. However, the time step of the traditional explicit FDTD(2, 2) method is limited by the Courant-Friedrichs-Lewy (CFL) condition. Besides, the FDTD(2, 2) method only achieves the second-order accuracy, which can result in significant error accumulation. To overcome this problem, we propose the SF-HO-FDTD(2, q) method with scalable time stability conditions for solving the multi-physics field coupled equations (q denotes q-th order spatial difference scheme). This approach integrates spatial filtering (SF) and higher-order FDTD (HO-FDTD) methods. Moreover, the proposed method requires no further derivation of the iterative equations of the traditional HO-FDTD(2, q) method. It only needs to introduce the SF operation in each numerical iteration process to filter out the spatial high-frequency components arising from the use of time steps that do not satisfy the CFL stability condition. This condition is applied to ensure the stability of the numerical method. Therefore, the proposed method has a high compatibility with the traditional HO-FDTD(2, q) method. Then, the numerical dispersion analysis of the SF-HO-FDTD(2, q) method is theoretically studied. Finally, numerical examples are carried out to confirm the accuracy and efficiency of the strategy suggested in this study.

Semi-Analytical Formula for Estimating Variance Response of Megawatt Wind Turbine Tower Under Parked Condition

As the supporting structure of the wind turbine, the tower is important for the safety of the wind turbine. The accurate estimation of tower response is critical for the wind turbine tower design. In the preliminary design, lots of calculations are necessary to determine design parameters. The traditional time domain calculation suffers its low efficiency and hinders the design progress. To overcome this difficulty, the frequency domain analysis should be developed, especially for the large wind turbine which may be significantly affected by the aerodynamic damping. In this study, a semi-analytical formula for the variance of the tower base bending moment is developed from the frequency domain analysis at parked condition. First, based on the quasi-steady theory and the three-component (mean, background and resonant components) method commonly used in the wind-resistant design of structure, the analytical formula of the variance for the tower base bending moment is presented considering the three components of the wind velocity. Second, the analytical formula is corrected based on the comparison with the calculation result by FAST. In the correction, the empirical aerodynamic damping is adopted. Finally, the applicability of the developed semi-analytical formula is verified by 5[Formula: see text]MW, 10[Formula: see text]MW and 15[Formula: see text]MW wind turbines.

Lost circulation detection method based on cepstrum analysis of transient pressure waves

Encountering natural fractures or unbalanced formation pressure during oil drilling can result in economic loss and environmental pollution due to well leakage. Existing detection methods encounter challenges such as high costs, complex downhole environments, and difficult data acquisition. To address these issues, we propose a well leakage detection method using cepstrum for analyzing transient pressure waves. Cepstrum is a signal Fourier transform after logarithmic operation and then Fourier inverse spectrum obtained. By studying the propagation of transient pressure waves in the wellbore, we identify drilling fluid leakage location and amount based on time-dependent and amplitude changes of pressure wave signal characteristic peaks. To handle noise in the pressure wave signal, we employ adaptive noise-complete ensemble empirical modal decomposition (CEEMDAN) and wavelet threshold (WT) joint denoising. Correlation coefficient (CCF) with the Hilbert joint spectrum (HJS) is used to extract main frequency components, achieving denoising. Experimental results confirm: ① Noise interference in transient pressure waves is effectively suppressed using the CEEMDAN-WT-CCF-HJS denoising method. ② Cepstrum analysis of the pressure wave signal during wellbore annulus system leakage reveals distinct reflected wave characteristic peaks, aiding in locating different leakage points, with the amplitude of these peaks reflecting the size of the leakage. ③ This method efficiently utilizes time-frequency information from the excitation pressure wave signal, offering advantages over traditional time-domain and frequency-domain analysis. Experiments covering various leakage scenarios, amounts, and borehole sizes yielded controlled experimental errors (2.25%–9.10%), within a reasonable range. The method's validity and reliability were confirmed, providing theoretical support and technical guidance for well leakage detection in oil drilling.

A Novel 3-D DGTD-FDTD Hybrid Method withOne Overlapping Virtual Layer

Compared with the traditional finite difference time-domain (FDTD) method, the discontinuous Galerkin time-domain (DGTD) method may face the issue of intense computation. In this paper, a novel 3-D DGTD-FDTD hybrid method is proposed to dramatically reduce the unknowns of the DGTD method. Instead of the common mass-lumped elements, this virtual layer of the Yee grid is implemented on the intersecting boundary, which simplifies the mesh generation and reduces the number of unknowns. To validate the proposed method, two examples of sphere scattering and horn antenna are considered. The simulation results demonstrate the effectiveness of the proposed method.

Visual analytics for process monitoring: Leveraging time-series imaging for enhanced interpretability

In the era of big data driven by the advent of the Internet of Things (IoT), process industries face the challenge of analyzing massive and complex data to extract relevant information for effective process monitoring. Despite exploring various approaches, scalability and interpretability continue to present practical limitations. To address these limitations, we propose a new framework called visual analytics. Visual analytics offers a new perspective on solving process monitoring problems. It involves transforming historical process data into visual clues, thereby converting traditional fault detection problems into image classification problems. This approach allows process experts to visually analyze patterns and textures within the data, making interpretation much easier compared to traditional time domain analysis. Moreover, by treating process data as images, visual analytics can leverage a wide range of computer vision techniques, including convolutional neural networks (CNNs), to accurately classify and detect faults. By integrating human visual perception with advanced computer vision techniques, visual analytics enables the effective analysis of massive and complex process data. To empirically validate the proposed visual analytics approach, we conduct experiments on both the simulated continuous stirred tank heater (CSTH) benchmark and the industrial arc loss benchmark. The experimental results from both benchmarks demonstrate that the proposed visual analytics approach yields competitive performance in predicting process faults while enhancing interpretability by providing meaningful and informative visual representations.

Numerical analysis of the performance of a composite marine propeller blade subject to structural blade oscillations

High-fidelity Large Eddy Simulations (LES) are conducted over the oscillating marine propeller blades to investigate the effects of blade oscillation on the unsteady flow behaviour as well as to analyse the performance of the composite marine propeller blades. The accuracy of the results is first verified against the reference experimental results. A nonlinear frequency-domain solution or harmonic solution method is also applied to this study, based on a high-fidelity LES model, in addition to the traditional time-domain solution method to investigate the capabilities of the frequency-domain method in predicting the unsteady flow parameters, vortex generation, and hydrodynamic damping to determine the stability of the propeller blades. Results obtained reveal that the oscillation of the blade has a great impact on the vortex generation process which could impose implications on the performance and structural integrity of the composite marine propeller blades.

Adaptive Satellite Navigation Anti-Interference Algorithm Based on Inverse Cosine Function

Contrasting the dilemma that the traditional time-domain least mean square (LMS) algorithm in the existing satellite navigation receiver anti-interference system cannot satisfy the convergence time is short and maintain a low level of steady-state error at the same time, an inverse cosine variable step size LMS algorithm (ICVS-LMS) is proposed. To begin with, the LMS algorithm, with a fixed step size focuses on its effectiveness in attenuating and suppressing interference signals, is analyzed, and then the proposed ICVS-LMS algorithm is analyzed. In conclusion, both the ICVS-LMS algorithm and the traditional algorithm are simulated and compared in terms of their effectiveness in suppressing interference in satellite navigation signals. The experimental results demonstrate that the improved algorithm significantly reduces convergence time while maintaining a small steady-state error. The improved algorithm demonstrates high robustness and an obvious suppression effect on interference signals. The anti-interference performance is 8.41–12.22% higher than that of the proposed algorithm.

Numerical Methods for Time-Domain Responses of the Frequency-Independent Damped System

The complex damping model can only be used to calculate the steady-state responses, while the transient responses are divergent. Based on the complex damping model, the Hilbert transform is introduced to establish a hysteretic damping model, eliminating the divergence phenomenon. However, with the increase of the loss factor, the damped natural frequency also increases. To overcome this shortcoming, a frequency-independent damping model is proposed based on the hysteretic damping model. However, traditional time-domain methods are no longer applicable to frequency-independent damping models. Therefore, the transient response and steady-state response are separated, and the assumption of external excitation acceleration is introduced. Time-domain methods-based linear polynomial assumption, quadratic polynomial assumption and trigonometric function assumption are proposed, respectively. Numerical examples show that the time-domain methods based on linear polynomial assumption and quadratic polynomial assumption have high computational efficiency. But these two methods cannot take into account the vibration frequency of external excitation acceleration. Hence, the computational accuracy is low. Compared with them, the time-domain method based on trigonometric function assumption has the lowest computational efficiency and the highest computational accuracy.

Piezoelectric and Machine Learning Based Keystroke Dynamics for Highly Secure User Authentication

Cyber security is of significance in today’s e-commerce applications. In this article, we present a piezoelectric touch sensing supported keystroke dynamics based identity authentication technique, for providing a secure access manner to smartphones. Here, the polyvinylidene fluoride (PVDF) based piezoelectric touch panel can learn detailed force touch habits of users. With a support vector machine (SVM) algorithm, our proposed frequency domain features experimentally demonstrate a better authentication accuracy of 98.3%, compared to the traditional time domain features. The work showcases a feasible method of combining functional materials and artificial intelligence (AI) for satisfying highly secure requirements.

Optimizing and Analyzing Performance of Motor Fault Diagnosis Algorithms for Autonomous Vehicles via Cross-Domain Data Fusion

Electric motors play a pivotal role in the functioning of autonomous vehicles, necessitating accurate fault diagnosis to ensure vehicle safety and reliability. In this paper, a novel motor fault diagnosis approach grounded in vibration signals to enhance fault detection performance is presented. The method involves capturing vibration signals from the motor across various operational states and frequencies using vibration sensors. Subsequently, the signals undergo transformation into frequency domain representations through fast Fourier transform. This includes normalizing and concatenating the amplitude frequency and phase frequency signals into comprehensive frequency domain information. Leveraging Gramian image-encoding attributes, cross-domain fusion of time-domain and frequency-domain data is achieved. Finally, the fused Gram angle field map is fed into the ConvMixer deep learning model, augmented by the ECA mechanism to facilitate precise motor fault identification. Experimental outcomes underscore the efficacy of cross-domain data fusion, showcasing improved pattern recognition and recognition rates for the models compared to traditional time-domain methods. Additionally, a comparative analysis of various deep learning models highlights the superior performance of the ECA-ConvMixer model. This study makes significant contributions by introducing a cross-domain data fusion method, merging time-domain and frequency-domain information to enhance motor vibration signal analysis. Additionally, the incorporation of the ECA-ConvMixer deep learning model, equipped with attention mechanisms, effectively captures critical features, thus serving as a robust tool for motor fault diagnosis. These innovations not only enhance diagnostic accuracy but also have broad applications in areas like autonomous vehicles and industry, leading to reduced maintenance expenses and enhanced equipment reliability.

Modeling the connection between oil painting creation and university students’ inspiration in the context of “Internet+”

Abstract In the Internet era, oil painting creation requires constant observation and learning before it can be inspired, and the inspiration for oil painting creation is like a godsend. This paper constructs a fast Fourier transform convolutional model based on fast Fourier transform and convolutional neural network to study the connection between oil painting creation and university students’ inspiration in the context of “Internet+”. The model was evaluated algorithmically, and a dataset of oil paintings was built to investigate the influence of the hue and brushwork technique on students’ inspiration. From the algorithm evaluation, the algorithm of this paper’s model reduces the floating-point computation from 34.76G to 20.08G than the traditional time-domain convolution algorithm, which reduces the floating-point computation by nearly 15G and greatly improves the running speed of this paper’s model. From the hue examples, the percentage of blue-gray, pale white, and light blue oil paintings are 34.95%, 18.59%, and 10.73%, respectively. Various hues will bring students different emotional expressions and induce their creative inspiration. In terms of brushwork techniques, the average percentages of the six brushwork techniques are 16.53%, 22.21%, 15.68%, 11.73%, 14.69%, and 19.16%, respectively. The fast Fourier transform convolution model established under the background of “Internet+” can effectively analyze the different tones and brushstroke techniques in oil paintings so that students can feel the emotion and vitality of the works in the process of observation and thus promote the generation of inspiration for oil painting creation of college students.