Fluctuation Components Research Articles

Upper limit assessment of GIS equipment failure based on time series model and deep learning algorithm

If a sudden fault of gas-insulated switchgear is not handled in time, it will delay production and cause certain economic losses to the enterprise. Therefore, this study proposes an upper limit assessment algorithm for equipment faults based on time series model and deep learning. The irregular fluctuation components in the time series model are stabilized by using empirical mode decomposition, and the long short-term memory network in the deep learning algorithm is introduced to combine the two. The equipment fault data is obtained by the joint algorithm. After empirical mode decomposition, the intrinsic mode function component of the data signal is obtained, and the sensitive component is extracted from it. The relationship value and mutual information between it and the original data signal are analyzed to complete the fault data feature extraction. In the process of equipment fault upper limit assessment, the parameter optimization and convergence are controlled, and the fault deep learning model is constructed. After the refraction coefficient and reflection coefficient are determined, the upper limit assessment of equipment faults is realized. The proposed method is experimentally tested. The results show that the proposed algorithm has an ideal upper limit assessment result of faults, and the obtained curve has a high degree of fit with the actual result curve. It can be proved that the proposed algorithm can scientifically evaluate the upper limit of equipment faults and has certain application value.

Steady secondary flow in a turbulent boundary layer past a slender axisymmetric body

The turbulent boundary layer in a viscous incompressible fluid developing longitudinally past the surface of a thin cone or cylinder at a finite distance from the laminar–turbulent transition zone is studied. The characteristic Reynolds number, determined from the external flow velocity and the length of the body, is assumed to be large, and the thickness of the boundary layer is small and comparable to the radius of the body. The asymptotic method of multiple scales is used to find solutions to the Navier–Stokes equations. Instead of the traditional decomposition of the solution into time-averaged values and their fluctuations, the velocities and pressure are expressed as an asymptotic series consisting of steady and perturbed terms. As a result, the viscous steady flow (‘secondary’) that arises in the boundary layer as a mandatory component of fast turbulent fluctuations was described. Analytical and numerical solutions for the radial steady velocity are presented, describing the self-induced suction of fluid from the external flow into the boundary layer. Further analytical solutions are obtained for the longitudinal and circumferential velocities, which differ markedly from the laminar regime. The solutions found are somewhat similar to the degenerate (one-dimensional) case of self-sustaining longitudinal thin structures in turbulent shear flows. A qualitative comparison with direct numerical simulations is presented.

TPTNet: A Data‐Driven Temperature Prediction Model Based on Turbulent Potential Temperature

AbstractA data‐driven model for predicting the surface temperature using neural networks was proposed to alleviate the computational burden of numerical weather prediction (NWP). Our model, named TPTNet uses only 2 m temperature measured at the weather stations of the South Korean Peninsula as input to predict the local temperature at finite forecast hours. The turbulent fluctuation component of the temperature was extracted from the station measurements by separating the climatology component accounting for the yearly and daily variations. The effect of station altitude was then compensated by introducing a potential temperature. The resulting turbulent potential temperature (TPT) data at irregularly distributed stations were used as input for predicting the TPT at forecast hours through three trained networks based on convolutional neural network, Swin Transformer, and a graph neural network. By comparing the prediction performance of our network with that of persistence and NWP, we found that our model can make predictions comparable to NWP for up to 12 hr.

A novel formulation of wind velocity spectrum incorporating rainfall influence

Wind-rain interaction constitutes a common physical phenomenon in nature. Nevertheless, in fluid mechanics and wind engineering, whether and how rainfall influences wind remains an unresolved challenge. This study relies on a substantial dataset obtained through on-site measurement. Initially, three distinct rainfall categories—light rain, moderate rain, and heavy rain—are established based on the magnitude of vertical wind velocity. Notably, significant disparities in the high-frequency constituents of the fluctuating wind velocity spectrum are discerned across various rainfall levels. Subsequently, a rigorous quantitative analysis explores the relationship between the high-frequency cumulative energy proportion of the fluctuating wind velocity spectrum and rainfall intensity, leading to formulations that delineate the high-frequency cumulative energy proportion in the alongwind, acrosswind, and vertical wind directions as explicit functions of rainfall intensity. Moreover, a comprehensive unified expression models the three-dimensional fluctuating wind velocity spectrum. This expression incorporates parameters encompassing the relative scales of fluctuation and mean components, the extent of conversion between high and low-frequency energy, and the scaling laws governing the inertial subrange. Additionally, explicit functional dependencies for these parameters concerning variations in rainfall intensity are provided. The novel formulation of the fluctuating wind velocity spectrum proposed in this study bears substantial scientific significance, as it facilitates a deeper understanding of wind-rain interaction and offers valuable insights for conducting intricate environmental flow field simulations.

Adaptive Sliding Window–Dynamic Time Warping-Based Fluctuation Series Prediction for the Capacity of Lithium-Ion Batteries

Accurately predicting the capacity of lithium-ion batteries is crucial for improving battery reliability and preventing potential incidents. Current prediction models for predicting lithium-ion battery capacity fluctuations encounter challenges like inadequate fitting and suboptimal computational efficiency. This study presents a new approach for fluctuation prediction termed ASW-DTW, which integrates Adaptive Sliding Window (ASW) and Dynamic Time Warping (DTW). Initially, this approach leverages Empirical Mode Decomposition (EMD) to preprocess the raw battery capacity data and extract local fluctuation components. Subsequent to this, DTW is employed to forecast the fluctuation sequence through pattern-matching methods. Additionally, to boost model precision and versatility, a feature recognition-based ASW technique is used to determine the optimal window size for the current segment and assist in DTW-based predictions. The study concludes with capacity fluctuation prediction experiments carried out across various lithium-ion battery models. The results demonstrate the efficacy and extensive applicability of the proposed method.

Interior noise prediction of inter-coach space of high-speed maglev trains based on wavenumber decomposition on aerodynamic excitation

Current research on noise and vibration control of high-speed maglev trains pays more attention to far-field noise, while the level of interior noise has a direct impact on the ride comfort and should be placed equal weight on. In this paper, inter-coach space, one of the main pressure fluctuation sources of a specific type of high-speed maglev train with a design speed of 600 km·h−1, is taken as the research object. The turbulent and acoustic components of wall pressure fluctuations (WPF) are separated based on a wavenumber-frequency analysis approach, and then each component is applied as different forms of source input to the vibroacoustic model, namely, finite element method-boundary element method (FEM-BEM) and statistical energy analysis (SEA) for low- and high-frequency ranges respectively, to investigate the contribution of both components to interior acoustic cavity in all frequency range. It can be seen quantitatively from the results that the amplitude of turbulent component is generally much higher than that of the acoustic one, but it can be vice versa when it comes to the interior response. The conclusion drawn in this paper are able to provide guidance for future researches on more targeted interior noise control of high-speed maglev trains.

Dual NWP wind speed correction based on trend fusion and fluctuation clustering and its application in short-term wind power prediction

With the continuous growth of grid-connected installed capacity of wind power, the inevitable gap, volatility and randomness of wind power pose challenges to the stability of real-time operation of power system. Accurate wind power prediction (WPP) can effectively ensure the safe and stable operation of power system. At present, the main input of short-term power prediction based on data-driven model is numerical weather prediction (NWP), and its prediction accuracy leads to the failure to effectively improve the accuracy of short-term power prediction. To solve this problem, this paper proposes a dual NWP wind speed (WS) correction method based on trend fusion and fluctuation clustering. First, the WS trend is used to construct a new input feature, and the NWP WS error distribution is determined to establish a more correct and stable mapping relationship with the NWP WS error. Secondly, the error is decomposed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the trend and fluctuation components are defined by Pearson coefficient. The trend curve features in the short 24h period were extracted from the two dimensions of time and value, and the trend and fluctuation curve databases under different scenarios were formed by K-Medoids clustering. Finally, the trend component is modified by the Attention-GRU model, and the corresponding trend component is searched and matched from the database to correct the fluctuation component. The NWP WS is modified by the superposition of the two components and the short-term WPP is made. Two wind farms in Mengxi, China were used for example analysis, and the prediction accuracy was improved by 10 % and 13.1 % respectively, which demonstrated the effectiveness of the proposed method.

A dynamic prediction model of landslide displacement based on VMD–SSO–LSTM approach

Addressing the limitations of existing landslide displacement prediction models in capturing the dynamic characteristics of data changes, this study introduces a novel dynamic displacement prediction model for landslides. The proposed method combines Variational Mode Decomposition (VMD) with Sparrow Search Optimization (SSO) and Long Short-Term Memory (LSTM) techniques to formulate a comprehensive VMD–SSO–LSTM model. Through the application of VMD, the method dissects cumulative displacement and rainfall data, thereby extracting distinct components such as trend, periodicity, and fluctuation components for displacement, as well as low-frequency and high-frequency components for rainfall. Furthermore, leveraging Gray Correlational Analysis, the interrelationships between the periodic component of displacement and the low-frequency component of rainfall, as well as the fluctuation component of displacement and the high-frequency component of rainfall, are established. Building upon this foundation, the SSO–LSTM model dynamically predicts the interrelated displacement components, synthesizing the predicted values of each component to generate real-time dynamic forecasts. Simulation results underscore the effectiveness of the proposed VMD–SSO–LSTM model, indicating root-mean-square error (RMSE) and mean absolute percentage error (MAPE) values of 1.2329 mm and 0.1624%, respectively, along with a goodness of fit (R2) of 0.9969. In comparison to both back propagation (BP) prediction model and LSTM prediction model, the VMD–SSO–LSTM model exhibits heightened predictive accuracy.

Improving Aeromechanical Performance of Compressor Rotor Blisk with Topology Optimization

When it comes to modern design of turbomachinery, one of the most critical objectives is to achieve higher efficiency and performance by reducing weight, fuel consumption, and noise emissions. This implies the need for reducing the mass and number of the components, by designing thinner, lighter, and more loaded blades. These choices may lead to mechanical issues caused by the fluid–structure interaction, such as flutter and forced response. Due to the periodic aerodynamic loading in rotating components, preventing or predicting resonances is essential to avoid or limit the dangerous vibration of the blades; thus, simulation methods are crucial to study such conditions during the machine design. The purpose of this paper is to assess a numerical approach based on a topology optimization method for the innovative design of a compressor rotor. A fluid-structural optimization process has been applied to a rotor blisk which belongs to a one-and-a-half-stage aeronautical compressor including static and dynamic loads coming from blade rotation and fluid flow interaction. The fluid forcing is computed by some CFD TRAF code, and it is processed via time and space discrete Fourier transform to extract the pressure fluctuation components in a cyclic-symmetry environment. Finally, a topological optimization of the disk is performed, and the encouraging results are presented and discussed. The remarkable mass reduction in the component (≈32%), the mode-shape frequency shift from a fluid forcing frequency, and an overall relevant reduction in the dynamic response around Campbell’s crossing confirm the efficacy of the presented methodology.

Parametric study of large-eddy simulation to capture scaling laws of velocity fluctuations in neutral atmospheric boundary layers

The development of digital twins for wind farms often involves the use of large-eddy simulation (LES) to model atmospheric boundary layers. Existing LES solvers primarily focus on accurately capturing streamwise fluctuations. They, however, overlook the less energetic cross-stream fluctuations, which play a crucial role in wind turbine wake evolution. In this study, we conduct a systematic parametric study and incorporate changes in an open-source LES solver. The improved solver is able to predict all three components of velocity fluctuations in alignment with the scaling laws derived from the attached-eddy hypothesis. In particular, we examine the impact of (i) the subgrid-scale model, (ii) the wall model, (iii) the von Kármán constant, and (iv) the grid-cell aspect ratio. We find that although all these factors influence the prediction of velocity fluctuations, the grid-cell aspect ratio has the greatest effect on the spanwise and vertical velocity components. Notably, utilizing nearly isotropic grid cells leads to the best alignment of all three velocity component fluctuations with the scaling laws. Spectral analysis further demonstrates that the present LES solver accurately predicts the characteristic length scales for all velocity fluctuation components, making it a reliable tool for obtaining turbulent inflow conditions for wind farm modeling.

Influence of excessive salt consumption on the functional state of microvascular vessels of the skin of rats with renal dysfunction

Background. Cardiovascular complications are common in chronic kidney disease (CKD). The relationship between the cardiovascular system and the kidneys is complex and diverse, but the question of the mechanisms of the influence of excess NaCl consumption on the functional state of blood vessels during renal dysfunction remains relevant.The aim: to evaluate the effect of a high-salt diet on the functional state of microcirculatory vessels (MCR) of the skin in rats at an early stage of renal dysfunction.Material and Methods. The study was conducted on Wistar rats. The first SO group included animals subjected to sham surgery (SO) and receiving a standard diet (0.34% NaCl); in the second, HS group – animals that also underwent a sham operation, but received a high-salt diet (4% NaCl); in the third, NE group – rats subjected to ¾ nephrectomy (NE) and receiving a standard diet; the fourth, HS+ NE group included rats subjected to ¾ NE and fed a high-salt diet (4% NaCl). Blood pressure (BP) was measured in the tail using the cuff method (Sistola, Russia), MCR was assessed using laser Doppler flowmetry (LDF). The reactivity of skin microvessels was assessed by changes in the MCR index before and after iontophoresis of acetylcholine (ACh) and sodium nitroprusside (NP). Based on the assessment of the spectral characteristics of fluctuations in the MCR index obtained using Wavelet analysis, the value of endothelial, neurogenic and myogenic microvascular tone was calculated.Results. 4 months after ¾ NE, rats showed a decrease in the excretory function of the kidneys. In rats on a highsalt diet, an increase in blood urea levels was noted. The blood pressure in rats of the SO+HS group did not significantly differ from the control animals of the SO group (131 ± 8 and 125 ± 4 mmHg, respectively, NS), NE led to a significant increase in blood pressure (135 ± 5 mmHg in rats of the NE group, and 145 ± 7 mmHg – in the NE+HS group). The average MCR was higher in rats receiving a high-salt diet (SO+HS and NE+HS groups), compared with animals of the corresponding group receiving a standard diet. The combination of NE and high-salt load has an inhibitory effect on the amplitude of fluctuations in the intensity of perfusion in all studied ranges: endothelial, neurogenic and myogenic, while the calculated value of skin microvascular tone in three ranges was the highest in NE+HS group rats. In all experimental groups, the reactivity to AH was reduced compared with the SO group, and after NP iontophoresis, the average perfusion rate significantly decreased only in groups of animals receiving a high-salt diet: SO+HS and NE+HS.Conclusion. A high-salt diet in rats with an initial stage of renal dysfunction contributes to an increase in blood pressure and a decrease in natriuresis compared with animals with NE who received a standard diet. With excessive consumption of table salt in rats after NE, the intensity of cutaneous MCR increases, while reducing the variability of the MCR index. A high-salt diet in rats with NE modifies the characteristics of the spectral components of fluctuations in the MCR index, indicating an increase in tonic effects on skin microvessels in the endothelial, neurogenic and myogenic ranges. With hypernatrial load, animals with ¾ NE have a decrease in the reactivity of cutaneous microvessels to AH and NP, which is mediated by both a decrease in endothelial NO production and a decrease in MMC sensitivity to NO.

Using spatio-temporal graph neural networks to estimate fleet-wide photovoltaic performance degradation patterns.

Accurate estimation of photovoltaic (PV) system performance is crucial for determining its feasibility as a power generation technology and financial asset. PV-based energy solutions offer a viable alternative to traditional energy resources due to their superior Levelized Cost of Energy (LCOE). A significant challenge in assessing the LCOE of PV systems lies in understanding the Performance Loss Rate (PLR) for large fleets of PV systems. Estimating the PLR of PV systems becomes increasingly important in the rapidly growing PV industry. Precise PLR estimation benefits PV users by providing real-time monitoring of PV module performance, while explainable PLR estimation assists PV manufacturers in studying and enhancing the performance of their products. However, traditional PLR estimation methods based on statistical models have notable drawbacks. Firstly, they require user knowledge and decision-making. Secondly, they fail to leverage spatial coherence for fleet-level analysis. Additionally, these methods inherently assume the linearity of degradation, which is not representative of real world degradation. To overcome these challenges, we propose a novel graph deep learning-based decomposition method called the Spatio-Temporal Graph Neural Network for fleet-level PLR estimation (PV-stGNN-PLR). PV-stGNN-PLR decomposes the power timeseries data into aging and fluctuation components, utilizing the aging component to estimate PLR. PV-stGNN-PLR exploits spatial and temporal coherence to derive PLR estimation for all systems in a fleet and imposes flatness and smoothness regularization in loss function to ensure the successful disentanglement between aging and fluctuation. We have evaluated PV-stGNN-PLR on three simulated PV datasets consisting of 100 inverters from 5 sites. Experimental results show that PV-stGNN-PLR obtains a reduction of 33.9% and 35.1% on average in Mean Absolute Percent Error (MAPE) and Euclidean Distance (ED) in PLR degradation pattern estimation compared to the state-of-the-art PLR estimation methods.

An ensemble model for short-term wind power prediction based on EEMD-GRU-MC

As a kind of clean and renewable energy, wind power is of great significance for alleviating energy crisis and environmental pollution. However, the strong randomness and large volatility of wind power bring great challenges to the dispatching and safe operation of the power grid. Hence, accurate and reliable short-term prediction of wind power is crucial for the power grid dispatching department arranging reasonable day-ahead generation schedules. Targeting the problem of low model prediction accuracy caused by the strong intermittency and large volatility of wind power, this paper develops a novel ensemble model for short-term wind power prediction which integrates the ensemble empirical mode decomposition (EEMD) algorithm, the gated recurrent unit (GRU) model and the Markov chain (MC) technique. Firstly, the EEMD algorithm is used to decompose the historical wind power sequence into a group of relatively stationary subsequences to reduce the influence of random fluctuation components and noise. Then, the GRU model is employed to predict each subsequence, and the predicted values of each subsequence are aggregated to get the preliminary prediction results. Finally, to further enhance the prediction accuracy, the MC is used to modified the prediction results. A large number of numerical examples indicates that the proposed EEMD-GRU-MC model outperforms the six benchmark models (i.e., LSTM, GRU, EMD-LSTM, EMD-GRU, EEMD-LSTM and EEMD-GRU) in terms of multiple evaluation indicators. Taking the spring dataset of the ZMS wind farm, for example, the MAE, RMSE and MAPE of the EEMD-GRU-MC model is 1.37 MW, 1.97 MW, and from 1.76%, respectively. Moreover, the mean prediction error of the developed model in all scenarios is less than or close to 2%. After 30 iterations, the proposed model uses an average of about 35 min to accurately predict the wind power of the next day, proving its high computation efficiency. It can be concluded that the ensemble model based on EEMD-GRU-MC is a promising prospect for short-term wind power prediction.

Multi-step power forecasting for regional photovoltaic plants based on ITDE-GAT model

With the integration of high proportion of distributed photovoltaic(PV), high-accuracy regional PV power forecasting technology can enhance the regional coordinated scheduling capability of the new power system. This paper proposes a regional PV power forecasting model based on an improved time-series dense encoder and graph attention network (ITDE-GAT), which takes into account the spatio-temporal correlations among the regional PV plants. Firstly, an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN) is used to extract the clear-sky and fluctuation components from PV data. Secondly, the combined ITDE-GAT is applied to perform the regional PV power forecasting. Considering the static information, an improved dense encoder network (ITDE) is constructed to extract the temporal and spatial relationships of regional PV. Graph attention network (GAT) is then utilized to explore the spatial correlations among the regional PV. Finally, case study from two actual PV region datasets shows that the proposed model achieves higher forecasting accuracy and exhibits stronger generalization capabilities. The results demonstrate that compared to various advanced deep learning methods, the R2 evaluation metric of the approach proposed in this paper demonstrates, respectively, maximum improvements of 3.4 %, 6.5 %, and 7.8 % for the 1 h, 3 h, and 6 h ahead predictions.

Investigation of the influence of a closed loop of longitudinal forces on the dynamics of bending and torsional vibrations of a package working body

It is shown that at bending and torsional fluctuation component design, within which there is closed sidebar internal longitudinal effort, exists its influence upon oscillatory process, defined by correlation of longitudinal acerbity compressed and sprained element and is shown the way of the analytical account of this influence. The decision of the problem is received about изгибных and torsional fluctuation packet worker of the organ with provision for specified phenomena, which has shown the need for the account for this influence under exact calculation.

Earthquake prediction based on the analysis of water level fluctuations in the control well

This study examines the methodology and results of earthquake forecasting based on the assumption that the earthquake preparation process can be linked to the development of rupture mechanisms. The analysis focuses on water fluctuations in a control monitoring well, which provides valuable information about the earthquake’s preparation period well in advance. Studies on the territory of the Kryvyi Rih iron ore basin (Kryvbas) confirmed that the underground waters in a deep well (815 m deep) depend on the processes of the current lithosphere deformations evidenced by the changing elastic-deformation state of the crust in tectonic areas. The Kryvyi Rih-Kremenchuk crust-mantle fault produces a system of different-scale faults in the crystalline basement which determine the hydrodynamics of the Kryvbas underground filtration. The earthquake preparation process exhibits clear indicators of the rupture process, characterized by a periodic component of water fluctuations superimposed on the main level-changing process during the preparatory phase. The frequency of the periodic component increases as the date of the earthquake approaches. With this approach, an earthquake with a magnitude of 4.1 inKryvbas was successfully predicted one and a half year before the event, based on monitoring water level fluctuations for 120 days between February and June 2016. The efficacy of forecasting local earthquakes was evaluated using the retro-forecast of earthquakes in Kryvbas on July 29, 2017. The methodology was validated through testing, confirming the initial assumptions. This technique was tested to predict the exacerbation of the seismic situation in the iron ore mining area caused by powerful blasting activities.

NUMERICAL INVESTIGATION OF TURBULENCE CHARACTERISTICS AND SELF-SIMILARITY IN A HIGHLY AERATED STABLE HYDRAULIC JUMP USING LARGE EDDY SIMULATION

This work presents a numerical study of a stable hydraulic jump at Froude number 4.25 and Reynolds number 1.15×105 inside a horizontal and rectangular channel with a length of 3.2 m, a width of 0.5 m and a height of 0.4 m using large eddy simulation (LES). Classical hydraulic jump characteristics are obtained, such as conjugate depths, jump length, void fraction and velocity profiles. The hydraulic jump maximum streamwise velocity decay and shear layer spreading rate are simulated and compared with experimental data. For these parameters, numerical results demonstrate that is possible to stablish an analogy with other shear flows, such as the horizontal plane wall jet. Profiles of streamwise and vertical components of mean velocity are simulated, and self-similarity is observed for cross-sections located at the recirculation region of the jump. Self-similarity is also observed in terms of turbulent fluctuations, insofar as LES simulations indicate a high level of turbulence in the recirculation region. The simulated root mean square of streamwise velocity fluctuations, , ranges from 0.5 to 0.7 of the maximum cross-sections velocity, whereas the root mean square of vertical component of velocity fluctuations, , stays around 0.5 of the maximum cross-sections velocitiy. All validation comparisons show good agreement with the selected experimental data of Kramer and Valero (2020) and Wang (2014), presenting average deviations always lesser than 5%.

Particle Image Velocimetry Measurements in a High-Speed Low-Reynolds Low-Pressure Turbine Cascade

Abstract Particle image velocimetry (PIV) measurements in the blade-to-blade (B2B) plane and cascade outlet plane (COP) of a high-speed low-pressure turbine (LPT) cascade were performed at engine-representative outlet Mach number (0.70–0.95), and Reynolds number (70–120 k) under steady flow conditions. The freestream turbulence characteristics were imposed by means of a passive turbulence grid. The PIV results on the B2B plane were compared against five-hole probe (5HP) and Reynolds-averaged Navier–Stokes (RANS) computations to assess the validity of measurement and simulation techniques in the engine-relevant LPT cascade flows. The PIV captured the wake depth and width measured by the 5HP whereas the RANS displayed an overprediction of the wake Mach number deficit. The 5HP was found to impose sinewave fluctuations of the measured flow angle downstream, around three times higher than PIV. Additionally, PIV estimated turbulence intensity (TI) in the cascade, showing TI decay along a streamline. At the highest Mach number, a peak TI occurred past a shock wave. Measurements of the outlet flow field highlighted a high TI in the secondary flow region whereas high degree of anisotropy (DA) was registered in the boundary of the secondary flow and freestream regions. The contribution of the streamwise fluctuation component was found to be less than the crosswise and radial components in the freestream region. Increasing the cascade outlet Mach number, the contribution of streamwise fluctuation to the DA was observed to decrease.

Influence Line Identification Method Based on VMD Combined with Improved Wavelet Threshold Denoising

The influence line of bridge can reflect the performance of the bridge under the moving vehicle load. It has a wide range of applications in structural damage detection, performance evaluation, model correction and bridge weigh-in-motion. Fast moving vehicles will cause the dynamic effect of the bridge, resulting in the change of the bridge response curve and the difficulty of identifying the influence line. In this paper, an influence line identification method based on variational mode decomposition (VMD) combined with improved wavelet threshold denoising is proposed. Firstly, the mathematical model of influence line identification is established based on vehicle load matrix and bridge response. Then, VMD combined with improved wavelet threshold denoising method is used to eliminate dynamic fluctuation effect and noise. Finally, the elastic net penalty term is introduced in the influence line identification problem, and the B-spline basis function is used to reconstruct the influence line. In order to verify the effectiveness of the proposed method, a vehicle-bridge coupling model is established for numerical calculation, and the identification of bridge displacement influence lines under different conditions such as vehicle speed, noise level, road roughness and vehicle weight is investigated. The results show that the proposed method can effectively eliminate the dynamic fluctuation components and noise of the bridge response. In the case of good road roughness, the influence line can still be accurately identified when the vehicle speed reaches 15 m/s.

Theory of Magnetic Switchbacks Fully Supported by Parker Solar Probe Observations

Magnetic switchbacks are rapid high-amplitude reversals of the radial magnetic field in the solar wind that do not involve a heliospheric current sheet crossing. First seen sporadically in the 1970s in Mariner and Helios data, switchbacks were later observed by the Ulysses spacecraft beyond 1 au and have been recently discovered to be a typical component of solar wind fluctuations in the inner heliosphere by the Parker Solar Probe spacecraft. While switchbacks are now well understood to be spherically polarized Alfvén waves thanks to Parker Solar Probe observations, their formation has been an intriguing and unsolved puzzle. Here we provide a simple yet predictive theory for the formation of these magnetic reversals: the switchbacks are produced by the distortion and twisting of circularly polarized Alfvén waves by a transversely varying radial wave propagation velocity. We provide an analytic expression for the magnetic field variation, establish the necessary and sufficient conditions for the formation of switchbacks, and show that the proposed mechanism works in a realistic solar wind scenario. We also show that the theoretical predictions are in excellent agreement with observations, and the high-amplitude radial oscillations are strongly correlated with the shear of the wave propagation speed. The correlation coefficient is around 0.3–0.5 for both encounter 1 and encounter 12. The probability of this being a lucky coincidence is essentially zero with p-values below 0.1%.