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ABSTRACT The goal of this paper is to present a new finite‐dimensional adaptive observer for some uncertain linear parabolic systems with delayed measurements. The observer is based on the modal decomposition approach and uses a classical persistent excitation condition. We prove that by using a finite‐dimensional state predictor, we ensure exponential convergence of both states parameters estimation error for arbitrary long constant delay. [ABSTRACT FROM AUTHOR]

This paper proposes a method for harmonic time–frequency analysis and detection based on an improved multi-synchrosqueezing transform (MSST). The aim is to address the significant endpoint problem of the synchrosqueezing transform (SST) in power harmonic analysis. This approach initially employs the Burg method to estimate the parameters of the auto-regressive (AR) model for the harmonic signal. Subsequently, it conducts multiple iterative computations on the SST results of the extended harmonic signal, further compressing the time–frequency spectrum energy to obtain a more precise time–frequency spectrum of the harmonic signal. Additionally, it utilizes the robust reconstruction capability of MSST to decompose the harmonic signal and obtain a series of intrinsic mode functions (IMF) with different frequencies. Finally, the Hilbert Transform is applied to identify the harmonic parameters of each IMF component and accomplish harmonic detection. The simulation experiments and measured data results demonstrate that the proposed method outperforms the Hilbert-Huang Transform (HHT) and SST methods in achieving more accurate time–frequency analysis and detection of harmonic signals. It also reveals the time–frequency characteristics and variation patterns of power grid harmonics, making it of great significance for harmonic control. [ABSTRACT FROM AUTHOR]

We propose a technique for performing spectral (in time) analysis of spatially-resolved flowfield data, without needing any temporal resolution or information. This is achieved by combining projection-based reduced-order modeling with spectral proper orthogonal decomposition. In this method, space-only proper orthogonal decomposition is first performed on velocity data to identify a subspace onto which the known equations of motion are projected, following standard Galerkin projection techniques. The resulting reduced-order model is then utilized to generate time-resolved trajectories of data. Spectral proper orthogonal decomposition (SPOD) is then applied to this model-generated data to obtain a prediction of the spectral content of the system, while predicted SPOD modes can be obtained by lifting back to the original velocity field domain. This method is first demonstrated on a forced, randomly generated linear system, before being applied to study and reconstruct the spectral content of two-dimensional flow over two collinear flat plates perpendicular to an oncoming flow. At the range of Reynolds numbers considered, this configuration features an unsteady wake characterized by the formation and interaction of vortical structures in the wake. Depending on the Reynolds number, the wake can be periodic or feature broadband behavior, making it an insightful test case to assess the performance of the proposed method. In particular, we show that this method can accurately recover the spectral content of periodic, quasi-periodic, and broadband flows without utilizing any temporal information in the original data. To emphasize that temporal resolution is not required, we show that the predictive accuracy of the proposed method is robust to using temporally-subsampled data. [ABSTRACT FROM AUTHOR]

Under the action of the fluctuating wind load, the low frequency part produces background response to the structures, while the high frequency part produces resonance response to the structures. In structural design, equivalent static wind load is usually used to equate the fluctuating wind load. Although there are various methods of evaluating the equivalent static wind load, they did not consider the correlation between modal responses or considered them insufficiently. Therefore, in this paper, the correlation between modal response is considered to evaluate the equivalent static wind load, the displacement response is decomposed by proper orthogonal decomposition (POD) method, the correlation between modal displacement is removed, the equivalent static wind load is expressed in the form of displacement mode, and then the correlation between modal response is fully considered in the extreme value combination. This paper combines the equivalent static wind loads in order to make the wind resistance design more reasonable for high‐rise buildings. First, the formulas of equivalent static wind loads expressed by displacement modes of background and resonant response are deduced based on modal decomposition and POD method. Second, the combination formulas of square‐root‐of‐sum‐square (SRSS) and complete‐quadratic‐combination (CQC) rules for the equivalent static wind loads considering the mean wind loads are proposed. Both the linear combination formula of SRSS for the equivalent static wind load and the weighting factor expressions of background and resonance equivalent static wind load are given. Third, the accuracy and validity of the formulas of equivalent static wind load are verified by a wind tunnel pressure test of a high‐rise building. Finally, the simplified combination coefficient formulas for the equivalent static wind loads are proposed, and the combination of the high‐rise building base's fluctuating equivalent static wind loads of along‐wind direction, across‐wind direction, and torsional direction is analyzed. [ABSTRACT FROM AUTHOR]

In order to improve the separation control effect of an oscillating jet, the external flow field of the actuators and the wing wake are obtained via hot-wire measurements to optimize the actuator and achieve wing separation flow control. The optimization objectives are to improve the sweeping uniformity and range of the jet. In the present study, the PCA method is used for the modal decomposition of the velocity distribution. The modal-based actuator evaluation parameters are proposed, and the kriging surrogate models of the modal coefficients (principal components) on the actuator parameters are established. The multi-objective genetic algorithm was utilized to complete the optimization of the actuator, and the effect of flow separation control on the wing was verified. The results show that three patterns exist in the time-averaged velocity distribution of the external flow field: unimodal, broad and bimodal, from unimodal to bimodal, the degree of the jet sweeping uniformity gradually decreases, and the sweeping range gradually increases. The pattern of the velocity distribution modals affects the degree of jet sweeping uniformity, while the distance of the modal peaks affects the jet sweeping range. The two evaluation parameters are negatively correlated: insufficient sweeping range or poor sweeping uniformity of the jet are not conducive to wing separation flow control, and the two must be coordinated to achieve the optimal control effect. [ABSTRACT FROM AUTHOR]

AbstractThis article aims to investigate the dynamics of clamped-clamped porous viscoelastic double microbeams interconnected via a viscoelastic layer within the framework of the modified couple stress theory (MCST). Different types of porosity distributions are considered to examine the material imperfection effects on the dynamics of the double microbeam. A closed-cell porosity model is employed to model the variation of the material properties in the thickness plane. The coupled longitudinal and lateral equations of motion are derived using Hamilton’s principle. The governing equations of motion are solved using the modal decomposition method. The equations of motion are verified against the literature for simplified cases (i.e., a non-porous single viscoelastic microbeam and a porous single microbeam resting on a viscoelastic foundation). The numerical results are validated for simpler versions of the system using the finite element method and against the existing literature for a simplified case of a uniformly distributed porous double macrobeam connected via an elastic layer. Influences of viscoelasticity in the double microbeam, viscoelastic layer, porosity, and small-scale influences on the dynamics of the viscoelastic double microbeams are studied. It was found that increasing the material length-scale factor causes the complex fundamental lateral vibrational frequencies to increase for all the porosity patterns. [ABSTRACT FROM AUTHOR]

An exact parallel algorithm of dynamic mode decomposition (DMD) with Hankel matrices for large-scale flow data is proposed. The proposed algorithm enables the DMD and the Hankel DMD for large-scale data obtained by high-fidelity flow simulations, such as large-eddy simulations or direct numerical simulations using more than a billion grid points, on parallel computations without any approximations. The proposed algorithm completes the computations of the DMD by utilizing block matrices of X T X ∈ R k × k (where X ∈ R n × k is a large data matrix obtained by high-fidelity simulations, the number of snapshot data is n ≳ 10 9 , and the number of snapshots is k ≲ O (10 3) ) without any approximations: for example, the singular value decomposition of X is replaced by the eigenvalue decomposition of X T X . Then, the computation of X T X is parallelized by utilizing the domain decomposition often used in flow simulations, which reduces the memory consumption for each parallel process and wall-clock time in the DMD by a factor approximately equal to the number of parallel processes. The parallel computation with communication is performed only for X T X , allowing for high parallel efficiency under massively parallel computations. Furthermore, the proposed exact parallel algorithm is extended to the Hankel DMD without any additional parallel computations, realizing the Hankel DMD of large-scale data collected by over a billion grid points with comparable cost and memory to the DMD without Hankel matrices. Moreover, this study shows that the Hankel DMD, which has been employed to enrich information and augment rank, is advantageous for large-scale high-dimensional data collected by high-fidelity simulations in data reconstruction and predictions of future states (while prior studies have reported such advantages for low-dimensional data). Several numerical experiments using large-scale data, including laminar and turbulent flows around a cylinder and transonic buffeting flow around a full aircraft configuration, demonstrate that (i) the proposed exact parallel algorithm reproduces the existing non-parallelized Hankel DMD, (ii) the Hankel DMD for large-scale data consisting of over a billion grid points is feasible by using the proposed exact parallel algorithm with high parallel efficiency on more than 6 thousand CPU cores, and (iii) the Hankel DMD has advantages for high-dimensional data such as n ≳ 10 9 . [ABSTRACT FROM AUTHOR]

Spectral proper orthogonal decomposition(SPOD) has been recently used to capture temporal and spatial characteristics of complex flows. Applying SPOD analysis to such flow fields can deepen the understanding of flow mechanism. To further study the external flow at high Reynolds numbers, large eddy simulation(LES) is applied to acquire the flow field data while SPOD is utilized to extract important flow structures. The considered external flow includes incompressible flow around the cylinder and transonic shock oscillation. For the flow around the cylinder, flow field under three different Reynolds numbers is studied. Power spectral density(PSD) results of lift coefficient show that the vortex shedding frequency of low Reynolds number is S t = 0. 24 , which of moderate Reynolds number is S t = 0. 203 , and S t = 0. 27 for high Reynolds number flow. The increment of Reynolds number will lead to early instability of shear layer on the cylindrical surface. The modal results of SPOD and dynamic mode decomposition(DMD) at different Reynolds numbers show two antisymmetric structures at shedding vortex frequencies, indicating the resemblance in the process of vortex formation. For the shock wave/boundary layer interaction(SBLI) of transonic Royal Aircraft Establishment(RAE) 2822 airfoil, flow field under M ∞ = 0. 729 , R e c = 1. 94 × 1 0 7 is studied. The results show that the occurrence of unsteadiness is closely related to the normal shock wave/turbulent boundary layer interaction happening on the upper surface of the airfoil. Modal results of SPOD near the characteristic frequency show strong discontinuity near the shock wave. • Spectral proper orthogonal decomposition is developed to capture the spatiotemporal modes. • The mechanism of Reynolds-number effect on the flow around circular cylinder is revealed. • The low frequency of transonic buffet on airfoil is identified. [ABSTRACT FROM AUTHOR]

Cavitation is a complex hydrodynamic phenomenon that occurs when the local pressure in a liquid drops below the saturation vapor pressure, resulting in the phase transition of the liquid into vapor. This process was characterized by intricate three-dimensional flow structures and highly unsteady, violent dynamics. For applications such as hydraulic machinery, ship propulsion, and hydraulic engineering, cavitation often induces detrimental effects, and has significant impact on performance and reliability in engineering systems. In order to investigate the structure of non-constant cavitation flow field, this paper adopted the experimental method of image particle velocimetry PIV-LIF, which used a high-speed camera to photograph the cavitation flow from the side of the experimental section and followed the liquid flow by fluorescent tracer particles, so as to measure the cavitation velocity field. Using the high-resolution velocity field and grayscale snapshot data, the dynamic mode decomposition (DMD) was carried out to analyze the structure of the non-constant cavitation flow field. The DMD method can effectively decompose the flow field into several modes and identify the dominant mode for analysis. Based on the DMD method, the decomposition of the cavitated flow field has the characteristics of a single mode frequency and growth rate, which is more advantageous in the analysis of cavitated periodic flow. By analyzing the main modes that account for higher energy and performing modal reconstruction, it is found that all these modes contain certain characteristics of vacuole motion, which are related to the structure of the re-entrant jet and the accompanying vortex in the flow field, and the main evolution frequency of the vacuole is consistent with the vortex shedding frequency. The results of the flow field characteristics of each mode show that the first-order mode of the cavitation flow corresponds to a frequency of 0, which represents the average flow field; the second-order mode corresponds to a frequency of about the vacuole shedding frequency, which reveals that the vacuole grows and sheds periodically at the leading edge of the occurrence; the third-order mode corresponds to a frequency about twice that of the second-order mode, which reveals the fusion behavior of two large-scale vortices behind the hydrofoil; the fourth-order mode corresponds to a frequency about three times higher than the second-order mode, which characterizes the fusion behavior of some small-scale vortices in the flow field. Finally, the modal decomposition analysis of the cavitation flow field with different cavitation numbers was carried out, and it is found that the vortex structure of the shedding vacuole increases with decreasing cavitation number, and the second-order mode frequency decreases with decreasing cavitation number.

Microseismic monitoring data plays a crucial role in predicting and warning dynamic hazards such as coal mine rockburst. Utilizing historical microseismic monitoring data to forecast the evolution characteristics of future microseismic events is an effective approach to enhance the timeliness and accuracy of rockburst prediction and early warning. However, due to the non-stationary nature of microseismic data, conventional time series prediction methods often struggle to achieve accurate forecasts. To address this issue, this paper proposes a microseismic time series prediction method based on the combination of modal decomposition technique and deep learning methods. Firstly, the adaptive noise complete ensemble empirical mode decomposition (CEEMDAN) is employed to decompose the microseismic data into multiple intrinsic mode sequences. Subsequently, the decomposed sequences are reconstructed into high-frequency, low-frequency, and trend sequences using sample entropy. Furthermore, the variable mode decomposition (VMD) method is utilized to further decompose the high-frequency and low-frequency sequences into multiple new intrinsic mode sequences, which serve as the multivariate feature time series of microseismic data. Finally, a convolutional neural network (CNN) and long short-term memory network (LSTM) combined microseismic time series prediction model is established, with the microseismic multivariate feature time series within a certain time window and historical microseismic data as inputs, and the next moment microseismic monitoring data as output. The Bayesian optimization method is employed to optimize the hyperparameters of the model, aiming to improve the accuracy of microseismic prediction. Based on the microseismic monitoring data from the multiple working faces of the Xinjulong coal mine, the proposed method is applied to predict the daily maximum energy, daily average energy, and daily frequency of microseismic events, and a series of comparative experiments are conducted on the prediction of daily maximum energy data. The results show that the proposed method can effectively predict the evolution trend of microseismic events, with small errors between the predicted results and actual monitoring values. It demonstrates a good predictive performance and generalization ability.

Accurate wind speed prediction is extremely critical to the stable operation of power systems. To enhance the prediction accuracy, we propose a new approach that integrates bidirectional long short-term memory (BiLSTM) with fully adaptive noise ensemble empirical modal decomposition (CEEMDAN), the RIME optimization algorithm (RIME), and a multi-head self-attention mechanism (MHSA). First, the historical data of wind farms are decomposed via CEEMDAN to extract the change patterns and features on different time scales, and different subsequences are obtained. Then, the parameters of the BiLSTM model are optimized using the frost ice optimization algorithm, and each subsequence is input into the neural network model containing the MHSA for prediction. Finally, the predicted values of each component are weighted and reconstructed to obtain the predicted values of wind speed time series. According to the experimental results, the method can predict the short-term wind speeds of wind farms more accurately. We verified the effectiveness of the method by comparing it with different models. [ABSTRACT FROM AUTHOR]

In order to improve the safety of wind turbine, this paper takes the high-speed spur gear on output shaft in the transmission system of the wind turbine as the study object. The original signals of the gear in different fault states which obtained from the experimental platform decompose by the empirical mode decomposition method (EMD) and the variational mode decomposition method (VMD), respectively. Then the different gear fault types' eigenvectors were built. Fuzzy neural network (FNN) is adopted to learn the fault types and eigenvector samples of gears, and then the fault types of gears are identified. It is found that the fault features decomposed by VMD method have reached a high accurate recognition rate point to the fact that VMD has good applicability in the fault recognition of gear in wind turbine. [ABSTRACT FROM AUTHOR]

Pressure pulsations are crucial data within the flow field of a pump, and the characteristics of these pulsations can reveal changes in the internal flow. Based on model experiments, this paper obtained pressure pulsation data under two blade conditions and compared direct time-domain observations, peak-to-peak value changes, and the VMD decomposition method. The results show that even when it is known that one blade condition has changed, it is not possible to determine this through direct observation of pressure pulsation changes. The peak-to-peak value changes indicate that under special flow conditions, they are easily affected by different operating conditions, which can interfere with the results. In contrast, the VMD method, which decomposes the signal into low-frequency components, can better display anomalies within the pressure pulsation cycle and is less susceptible to the interference of flow conditions, offering some reference significance for diagnosing the blade operating conditions of the main pump. [ABSTRACT FROM AUTHOR]

Air pollution, particularly PM2.5, has long been a critical concern for the atmospheric environment. Accurately predicting daily PM2.5 concentrations is crucial for both environmental protection and public health. This study introduces a new hybrid model within the "Decomposition-Prediction-Integration" (DPI) framework, which combines variational modal decomposition (VMD), causal convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and attention mechanism (AM), named as VCBA, for spatio-temporal fusion of multi-site data to forecast daily PM2.5 concentrations in a city. The approach involves integrating air quality data from the target site with data from neighboring sites, applying mathematical techniques for dimensionality reduction, decomposing PM2.5 concentration data using VMD, and utilizing Causal CNN and BiLSTM models with an attention mechanism to enhance performance. The final prediction results are obtained through linear aggregation. Experimental results demonstrate that the VCBA model performs exceptionally well in predicting daily PM2.5 concentrations at various stations in Taiyuan City, Shanxi Province, China. Evaluation metrics such as RMSE, MAE, and R2 are reported as 2.556, 1.998, and 0.973, respectively. Compared to traditional methods, this approach offers higher prediction accuracy and stronger spatio-temporal modeling capabilities, providing an effective solution for accurate PM2.5 daily concentration prediction. [ABSTRACT FROM AUTHOR]

The interactions between the internal tide and the mesoscale circulation are studied from the internal tide energy budget perspective. To that end, the modal energy budget of the internal tide is diagnosed using a high resolution numerical simulation covering the North Atlantic. Compared to the topographic contribution, the advection of the internal tide by the low‐frequency flow component and the horizontal and vertical shear are found to be significant at global scale, while the buoyancy contribution is important locally. The advection of the internal tide by the low‐frequency currents is responsible for a net energy transfer from the large scale to smaller scale internal tide, without exchanges with the low‐frequency flow. On the opposite, the shear of the mesoscale circulation and the buoyancy field are responsible for exchanges between the internal tide and the low‐frequency flow. The importance of the shear increases in the northernmost part of the domain, and a partial compensation between the buoyancy and the shear contributions is found in some areas of the North Atlantic, such as in the Gulf Stream region. In addition, the temporal variability of these energy transfers is investigated. In contrast to topographic scattering, for which the spring‐neap cycle is the dominant frequency, the energy transfer terms driven by low‐frequency motions in areas of strong mesoscale activity are also modulated by variations of the low‐frequency current spatial distribution. Plain Language Summary: Internal tides are waves generated when the tidal motions, induced by the Moon and Sun gravitational attraction on the ocean, interact with topographical features. These waves, which are ubiquitous in the ocean, then propagate inside of the ocean interior and interact with other types of currents. This study investigates the wave‐currents interactions and their impact on the energy exchanges in the ocean using a high‐resolution realistic numerical simulation. We find that, although the scattering of internal tides by the topography is dominant in their energy budget, the interaction with the currents are significant at the basin scale. They lead to transfers of energy from large scale internal waves to smaller ones, and between currents and internal tides in the Labrador sea and Faroe islands vicinity. This transfer of energy is likely to impact the ocean mixing driven by the small scale internal tide, and thereby to affect the global ocean circulation. The temporal variability of these current‐internal tides interactions shows the same period as the variability of the semi‐diurnal astronomical tide, with a modulation by the temporal variability of the currents. Key Points: Although topographic scattering is dominant, low‐frequency flow has a strong impact on the internal tide energy cycleThe dominant effect of low‐frequency flow on internal tide is a transfer of energy toward smaller scaleLow‐frequency flow induced interactions show an imprint of the spring neap cycle modulated by variations in the currents [ABSTRACT FROM AUTHOR]

AbstractIdentifying modal parameters for the different vibrational conditions for any structure or machine is very important for its structural health monitoring. Researchers have proposed different methodologies to determine that structure’s excitation or natural frequency. In the initial design phase, researchers have to rely on building the FE model and then analyze its natural and excitation frequencies. However, for the complex structure, it is difficult to handle a huge dataset of the vibrational information of the mesh for multi-frequency excitations and it becomes computationally expensive to analyze the system’s performance. This work presents the data-driven technique called Dynamic Mode Decomposition (DMD) which addresses this problem by discovering the system’s dynamics using system’s high-dimensional spatiotemporal data. The present work studies the FE vibration of fixed-fixed beams with single and multi-frequency harmonic forcing functions with and without noise for real-time scenarios. Here, DMD is applied to the sequential spatiotemporal data of displacement of the vibrating beams, and dominant frequencies and corresponding mode shapes are identified to predict the actual time dynamics of the system under different conditions in simulation and experimental conditions. This study also involves the failure of the DMD to capture the periodic oscillation (standing wave) in the beam and proposes the application of the Hankel matrix (time-delay coordinate) to capture the actual dynamical parameters of the system. The resulting predicted frequencies match the actual excitation frequencies, and the accuracy is improved with a number of stackings of the given time series data through the Hankel matrix at different rank truncations. [ABSTRACT FROM AUTHOR]

Centrifugal pump pressure pulsation contains various signals in different frequency domains, which interact and superimpose on each other, resulting in characteristics such as intermittency, non-stationarity, and complexity. Computational Fluid Dynamics (CFD) and traditional time series models are unable to handle nonlinear and non-smooth problems, resulting in low accuracy in the prediction of pressure fluctuations. Therefore, this study proposes a new method for predicting pressure fluctuations. The pressure pulsation signals at the inlet of the centrifugal pump are processed using Variational Mode Decomposition–Particle Swarm Optimization (VMD-PSO), and the signal is predicted by Convolutional Neural Networks–Long Short-Term Memory (CNN-LSTM) model. The results indicate that the proposed prediction model combining VMD-PSO with four neural networks outperforms the single neural network prediction model in terms of prediction accuracy. Relatively high accuracy is achieved by the VMD-PSO-CNN-LSTM model for multiple forward prediction steps, particularly for a forward prediction step of 1 (Pre = 1), with a root mean square error of 0.03145 and an average absolute percentage error of 1.007%. This study provides a scientific basis for the intelligent operation of centrifugal pumps. [ABSTRACT FROM AUTHOR]

Short-term power load forecasting is the basis for ensuring the safe and stable operation of the power system. However, because power load forecasting is affected by weather, economy, geography, and other factors, it has strong instability and nonlinearity, making it difficult to improve the accuracy of short-term power load forecasting. To solve the above problems, a load forecasting method combining empirical modal decomposition (EMD) and long short-term memory neural network (LSTM) has been proposed. The original signal is first decomposed into a series of eigenmode functions and a residual quantity using the EMD algorithm. Subsequently, all the components are fed into the LSTM network. To further improve the load prediction accuracy, a self-attention mechanism is introduced for large component signals to further explore the internal correlation of the data, and the Sparrow Optimisation Algorithm (SSA) is used to optimize the LSTM hyperparameters. Combining EMD, LSTM, self-attention mechanism (SAM), and SSA, the EMD-SSASAM -LSTM method for short-term power load forecasting is further proposed. The results show that the coefficient of determination (R2) of the method is 0.98, the mean absolute error (MAE) is 0.013, the root mean square error (RMSE) is 0.018, and the mean absolute percentage error (MAPE) is 2.57%, which verifies that the proposed model can improve the accuracy of load forecasting, and has a certain application prospect. [ABSTRACT FROM AUTHOR]

In a “low-carbon” context, the power load is affected by the coupling of multiple factors, which gradually evolves from the traditional “pure load” to the generalized load with the dual characteristics of “load + power supply.” Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized loads. From the perspective of image processing, this study proposes a graphical short-term prediction method for generalized loads based on modal decomposition. First, the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting, gradient boosted decision tree, and random forest algorithms. Subsequently, the generalized load data are decomposed into three sets of modalities by modal decomposition, and red, green, and blue (RGB) images are generated using them as the pixel values of the R, G, and B channels. The generated images are diversified, and an optimized DenseNet neural network was used for training and prediction. Finally, the base load, wind power, and photovoltaic power generation data are selected, and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise algorithm. Based on the proposed graphical forecasting method, the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method.

The structural dynamic response reconstruction of large-scale civil engineering structures using traditional methods results in heavy computational burden with low efficiency. To improve efficiency, a novel time-domain response reconstruction method based on model condensation and modal decomposition is proposed in this paper. The finite element model of the target structure is transformed and condensed. The system matrices of the condensed model have lower dimensionality than those of the original finite element model. A checking procedure based on the modal assurance criterion is developed to ensure the accuracy of the condensed system. Measured dynamic responses are decomposed to time-domain modal responses at measured degrees of freedom (DOFs) using band-pass filters. The mode shape matrix of the condensed model is used to calculate the modal responses at the unmeasured DOFs. Following modal superposition and coordinate transformation, the dynamic responses at unmeasured DOFs are reconstructed without knowledge of the excitation. The accuracy and efficiency of the proposed methodology are verified in a numerical case study of a three-dimensional building frame model and an experimental case study of a two-story steel frame under laboratory conditions. In contrast with the numerical case study, the effects of modeling error and measurement noise are considered in the experiment verification. The results show the outstanding performance of the proposed methodology when compared with a traditional response reconstruction method. [ABSTRACT FROM AUTHOR]

In this work, a surrogate model for structural, transient and discontinuously excited finite element method simulations is developed. This allows to reduce the computational effort of repeated calculations of identical models under different load cases. The architecture of the surrogate combines fully connected neural network layers with long short-term memory layers. For the reproduction of different damping ratios, a categorical variable is added to the continuous input data. Based on a recursive flow of the predicted data back to the input layer, long-term dependencies do not vanish due to short-input sequences. The system dimension is reduced by applying the model-order reduction technique for modal decomposition. The high accuracy of the surrogate and the reduction of computational costs are shown on an academic example of a cantilever beam and a real-world example of a robot. The advantages of our approach are illustrated in comparison with state-of-the-art surrogates for transient finite element analysis. By using the surrogate proposed in this study, oscillations due to discontinuous excitation of mechanical structures can be reproduced. For this purpose, only short-input sequences are necessary since the excitation of the oscillations does not have to be part of the input sequence during the whole duration of the oscillations. Due to the categorical variable for the damping ratio, the surrogate can account for the influence of different damping in parameter studies. [ABSTRACT FROM AUTHOR]

The density of the ocean is unevenly distributed along the depth direction, showing a stratified structure. When there is an external disturbance, large-scale internal solitary waves are easily generated. The internal solitary waves are bounded by the intermediate pycnocline, and the currents in the upper and lower layers will flow in opposite directions. This generates strong shear forces that threaten the safety of marine structures. In this paper, the flow field distribution characteristics of a cylinder under the action of internal solitary waves at different scales are analyzed as a research object. The whole cylinder is discretized into 40 regions, and the horizontal force applied to each section of the cylinder is extracted. The force characteristics of the cylinder are analyzed. It is concluded that the pressure is the main factor determining the magnitude of the total combined force. In addition, the paper extracts the main flow structures from the modal decomposition point of view and explains the reasons affecting the force behavior of the cylinder. [ABSTRACT FROM AUTHOR]

Cavitation is a quasi-periodic process, and its non-stationarity leads to increasingly complex flow field structures. On the other hand, characterizing the flow field with greater precision has become increasingly feasible. However, accurately and effectively extracting the most representative vibration modes and spatial structures from these vast amounts of data has become a significant challenge. Researchers have proposed data-driven modal decomposition techniques to extract flow field information, which have been widely applied in various fields such as signal processing and fluid dynamics. This paper addresses the application of modal decomposition methods, such as dynamic mode decomposition (DMD), Proper Orthogonal Decomposition (POD), and Spectral Proper Orthogonal Decomposition (SPOD), in cavitation feature detection in hydraulic machinery. It reviews the mathematical principles of these three algorithms and a series of improvements made by researchers since their inception. It also provides examples of the applications of these three algorithms in different hydraulic machinery. Based on this, the future development trends and possible directions for the improvement of modal decomposition methods are discussed. [ABSTRACT FROM AUTHOR]

Pipelines constitute a vital component in offshore oil and gas operations, subjected to prolonged exposure to a range of alternating loads. Safeguarding their integrity, particularly through meticulous leak detection, is essential for ensuring safe and reliable operation. Acoustic emission detection emerges as an effective approach for monitoring pipeline leaks, demanding subsequent rigorous data analysis. Traditional analysis techniques like wavelet analysis, empirical mode decomposition (EMD), variational mode decomposition (VMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) often yield results with considerable randomness, adversely affecting leak detection accuracy. This study introduces an enhanced damage recognition methodology, integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and probabilistic neural networks (PNN) for more accurate pipeline leak identification. This novel approach combines laboratory-acquired acoustic emission signals from leaks with ambient noise signals. Application of ICEEMDAN to these composite signals isolates eight intrinsic mode functions (IMFs), with subsequent time–frequency analysis providing insight into their frequency structures and feature vectors. These vectors are then employed to train a PNN, culminating in a robust neural network model tailored for leak detection. Conduct experimental research on pipeline leakage identification, focusing on the local structure of offshore platforms, experimental research validates the superiority of the ICEEMDAN–PNN model over existing methods like EMD, VMD, and CEEMDAN paired with PNN, particularly in terms of stability, anti-interference capabilities, and detection precision. Notably, even amidst integrated noise, the ICEEMDAN–PNN model maintains a remarkable 98% accuracy rate in identifying pipeline leaks. [ABSTRACT FROM AUTHOR]

In scenarios involving photovoltaic power generation and electrical dynamic loads, complex electricity metering signals often exhibit strong randomness and rapid fluctuations. These characteristics frequently lead to substantial errors in electricity metering, thereby affecting fair and equitable energy transactions. This paper presents a novel modal decomposition theory in the run-length domain. Such a theory is developed to map signals from the amplitude domain to run sequences in the run-length domain. Subsequently, it decomposes signals into a quasi-steady, slowly varying mode, and another dynamic, rapidly fluctuating mode, facilitating the extraction of sensitive characteristics over prolonged durations. Furthermore, the paper proposes characteristics representation method for complex electricity metering signals by constructing parameters and characteristic functions in the run-length domain. Additionally, the paper specifically extracts sensitive characteristics for photovoltaic complex electricity metering signals using the modal decomposition theory in the run-length domain. Finally, experimental validation is conducted to elucidate the impact of these sensitive characteristics on electricity meter errors with the maximum dynamic error observed at −15.53%.

In order to improve the separation control effect of an oscillating jet, the external flow field of the actuators and the wing wake are obtained via hot-wire measurements to optimize the actuator and achieve wing separation flow control. The optimization objectives are to improve the sweeping uniformity and range of the jet. In the present study, the PCA method is used for the modal decomposition of the velocity distribution. The modal-based actuator evaluation parameters are proposed, and the kriging surrogate models of the modal coefficients (principal components) on the actuator parameters are established. The multi-objective genetic algorithm was utilized to complete the optimization of the actuator, and the effect of flow separation control on the wing was verified. The results show that three patterns exist in the time-averaged velocity distribution of the external flow field: unimodal, broad and bimodal, from unimodal to bimodal, the degree of the jet sweeping uniformity gradually decreases, and the sweeping range gradually increases. The pattern of the velocity distribution modals affects the degree of jet sweeping uniformity, while the distance of the modal peaks affects the jet sweeping range. The two evaluation parameters are negatively correlated: insufficient sweeping range or poor sweeping uniformity of the jet are not conducive to wing separation flow control, and the two must be coordinated to achieve the optimal control effect.

Vortex shedding at the tail of a high-speed train changes the aerodynamic characteristics of the train, which affects the safety and stability of train operation. This paper takes CR400AF as the research object, and uses dynamic monitoring points to realize the whole process monitoring of the flow field at the tail of the train running in open air and in tunnel for the first time. The wake of the train in different infrastructure scenarios is analyzed by the proper orthogonal decomposition method. The study found that the wake vortex structure is quite different when the train runs in different scenarios, and the turbulent kinetic energy intensity of the wake in tunnel is higher than that of the open air running. Modal decomposition method can identify flow structures that have a large impact on train aerodynamics. Through frequency analysis, it is found that the modal frequency obtained from the decomposition is higher when running in open air than when running in tunnel. With the increase of train speed, the modal strouhal number increases when the train is running in open air, and decreases when the train runs in tunnel. After the train enters the tunnel, the reverse movement of the air around the train body suppresses the development and separation of the boundary layer, which is the main reason for the low frequency of wake vortex shedding in tunnel. The stability of the train running in tunnel is worse than that when running in open air, which is closely related to the more complex flow structure around the car body and the drastic change of aerodynamic force when running in the tunnel. [ABSTRACT FROM AUTHOR]

In this work, modal decomposition is employed to generate more data for matching scenarios with more complex topography for predicting wind power output in the case of complex terrain. The existing literature shows that a single wind power output forecast model is difficult to cope with complex terrain and thus the accuracy of wind power output forecast is limited. This work combines the advantages of attention mechanism and convolutional neural network for a novel network based on modal decomposition of historical data for wind power output forecast on complex terrain. The proposed novel network can break through the limitations of a single wind power output forecast model. In addition, the signals that are modally decomposed can be predicted more accurately. The presented method is contrasted with various other algorithms for the wind power output prediction problem in complex terrain. Comparative experiments show that the proposed network achieves a higher accuracy rate. [ABSTRACT FROM AUTHOR]

Short-term power load forecasting is the basis for ensuring the safe and stable operation of the power system. However, because power load forecasting is affected by weather, economy, geography, and other factors, it has strong instability and nonlinearity, making it difficult to improve the accuracy of short-term power load forecasting. To solve the above problems, a load forecasting method combining empirical modal decomposition (EMD) and long short-term memory neural network (LSTM) has been proposed. The original signal is first decomposed into a series of eigenmode functions and a residual quantity using the EMD algorithm. Subsequently, all the components are fed into the LSTM network. To further improve the load prediction accuracy, a self-attention mechanism is introduced for large component signals to further explore the internal correlation of the data, and the Sparrow Optimisation Algorithm (SSA) is used to optimize the LSTM hyperparameters. Combining EMD, LSTM, self-attention mechanism (SAM), and SSA, the EMD-SSA- SAM -LSTM method for short-term power load forecasting is further proposed. The results show that the coefficient of determination (R2) of the method is 0.98, the mean absolute error (MAE) is 0.013, the root mean square error (RMSE) is 0.018, and the mean absolute percentage error (MAPE) is 2.57%, which verifies that the proposed model can improve the accuracy of load forecasting, and has a certain application prospect.

This study presents a novel application of data-driven resolvent analysis algorithm for flow control. The objective is to identify key coherent structures connected to regions of the flow that are highly sensitive to structural changes. Modifying such regions, i.e., including momentum source terms, flow stabilizers, or changing the shape of the body under study, we can control the appearance of flow instabilities. The method is tested in two different applications: the laminar flow behind a two-dimensional circular cylinder and a turbulent channel flow including heat transfer to the endwall. In the first test case, the flow unsteadiness is controlled by including two stabilizers (formed by two small cylinders) in the areas suggested by resolvent analysis. This is a benchmark problem in fluid dynamics that serves to validate the idea of using this tool for flow control. In the second test case, data-driven resolvent analysis is applied as a mean to control, enhance or reduce, convective heat transfer. Modifications in the geometry, in the form of cavities and ribs included in the areas pointed by resolvent analysis, show the possibility of enhancing heat transfer while reducing drag. The findings highlight the importance of resolvent analysis in understanding flow dynamics and designing effective flow control strategies.

Accurate wind speed prediction is extremely critical to the stable operation of power systems. To enhance the prediction accuracy, we propose a new approach that integrates bidirectional long short-term memory (BiLSTM) with fully adaptive noise ensemble empirical modal decomposition (CEEMDAN), the RIME optimization algorithm (RIME), and a multi-head self-attention mechanism (MHSA). First, the historical data of wind farms are decomposed via CEEMDAN to extract the change patterns and features on different time scales, and different subsequences are obtained. Then, the parameters of the BiLSTM model are optimized using the frost ice optimization algorithm, and each subsequence is input into the neural network model containing the MHSA for prediction. Finally, the predicted values of each component are weighted and reconstructed to obtain the predicted values of wind speed time series. According to the experimental results, the method can predict the short-term wind speeds of wind farms more accurately. We verified the effectiveness of the method by comparing it with different models.

Pressure pulsations are crucial data within the flow field of a pump, and the characteristics of these pulsations can reveal changes in the internal flow. Based on model experiments, this paper obtained pressure pulsation data under two blade conditions and compared direct time-domain observations, peak-to-peak value changes, and the VMD decomposition method. The results show that even when it is known that one blade condition has changed, it is not possible to determine this through direct observation of pressure pulsation changes. The peak-to-peak value changes indicate that under special flow conditions, they are easily affected by different operating conditions, which can interfere with the results. In contrast, the VMD method, which decomposes the signal into low-frequency components, can better display anomalies within the pressure pulsation cycle and is less susceptible to the interference of flow conditions, offering some reference significance for diagnosing the blade operating conditions of the main pump.

Metal cutting is a complex process with strong randomness and nonlinear characteristics in its dynamic behavior, while tool wear or fractures will have an immediate impact on the product surface quality and machining precision. A combined prediction method comprising modal decomposition, multi-channel input, a multi-scale Convolutional neural network (CNN), and a bidirectional long-short term memory network (BiLSTM) is presented to monitor tool condition and to predict tool-wear value in real time. This method considers both digital signal features and prediction network model problems. First, we perform correlation analysis on the gathered sensor signals using Pearson and Spearman techniques to efficiently reduce the amount of input signals. Second, we use Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to enhance the local characteristics of the signal, then boost the neural network's identification accuracy. In addition, the deconstructed signal is converted into a multi-channel input matrix, from which multi-scale spatial characteristics and two-way temporal features are recovered using multi-scale CNN and BiLSTM, respectively. Finally, this strategy is adopted in simulation verification using real PHM data. The wear prediction experimental results show that, in the developed model, C1, C4, and C6 have good prediction performance, with RMSE of 8.2968, 12.8521, 7.6667, and MAE of 6.7914, 9.9263, and 5.9884, respectively, significantly lower than SVR, B-BiLSTM, and 2DCNN models. [ABSTRACT FROM AUTHOR]

Centrifugal pump pressure pulsation contains various signals in different frequency domains, which interact and superimpose on each other, resulting in characteristics such as intermittency, non-stationarity, and complexity. Computational Fluid Dynamics (CFD) and traditional time series models are unable to handle nonlinear and non-smooth problems, resulting in low accuracy in the prediction of pressure fluctuations. Therefore, this study proposes a new method for predicting pressure fluctuations. The pressure pulsation signals at the inlet of the centrifugal pump are processed using Variational Mode Decomposition–Particle Swarm Optimization (VMD-PSO), and the signal is predicted by Convolutional Neural Networks–Long Short-Term Memory (CNN-LSTM) model. The results indicate that the proposed prediction model combining VMD-PSO with four neural networks outperforms the single neural network prediction model in terms of prediction accuracy. Relatively high accuracy is achieved by the VMD-PSO-CNN-LSTM model for multiple forward prediction steps, particularly for a forward prediction step of 1 (Pre = 1), with a root mean square error of 0.03145 and an average absolute percentage error of 1.007%. This study provides a scientific basis for the intelligent operation of centrifugal pumps.

The density of the ocean is unevenly distributed along the depth direction, showing a stratified structure. When there is an external disturbance, large-scale internal solitary waves are easily generated. The internal solitary waves are bounded by the intermediate pycnocline, and the currents in the upper and lower layers will flow in opposite directions. This generates strong shear forces that threaten the safety of marine structures. In this paper, the flow field distribution characteristics of a cylinder under the action of internal solitary waves at different scales are analyzed as a research object. The whole cylinder is discretized into 40 regions, and the horizontal force applied to each section of the cylinder is extracted. The force characteristics of the cylinder are analyzed. It is concluded that the pressure is the main factor determining the magnitude of the total combined force. In addition, the paper extracts the main flow structures from the modal decomposition point of view and explains the reasons affecting the force behavior of the cylinder.

Cavitation is a quasi-periodic process, and its non-stationarity leads to increasingly complex flow field structures. On the other hand, characterizing the flow field with greater precision has become increasingly feasible. However, accurately and effectively extracting the most representative vibration modes and spatial structures from these vast amounts of data has become a significant challenge. Researchers have proposed data-driven modal decomposition techniques to extract flow field information, which have been widely applied in various fields such as signal processing and fluid dynamics. This paper addresses the application of modal decomposition methods, such as dynamic mode decomposition (DMD), Proper Orthogonal Decomposition (POD), and Spectral Proper Orthogonal Decomposition (SPOD), in cavitation feature detection in hydraulic machinery. It reviews the mathematical principles of these three algorithms and a series of improvements made by researchers since their inception. It also provides examples of the applications of these three algorithms in different hydraulic machinery. Based on this, the future development trends and possible directions for the improvement of modal decomposition methods are discussed.

Pipelines constitute a vital component in offshore oil and gas operations, subjected to prolonged exposure to a range of alternating loads. Safeguarding their integrity, particularly through meticulous leak detection, is essential for ensuring safe and reliable operation. Acoustic emission detection emerges as an effective approach for monitoring pipeline leaks, demanding subsequent rigorous data analysis. Traditional analysis techniques like wavelet analysis, empirical mode decomposition (EMD), variational mode decomposition (VMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) often yield results with considerable randomness, adversely affecting leak detection accuracy. This study introduces an enhanced damage recognition methodology, integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and probabilistic neural networks (PNN) for more accurate pipeline leak identification. This novel approach combines laboratory-acquired acoustic emission signals from leaks with ambient noise signals. Application of ICEEMDAN to these composite signals isolates eight intrinsic mode functions (IMFs), with subsequent time–frequency analysis providing insight into their frequency structures and feature vectors. These vectors are then employed to train a PNN, culminating in a robust neural network model tailored for leak detection. Conduct experimental research on pipeline leakage identification, focusing on the local structure of offshore platforms, experimental research validates the superiority of the ICEEMDAN–PNN model over existing methods like EMD, VMD, and CEEMDAN paired with PNN, particularly in terms of stability, anti-interference capabilities, and detection precision. Notably, even amidst integrated noise, the ICEEMDAN–PNN model maintains a remarkable 98% accuracy rate in identifying pipeline leaks.

Electrical load forecasting is important to ensuring power systems are operated both economically and safely. However, accurately forecasting load is difficult because of variability and frequency aliasing. To eliminate frequency aliasing, some methods set parameters that depend on experiences. The present study proposes an adaptive hybrid model of modal decomposition and gated recurrent units (GRU) to reduce frequency aliasing and series randomness. This model uses average sample entropy and mutual correlation to jointly determine the modal number in the decomposition. Random adjustment parameters were introduced to the Adam algorithm to improve training speed. To assess the applicability and accuracy of the proposed hybrid model, it was compared with some state of the art forecasting methods. The results, which were validated by actual data sets from Shaanxi province, China, show that the proposed model had a higher accuracy and better reliability compared to the other forecasting methods. [ABSTRACT FROM AUTHOR]

A new approach for modal decomposition through re-interpretation of unsteady dynamics, termed time-axis clustering, is developed in this work and is demonstrated on an experimental turbulent reacting flow dataset consisting of simultaneously measured planar OH-PLIF and PIV fields in a model combustor. The method executes a K-Means clustering algorithm on an alternative representation of the input snapshot dataset: the dataset is interpreted here as a collection of one-dimensional time series, where each time series represents the time evolution of some flow quantity of interest (QoI) at a fixed point in physical space (i.e. pixel locations). The clustering algorithm in the modal decomposition context produces (a) spatial modes (called time-axis modes) that identify localized regions of dynamical similarity in physical space and (b) temporal coefficients that represent average trajectories of the flow QoI conditioned on the regions in physical space identified by the corresponding spatial mode. Due to the non-overlapping nature of K-Means clusters, visualization of the modes provides a unique pathway for flow feature extraction based on dynamical similarity. Ultimately, this work shows how time-axis clustering provides a promising avenue for domain-localized data-based modelling of complex fluid flows. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases, especially myocardial infarction and heart failure, are among the most common causes of death. Proper, timely diagnosis can be a key factor in reducing the mortality of these diseases. In the present paper, statistical data analysis of left ventricle of human heart is presented. Raster DICOM images are processed, segmented and registered, in order to mark the left ventricle on medical images, and then to obtain its geometrical 3D models of constant topology. Registered, geometrical data, obtained for whole cardiac cycle of patients with healthy hearts, hypertrophy and heart failure, is then decomposed using Principal Component Analysis. The obtained modes represent the movement of the ventricle during one heart cycle. The proposed approach allows neglecting unimportant, noisy signal and enables the interpretation of the heart cycle. It is shown that modal decomposition might be used to distinguish the hearts with heart failure and the group containing healthy hearts and the ones with hypertrophy. Being a non-invasive method, this approach enables the diagnosis of various hearts, including prenatal ones. [ABSTRACT FROM AUTHOR]

Bearing effects have not been deeply investigated in the dynamic analysis of a train–bridge coupled system subjected to earthquake motions, and existing studies on base isolation of railway bridges have typically not considered the running train. To fill these gaps, this paper develops the numerical model of a three‐dimensional train–wheel–bridge–bearing (TWBB) system for seismic analysis. The substructuring method is implemented to separate the TWBB system into the train, wheel–rail, bridge and bearing subsystems, and couple them using state‐space representations. The complete model of the TWBB system has three main features: fewer degrees of freedom (DOF) than traditional finite element methods, applicability to different types of bearings, and simplicity as no iterations in the simulation are required. Therefore, the proposed procedure demonstrates that the TWBB model is an efficient and flexible representation to obtain and predict realistic demands for engineering purposes. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) testing and Lamb wave inspection techniques have been widely used in non-destructive testing and structural health monitoring. For thin plates, the AEs arising from structural defect development (e.g., fatigue crack propagation) propagate as Lamb waves, and Lamb wave modes can be used to provide important information about the growth and localisation of defects. However, few sensors can be used to achieve the in situ wavenumber–frequency modal decomposition of AEs. This study explores the ability of a new multi-element piezoelectric sensor array to decompose AEs excited by pencil lead breaks (PLBs) on a thin isotropic plate. In this study, AEs were generated by out-of-plane (transverse) and in-plane (longitudinal) PLBs applied at the edge of the plate, and waveforms were recorded by both the new sensor array and a commercial AE sensor. Finite element analysis (FEA) simulations of PLBs were also conducted and the results were compared with the experimental results. To identify the wave modes present, the longitudinal and transverse PLB test results recorded by the new sensor array at five different plate locations were compared with FEA simulations using the same arrangement. Two-dimensional fast Fourier Transforms were then applied to the AE wavefields. It was found that the AE modal composition was dependent on the orientation of the PLB direction. The results suggest that this new sensor array can be used to identify the AE wave modes excited by PLBs in both in-plane and out-of-plane directions. [ABSTRACT FROM AUTHOR]

The direction of arrival (DOA) and number of sound sources is usually estimated by short-time Fourier transform and the conjugate cross-spectrum. However, the ability of a single AVS to distinguish between multiple sources will decrease as the number of sources increases. To solve this problem, this paper presents a multimodal fusion method based on a single acoustic vector sensor (AVS). First, the output of the AVS is decomposed into multiple modes by intrinsic time-scale decomposition (ITD). The number of sources in each mode decreases after decomposition. Then, the DOAs and source number in each mode are estimated by density peak clustering (DPC). Finally, the density-based spatial clustering of applications with the noise (DBSCAN) algorithm is employed to obtain the final source counting results from the DOAs of all modes. Experiments showed that the multimodal fusion method could significantly improve the ability of a single AVS to distinguish multiple sources when compared to methods without multimodal fusion. [ABSTRACT FROM AUTHOR]