Operational modal analysis provides critical value for structural health monitoring of arch dams, where ambient vibration-derived modal parameters characterize operational dynamic properties. This paper presents an automated method for high-fidelity identification of modal parameters. The proposed method enhances the covariance-driven stochastic subspace identification (SSI-COV) algorithm by introducing the adaptive multivariate variational mode decomposition (MVMD), the three-dimensional stabilization diagram, and an improved fuzzy c-means (FCM) clustering algorithm. Initially, the adaptive MVMD algorithm is utilized to decompose the multichannel vibration response signals. The multichannel signals are adaptively denoised by extracting and superposing sensitive signal components. Subsequently, the modal parameters of the high arch dam are obtained using the SSI-COV, and a three-dimensional stabilization diagram is developed for the automatic determination of the model order. Finally, an improved FCM clustering algorithm that integrates the local outlier factor for abnormal poles and the coati optimization algorithm for global optimization of the initial cluster centers is proposed to group physical modes, thereby facilitating automatic modal parameter estimation. Validation employs a four-DOF numerical model, a physical arch dam model, and a prototype arch dam. Results demonstrate effective noise suppression and reliable automatic modal identification under varying water discharge conditions, providing a new idea supporting continuous long-term observation of dynamic characteristics in high arch dams.

The slender and lightweight characteristics of large discharge sluice structures, coupled with their high-discharge operation and fluid-structure interaction effects, make them particularly vulnerable to severe vibrations or even failure when subjected to high-velocity discharge flows with tremendous energy. To address vibration safety concerns, this study proposes a mitigation design method based on Gaussian process regression (GPR) surrogate modeling and the ivy optimization algorithm. First, an improved direct displacement method is used to determine the parameter design space for viscous dampers installed on the sluice structure. Subsequently, a GPR surrogate model is developed to map the relationship between viscous damper parameters and the dynamic response of the sluice structure, using a sample set obtained from finite element calculations. This set includes damper parameters, maximum dynamic displacement, and maximum dynamic stress response. Finally, the ivy algorithm is applied to optimize damper parameters for effective vibration suppression. The proposed method was applied to the flood discharge sluice structure at the overflow dam section of a large hydraulic complex project in China. While preserving the original configuration and functionality of the sluice, the dynamic displacement and stress under the checked flood case were successfully reduced below permissible thresholds (dynamic displacement < 402 mu m, dynamic stress < 0.57 MPa). Specifically, maximum dynamic displacement decreased by 54.0 %, and maximum dynamic stress decreased by 2.1 %. This study provides a novel approach to vibration mitigation design for large discharge sluice structures under flow excitation.

Accurate finite element models (FEMs) are crucial for the structural health monitoring of high arch dams. Aiming to address challenges of automatic operational modal extraction and the limited accuracy of traditional FEM updating (FEMU) methods, a deep learning-based FEMU framework combining automatic operational modal analysis and multi-objective optimization is proposed. First, an automatic operational modal identification approach is developed based on the covariance-driven stochastic subspace identification method and the hierarchical density-based spatial clustering of applications with noise algorithm, facilitating the accurate and automatic extraction of structural modal parameters. Next, a multi-output deep hybrid kernel extreme learning machine model, optimized using the generalized normal distribution optimization (GNDO) algorithm, is employed to establish a computationally efficient and accurate surrogate model that maps material parameters to the modal parameters of the FEM. A multi-objective optimization function is then constructed to minimize discrepancies between identified modal parameters and their corresponding calculated values, aiming to avoid the subjective selection of weights of the objective functions. Finally, the multi-objective GNDO algorithm is employed to determine the optimal material parameters, achieving efficient and accurate FEMU of high arch dams under operational conditions. The physical model of an arch dam and an engineering example demonstrate that the updated FEM modal parameters closely match the identified ones, revealing frequency errors below 5% and modal assurance criterion values exceeding 0.94. Overall, the proposed approach offers a novel concept and method for efficient FEMU of high arch dams.

High arch dams are typical three-dimensional statically indeterminate structures, and their displacements exhibit pronounced spatial correlations. Existing single-point displacement prediction models neglect these spatial interdependencies, while conventional point prediction approaches cannot quantify predictive uncertainty. To address these limitations, this study proposes a multi-point displacement interval prediction framework for arch dams that integrates graph-embedding clustering, hybrid deep learning, and adaptive bandwidth kernel density estimation (ABKDE). The framework first applies graph embedding clustering to identify spatial correlations among monitoring points, thereby achieving consistency-based zoning of monitoring data. It then integrates the inverted Transformer (iTransformer) and patching-enabled scalar long short-term memory neural networks (psLSTM) to extract global features from multivariate time series. The Cuckoo catfish optimization (CCO) algorithm is employed to adaptively optimize hyperparameters, enabling collaborative multi-point displacement prediction for high arch dams. Finally, ABKDE is used to estimate the probability distribution of prediction errors and generate reliable prediction intervals, thereby enabling multi-point displacement interval prediction for each zone of the high arch dam. Validation using 18 years of measured displacement data from a high arch dam demonstrates that the proposed framework significantly outperforms the benchmark models in both multi-point collaborative prediction accuracy and prediction-interval reliability, providing technical support for the long-term safety monitoring of high arch dams.

The large-scale integration of renewable energy, driven by China's carbon peaking and neutrality goals, challenges power system stability and economy operation due to inherent stochasticity and volatility. This study develops a capacity configuration optimization model for a thermal–wind–photovoltaic–pumped storage integrated system, using a prefecture-level city in East China as a case. The Synchronous Backward Reduction algorithm generates eight typical scenarios from 2019 data to characterize uncertainty. A bi-level framework is established: the outer level optimizes renewable capacity via the Multi-Objective Multi-Verse Optimizer, while the inner level solves a mixed-integer linear programming model via CPLEX with the ε-constraint method. The Pareto frontier is evaluated through entropy-weighted TOPSIS. Results show that pumped storage reduces optimal wind and photovoltaic capacities from 2,430 MW and 2,388 MW to 2,203 MW and 1,003 MW, respectively, while curtailing wind and photovoltaic to below 0.53% and 1.57%. The static payback period of the system is approximately 6.74 years, with an incremental payback period of 2.17 years for pumped storage investment. Annual simulation verifies over 90% efficient operation, with curtailment confined to extreme events. The findings confirm that pumped storage enhances system flexibility, promotes renewable integration, and ensures economic performance in high-renewable systems.

During startup transients of high-head Francis pump-turbines, rotational speed, flow rate, and hydraulic pressure undergo severe fluctuations. Traditional single-objective or weighted optimization methods require subjective weight assignment and fail to yield a Pareto-optimal solution set, with open-loop and closed-loop parameters often tuned independently, neglecting dynamic coupling effects. This paper proposes a collaborative parameter optimization method based on a reference-point-based non-dominated sorting genetic algorithm for a high-head pumped storage power station. A water hammer simulation model is established using the method of characteristics, and the pump-turbine's four-quadrant characteristics are transformed via Suter transformation to eliminate low-speed singularities, constructing a high-fidelity nonlinear dynamic model encompassing the governor and generator. Five guide vane trajectory parameters from the composite control strategy of early-stage two-phase open-loop startup combined with late-stage proportional-integral-derivative closed-loop regulation, together with three proportional-integral-derivative control gains parameters, form an 8-dimensional decision vector. Three objectives are formulated: the integral of the absolute relative speed error (J1), the integral of the absolute relative pressure error at the spiral casing (J2), and the maximum relative axial hydraulic thrust (J3). The reference-point-based selection mechanism yields a well-distributed Pareto front. Comparing projections of the three-objective Pareto front onto bi-objective planes against corresponding bi-objective fronts reveals that incorporating J1 fundamentally restructures the J2–J3 competitive relationship, demonstrating the intrinsic necessity of three-objective optimization. Time-domain simulations confirm the balanced 3-obj solution achieves comprehensive equilibrium across speed regulation, pressure suppression, and axial thrust control, effectively avoiding performance skewness caused by objective omission in bi-objective schemes.

To address the problems of subjectivity in parameter adjustment and complex operation process of the traditional floodgate vibration signal denoising method, this study proposes an intelligent denoising method based on a convolutional denoising autoencoder (CDAE). First, the measured vibration signals are denoised by combining singular spectrum analysis (SSA) with the successive variational mode decomposition (SVMD) method. Then, a training dataset is constructed on the basis of the original and denoised signals. Finally, an intelligent denoising model for vibration response signals based on a CDAE is developed. Results of digital signal denoising show that the signal-to-noise ratio of the denoised signal is improved by more than 16.55 dB, along with a significant reduction in computation time compared with that of SSA-SVMD. Vibration signals from a specific flood discharge sluice are denoised using the proposed method. Findings demonstrate that the technique can effectively suppress the false mode interference induced by noise in the identification of dynamic characteristics, as well as eliminate the signal's low-frequency water flow noise and background white noise. Experiments demonstrate that CDAE achieves effective denoising under various noise conditions, striking a favorable balance between accuracy and efficiency, making it particularly suitable for the online processing of engineering vibration signals. The technique is expected to successfully realize online continuous intelligent denoising of actual engineering vibration signals and accurate identification of the complex environmental dynamic characteristics of flood discharge sluices.

Industrial power plant circulating cooling systems feature large flow rates, large pipe diameters, and severe hydraulic transients. To address excessive negative pressure and water hammer caused by flow interruption during pump shutdowns, a collaborative optimization method is proposed. It combines a LightGBM-TPE (Light Gradient Boosting Machine with Tree-structured Parzen Estimator) surrogate model with an improved multi-objective Gray Wolf algorithm (DEMOGWO). First, a hydraulic transient numerical model is constructed using the method of characteristics. Air valve size, inlet/outlet coefficients, and two-stage butterfly valve closing times are selected as decision variables. Using Latin hypercube sampling, a dataset is generated to train the LightGBM-TPE surrogate model for efficient water hammer simulation, while the SHAP method identifies core influencing parameters. Next, incorporating a nonlinear convergence factor, differential evolution mutation, and elite archiving, the DEMOGWO algorithm achieves dual-objective collaborative optimization of minimum system pressure (safety) and air valve size (economy). Results show the LightGBM-TPE model significantly outperforms CNN, BP, and LSTM networks in prediction accuracy. SHAP analysis reveals the butterfly valve's first-stage closing time and air valve size dominate negative pressure control. Post-optimization, the minimum system pressure improved from −3.98 m to −1.16 m (a 70.9% increase), with all transient values meeting safety limits. This “surrogate modeling-interpretability-optimization” framework provides an efficient solution for the intelligent tuning of water hammer protection parameters in power plants.

To mitigate the risks of pressure surges and water hammer during accidental pump trips in industrial cooling water systems, accurate boundary modeling of cooling towers is essential. This study employs the Method of Characteristics (MOC) to evaluate four equivalent models for the central riser shaft: Model A (constant level), Model B (two-way surge tank), Model C (dynamic coupling of shaft and distribution channel), and Model D (composite structure). Results indicate that Model A fails to reflect actual hydraulic states, producing an unrealistic pump reverse speed of -253.24 r/min and overly conservative estimates. While Models B, C, and D exhibit similar pressure trends, Model C most accurately captures the physical drainage process, realistically simulating how the shaft level stabilizes at the distribution channel elevation before declining. By accurately reflecting engineering hydraulics, Model C provides the most reliable basis for water hammer safety assessments. It is recommended for optimizing pump valve closure strategies, vacuum breaker installations, and siphon protection designs in power plant systems.

BACKGROUND AND OBJECTIVE:Essential tremor (ET) is a common movement disorder (MD) with significant genetic contributions, yet its genetic basis remains poorly understood. To clarify ET's genetic architecture and improve clinical diagnostics, we investigated pathogenic variants and their clinical implications in a large Chinese cohort. METHODS:Whole-genome sequencing was conducted on 3097 Chinese patients with ET and 2050 healthy control subjects. We analyzed variants within 26 known ET-associated genes and 437 broader MD-associated genes. Variants were classified as pathogenic or likely pathogenic (P/LP) following American College of Medical Genetics and Genomics guidelines. RESULTS:Twenty-six patients with ET (0.84%) harbored P/LP variants in ET-associated genes, involving eight genes and 24 distinct variants, with CACNA1G (n = 9) and GPR151 (n = 4) most frequently implicated. Only two patients carried previously reported ET-associated variants. Genetic segregation analysis in five families did not confirm clear cosegregation. In addition, 83 patients (2.68%) carried P/LP variants in broader MD-associated genes, notably, GSN (n = 7), FAT2 (n = 6), and FIG4 (n = 6). Clinical follow-up rediagnosed six individuals carrying GCH1, SGCE, GRN, and NOTCH3 variants with alternative MDs, and two individuals with PRKN and PSEN1 variants developed additional MDs. The remaining patients maintained ET diagnosis without MD progression. Six individuals (0.24%) were identified with Klinefelter's syndrome (47, XXY). CONCLUSIONS:Our findings underscore ET's genetic heterogeneity. Integrating genetic screening and longitudinal clinical evaluation is critical for precise diagnosis and identifying patients at risk for alternative or concurrent MDs. © 2025 International Parkinson and Movement Disorder Society.

Directly measuring the flow loads and the overall dynamic responses field of the sluice structure is usually difficult. Therefore, the indirect determination of the dynamic loads and overall dynamic responses field through the measured response of limited measuring points is of great significance for studying the flow-induced vibration mechanism of the sluice structure and evaluating structural vibration safety. In this paper, a fast time-domain identification method for the dynamic loads of flood discharge sluice structure based on data-level information fusion is proposed. Through data-level fusion of dynamic response information of limited measuring points of the flood discharge sluice structure, the optimal analysis frequency of the load identification shape functions was determined, and the response matrix of the load shape functions was constructed, which greatly reduced the calculation dimension of the system matrix and improved the time-domain identification efficiency of dynamic loads. The accuracy and efficiency of the method were verified using a numerical model of flood discharge sluice. Combined with the prototype vibration test data of the flood discharge sluice structure of a large hydropower station in China, multi-source dynamic loads of the flood discharge sluice structure were identified efficiently and accurately in the time domain. A positive dynamic analysis of the flood discharge sluice structure was carried out using the identified dynamic loads, and the inversion of the overall dynamic responses field of the flood discharge sluice structure under the condition of limited measured response points was realized. This is crucial for evaluating the vibration safety state of the flood discharge sluice structure in a hydropower station.

The friction factor is widely recognized as a pivotal parameter in the analysis of fluid–boundary interactions; however, a comprehensive grasp of friction mechanics remains elusive. This investigation revisits measurements from the benchmark Nikuradse measurements, furnishing indirect evidence of two critical points in pipe turbulence. It underscores that friction factors within laminar and turbulent regimes are intimately interconnected, bearing significant relations to subcritical and critical phenomena. The two critical points directing the laminar–turbulent transition consist of a standard non-equilibrium phase transition and a fully matured turbulent regime, accompanied by an extensive crossover to its asymptotic scaling. Relying on a mathematical model, the scaled friction factor for rough pipes converges into a unified curve. New formula of friction factor of pipe flow is derived, and it was illustrated that it can be derived as a geometric weighted parameter, bridging the laminar and turbulent friction factors. Conclusively, the proffered model was juxtaposed with pipe experimental data from the antecedent study and more contemporaneous transitional pipe data to authenticate the aptness of the suggested model, and it united the friction factor in all three regimes.

The problem of high-speed water flow induced structural vibration during the flood discharge process of hydraulic structures has received increasing attention. This study reveals that the amplitude of the vibration response during flood discharge in the vibration test of a prototype overflow dam pier at a hydropower station varies significantly over time, indicating the existence of impact-type beat vibration, which can easily induce strong vibration of the structure and even cause damage. A beat vibration analysis model for the overflow dam pier based on adaptive variational modal decomposition (VMD) and automatic operational modal analysis is proposed to further analyze the mechanism of beat vibration generation. First, a mathematical model of beat vibration is developed to investigate the production conditions of beat vibration. Secondly, based on adaptive VMD and automatic operational modal analysis method, the main working modal shapes, frequency ratio and amplitude ratio of dam piers are determined, and the main vibration components causing beat vibration are quantitatively extracted to analyze the internal cause of beat vibration. Finally, the external cause of beat vibration were analyzed from the perspective of hydrodynamic load characteristics by combining the hydraulic and hydroelastic physical model tests of the overflow dam pier. The results show that the proposed beat vibration analysis model can effectively explain the occurrence mechanism of beat vibration and determining and extracting the main vibration modal shapes that cause beat vibration, which can provide a theoretical basis for its structural vibration reduction.

Multiple subvortices corresponding to suction vortices in observations are obtained within a simulated tornado for the EF4 tornado case of Funing, China, on 23 June 2016. Within the simulation, the tornado evolves from a one-cell structure with vorticity maximum at its center to a two-cell structure with a ring of vorticity maximum. Five well-defined subvortices develop along the ring. The radial profile of tangential wind across the vorticity ring satisfies the necessary condition of barotropic instability associated with phase-locked, counterpropagating vortex Rossby waves (VRWs) along the ring edges. The phased-locked waves revolve around the parent vortex at a speed less than the maximum azimuthal-mean tangential velocity, agreeing with theoretically predicted VRW phase speed. The radii within which the wave activities are confined are also correctly predicted by the VRW theory where radial group velocity approaches zero. Several other characteristics related to the simulated subvortices agree with VRW theories also. The most unstable azimuthal wavenumber depends on the width and the relative magnitude of vorticity of the vortex ring. Their values estimated from the simulation prior to subvortex formation correctly predict wavenumber 5 as the most unstable. The largest contribution to wave kinetic energy is diagnosed to be from the radial shear of azimuthal wind term, consistent with barotropic instability. Vorticity diagnostics show that vertical vorticity stretching is the primary vorticity source for the intensification and maintenance of the simulated subvortices. Significance Statement Multiple subvortices or suction vortices in tornadoes can produce extreme damage but their cause is not well understood. An intense tornado from China that developed five strong subvortices, along a vorticity ring a distance from the tornado vortex center, was successfully simulated. By examining the propagation and other characteristics of these subvortices and comparing them with theoretical models of vortex Rossby waves (VRWs) that have been studied mostly in the context of typhoons/hurricanes, it is believed that nonlinear growth of unstable VRWs associated with barotropic instability is the primary reason for the development of subvortices within the tornado. The conclusion is further supported by analyses of the primary source of wave growth energy. Vertical vorticity stretching is the main vorticity source for intensifying and maintaining the subvortices at their development and mature stages. The unstable growth of VRWs as the cause of tornado suction vortices has not been analyzed in detail for realistic tornadoes until now.

Accurate dynamic model of the sluice plays an important role in the structural health monitoring and performance evaluation. It is difficult for the established model to truly and comprehensively reflect the overall mechanical characteristics of the sluice because of parameter uncertainty. Structural dynamic model updating based on vibration response is a feasible approach, but it relies on the modal parameters identification and reliable surrogate model. Therefore, a novel dynamic model parameter updating methodology of a sluice is proposed. Firstly, an improved stochastic subspace identification method based on adaptive variational mode decomposition is proposed to accurately obtain sluice modal parameters. Secondly, Kriging surrogate model based on orthogonal test method and particle swarm optimization is introduced. The mathematical surrogate model reflecting the strong non-linear relationship between modal parameters and material parameters with high sensitivity is constructed. Finally, taking the minimum relative error between the calculated and identified values of the sluice modal parameters as the objective function, whale optimization algorithm is used to optimize and the dynamic model parameter of the sluice is updated. A case of sluice physical model shows that the proposed method is feasible and reliable, which lays a great foundation for the structural health diagnosis of sluices.

The closing law of guide vane in hydropower station affects the resulting hydraulic transients, which should have good robust performance. A comprehensive evaluation method for the robustness of guide vane closing law is constructed by using a penalty function and a weight function to generate the evaluation indexes of the hydraulic turbine's large fluctuation transition process from maximum rise rate of spiral case pressure, maximum draft tube vacuum, and maximum rise rate of rotational speed. The effect of various inflection point times, inflection point opening degrees, and guide vane effective closing times for a moderately-high head hydropower station on these three regulating guarantee parameters were evaluated by the orthogonal experimental method. The results show that the effective closing time has a relatively small influence on these three regulating guarantee parameters, while the inflection point opening degree and the inflection point time significantly affect the balance between the water hammer pressure and the unit's speed. These evaluation results for the guide vane closing process not only lead to each regulating guarantee parameter having sufficient safety margin, but also to a robust control system. This robustness evaluation method provides a reference for reasonable selection of guide vane closing law of moderately-high head hydropower plants.

To address the issue of the vibration characteristic signals of floodgates being affected by background white noise and low-frequency water flow noise, a noise reduction method combining the improved adaptive singular value decomposition algorithm (ASVD) and the improved complete ensemble EMD with adaptive noise (ICEEMDAN) is proposed. Firstly, a Hankel matrix is constructed based on the collected discrete time signals. After performing SVD on the Hankel matrix, the ASVD algorithm is used to automatically select the effective singular values to filter out most of the background white noise and retain the useful frequency components with similar energy in the signal. Then, ICEEMDAN combined with the Spearman correlation coefficient method is used to further filter out residual white noise and low-frequency water flows. The noise reduction performance of this combined method is verified through simulation experiments. Filtered by the ASVD-ICEEMDAN method, the signal-to-noise ratio of the simulation signal (50% noise level) is increased from 4.417 to 16.237, and the root mean square error is reduced from 2.286 to 0.586. Based on the practically measured vibration signals of a floodgate at a large hydropower station, the result shows that the ASVD-ICEEMDAN method exhibits good noise reduction performance and feature information extraction abilities for floodgate vibration signals, and can provide support for operational mode analysis and damage identification of practical structures under complex interference conditions.

Operational modal analysis plays an important role in the structural health monitoring and safety diagnosis of arch dam. However, due to background noise, it is difficult to accurately extract the effective characteristics information of arch dam from the vibration responses whose amplitudes are too small under ambient vibration, and the deviation caused by traditional identification methods will directly affect the estimation accuracy for structural modal parameters. Therefore, a novel methodology for modal parameter identification of arch dam based on multi-level information fusion is proposed in this paper. The proposed method is based on multi-sensor data-level fusion to identify the structural natural frequency and damping ratio, which greatly preserves and extracts structural modal properties in the vibration responses. Meanwhile, structural mode shapes are identified based on dynamic feature-level fusion, which significantly improves the identification accuracy. The effectiveness and feasibility of the proposed method are verified by the modal results of digital signals and simulated signals in the 7-DOF system. Prototype engineering case shows that the closely spaced and high-frequency modes can be decomposed and identified by the proposed method, and this method has a higher identification accuracy, which can provide a new idea for modal parameter identification of arch dam.

针对长距离重力流输水工程中因调节阀的启闭操作产生的正、负水锤问题,本文利用特征线法和水锤波传播理论,结合工程实例,分别对采用末端阀控制、在线阀控制以及在线阀与末端阀联合控制等水力控制方案开展了数值模拟与对比分析.结果表明:采用在线阀与末端阀联合阀组控制方案可有效解决在线阀控制方案中下游管道的负压问题,同时相较于末端阀控制方案,在线阀下游近99 km管道的最大正压降低了15.0～18.6 mH2 O.基于联合阀组方案的控制优势,优化设计了调节阀的关闭时间与方式,提出在线阀的关闭时间宜适当小于末端阀,且宜采用线性或先快后慢的两阶段关阀方式;优化后的联合防护方案不仅保证了管道沿程无负压,而且进一步抑制了在线阀下游管段的水锤正压,极大地降低了管道投资,为类似工程的水力控制方案提供重要参考.

Vibration-based dam safety monitoring methods have increasingly become a research hotspot for assessing dam safety. The accurate material parameters of a high arch dam and its foundation are critical for monitoring vi-bration safety. Accordingly, a dynamic material parameter inversion framework for an arch dam is developed based on modal parameters and deep learning. Firstly, an determined-order stochastic subspace identification method with adaptive variational modal decomposition is proposed based on the prototype vibration signal obtained under the discharge excitation, which can effectively identify the modal parameters. Secondly, the sensitivity of the dynamic elastic modulus (DEM) in different regions to the modal parameters of the arch dam was analysed using the orthogonal test method and variance analysis method, and it was utilised to ascertain the DEM to be inverted. Finally, a Bayesian optimised multi-output long short-term memory neural network is used to establish a nonlinear mapping relationship between the DEM and the modal parameters as an alternative to finite element calculations, and the identified modal parameters are employed as network inputs to inverse the actual DEM of each zoning. An engineering example shows that the proposed DEM inversion method for high arch dams is effective and accurate, providing a good basis for the vibration safety analysis of arch dams. This study overcomes the limitations of difficult to effectively extract modal parameters of arch dams under discharge excitation, and advances the application of deep learning technology in hydraulic engineering by combining modal parameters.