Search Results (606)

Based on the characteristics of offshore pile foundation engineering, this study proposes a novel interpretation method for pile settlement time history signals in Rapid Load Testing (RLT). The approach utilizes Variational Mode Decomposition (VMD) to decompose and reconstruct the originally acquired acceleration signals, effectively eliminating high-frequency noise and significantly enhancing signal quality. After obtaining a purified acceleration signal, the study further refines the velocity signal based on the velocity characteristics at the beginning and end of the loading process, aiming to mitigate the influence of initial and boundary conditions on the velocity data. This process yields a highly accurate displacement time history curve. To validate the superiority of VMD in acceleration signal processing, a signal model test was conducted. Comparative experimental results demonstrate that the displacement time history curve derived from VMD-processed signals not only exhibits smaller relative errors and higher precision but also shows significant waveform improvements compared to curves obtained through direct integration of filtered signals. The research indicates that for marine pile foundations, using VMD to decompose and reconstruct the signals, and applying the continuous mean square error theory to identify the critical components of noise and effective signals has significant advantages in the processing of displacement signals using RLT. Compared with traditional analysis methods, the study successfully achieved the effective removal of high-frequency noise in the signal by applying the VMD technique to the decomposition and reconstruction of acceleration signals, significantly improving the quality of the signal. The assumption of zero pile head velocity before and after loading enables accurate determination of the actual pile head displacement Full article

Aluminum alloys under long-term service or repetitive stress are prone to small fatigue cracks (FCs) with arbitrary orientations, necessitating eddy current probes with focused magnetic fields and directional selectivity for reliable detection. This study presents a flexible printed circuit board (FPCB) probe with a double-layer planar excitation coil and a double-layer differential receiving coil. The excitation coil employs a reverse-wound design to enhance magnetic field directionality and focusing, while the differential receiving coil improves sensitivity and suppresses common-mode noise. The probe is optimized by adjusting the excitation coil overlap and the excitation–receiving coil angles to maximize eddy current concentration and detection signals. Finite element simulations and experiments confirm the system’s effectiveness in detecting surface cracks of varying sizes and orientations. To further characterize these defects, two time-domain features are extracted: the peak-to-peak value ($\Delta$P), reflecting amplitude variations associated with defect size and orientation, and the signal width ($\Delta$W), primarily correlated with defect angle. However, substantial overlap in their value ranges for defects with different parameters means that these features alone cannot identify which specific parameter has changed, making prior defect classification using a Transformer-based approach necessary for accurate quantitative analysis. The proposed method demonstrates reliable performance and clear interpretability for defect evaluation in aluminum components. Full article

This paper addresses the challenges of fault diagnosis in gas turbines (GTs) utilized in oil and gas pipeline systems by proposing a novel multiparameter analysis framework that integrates dynamic, quantile-based Statistical Process Control (SPC) with prior domain knowledge. The proposed approach initially employs a dynamic quantile SPC model to establish adaptive control limits, effectively handling the non-stationarity and non-normality of gas turbine operational data. By analyzing parameter variations under typical operating conditions and incorporating expert insights, a multiparameter fault analysis matrix and corresponding weighting factors are constructed to facilitate fault diagnosis with prior knowledge. Furthermore, a fault probability model based on parameter change rates and weighting factors is developed to quantify the likelihood of different fault modes. An operating condition clustering and correction mechanism enables the dynamic adjustment of control limits, thereby preventing misdiagnoses caused by varying operational states. The validity of the proposed method is demonstrated using real data from a domestic pipeline gas turbine, validated by real domestic pipeline GT data, outperforming existing models, with a fault accuracy up to 10%. The approach efficiently estimates fault probabilities and accurately detects both sudden and gradual faults, significantly enhancing intelligent fault diagnosis capabilities for gas turbines. Full article

To address the challenge of effectively filtering out noise components in GPS coordinate time series, we propose a denoising method based on parameter-optimized variational mode decomposition (VMD). The method combines permutation entropy with mutual information as the fitness function and uses the crayfish (COA) algorithm to adaptively obtain the optimal parameter combination of the number of modal decompositions and quadratic penalty factors for VMD, and then, sample entropy is used to identify effective mode components (IMF), which are reconstructed into denoised signals to achieve effective separation of signal and noise The experiments were conducted using simulated signals and 52 GPS station data from CMONOC to compare and analyze the COA–VMPE–WD method with wavelet denoising (WD), empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) methods. The result shows that the COA–VMPE–WD method can effectively remove noise from GNSS coordinate time series and preserve the original features of the signal, with the most significant effect on the U component. The COA–VMPE–WD method reduced station velocity by an average of 50.00%, 59.09%, 18.18%, and 64.00% compared to the WD, EMD, EEMD, and CEEMDAN methods. The noise reduction effect is higher than the other four methods, providing reliable data for subsequent analysis and processing. Full article

This paper presents a new compact and efficient first-order all-pass filter in voltage mode based on a second-generation voltage conveyor, along with two resistors, and a capacitor. This circuit delivers an all-pass response from the low-impedance node and eliminates the need for a voltage buffer in cascading configurations. A thorough non-ideal analysis, accounting for parasitic impedances and the non-ideal gains of the active module, shows negligible effects on the filter performance. Furthermore, a sensitivity analysis with respect to both active and passive components further validates the robustness of the design. The proposed all-pass filter is validated by Cadence PSPICE simulations, utilizing 0.18 µm TSMC CMOS process parameter and ±0.9 V power supply, including Monte Carlo analysis and temperature variations. Additionally, experimental validation is carried out using commercially available IC AD844, showing great consistency between theoretical and experimental results. Resistor-less realization of the proposed filter provides tunability feature. A quadrature sinusoidal oscillator is presented to validate the proposed structure. The introduced circuit provides a simple and effective solution for low-power and compact analog signal processing applications. Full article

This work introduces a unified Artificial Intelligence-based framework for the optimal tuning of gains in a neural discrete-time sliding mode controller (SMC) applied to a two-degree-of-freedom robotic manipulator. The novelty lies in combining surrogate-assisted optimization with normalized search spaces to enable a fair comparative analysis of three metaheuristic strategies: Bayesian Optimization (BO), Particle Swarm Optimization (PSO), and Genetic Algorithms (GAs). The manipulator dynamics are identified via a discrete-time recurrent high-order neural network (NN) trained online using an Extended Kalman Filter with adaptive noise covariance updates, allowing the model to accurately capture unmodeled dynamics, nonlinearities, parametric variations, and process/measurement noise. This neural representation serves as the predictive plant for the discrete-time SMC, enabling precise control of joint angular positions under sinusoidal phase-shifted references. To construct the optimization dataset, MATLAB^® simulations sweep the controller gains $(k_0^{*},k_1^{*})$ over a bounded physical domain, logging steady-state tracking errors. These are normalized to mitigate scaling effects and improve convergence stability. Optimization is executed in Python^® using integrated scikit-learn, DEAP, and scikit-optimize routines. Simulation results reveal that all three algorithms reach high-performance gain configurations. Here, the combined cost is the normalized aggregate objective $\tilde{J}$ constructed from the steady-state tracking errors of both joints. Under identical experimental conditions (shared data loading/normalization and a single Python pipeline), PSO attains the lowest error in Joint 1 ($7.36\times {10}^{-5}$ rad) with the shortest runtime (23.44 s); GA yields the lowest error in Joint 2 ($8.18\times {10}^{-3}$ rad) at higher computational expense (≈69.7 s including refinement); and BO is competitive in both joints ($7.81\times {10}^{-5}$ rad, $8.39\times {10}^{-3}$ rad) with a runtime comparable to PSO (23.65 s) while using only 50 evaluations. Full article

The mechanical properties of the joint are a key factor influencing the overall structural performance of segmental precast beams. This study investigates the shear performance of large keyed dry joints in segmental precast beam specimens under six different conditions, including variations in the base height of the key, depth-to-height ratio, number of keys, and prestressing reinforcement ratio, using direct shear tests and numerical simulations. The mechanical performance of the joints in segmental precast bridges under combined bending and shear forces is also studied using finite element analysis software. Additionally, a prediction model for the shear strength of the large keyed dry joints is established using machine learning methods. The results show that increasing the base height, depth-to-height ratio, and overall dimensions of the key can enhance the shear strength of dry joints. The depth-to-height ratio of the key not only affects the shear strength of the dry joint but also determines the failure mode of the joint. Furthermore, the shear bearing capacity and displacement stiffness of the keyed dry joint increase with the reinforcement ratio of the prestressing tendons. Compared to smaller keyed joints, larger keyed dry joints exhibit higher shear bearing capacity, smaller relative slip at failure, and a simpler casting process, making them more suitable for application in segmental precast bridges. The influence of bending moment on the shear bearing capacity of the joint section is limited, with the relative variation compared to the pure shear condition being less than 10%. The shear bearing capacity of the joint section in segmental precast bridges can be designed based on its direct shear performance. The developed interface shear strength prediction model effectively captures the nonlinear relationship between various parameters and shear strength, demonstrating strong adaptability and accuracy. Full article

The El Niño–Southern Oscillation (ENSO) is the most consequential mode of interannual climate variability on the planet, yet its prediction has become complex due to the inability of classical paradigms to explain the observed co-evolution of the tropical mean state and interannual variability on decadal timescales. This article synthesizes the extensive research on ENSO rectification, exploring a paradigm that resolves this causality problem by recasting ENSO as an active architect of its own mean state. Tracing the intellectual development of this theory, starting from fundamental concepts such as the “dynamical thermostat” and “heat pump” hypotheses, modern analysis has identified the core physical mechanism as nonlinear dynamical heating (NDH), which is rooted in nonlinear heat advection during asymmetric ENSO cycles. The convergence of evidence from forced ocean models and observational diagnostics confirms a rectified signal characterized by an off-equatorial spatial pattern, providing a primary mechanism for tropical Pacific decadal variability (TPDV). By establishing a coherent framework linking high-frequency asymmetry with low-frequency variations, this review lays the foundation for future research and emphasizes the critical role of the rectification effect in improving decadal climate prediction. Full article

To investigate the local compressive bearing capacity of concrete reinforced with spiral stirrups, a study was conducted on 40 specimens, focusing on the analysis of failure modes and strain variations. The study investigated the effects of concrete strength grade fcg, stirrup diameter d, volumetric ratio of the spiral stirrups ρv, and ratio of core area to load area Acor/Al on the local bearing capacity Fl. The experimental results indicated that the local bearing capacity Fl increased as the ratio of core area to load area Acor/Al decreased, with an observed increase of 23.0%. Furthermore, the local bearing capacity Fl increased with a reduction in stirrup spacing s, showing a 65.4% increase compared to the initial value. An increase in stirrup diameter d also would contribute to a higher Fl, with an improvement of 24.7%. Additionally, the local bearing capacity Fl exhibited a directly proportional relationship with the concrete strength grade fcg. Based on the theoretical analyses, the local bearing capacity of calculation equation Nu′ was established with the reinforcement term ρvfyAl and the concrete term fcAl. The experimental data on concrete reinforced with spiral stirrups were collected to verify the accuracy of calculation equation. The results show that there was a high calculation accuracy for the mechanical model and could provide a reference for local bearing capacity calculation. Full article

As a critical component in tunnel construction, the segment erector of shield tunneling machines critically influences segment assembly quality and construction efficiency, largely determined by its dual-cylinder synchronization control. Addressing challenges such as dynamic coupling, nonlinear disturbances, and significant inertial force fluctuations inherent in hydraulic cylinder synchronization under large-inertia loads and variable working conditions, this study proposes an optimized inertial force suppression strategy utilizing an improved sliding mode control (SMC). Mechanical and hydraulic dynamic models of the dual-cylinder lifting mechanism were established to analyze load distribution and force-arm variation patterns, thereby elucidating the influence of inertial forces on synchronization accuracy. Based on this analysis, an adaptive boundary-layer SMC, incorporating real-time inertial force compensation, was designed. This design effectively suppresses system chattering and enhances robustness. Simulation and experimental results demonstrate that the proposed method achieves synchronization errors within ±0.5 mm during step responses, reduces inertial force peaks by 50%, and exhibits significantly superior anti-interference performance compared to conventional PID control. This research provides theoretical foundations and practical engineering insights for high-precision synchronization control in shield tunneling, demonstrating substantial application value. Full article

Accurate prediction of transformer top-oil temperature is crucial for insulation ageing assessment and fault warning. This paper proposes a novel prediction method based on Variational Mode Decomposition (VMD), kernel principal component analysis (Kernel PCA), a Time-aware Shapley Additive Explanations–Multilayer Perceptron (TSHAP-MLP) feature selection method, enhanced Bayesian optimization, and a Self-organized Time Series Forecasting System (SOFTS). First, the top-oil temperature signal is decomposed using VMD to extract components of different frequency bands. Then, Kernel PCA is employed to perform non-linear dimensionality reduction on the resulting intrinsic mode functions (IMFs). Subsequently, a TSHAP-MLP approach—incorporating temporal weighting and a sliding window mechanism—is used to evaluate the dynamic contributions of historical monitoring data and IMF features over time. Features with SHAP values greater than 1 are selected to reduce input dimensionality. Finally, an enhanced hierarchical Bayesian optimization algorithm is used to fine-tune the SOFTS model parameters, thereby improving prediction accuracy. Experimental results demonstrate that the proposed model outperforms transformer, TimesNet, LSTM, and BP in terms of error metrics, confirming its effectiveness for accurate transformer top-oil temperature prediction. Full article

Integration of thermographic imaging with in situ scanning electron microscopy (SEM) analysis may aid in quantifying thermal–mechanical behavior during tensile testing of 3D-printed polymers, which gives information about fracture mechanics, including the associated thermal phenomena. Upon fracture, samples exhibit changes in the thermal field, which is interesting because temperature fluctuations can affect material integrity. The paper introduces printing parameters to demonstrate a thermal measurement system’s sensitivity in detecting variations in mechanical response due to controlled changes in the process. Employing scientific methods, one can extrapolate results to a wider class of materials such as thermoplastics. Analysis of variance (ANOVA) is key in the design of experiments (DOE) if one wants to analyze the effect of factors and interactions. It has been used with the purpose of reducing the risk of type I errors (i.e., false positives). The finite element method (FEM) highlights temperature distribution in the area of interest and confirms recorded data. The particularly developed research experiments are carried out in a laboratory environment. Different samples are subjected to tensile tests under the evaluation of changes in the thermal field. SEM analysis is also widely used in fracture analysis to understand failure modes (ductile vs. brittle, crazing, delamination, and others). Thus, the paper aims to present a custom setup comprised a thermal camera pointed at samples during tensile testing that would serve as a reliable assessment system that accounts for the substitution of a sensor-based environment but is still fully capable of validating the measurement approach. Full article

Numerous thermal practical applications utilize shell and tube heat exchanger appliances to transfer heat energy between hot and cold working fluids. Incorporating metal foam to the outer periphery of inner tube improves the heat transfer process from hot water in the tube side to cold water in the shell side and consequently improves heat exchanger performance. In this study, the integration of use of a porous material together with designing a robust adaptive controller could efficiently regulate the outlet cold water temperature to the desired value. This is achieved with respect to the time required for cold water to reach the desired temperature (settling time) and the amount of hot water volume flow during a certain time span. A barrier function-based adaptive sliding mode controller (BF-based adaptive SMC) is proposed, which requires only the information of temperature measurement of cold water. The stability of BF-based adaptive SMC is proved utilizing Lyapunov function analysis. The effectiveness of proposed controller is verified via numerical results, which showed that the proposed controller could achieve considerable accuracy of cold water temperature using suitable design parameters. In addition, the robustness of controller against variation in inlet temperature is also verified. Another improvement to performance of heat exchanger system is achieved by adding the metal foam of aluminum material on inner pipe perimeter with wide range of metal foam to outer inner pipe diameters ratio ($1\le s\le 1.8$). The results showed that the settling time is significantly reduced which enables outlet cold water to reach the required temperature faster. With respect of the case of non-adding metal foam on inner pipe outer circumference, when $s=1.2$, the settling time and hot water temperature are reduced by $1/2$ and $17.3\%$, respectively, while for $s=1.8$, they are decreased by $\le 1/20$ and $35.3\%$ correspondingly. Accordingly, the required volume flow for hot water is reduced considerably. Full article

Model predictive control (MPC) is significantly affected by parameter mismatch in inverter applications, whereas model-free predictive control (MFPC) avoids parameter dependence through the ultra-local model (ULM). However, the traditional MFPC based on the algebraic method needs to store historical data for multiple cycles, which results in a sluggish dynamic response. To address the above problems, this paper proposes a model-free predictive control method based on the ultra-local model and an adaptive super-twisting sliding mode observer (ASTSMO). Firstly, the effect of parameter mismatch on the current prediction error of conventional MPC is analyzed through theoretical analysis, and a first-order ultra-local model of the inverter is established to enhance robustness against parameter variations. Secondly, a super-twisting sliding mode observer with adaptive gain is designed to estimate the unknown dynamic terms in the ultra-local model in real time. Finally, the superiority of the proposed method is verified through comparative validation against conventional MPC and the algebraic-based MFPC. Simulation results demonstrate that the proposed method can significantly enhance robustness against parameter variations and shorten the settling time during dynamic transients. Full article

In this work, a set of methods for the study of the physical–chemical properties of flotation processing products was applied to establish parameters for the technological mode of anthropogenic raw material flotation processing using waste paper as an example. The proposed methods include the criterion E_f estimation, which characterizes the interfacial characteristics during flotation, and the criterion J determination, which characterizes the degree of purification of the obtained paper mass. The estimation of criterion E_f is based on the measurement of electric potential difference during flotation. The estimation of criterion J is based on spectrophotometric studies of the flotation product’s optical properties. Based on dispersion analysis, it was established that the proposed criteria are statistically dependent on the variation of the flotation purification mode parameters. The results of the study of flotation processing products show that the criterion E_f is sensitive to the recovery selectivity of dye particles in the froth product. In conjunction with the criterion of optical purity, J, it can be used to assess the effectiveness of proposed solutions of hardware design and the technological mode of flotation separation. Full article