Practical Engineering Applications Research Articles

Gaussian bistable cascade double feedback stochastic resonance weak signal enhancement detection

Stochastic resonance (SR), as a nonlinear weak signal enhancement detection method, has found extensive applications in fields like fault diagnosis and biological information processing. However, further research has revealed that single stochastic resonance systems often fail to meet the requirements of practical engineering applications, leading to a growing interest in multi-system collaborative stochastic resonance. This article first constructs a cascaded SR system based on the Gaussian bistable model, and introduces the idea of feedback into the cascaded system. A Gaussian bistable cascaded dual feedback SR system is proposed to improve the output signal enhancement performance of the system. This system effectively utilizes the memory characteristics inherent in the output feedback and enables multi-level particle transitions and energy transfers in noisy input signals, thereby reducing noise and enhancing the extraction of weak signal features. Furthermore, the BSO optimization algorithm was utilized to optimize the system parameters of the model, resulting in an adaptive Gaussian bistable cascaded double feedback stochastic resonance (GBCDFSR) system. This system is applied to the processing of noisy periodic signals across various noise types. Simulation experiments and engineering applications show that the proposed model has stronger signal detection capabilities.

A generalized grey model with symbolic regression algorithm and its application in predicting aircraft remaining useful life

As a sparse data analysis method, a grey model faces challenges in interpretability for its effective application in uncertain systems. This study proposes a generalized grey model (GGM) based on symbolic regression, designed to improve the intelligence and adaptability of grey models. The GGM serves as a unified framework, integrating various grey model families and addresses regression challenges to determine the model structure. Symbolic regression in the GGM identifies symbolic input-output relationships, offering an interpretable approach for structure determination. By leveraging the non-uniqueness principle in grey system theory and employing structural penalty parameters, the model balances complexity and interpretability. A comparative analysis between GGM and conventional grey function models is conducted focusing on the differences in modeling, structure identification, and parameter optimization. Validation on the M3 competition dataset demonstrated the GGM's superior performance, achieving a significant reduction in prediction error compared to other grey forecasting models. Additionally, a rigorous analysis of aircraft lifespan data underscored the robustness and accuracy of GGM in practical engineering applications.

Effects of Resting Conditions on Tensile Properties of Acid Aggregate Hydraulic Asphalt Concrete.

This study addresses the issue of construction stagnation affecting the adhesion and tensile properties of hydraulic asphalt concrete with acid aggregate. It investigates the impact of rest periods on the tensile characteristics of such materials under standard construction conditions. The influence of varying rest durations and asphalt temperatures on the tensile behavior of the concrete is assessed through indoor experiments. The bonding between asphalt and aggregate is examined, along with the tensile property variations of the concrete. The study found that the standstill time significantly affects the adhesion of asphalt, with the adhesion decreasing progressively with increased temperature and rest time, irrespective of the addition of anti-stripping agents. However, the inclusion of these agents can mitigate the reduction in adhesion. Furthermore, the study identified that rest duration has a more substantial impact on adhesion than temperature. The splitting tests demonstrate that the tensile properties of asphalt concrete are considerably affected by the resting time. Over a period of 0, 10, 20, and 30 days of rest, an increase in splitting strength and a decrease in splitting displacement were observed. The findings offer valuable insights for predicting the tensile performance of asphalt concrete in practical engineering applications after a period of rest.

Enhanced oriented oxidation of medium and long chain alkanes by inactivating hydrophilic organics of soil organic matter

Aiming to explore the effect of deactivation of soil organic matter (SOM) on the oriented oxidation of n-alkanes, the petroleum-contaminated soil was pretreated with K2HPO4, and then the soil was treated with Fenton oxidation. After the soil was regulated by K2HPO4, the hydrophilic organics (Protein II and Humic acid-like) in SOM were deactivated, which made the ·OH transfer to oxidize TPH, to achieve efficient oriented oxidation of TPH. After the passivation of hydrophilic organics, the TPH oxidation amount of the efficient group was as high as 6007 mg/kg, which was 1.57 times than unregulated group. In efficient oriented oxidation group, the TPH oriented oxidation amount reached 2186 mg/kg, and the TPH oxidation amounts of unit hydroxyl strength reached 759.42 mg/kg, and the oxidation effect of the medium and long chain alkanes was improved. The results showed that 22.60 % ·OH was transferred to TPH, and the relative reactivity coefficient increased from 0.7 to 1.8(with k>1), which was the reason for improving the oriented oxidation effect of medium and long chain alkanes. It was also found that the two hydrophilic organics had high correlations with TPH oxidation (R2 = 0.9). Further analysis found that the passivation of hydrophilic organics was mainly through the reaction of PO43- with protein and humus in soil organic matter. In general, this study greatly reduced the ineffective consumption of ·OH by SOM, thereby improving the utilization efficiency of H2O2 and realizing the efficient oriented oxidation of petroleum hydrocarbons, which provided great reference value for practical engineering applications.

Seismic performance of frame with middle partially encased composite brace and steel-hollow core partially encased composite spliced frame beam

Steel-hollow core partially encased composite spliced frame beam (SHSFB) and middle partially encased composite brace (MPECB) demonstrate the potential to economize on steel consumption while exhibiting superior performance. To investigate the seismic performance of frames with SHSFBs and MPECBs, horizontal low-cyclic loading tests were conducted on four frame specimens, involving two two-story frames with no brace and two multi-story X-braced frames. Numerical simulations were performed using the “Open System for Earthquake Engineering Simulation” (OpenSees). The results indicate that the novel composite frames exhibit commendable seismic performance and energy dissipation capacity. The unbraced frame with SHSFBs has deformation capacity and mechanical properties comparable to bare steel frame. In contrast to the frame specimen equipped with bare steel braces featuring a 6 mm flange thickness, the specimen utilizing MPECBs with 4 mm flange thickness exhibits only a slight reduction of 3.60 % in maximum bearing capacity and 2.68 % in initial lateral stiffness. Significantly, each SHSFB and MPECB component reduces steel consumption by 16.71 % and 24.79 %, respectively, compared to bare steel components. Therefore, while ensuring adequate mechanical properties, frame structures equipped with SHSFBs and MPECBs will require less steel. Based on both experimental and simulation results, the formulas for predicting the ultimate bearing capacity and initial lateral stiffness of the novel composite frame were proposed, and the structural design for the multi-story X-braced frame was analyzed, offering valuable insights for practical engineering applications.

Research on Distribution Substation Topology Identification Methods

With the advancement of digital transformation in distribution substations, a large number of smart devices are being integrated into substations. Addressing the challenges of automatic topology recognition and the issue of unstable recognition accuracy in distribution substations has become crucial. This paper proposes a substation topology recognition method based on an improved matrix approach and the Minimum Conditional Probability of Packet Loss Theorem. The improved matrix approach is utilized to calculate the topological signals, enabling automatic bottom-up topology recognition within the substation. The application of the Minimum Conditional Probability of Packet Loss Theorem in processing topological data significantly enhances the accuracy of substation topology recognition, reducing the impact of external factors on recognition accuracy. Experimental validation demonstrates that the proposed method is highly feasible and exhibits fault tolerance, indicating practical engineering applications.

Modeling time-dependent deformation in concrete: A fractional calculus method with finite element implementation

This study presents a novel numerical method to simulate the time-dependent behavior of concrete materials. The deformation response of both traditional and fractional calculus (FC) viscoelastic models is analyzed by the proposed method, in which models’ one-dimensional constitutive relationships are extended to the three-dimensional form. By applying numerical and finite difference methods based on Caputo-type fractional integration, the stress-strain behavior of the FC viscoelastic model is discretized over the time scale. This method exhibits broad application prospects, and can be employed to represent various forms of viscoelastic models. For the concrete material, suitable FC viscoelastic models are developed. To facilitate practical engineering applications of the proposed model, a numerical solution algorithm is implemented in the finite element (FE) analysis through the User-defined Material Mechanical Behavior (UMAT) interface of the commercial FE software ABAQUS. Finally, FE results are compared with the experimental results from several concrete creep tests. The consistency between the FE results and experimental data confirms the effectiveness of the proposed model in describing concrete behavior. The proposed method and model provide theoretical and numerical support for a more profound understanding and simulation of the time-dependent deformation of RC specimens in practical engineering applications.

Inverse parameter identification and model updating for Cross-laminated Timber substructures

Finite Element (FE) model updating is crucial for identifying key parameters in structural design and improving predictive accuracy. Despite extensive research on advanced FE procedures approved for user applications, persistent disparities remain in real-world scenarios, especially for complex materials like wood. Capturing accurate mechanical characteristics with traditional models poses challenges in sustainability projects. This study introduces a derivative-free model updating procedure using a Single-Objective Optimisation (SOO) incorporating observed and predicted natural frequencies and vibration modes. The objective function optimises tuning parameters to minimise discrepancies between predicted and observed outcomes. The focus is on Cross-laminated Timber (CLT), a composite wooden structure gaining traction as a sustainable alternative to materials like reinforced concrete and steel. However, the mechanical properties of CLT can vary due to inherent variability in wood’s mechanical characteristics. This research identifies sensitive mechanical properties — longitudinal Young’s modulus, internal shear moduli, and rolling shear modulus of CLT — using a model updating procedure based on a comprehensive set of data from Experimental Modal Analysis (EMA). The study provides mathematical algebraic derivations of the updating procedure and a step-by-step implementation algorithm to facilitate practical application in structural engineering.

An adaptive multi-output Gaussian process surrogate model for large-scale parameter estimation problems

PurposeParameter estimation in complex engineering structures typically necessitates repeated calculations using simulation models, leading to significant computational costs. This paper aims to introduce an adaptive multi-output Gaussian process (MOGP) surrogate model for parameter estimation in time-consuming models.Design/methodology/approachThe MOGP surrogate model is established to replace the computationally expensive finite element method (FEM) analysis during the estimation process. We propose a novel adaptive sampling method for MOGP inspired by the traditional expected improvement (EI) method, aiming to reduce the number of required sample points for building the surrogate model. Two mathematical examples and an application in the back analysis of a concrete arch dam are tested to demonstrate the effectiveness of the proposed method.FindingsThe numerical results show that the proposed method requires a relatively small number of sample points to achieve accurate estimates. The proposed adaptive sampling method combined with the MOGP surrogate model shows an obvious advantage in parameter estimation problems involving expensive-to-evaluate models, particularly those with high-dimensional output.Originality/valueA novel adaptive sampling method for establishing the MOGP surrogate model is proposed to accelerate the procedure of solving large-scale parameter estimation problems. This modified adaptive sampling method, based on the traditional EI method, is better suited for multi-output problems, making it highly valuable for numerous practical engineering applications.

Computational Simulation of Monopile Scour under Tidal Flow Considering Suspended Energy Dissipation

Local scour around bridge foundations significantly impacts the stability and safety of marine structures. The development of scour holes adjacent to the pile foundations of sea-crossing bridges, influenced by tidal currents, involves multidimensional physical fields, multiscale coupling, and complex variations in marine loads. However, experimental models alone are inadequate for investigating the underlying mechanisms. Numerical simulation, a critical tool for studying local scour processes, faces the challenge of accurately modeling sediment transport, particularly under tidal flow conditions near pile foundations. To solve this challenge, this research considers the effect of reciprocating flow on sediment shear as well as its characteristic dissipation based on the immersed boundary method, introduces a reciprocating flow dissipation mechanism, and adds a momentum exchange term between the fluid and the sediment to derive a new controlling equation; a new tidal flow localized scour solver is ultimately constructed, termed TidalflowFOAM. The solver effectively simulates complex flow conditions under tidal currents, extending the modeling capabilities to more realistic three-dimensional bridge scour scenarios under combined wave and current conditions. Validation through cases reported in the literature and a series of controlled experiments, encompassing varying depths, flow velocities, and pile diameters, demonstrates the solver’s proficiency in capturing post-vortex data and accurately reflecting the influence of key factors on scour depth. However, the fidelity of the simulated scour hole morphology under tidal flow conditions behind the piles requires enhancement. The proposed numerical model for tidal flow conditions has high solution accuracy and can guide practical engineering applications.

Harnessing the power of Wav2Vec2 and CNNs for Robust Speaker Identification on the VoxCeleb and LibriSpeech Datasets

Speaker identification, a cornerstone of speech processing, involves associating individuals with spoken segments within a known speaker pool. This paper presents a significant AI contribution: an innovative framework tailored for closed-set speaker identification. It concurrently emphasizes its practical engineering application in the realm of speech analysis. This paper introduces a pioneering AI framework with substantial neural network architecture enhancements, particularly focusing on optimizing the Log-Softmax function—a linchpin for speaker attribution. Additionally, we seamlessly incorporate cutting-edge data augmentation techniques into the Wav2Vec2 framework. These innovations push the boundaries of current Speaker Identification methodologies. Empirical validation demonstrates our framework’s efficacy, yielding a remarkable relative improvement of up to 3.16% in top-1% accuracy compared to the state-of-the-art. This research sets a new benchmark, surpassing existing standards and unlocking the full potential of closed-set Speaker Identification functions. In addition, the methodology presented in this paper serves as a catalyst for advancing Speaker Identification methodologies in engineering applications, underlining the transformative potential of AI-driven innovations in this domain.

Investigation on tribological behaviors of a novel generation polymer-coated ceramsite particle for fracturing

During fracturing, proppant particles can create favorable pathways for shale gas production. To solve the problem of low production efficiency of shale gas, a new polymer coated particle was prepared by experiment. And its mechanical properties were tested and tribological properties were studied under different speeds, loads and medium states. The results show that the new polymer coated particles have excellent tribological and mechanical properties. The friction coefficient between proppant particles and fracture surface decreases with increasing velocity. In addition, the new proppant particles have excellent hydrophobicity under lubricating conditions, indicating that the proppant has excellent migration efficiency. When proppants just pumped into the fracture, it can reach the fracture depth quickly. In addition, the friction coefficient between proppant particles and fracture surface gradually increases with the increase of load. It is further proved that when the proppant particles enter the fracture, the friction coefficient increases due to the extrusion of the fracture, and the reverse displacement can be effectively reduced. This research has important theoretical and practical engineering application value for improving the efficiency of shale gas stimulation.

Investigation of mixing techniques for full-strength-grade engineered cementitious composites (ECCs): Mechanical properties and microstructure

Mixing techniques significantly influence the performance of ECC by affecting both macro- and microstructural properties. Despite its importance, research on ECC mixing techniques remains limited, restricting broader exploration and application. This study investigates the effects of three mixing techniques using pan, handheld, and planetary mixers on the performance of full-strength-grade ECC, focusing on flowability, compressive strength, elasticity, tensile and flexural properties, and fiber bridging ability. CT scan-based 3D reconstructions provided insights into pore and fiber distribution. The findings indicate minimal variations in flowability, compressive strength, and elasticity across mixer types, all within 10 % of optimal performance. However, tensile strength showed significant variability at higher strength levels, with pan mixers exhibiting up to 72.25 % performance drop, while handheld mixers showed reductions of up to 25.78 %. Flexural performance remained robust across all mixers, with pan and handheld mixers achieving over 82 % and 92 % of the performance seen with planetary mixers, respectively. While porosity was similar across mixers at identical strength levels, pore size diversity increased with higher strength levels. Additionally, fiber distribution varied significantly. Planetary mixers achieved superior uniformity, whereas pan mixers exhibited significant clustering. These results provide a quantitative assessment of the mixing performance of different mixers, offering valuable guidance for both research and practical engineering applications.

Study of Concrete Properties of Recycled Glass Fibres from Decommissioned Wind Turbine Blades

As an environmentally friendly and renewable energy solution, wind power is rapidly gaining favour worldwide due to its gentle impact on the environment. Nevertheless, the potential environmental risks posed by discarded wind turbine blades still need to be brought to our attention. Therefore, exploring ways to recycle and reuse discarded wind turbine blades has become an urgent task in the field of environmental protection. This study focuses on the incorporation of recycled glass fibres from crushed wind turbine blades into concrete to assess their benefits in engineering practice. In this study, we used four different particle sizes of recycled glass fibres, 0-5mm, 5-10mm, 10-15mm and 15-20mm, and incorporated them into the concrete at four different admixture levels of 0.2%, 0.4%, 0.6% and 0.8%. By comprehensively examining its workability, mechanical properties and microstructure, we found that although the incorporation of glass fibres reduced the apparent density, slump and compressive strength of the concrete to a certain extent, it significantly improved the split tensile and flexural strengths of the concrete, as well as effectively improved the brittleness of the material and enhanced its toughness. These findings reveal the feasibility of recycling glass fibres from decommissioned wind turbine blades and applying them to concrete. This study not only opens up a new path for environmentally friendly recycling and reuse of wind turbine blades, but also provides a valuable reference for practical engineering applications, with significant social and economic benefits.

Stability of Prestressed Stayed Steel Columns Under Eccentric Compression

Prestressed stayed steel columns (PSSCs) are structural components notable for their exceptional stability and load capacity. However, their behavior under eccentric compression remains poorly understood compared to their performance under axial compression. This study conducted tests and finite element analysis (FEA) to investigate the stability of PSSCs under eccentric compression loading. The study focused on examining the overall stability, buckling modes, and ultimate load capacity of PSSCs under various scenarios. The findings revealed that PSSCs exhibit significantly higher stability and load capacity than conventional columns. However, when subjected to eccentric compression, they experience a substantial decrease in stability. The results of the linear and nonlinear buckling analyses suggest that interactive buckling may occur under certain conditions, thereby influencing the buckling load. These findings clarify the correlation between stability and eccentric compression, offering valuable insights for future research and practical engineering applications.

Improved Cell Adhesion on Self-Assembled Chiral Nematic Cellulose Nanocrystal Films.

Chirality is ubiquitous in nature, and closely related to biological phenomena. Nature-originated nanomaterials such as cellulose nanocrystals (CNCs) are able to self-assemble into hierarchical chiral nematic CNC films and impart handedness to nano and micro scale. However, the effects of the chiral nematic surfaces on cell adhesion are still unknown. Herein, this work presents evidence that the left-handed self-assembled chiral nematic CNC films (L-CNC) significantly improve the adhesion of L929 fibroblasts compared to randomly arranged isotropic CNC films (I-CNC). The fluidic force microscopy-based single-cell force spectroscopy is introduced to assess the cell adhesion forces on the substrates of L-CNC and I-CNC, respectively. With this method, a maximum adhesion force of 133.2 nN is quantified for mature L929 fibroblasts after culturing for 24h on L-CNC, whereas the L929 fibroblasts exert a maximum adhesion force of 78.4 nN on I-CNC under the same condition. Moreover, the instant SCFS reveals that the integrin pathways are involved in sensing the chirality of substrate surfaces. Overall, this work offers a starting point for the regulation of cell adhesion via the self-assembled nano and micro architecture of chiral nematic CNC films, with potential practical applications in tissue engineering and regenerative medicine.

LTFM-net framework: Advanced intelligent diagnostics and interpretability of insulated bearing faults in offshore wind turbines under complex operational conditions

The rapid advancement of intelligent theories and models, exemplified by deep learning, has achieved remarkable success across numerous fields. However, given that the complexity and variability of offshore wind turbine systems, particularly in the context of high-power variable-frequency control of insulated bearings in offshore wind turbines, the problem of fault identification has become a recognized technical challenge. Additionally, fully exploiting the temporal characteristics of insulated bearing fault data poses a key problem that demands urgent resolution. To address these gaps, this paper pioneers a novel Lightweight Temporal Feature-focused framework, named LTFM-Net, aimed at solving the difficult problem of identifying insulated bearing faults in offshore wind turbines in practical engineering applications. Specifically, this framework enables the intelligent identification of insulated bearing faults under harsh operating conditions such as alternating voltage and variable loads, marking a first in this field. Furthermore, an innovative strategy named Weighted Diminish Recurrent Unit (WDRU) was developed, along with the derivation of its backpropagation formula, which is innovatively applied to the feature extraction module of the LTFM-Net framework for the first time. Thus, an efficient method for acquiring fault data from insulated bearings in offshore wind turbines was proposed. Based on a unified dataset, the diagnostic performance of the LTFM-Net framework was evaluated and compared with seven advanced methods. The results demonstrate that the LTFM-Net achieves precise identification of insulated bearing faults, confirming its excellent generalization, robustness, and superiority. The introduction of t-SNE for visualizing the fault characteristics of insulated bearings uncovered by the LTFM-Net framework further enhances its reliability, accuracy, and credibility.

Research on the optimal design of roadway support for fully mechanized coal seam working face

Abstract For the optimal design of surrounding rock deformation and support in the shallow buried coal seam of large-scale fully mechanized mining face, this paper took the surrounding rock of the roadway as the research object, used theoretical calculation and numerical simulation to establish the mechanical model of the deformation of the surrounding rock structure, and proposed and analyzed the reasonable roadway support scheme of the shallow buried coal seam. The numerical simulation results showed that the top anchor rod with Φ18×2200 mm and the inter-row distance of 1100×1000 mm, the anchor cable with Φ15.24×6300 mm and the inter-row spacing of 2200×2000 mm, the upper part anchor of Φ16×2200 mm, and the C30 concrete with a thickness of 200 mm for laying the bottom plate are selected, which was certified as the optimal support scheme. In practical engineering applications, the deformation of surrounding rock has been effectively controlled, and the supporting effect of surrounding rock was obvious, which was of great significance to ensure the safe production of coal mines.

Control-oriented dynamic modeling and GPC for single-tower double-circulation wet flue gas desulfurization system

In China, the current situation of large-scale grid integration of renewable energy generation and the government's introduction of capacity-based electricity pricing policies have compelled coal-fired power plants to gradually enhance their load adjustment capabilities and expand their load adjustment ranges. Simultaneously, the national environmental requirements for these plants have become increasingly stringent. However, the economic control of wet flue gas desulfurization (WFGD) systems is still far from meeting practical requirements. In this paper, starting from practical engineering applications and utilizing historical operational data, considering the actual system operation process, the impact of the absorption tower variable-frequency slurry circulation pump on the concentration of sulfur dioxide (SO2) in flue gas under different slurry pH values is investigated. This led to the establishment of a dynamic model for a single-tower double-circulation wet flue gas desulfurization (SD-WFGD) system. Recognizing the significant delay and inertia characteristics of pH value control in the SO2 absorption tank, a desulfurization system pH value desulfurization capacity-based optimization control scheme for the SD-WFGD system is proposed, termed as the Direct Sulfur Control (DSC) strategy. Based on the established dynamic model, a SD-WFGD system series control system is developed using a step generalized predictive control algorithm (GPC) combined with the DSC strategy. Validation results indicate that, compared to SGPC control schemes and traditional PID control schemes, this approach reduces fluctuations in slurry supply and outlet SO2 concentration, significantly enhancing the control performance of the SD-WFGD system. This ensures the safe, stable, economical, and flexible operation of the desulfurization system against the backdrop of flexible operation in coal-fired power plants.

A shared-link type wireless power and data simultaneous transfer system based on a series–parallel resonant network

Wireless Power Transmission (WPT) is widely used. In practical engineering applications, the transmitter and receiver of a stable and reliable WPT system need to establish not only a power transmission channel but also a data transmission channel to realize closed-loop control and information interaction. In this paper, a novel simultaneous wireless power and data transfer system based on a series–parallel (SP) resonant network is proposed. The system integrates an SP higher-order resonant network with series carrier injection, which makes power transmission and data transmission share a magnetic coupling channel. The isolation between power and data is realized by exploiting the frequency characteristics of the compensation network, thus realizing efficient power transmission and data transmission. In this paper, accurate and detailed mathematical modeling of the system is conducted, and the gain characteristics of the power transmission channel and the data transmission channel are demonstrated. Meanwhile, the mutual interference between the two channels is analyzed, and a feasible and effective parameter design method is developed. Finally, an experimental prototype with an output power of 16 W and a backward data rate of 100 Kbps is constructed to verify the feasibility of the proposed system and the correctness of the parameter design.