Practical Engineering Applications Research Articles

A hybrid FEM-IBM-level set algorithm for water entry of deformable body

Water entry of deformable bodies has been investigated extensively despite the major challenges in precise simulations of the whole physical process. Inspired by the basic idea of immersed boundary method (IBM), a hybrid FEM-IBM-level set algorithm is proposed where structural compliance is considered. The flow is described by Navier-Stokes equations with an added body force while the drastically moving object of arbitrary geometry is represented by several Lagrangian points on solid boundary. The direct forcing IBM is enhanced and coupled with finite element method (FEM) to facilitate simulation of severe fluid-structure interactions. Consequently, the added body force is calculated implicitly based on the exact velocity boundary condition with divergence-free condition ensured simultaneously. The improved conservative level set technique is adopted for accurate depiction of instantaneously distorted water surface, and the strongly coupled system is resolved iteratively through a partitioned scheme. Examples including sedimentation of a disk, water entry of deformable wedge and cylindrical shell are performed for validation. Specifically, hydroelastic impact of a three-dimensional conical shell is investigated, and the influence factors of entry dynamics are discussed. The results demonstrate the reliability and great potential of present algorithm for practical applications in ocean engineering, naval architecture and military engineering, etc.

Structural damage identification by using physics-guided residual neural networks

In recent years, vibration-based structural damage identification has made significant progress by exploiting data-driven deep learning techniques, which can efficiently extract damage-sensitive features from a large amount of data. However, in some practical engineering applications, large volumes of measurement data are not readily available. This paper proposes a novel physics-guided residual neural network (PhyResNet) framework to improve the robustness and accuracy of structural damage identification under data-scarce conditions. In contrast to the state-of-the-art purely data-driven ResNet, the proposed method embedded available physics knowledge (e.g., governing equations of dynamics) of structures into the feature learning process via a novel physics-based loss function. The input-output relationship of the network is constrained to retain its physical meaning implicitly while the demand for large amounts of labeled training data is reduced. Notably, even with only 5 % of the dataset used for training, PhyResNet achieves a 13.1 % improvement in R-Value. The performance of the proposed approach is evaluated through both numerical and experimental verifications. Results demonstrate that damage localization and quantification are achieved with high accuracies and good robustness.

AI-based rock strength assessment from tunnel face images using hybrid neural networks

In geological engineering and related fields, accurately and quickly identifying lithology and assessing rock strength are crucial for ensuring structural safety and optimizing design. Traditional rock strength assessment methods mainly rely on field sampling and laboratory tests, such as uniaxial compressive strength (UCS) tests and velocity tests. Although these methods provide relatively accurate rock strength data, they are complex, time-consuming, and unable to reflect real-time changes in field conditions. Therefore, this study proposes a new method based on artificial intelligence and neural networks to improve the efficiency and accuracy of rock strength assessments. This research utilizes a Transformer + UNet hybrid model for lithology identification and an optimized ResNet-18 model for determining rock weathering degrees, thereby correcting the strength of the tunnel face surrounding rock. Experimental results show that the Transformer + UNet hybrid model achieves an accuracy of 95.57% in lithology identification tasks, while the optimized ResNet model achieves an accuracy of 96.13% in rock weathering degree determination. Additionally, the average relative error in tunnel face strength detection results is only 9.33%, validating the feasibility and effectiveness of this method in practical engineering applications. The multi-model neural network system developed in this study significantly enhances prediction accuracy and efficiency, providing robust scientific decision support for tunnel construction, thereby improving construction safety and economy.

Characterization of embankment material using sand tire mix as additive and validating the results obtained from large scale direct shear test in Abaqus software

Laboratory direct shear tests often overlook larger particles due to limitations in shear box design, resulting in an inadequate portrayal of real-world soil conditions. To address this gap, the use of large-scale direct shear apparatus capable of accommodating larger particles is essential. However, conducting such tests with varied sizes of tire shreds, mixing ratios, and loads is intricate and resource-intensive. Therefore, the utilization of numerical modeling is suggested to simulate sand-tire mixtures under diverse conditions. This study presents conclusions derived from comparing numerical results with experimental observations, demonstrating a close correspondence between the two. It offers a comprehensive examination of the impact of sand and shredded tires mixtures on shear strength, indicating a consistent pattern: increased quantities of tire shreds correspond to heightened friction angles. Furthermore, specimens containing larger shredded tire particles display elevated friction angles, implying a possible connection between particle size and frictional behavior. This investigation underscores the importance of numerical modeling as a cost-efficient and effective approach to complement experimental inquiries, enriching our comprehension of soil-tire mixture dynamics and guiding practical applications in transportation and geotechnical engineering.

A systematic investigation of peracetic acid oxidation and polymeric coagulants re-flocculation to enhance activated sludge dewatering: Multi-porous skeleton structures

In this research, the effects of peracetic acid (PAA), polymeric flocculants, and their combined conditioning on improving the dewatering performance were comprehensively evaluated. The results showed that sludge cake moisture content, capillary suction time (CST), and specific resistance to filtration (SRF) were 70.6%, 48.1 s, and 3.42 × 1012 m/kg after adding 0.10 g/gMLSS PAA for 50 min, representing reductions of 12.60%, 40.32%, and 33.98%, respectively. Additionally, conditioning of sludge with polyferric sulfate (PFS), polyaluminum chloride (PAC), and cationic polyacrylamide (CPAM) enhanced sludge properties in the following order: CPAM > PAC > PFS. After the PAA oxidation and re-flocculation process, the optimal dosages of PFS, PAC, and CPAM were reduced to 1.5 g/L, 0.9 g/L, and 0.04 g/L, respectively. The sludge dewatering performance significantly improved, with sludge cake moisture content measuring 65.8%, 66.3%, and 61.7%, respectively. Moreover, the spatial multi-porous skeleton structures were formed via re-flocculation to improve the sludge dewatering. Furthermore, economic evaluation validated that the pre-oxidation and re-flocculation process could be considered an economically viable option. These research findings could serve as a valuable reference for practical engineering applications.

State of the Art of Coupled Thermo–hydro-Mechanical–Chemical Modelling for Frozen Soils

AbstractNumerous studies have investigated the coupled multi-field processes in frozen soils, focusing on the variation in frozen soils and addressing the influences of climate change, hydrological processes, and ecosystems in cold regions. The investigation of coupled multi-physics field processes in frozen soils has emerged as a prominent research area, leading to significant advancements in coupling models and simulation solvers. However, substantial differences remain among various coupled models due to the insufficient observations and in-depth understanding of multi-field coupling processes. Therefore, this study comprehensively reviews the latest research process on multi-field models and numerical simulation methods, including thermo-hydraulic (TH) coupling, thermo-mechanical (TM) coupling, hydro-mechanical (HM) coupling, thermo–hydro-mechanical (THM) coupling, thermo–hydro-chemical (THC) coupling and thermo–hydro-mechanical–chemical (THMC) coupling. Furthermore, the primary simulation methods are summarised, including the continuum mechanics method, discrete or discontinuous mechanics method, and simulators specifically designed for heat and mass transfer modelling. Finally, this study outlines critical findings and proposes future research directions on multi-physical field modelling of frozen soils. This study provides the theoretical basis for in-depth mechanism analyses and practical engineering applications, contributing to the advancement of understanding and management of frozen soils.

Multiple Object Tracking in Drone Aerial Videos by a Holistic Transformer and Multiple Feature Trajectory Matching Pattern

Drone aerial videos have immense potential in surveillance, rescue, agriculture, and urban planning. However, accurately tracking multiple objects in drone aerial videos faces challenges like occlusion, scale variations, and rapid motion. Current joint detection and tracking methods often compromise accuracy. We propose a drone multiple object tracking algorithm based on a holistic transformer and multiple feature trajectory matching pattern to overcome these challenges. The holistic transformer captures local and global interaction information, providing precise detection and appearance features for tracking. The tracker includes three components: preprocessing, trajectory prediction, and matching. Preprocessing categorizes detection boxes based on scores, with each category adopting specific matching rules. Trajectory prediction employs the visual Gaussian mixture probability hypothesis density method to integrate visual detection results to forecast object motion accurately. The multiple feature pattern introduces Gaussian, Appearance, and Optimal subpattern assignment distances for different detection box types (GAO trajectory matching pattern) in the data association process, enhancing tracking robustness. We perform comparative validations on the vision-meets-drone (VisDrone) and the unmanned aerial vehicle benchmarks; the object detection and tracking (UAVDT) datasets affirm the algorithm’s effectiveness: it obtained 38.8% and 61.7% MOTA, respectively. Its potential for seamless integration into practical engineering applications offers enhanced situational awareness and operational efficiency in drone-based missions.

Design of hybrid controller for broadband vibration isolation with active negative stiffness and ground-hook

This paper presents a novel hybrid control strategy that integrates active negative stiffness (ANS) with the ground-hook principle, aiming to enhance the vibration isolation system’s performance across both low and high-frequency ranges. Firstly, to improve vibration isolation performance, this study analyzes the existing ANS model. It identifies key factors affecting ANS stability and proposes a control parameter adjustment scheme based on stability conditions. This approach effectively refines ANS control theory and provides theoretical support for the application of ANS by ensuring stability. Secondly, addressing the limitations of ANS in high-frequency vibration isolation, this study proposes targeted solutions based on the ground-hook principle. While ANS excels in low-frequency isolation, this work tackles its shortcomings to enhance high-frequency vibration isolation. These solutions include the implementation of relative velocity feedback (RVFB) control and absolute velocity feedforward (AVFF) control to enhance high-frequency vibration isolation performance, and AVFF exhibits superior advantages over RVFB. Furthermore, the stability of different control strategies is analyzed. The findings reveal that sky-hook significantly enhances the control stability margin of the ANS loop, while RVFB decreases it, with AVFF having a minimal impact. Finally, comprehensive experiments validate a significant enhancement in both low and high-frequency vibration isolation performance, providing robust evidence for the practical engineering application of the vibration isolation system.

CFD-Powered Ship Trim Optimization: Integrating ANN for User-Friendly Software Tool Development

This study presents a comprehensive approach to trim optimization as an energy efficiency improvement measure, focusing on reducing fuel consumption for one RO-RO car carrier. Utilizing Computational Fluid Dynamics (CFD) software, the methodology incorporates artificial neural networks (ANNs) to develop a mathematical model for estimating key parameters such as the brake power, daily fuel oil consumption (DFOC) and propeller speed. The complex ANN model is then integrated into a user-friendly software tool for practical engineering applications. The research outlines a seven-phase trim optimization process and discusses its potential extension to other types of ships, aiming to establish a universal methodology for CFD-based engineering analyses. Based on the trim optimization results, the biggest DFOC goes up to 10.5% at 7.5 m draft and up to 8% for higher drafts. Generally, in every considered case, it is recommended to sail with the trim towards the bow, meaning that the ship’s longitudinal center of gravity should be adjusted to tilt slightly forward.

Three-dimensional line-of-sight-angle-constrained leader-following cooperative interception guidance law with prespecified impact time

To address the problem of multi-missile cooperative interception against maneuvering targets at a prespecified impact time and desired Line-of-Sight (LOS) angles in Three-Dimensional (3D) space, this paper proposes a 3D leader-following cooperative interception guidance law. First, in the LOS direction of the leader, an impact time-controlled guidance law is derived based on the fixed-time stability theory, which enables the leader to complete the interception task at a prespecified impact time. Next, in the LOS direction of the followers, by introducing a time consensus tracking error function, a fixed-time consensus tracking guidance law is investigated to guarantee the consensus tracking convergence of the time-to-go. Then, in the direction normal to the LOS, by combining the designed global integral sliding mode surface and the second-order Sliding Mode Control (SMC) theory, an innovative 3D LOS-angle-constrained interception guidance law is developed, which eliminates the reaching phase in the traditional sliding mode guidance laws and effectively saves energy consumption. Moreover, it effectively suppresses the chattering phenomenon while avoiding the singularity issue, and compensates for unknown interference caused by target maneuvering online, making it convenient for practical engineering applications. Finally, theoretical proof analysis and multiple sets of numerical simulation results verify the effectiveness, superiority, and robustness of the investigated guidance law.

Axial compressive performance of CFRP-steel composite tube confined seawater sea-sand concrete intermediate slender columns

To alleviate the scarcity of river sand resources and fully utilize the advantages of Carbon Fiber Reinforced Polymer (CFRP), a new type of CFRP-steel composite tube was developed to confine seawater-sea sand concrete (FCTSSC) column structures. This study conducted experimental research on FCTSSC intermediate slender columns for the first time, analyzing their failure modes and working mechanisms. The investigation explored the effects of outer CFRP layers and slenderness ratios on the mechanical performance. The results indicate that the FCTSSC intermediate slender column specimens exhibited global buckling accompanied by local bulging of steel tubes as their failure mode. The load-displacement curves can be divided into strong confinement and weak confinement models. Specimens confined by CFRP exhibited a 12.51–31 % increase in ultimate bearing capacity. The ultimate bearing capacity and its enhancement rate were positively correlated with the outer CFRP layers and negatively correlated with the slenderness ratio. Peak lateral displacements were positively correlated with both parameters. Finally, the model for calculating the axial compression ultimate bearing capacity of FCTSSC intermediate slender columns was proposed, providing a reference for future practical applications in offshore engineering.

Multi-strategy improved seagull optimization algorithm and its application in practical engineering

Metaheuristic algorithms play a crucial role in engineering optimization, as they can find the optimal parameter configuration in engineering systems. This article proposes a multi-strategy improved seagull optimization algorithm (OPSOA) to solve engineering application problems. Aiming to solve the problems of slow search speed and low convergence accuracy of the standard seagull optimization algorithm (SOA), four strategies, including Lévy flight and Cauchy mutation, were introduced to improve its performance. Comparison shows that OPSOA and its incomplete algorithms are better than SOA, indicating that each improvement is effective. By testing the benchmark functions of CEC 2017 and CEC 2022, it is shown that OPSOA has a strong ability to find the optimal solution and is superior to other algorithms in terms of convergence accuracy and search speed. The application of this algorithm in practical engineering problems proves that it has significant advantages in solving complex problems.

Investigation of a newly developed shear built-in valve type MR damper with high damping: Theories and experiments

Magnetorheological (MR) dampers are widely used as semi-active energy-dissipation devices in structural vibration control. Traditional MR dampers, particularly their working mode, often struggle to achieve a high damping force output and sometimes fail to meet the needs of practical engineering. Therefore, it is necessary to study MR dampers that can provide a high damping force. This study proposes an MR damper that combines a built-in shear valve with various working modes of shear extrusion. The damper generates a higher damping force by applying a current to the coils at different positions and altering its working mode. First, the ANSYS Maxwell software simulates the internal magnetic circuit of the damper to assess whether the magnetic field intensity distribution and circuit direction meet the design requirements. Analysis revealed that the internal magnetic circuit distribution was reasonable and satisfied the operating requirements of the damper. Furthermore, the mechanical models of the two working modes were derived based on the Bingham and double-viscous models. Finally, the accuracies of the mechanical models were verified experimentally. The experimental results showed that the simulation values closely matched the experimental values under all the working conditions. Therefore, the proposed mechanical models are reasonable, and can provide significant reference values for practical engineering applications.

Feasibility of intimately coupled CaO-catalytic-ozonation and bio-contact oxidation reactor for heavy metal and color removal and deep mineralization of refractory organics in actual coking wastewater

Considering the challenges for both single and traditional two-stage treatments, advanced oxidation and biodegradation, in the treatment of actual coking wastewater, an intimately coupled catalytic ozonation and biodegradation (ICOB) reactor was developed. In this study, ICOB treatment significantly enhanced the removal of Cu2+, Fe3+, and color by 39 %, 45 %, and 52 %, respectively, outperforming biodegradation. Catalytic ozonation effectively breaking down unsaturated organic substances and high-molecular-weight dissolved organic matter into smaller, more biodegradable molecules. Compared with biodegradation, the ICOB system significantly increased the abundances of Pseudomonas, Sphingopyxis, and Brevundimonas by ∼ 96 %, ∼67 %, and ∼ 85 %, respectively. These microorganisms, possessing genes for degrading phenol, aromatic compounds, polycyclic aromatics, and sulfur metabolism, further enhanced the mineralization of intermediates. Consequently, the ICOB system outperformed biodegradation and catalytic ozonation treatments, exhibiting chemical oxygen demand removal rate of ∼ 58 % and toxicity reduction of ∼ 47 %. Overall, the ICOB treatment showcases promise for practical engineering applications in coking wastewater treatment.

Experimental research on damage characteristics of red sandstone under the combined action of temperature, water and stress

In order to study the damage properties of red sandstone under combined action of temperature, water and stress, the uniaxial compression test was carried out for red sandstone after immersing in water for 0.5 h, 1 h and 3 h at 25 ℃, 55 ℃, 85 ℃ and 95 ℃, the mechanical properties, characteristic stress points, acoustic emission RA-AF parameters and failure forms of red sandstone were analyzed, the damage constitutive equation was established, and the damage properties of red sandstone were studied. The results showed: (1) temperature could significantly improve the saturated water content percentage of red sandstone. With the increase of temperature, the saturated water content percentage showed a trend of increase first and then decrease, and was the largest at 55 ℃; with the increase of temperature, the mechanical parameters (peak intensity, elasticity modulus, and stress at start point of expansion) decreased first and then increased, and was the smallest at 55 ℃; (2) the ratio of crack initiation stress to peak stress (σci/σf) initially increases and then decreases with rising temperatures for the same soaking duration. Conversely, it initially decreases and then increases with extended soaking time at the same temperature. The RA-AF acoustic emission signals predominantly occur in the tensile region at each stage account for a relatively large proportion, with the proportion of signals in the shear region significantly increasing as soaking time extends, corresponding well with the observed damage patterns. (3) By considering the damage due to temperature and water as Initial damage to red sandstone and the damage due to stress as load-induced damage, a damage variable based on the Weibull distribution function was established. The comprehensive damage constitutive equation derived from this can be applied to water-saturated red sandstone below 100 ℃. The findings provide theoretical reference for practical engineering applications.

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.

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.

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.