Modelling, Volume 6, Issue 4 (December 2025) – 33 articles

This work presents a time-domain approach for characterizing the Ground Reaction Forces (GRFs) exerted by a pedestrian during running. It is focused on the vertical component, but the methodology is adaptable to other components or activities. The approach is developed from a statistical perspective. It relies on experimentally measured force-time series obtained from a healthy male pedestrian at eight step frequencies ranging from 130 to 200 steps/min. These data are subsequently used to build a stochastic data-driven model. The model is composed of multivariate normal distributions which represent the step patterns of each foot independently, capturing potential disparities between them. Additional univariate normal distributions represent the step scaling and the aerial phase, the latter with both feet off the ground. A dimensionality reduction procedure is also implemented to retain the essential geometric features of the steps using a sufficient set of random variables. This approach accounts for the intrinsic variability of running gait by assuming normality in the variables, validated through state-of-the-art statistical tests (Henze-Zirkler and Shapiro-Wilk) and the Box-Cox transformation. It enables the generation of virtual GRFs using pseudo-random numbers from the normal distributions. Results demonstrate strong agreement between virtual and experimental data. The virtual time signals reproduce the stochastic behavior, and their frequency content is also captured with deviations below 4.5%, most of them below 2%. This confirms that the method effectively models the inherent stochastic nature of running human gait. Full article

Conical screw compressors are increasingly used in applications that require quiet operation and reduced dimensions. One of the important problems in the case of these compressors is the profiling of the active elements, which are conical screws, with constant or variable pitch. This problem can be solved using programs that allow for the creation of a virtual solid that represents the trace left by a body during its movement. In this paper, the profiling of the outer rotor of a conical screw compressor, with variable pitch, is pro-posed by numerical modeling of the space swept by the inner rotor while rotating. After obtaining the numerical model, a physical replica of it was made by 3D printing. The obtained part was scanned using an ATOS Core 500 video measurement system. Subsequently, the model obtained by scanning was compared with the numerical model of the rotor. The numerical model was obtained first using a part with known shape and dimensions. Since the helical–conical surface has variable pitch, it is possible to analyze the influence of the helix inclination angle on the modeling accuracy in parallel with the analysis of the contact between the two rotors. Full article

Wire rope joints are critical components requiring detailed mechanical analysis. This study investigates the stress/strain characteristics at the joint root under axial impact and combined tension-bending loads. A mathematical model was derived from the rope’s spatial structure, enabling the construction of 3D simulation and finite element models. Explicit dynamic analysis revealed distinct stress evolution patterns. Under axial impact, the joint root wires experience instantaneous peak stress causing core, inner, and outer wire yielding, though stress rapidly decreases and stabilizes. During stable loading, maximum stress (67% of impact peak) occurs on the joint root’s secondary outer wire. Under combined tension-bending, maximum stress dynamically shifts to the tension-side secondary outer wire at the joint root. Critically, both loading conditions identify the joint root’s secondary outer wire as the primary danger zone, with combined tension-bending producing a maximum local stress 1.04 times higher than axial impact. These findings highlight consistent failure locations and quantify relative stress magnitudes under complex loading. Full article

Urban underground pipeline aging and leakage can result in soil erosion and ground collapse, constituting a major threat to urban public safety. To investigate this disaster mechanism, this present study established a two-dimensional numerical model based on the computational fluid dynamics–discrete element method (CFD–DEM) two-way fluid–solid coupling approach, simulating and reproducing the entire process from soil erosion, soil arch evolution to ground collapse caused by underground pipeline leakage in water-rich sand layers. The simulation shows that under the action of seepage pressures, soil particles are eroded and lost, forming a cavity above the pipeline defect. As soil continues to be lost, the disturbed zone expands toward the ground surface, causing ground settlement, and in water-rich sand layers, a funnel-shaped sinkhole is eventually formed. The ground collapse process is closely related to the groundwater level and the thickness of the overlying soil layer above the pipeline. Rising groundwater levels reduce the effective stress and shear strength of the soil, significantly exacerbating seepage erosion. Increasing the thickness of the overlying soil layer can enhance the confining pressure, improve soil compactness, and promote the formation of soil stress arch, thereby effectively slowing down the rate of ground collapse. This study reproduces the process of ground collapse numerically and reveals the mechanism of ground collapse induced by underground pipeline leakage in water-rich sand layers. Full article

In recent years, there has been a growing shift toward the use of screw extruders in the pitch modification process. To further improve the mixing efficiency of twin-screw extruders in pitch processing, this study focuses on redesigning the mixing elements of a co-rotating twin-screw extruder. By integrating the conventional kneading block assembly with PTA technology, three innovative screw mixing elements were developed. In this study, numerical simulations were performed using the finite element method (FEM) in the ANSYS Polyflow 2022 R1 software. The dynamic mesh technique was employed to model the screw rotation. The mixing performance of these novel screw elements was then evaluated in terms of distribution, mixing, and shear effects by utilizing the Particle Tracking Analy sis (PTA) technique within the Polyflow statistical module. The results demonstrate that the configuration and structural design of the mixing screw elements significantly influence the mixing effectiveness of spinnable pitch. Among the tested configurations, the slotted thread mixing element with six slots and a 30° slot angle (Model 2) was identified as the optimal design, exhibiting markedly superior mixing performance compared to the traditional kneading block (Model 4). Full article

The design of tubular flocculators has advanced in the pursuit of more efficient and compact water treatment systems. Helically coiled tube flocculators (HCTFs) are known for generating stable secondary flows and uniform hydrodynamic patterns after the development length. However, their constant geometry restricts the hydrodynamic variability required for optimized flocculation. This study introduces the spirally coiled tube flocculator (SCTF), characterized by a winding diameter that varies along its length. CFD simulations and laboratory-scale experiments compared HCTFs and SCTFs in terms of turbidity removal capacity, axial velocity profiles, secondary flows, streamlines, and global velocity gradients. The SCTF outperformed the HCTFs under all evaluated configurations, achieving up to 98.2% turbidity removal. The results emphasize the potential of spiral geometries to enhance process efficiency and highlight the need to reconsider hydrodynamic strategies in the design of tubular flocculators. Full article

Spiral finned tube heat exchangers are extensively used in petrochemical, power electronics, and metallurgical industries due to their high efficiency and compact design. However, fouling accumulation during operation significantly reduces heat transfer efficiency and increases pressure loss. This study develops a hybrid approach integrating discrete element method (DEM), finite element analysis (FEA), and HTRI Xchanger Suite 7 software to correlate fouling thickness with thermal performance and establish a prediction model for tube-side outlet temperature under varying conditions. DEM simulations analyze dust deposition patterns and determine equivalent fouling thickness distribution. A fouling-integrated FE model then evaluates how fouling thickness affects both heat transfer and flow resistance coefficients. Through orthogonal experimental design considering fouling thickness, ambient temperature, and inlet air velocity, thermal resistance values calculated from FEA are imported into HTRI to predict outlet temperature. A random forest algorithm is subsequently employed to develop a multivariable prediction model. Validation conducted on a spiral finned tube heat exchanger at Chongqing Xiangguosi Underground Gas Storage Co., Ltd. (Chongqing, China) confirmed close agreement between simulated and actual fouling patterns. The maximum relative error of the predicted outlet temperatures on the testing dataset was 0.1869%, demonstrating the proposed method’s potential to support performance evaluation and operational optimization of fouled heat exchangers. Full article

This paper addresses periodic event-triggered output-feedback control (PETOFC) and event-triggered state-feedback control (ETSFC) for polynomial systems modeled by a linear-like representation with state-dependent coefficients. Periodic event-triggering evaluates conditions at fixed intervals, preventing Zeno behavior, while state-feedback control guarantees a minimum inter-event interval. Stability is analyzed using linear matrix inequalities. Under the proposed event-triggered controllers and using the sum-of-squares programming, the asymptotic stability of the closed-loop systems is ensured. Finally, the effectiveness of the proposed controllers are illustrated through two numerical examples. Full article

Plasma-facing components (PFCs) are among the most critical functional components in a nuclear fusion device. Their reliability and durability under high heat loads are directly tied to the safe operation and lifetime of the fusion device. Under cyclic high thermal loads, accumulated plastic strain can lead to material property degradation. Furthermore, severe temperature gradients generate alternating tensile and compressive stresses within the material, resulting in the initiation and propagation of microcracks, ultimately causing structural failure of the PFCs. This study focuses on the issues of thermal stress concentration and plastic strain accumulation at the tungsten (W)/copper (Cu) joint interface and proposes an optimized design scheme based on a laminated tungsten structure. Using a combined approach of finite element simulation and theoretical analysis, the effects of tungsten layer thickness and interface geometry on the thermomechanical performance of the PFC joint were systematically investigated. The results indicate that reducing the thickness of tungsten sheet can significantly decrease the interfacial stress level. As the tungsten sheet thickness is reduced from the millimeter scale to the micrometer scale, the thermal mismatch at the W/Cu interface is reduced, thereby leading to a notable reduction in normal stress along the axial direction. In particular, when the thickness falls below 10 μm, the axial normal stress approaches zero, and the equivalent stress at the interface is effectively mitigated. Further research indicates that optimizing the flat W/Cu interface into an arc-shaped interface can alter the location of stress concentration. When the ascending distance of the Cu exceeds 600 μm, the stress concentration at the interface vertex is essentially eliminated. However, an excessively ascending distance of the Cu can exacerbate plastic deformation in the copper layer. By optimizing the extended distance of the Cu, a balance between stress relief and plastic strain accumulation can be effectively achieved. Full article

Laser powder bed fusion (LPBF) is an advanced additive manufacturing method, but its productivity is relatively low, which limits its application. Performance can be increased without hardware modifications by enlarging the powder-layer thickness. However, this approach requires deeper investigation because the probability of defects (keyhole porosity, lack of fusion) rises substantially, and experiments become costly since each thickness value requires a separate LPBF run. High-fidelity simulation under such conditions can reduce the experimental workload. Reliable predictions, however, require numerous thermophysical parameters; reported values are often inconsistent or unavailable, and few studies have quantified their influence on simulation outcomes. A Lattice Boltzmann-based model is adopted to simulate the keyhole melting mode of AlSi10Mg. The effects of laser spot diameter, laser absorptivity, and the temperature dependence of thermal diffusivity and surface tension on the results are investigated. Predicted melt-pool morphologies are compared with cross-sections of experimental single tracks. Full article

This scientific research examines the intricate dynamics of Maxwell nanofluid flow across a stretching surface with Stefan blowing impacts, with a particular focus on maritime thermal management applications. The analysis integrates multiple physical phenomena including magnetohydrodynamic forces, the energy dissipation phenomenon, and thermal density variations within Darcy porous media. Special attention is given to Stefan blowing’s role in modifying thermal and mass transfer boundary layers. We derive an enhanced mathematical formulation that couples Maxwell fluid properties with nanoparticle transport under combined magnetic and density-gradient conditions. Computational results demonstrate the crucial influence of viscous heating and blowing intensity on thermal performance, with direct implications for naval cooling applications. The reduced governing equations form a nonlinear system that requires robust numerical treatment. We implemented the shooting technique to solve this system, verifying its precision through systematic comparison with established benchmark solutions. The close correspondence between results confirms both the method’s reliability and our implementation’s accuracy. The primary results of this study indicate that raising the Stefan blowing and density parameters causes notable changes in the temperature and concentration fields. The Stefan blowing parameter enhances both temperature and concentration near the wall by affecting thermal diffusion and nanoparticle distribution. In contrast, the density parameter reduces these values because of increased fluid resistance. Full article

For urban rail vehicles operating at speeds ranging from 60 to 250 km/h, the dominant source of radiated noise is the wheel–rail interaction. Finite element modal analysis was conducted on the wheelset, rails, and track slab. A multibody dynamics model under straight-line condition was established. It was a rigid–flexible coupling dynamics model, including the rigid vehicle body, flexible wheelsets, flexible rails, and flexible track slabs. Dynamic simulation calculations were carried out in this model to obtain the wheel–rail forces. The finite element and boundary element models of wheels and rails were established using simulation software to obtain the results of wheel–rail noise. The sound pressure levels on the surfaces of wheels and rails were calculated under the operating conditions of 120 km/h, 140 km/h, 160 km/h, and 200 km/h in the straight-line condition. The variation law of the frequency distribution of wheel–rail noise with the change in speed was obtained. The variation fitting function of wheel–rail noise SPL with speeds was obtained. Within the speed of 200 km/h, as the speed increased, the total value of wheel–rail SPL basically shows a linear growth. The simulation analysis results were compared with the experiment results. It indicated that the simulation results were reasonable. The simulation models are of great significance for the noise prediction in train design and manufacturing. Full article

Tandem cylinders, widely used in heat exchangers, water storage units, and electronic cooling, require optimized flow and heat transfer to enhance engineering performance. However, the combined effects of various factors in tandem configurations remain insufficiently explored. This study proposes an innovative approach that integrates multiple parameters to systematically investigate the influence of surface pattern characteristics and rotational speed on the fluid dynamics and heat transfer performance of tandem cylinders. Numerical simulations are conducted to evaluate the effects of various pattern dimensions (w/D = 0.12–0.18), surface shapes (square, triangular, and dimpled grooves), rotational speeds (|Ω| ≤ 1), and frequencies (N = 2–10) on fluid flow and heat transfer efficiency at Re = 200. The study aims to establish the relationship between the complexity of the coupling effects of the considered parameters and the heat transfer behavior as well as fluid dynamic variations. The results demonstrate that, under stationary conditions, triangular grooves exhibit larger vortex structures compared to square grooves. When a positive rotation is applied, coupled with increases in w/D and N, square grooves develop a separation vortex at the front. Furthermore, the square and dimpled grooves exhibit significant phase control capabilities in the time evolution of lift and drag forces. Under conditions of w/D = 0.12 and w/D = 0.18, the C_L of the upstream cylinder decreases by 17.2% and 20.8%, respectively, compared to the standard smooth cylinder. Moreover, the drag coefficient C_D of the downstream cylinder is reduced to half of the initial value of the upstream cylinder. As the surface amplitude increases, the C_D of the smooth cylinder surpasses that of the other groove types, with an approximate increase of 8.8%. Notably, at Ω = −1, the downstream square-grooved cylinder’s C_L is approximately 12.9% lower than that of other groove types, with an additional 6.86% reduction in amplitude during counterclockwise rotation. When N increases to 10, the ${\overline{C}}_D$ of the upstream square-grooved cylinder at w/D = 0.18 decreases sharply by 20.9%. Conversely, the upstream dimpled-groove cylinder significantly enhances ${\overline{C}}_p$ at w/D = 0.14 and N = 4. However, the upstream triangular-groove cylinder achieves optimal ${\overline{C}}_p$ stability at w/D ≥ 0.16. Moreover, at w/D = 0.18 and N = 6, square grooves show the most significant enhancement in vortex mixing, with an increase of approximately 42.7%. Simultaneously, the local recirculation zones in dimpled grooves at w/D = 0.14 and N = 6 induce complex and geometry-dependent heat transfer behaviors. Under rotational conditions, triangular and dimpled grooves exhibit superior heat transfer performance at N = 6 and w/D = 0.18, with TPI values exceeding those of square grooves by 33.8% and 28.4%, respectively. A potential underlying mechanism is revealed, where groove geometry enhances vortex effects and heat transfer. Interestingly, this study proposes a correlation that reveals the relationship between the averaged Nusselt number and groove area, rotational speed, and frequency. These findings provide theoretical insights for designing high-efficiency heat exchangers and open up new avenues for optimizing the performance of fluid dynamic systems. Full article

Short-range forces enable peridynamics to simulate impact, yet it demands a computationally expensive contact search and includes no intrinsic damping. A significantly more efficient solution is the coupled dual-horizon peridynamics–discrete element method approach, which provides a robust framework for modeling fracture. The peridynamics component handles the nonlocal continuum mechanics capabilities to predict material damage and fracture, while the discrete element method captures discrete particle behavior. Whereas existing peridynamics–discrete element method approaches assign discrete element method particles to many or all surface peridynamics points, the proposed method integrates dual-horizon peridynamics with a single discrete element particle representing each object. Contact forces are computed once per discrete element pair and mapped to overlapping peridynamics points in proportion to shared volume, conserving linear momentum. Benchmark sphere-on-plate impact demonstrates prediction of peak contact force, rebound velocity, and plate deflection within 5% of theoretical results found in the literature, while decreasing neighbour-search cost by more than an order of magnitude. This validated force-transfer mechanism lays the groundwork for future extension to fully resolved fracture and fragmentation. Full article

This study has investigated the structural and seismic performance of monolithic stone columns in the historical Mosque–Cathedral of Córdoba, with a focus on the earliest section constructed during the reign of Abd al-Rahman I (VIII century). An advanced 3D finite element (FE) model has been developed to assess the effects of geometric imperfections and component interactions on the stability of columns under both vertical and horizontal static loading. Three distinct modelling strategies have been employed in OpenSees 3.7.1, incorporating column inclination and contact elements to simulate mortar interfaces. Material properties have been calibrated using experimental data and in situ observations. The gravitational analysis has shown no significant damage in any of the configurations, aligning with the observed undamaged state of the structure. Conversely, horizontal analyses have revealed that tensile damage has predominantly occurred at the lower shaft. The inclusion of contact elements has led to a significant reduction in lateral resistance, highlighting the importance of accounting for friction and interface behaviour. Column inclination has been found to have a significant influence on failure patterns. These findings have highlighted the critical role of detailed modelling in evaluating structural vulnerabilities. Such features are generally included in the numerical modelling and evaluation of heritage buildings. Consequently, they can contribute to a better understanding of the seismic behaviour of historic masonry structures. Full article

Rapid urbanization and global climate change are leading to intensified Urban Heat Island (UHI) in tropical regions. This study examined and analyzed urban ventilation corridors to mitigate UHI, paying particular attention to the building arrangement and wind environment. The comprehensive review emphasizes the importance of macro-scale urban planning, including the orientation of street grids and the design of breezeways and air paths. After analyzing these strategies, CFD simulations were applied to the design of high-rise buildings in Semarang and residential areas in Jakarta. These studies revealed that in high-rise building areas in Semarang, the proposed design configuration resulted in a 62% increase in ground-level wind speeds. A further analysis of residential areas in Jakarta revealed that the most comfortable location within a house was in the second row, facing the wind, where the distance between houses was 8.5 m, and the average velocity was 2.78 m/s. Research conducted in this area may contribute to the development of more sustainable and resilient urban areas in tropical climates, as well as assist local governments in planning for these areas. Full article

Beta regression models are a class of models used frequently to model response variables in the interval $(0, 1)$. Although there are articles in which these models are used to model clustered and longitudinal data, the prediction of random effects is limited, and residual analysis has not been implemented. In this paper, a random intercept beta regression model is proposed for the complete analysis of this type of data structure. We proposed some types of residuals and formulate a methodology to obtain the best prediction of random effects. This model is developed through the parameterisation of beta distribution in terms of the mean and dispersion parameters. A log-likelihood function is approximated by the Gauss–Hermite quadrature to numerically integrate the distribution of random intercepts. A simulation study is used to investigate the performance of the estimation process and the sampling distributions of the residuals. Full article

Heat exchangers are widely used process equipment in industrial sectors, making the study of their temperature dynamics particularly appealing due to the nonlinearities involved. Model-free approaches enable the use of input and output data to generate specific and accurate estimations for each proposed system. In this work, a model-free identification strategy is proposed using a convolutional neural network to estimate the system’s behavior. Notably, the model does not rely on direct temperature measurements; instead, temperature is inferred from other system signals such as reference, flow, and control inputs. This data-driven approach offers greater specificity and adaptability, often outperforming manufacturer-provided coefficients whose performance may vary from design expectations. The results yielded an $R^2$ index of 0.9951 under nominal conditions and 0.9936 when the system was subjected to disturbances. Full article

Graphite nodularity is a key indicator for evaluating the microstructure quality of ductile iron and plays a crucial role in ensuring product quality and enhancing manufacturing efficiency. Existing research often only focuses on a single type of feature and fails to utilize multi-source information in a coordinated manner. Single-feature methods are difficult to comprehensively capture microstructures, which limits the accuracy and robustness of the model. This study proposes a hybrid estimation model for the graphite nodularity of ductile cast iron based on multi-source feature extraction. A comprehensive feature engineering pipeline was established, incorporating geometric, color, and texture features extracted via Hue-Saturation-Value color space (HSV) histograms, gray level co-occurrence matrix (GLCM), Local Binary Pattern (LBP), and multi-scale Gabor filters. Dimensionality reduction was performed using Principal Component Analysis (PCA) to mitigate redundancy. An improved watershed algorithm combined with intelligent filtering was used for accurate particle segmentation. Several machine learning algorithms, including Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Random Forest (RF), Gradient Boosting Regressor (GBR), eXtreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost), are applied to estimate graphite nodularity based on geometric features (GFs) and feature extraction. Experimental results demonstrate that the CatBoost model trained on fused features achieves high estimation accuracy and stability for geometric parameters, with R-squared (R²) exceeding 0.98. Furthermore, introducing geometric features into the fusion set enhances model generalization and suppresses overfitting. This framework offers an efficient and robust approach for intelligent analysis of metallographic images and provides valuable support for automated quality assessment in casting production. Full article

To meet the stringent reliability requirements of rotor blades in turbomachines, greater effort should be devoted to improving both aerodynamic and structural performance in blade design. This paper introduces an aero-structural multi-disciplinary design optimization (MDO) method for compressor rotor blades using a discrete adjoint method and an equivalent stress model (ESM). The principles of the ESM are firstly introduced, and its accuracy in calculating equivalent stress is validated through comparison with a commercial program. Both the aerodynamic performance and the maximum equivalent stress (MES) are selected as optimization objectives. To modify the blade profile, the steepest descent optimization method is utilized, in which the necessary sensitivities of the cost function to the design parameters are calculated by solving the adjoint equations. Finally, the aero-structural MDO of a transonic compressor rotor, NASA Rotor 67, is conducted, and the Pareto solutions are obtained. The optimization results demonstrate that the adiabatic efficiency and the MES are competitive in improving multi-disciplinary performance. For most of the Pareto solutions, the MES can be considerably reduced with increased adiabatic efficiency. Full article

The construction industry remains one of the most hazardous sectors despite its economic importance, with high-altitude fall accidents being the most prevalent and deadly type of incident. This paper aimed to study and analyze the accident data of the past accident cases in China and find out the key causes and rules of the accidents. This research analyzed 1223 Chinese accident reports (2014–2023) using Latent Dirichlet Allocation topic modeling to identify causal factors, followed by Apriori algorithm correlation analysis to reveal accident causation patterns. This study comprehensively uses topic model, association rules and visualization methods to systematically analyze the causes of high-altitude fall accidents. The research identified 24 distinct accident cause topics across personnel, equipment, management, and environmental dimensions. Key findings revealed that incorrect use of labor protective equipment, inadequate safety inspections, and failure to implement safety management protocols were persistent issues throughout the study period. Notably, the post COVID-19 pandemic introduced new safety challenges, with the intensity of topics related to “subject of responsibility for safety production has not been implemented” showing significant post-pandemic increases. These findings highlight the evolving nature of construction safety challenges and the need for targeted interventions to address persistent and emerging risks. Full article

With the frequent occurrence of stuck pipe incidents during the ultra-deep well drilling operation, the hydraulic-while-drilling (HWD) jar has become a critical component of the bottom hole assembly (BHA). However, during jarring operations for stuck pipe release, the drill string experiences severe vibrations induced by the impact loads from the jar, which significantly alter the stress state and dynamic response of the threaded connections—the structurally weakest elements—under cyclic dynamic loading, often leading to fracture failures. here, a thread failure incident of a hydraulic jar in an ultra-deep well in the Tarim Basin, Xinjiang, is investigated. A drill string dynamic impact model incorporating the actual three-dimensional wellbore trajectory is established to capture the time-history characteristics of multi-axial loads at the threaded connection during up and down jarring. Meanwhile, a three-dimensional finite element model of a double-shouldered threaded connection with helix angle is developed, and the stress distribution of the joint thread is analyzed on the boundary condition acquired from the time-history characteristics of multi-axial loads. Numerical results indicate that the axial compression induces local bending of the drill string during down jarring, resulting in significantly greater bending moment fluctuations than in up jarring and a correspondingly higher amplitude of circumferential acceleration at the thread location. Among all thread positions, the first thread root at the pin end consistently experiences the highest average stress and stress variation, rendering it most susceptible to fatigue failure. This study provides theoretical and practical insights for optimizing drill string design and enhancing the reliability of threaded connections in deep and ultra-deep well drilling. Full article

As one of the most important finishing machining means of internal helical gear, the residual stress that appears during profile grinding plays an important role in transmission performance and the service internal helical gear. In this research, the residual stress simulation model of internal helical gear profile grinding is established to optimize and predict grinding parameters by means of a neural network. The grinding process parameters (including grinding depth, grinding feed speed, and grinding wheel linear speed) are taken as variable factors. Through experimental verification, the maximum error of the simulation value is 12.8%. The radial basis function (RBF) neural network is introduced, and simulation data samples are used to train and test the residual stress prediction model. Three groups of unknown grinding parameters are predicted, and the relative errors between the predicted and measured values are 5.16%, 1.63%, and 3.39%, respectively. The results demonstrate that the RBF neural network residual stress prediction model proposed in this paper is accurate and feasible. At the same time, the residual stress prediction method provides a theoretical basis for optimizing and controlling the precision of internal helical gear profile grinding. Full article

High-pressure, high-volume fracturing in unconventional reservoirs often induces perforation erosion damage, endangering operational safety. This paper employs fluid–solid coupling theory to analyze the flow characteristics of fracturing fluid inside the casing during fracturing. Combined with strength theory, the stress distribution and variation law are investigated, revealing the mechanical mechanism of casing perforation erosion damage. The results indicate that the structural discontinuity at the entrance of the perforation tunnel causes an increase in fracturing-fluid velocity, and this is where the most severe erosion happens. The stress around the perforation is symmetrically distributed along the perforation axis. The casing inner wall experiences a combined tensile–compressive stress state, while non-perforated regions are under pure tensile stress, with the maximum amplitudes occurring in the 90° and 270° directions. Although the tensile and compressive stress do not exceed the material’s allowable stress, the shear stress exceeds the allowable shear stress, indicating that shear stress failure is likely to initiate at the perforation, inducing erosion. Moreover, under the impact of fracturing fluid, the contact forces at the first and second interfaces of the casing are unevenly distributed, reducing cement bonding capability and compromising casing integrity. The findings provide a theoretical basis for optimizing casing selection. Full article

This study investigates the effect of incorporating an electromagnetic harvester inside the bluff body of a 2-DoF hybrid harvester in comparison to a standalone piezoelectric harvester for various external loads. The harvester is excited through a vortex-induced vibration owing to the resultant wake vortices created behind the bluff body. The coupled dynamics of the two harvester components are modeled, and numerical simulations are conducted to evaluate the system’s performance under varying electrical loads. Numerical results show that at high, optimum electrical load, the standalone piezoelectric harvester outperforms the hybrid harvester. Nevertheless, for small electrical loads, the results show that the hybrid harvester outperforms the standalone PZT harvester by up to 18% in peak power output, while reducing the bandwidth by approximately 10% compared to the standalone piezoelectric harvester. Optimal spring stiffness values were identified, with the hybrid harvester achieving its maximum output power at a spring stiffness of 83.56 N/m. These findings underscore the need for careful design considerations, as the hybrid harvester may not achieve enhanced power output and bandwidth under higher electrical loads. Full article

Bi₂Sr₂CaCu₂O_8+x (Bi2212) high-temperature superconductor exhibits broad application prospects in strong magnetic fields, superconducting magnets, and power transmission due to its exceptional electrical properties. However, during practical applications, Bi2212 superconducting round wires are prone to mechanical loading effects, leading to crack propagation and degradation of superconducting performance, which severely compromises their reliability and service life. To elucidate the damage mechanisms under mechanical loading and their impact on critical current, this study establishes a two-dimensional model with existing cracks based on phase field fracture theory, simulating crack propagation behaviors under varying conditions. The results demonstrate that crack nucleation and propagation paths are predominantly governed by stress concentration zones. The transition zone width of cracks is controlled by the phase field length scale parameter. By incorporating electric fields into the phase field model, coupled mechanical-electrical simulations reveal that post-crack penetration causes significant current shunting, resulting in a marked decline in current density. The research quantitatively explains the mechanism of critical current degradation in Bi2212 round wires under tensile strain from a mechanical perspective. Full article

The paper presents a comparative analysis of various machine learning algorithms for the classification of epoxy composites reinforced with basalt fiber and modified with inorganic fillers. The classification is based on key thermophysical characteristics, in particular, the mass fraction of the filler, temperature, and thermal conductivity coefficient. A dataset of 16,056 interpolated samples was used to train and evaluate more than a dozen models. Among the tested algorithms, the MLP neural network model showed the highest accuracy of 99.7% and balanced classification metrics F1-measure and G-Mean. Ensemble methods, including XGBoost, CatBoost, ExtraTrees, and HistGradientBoosting, also showed high classification accuracy. To interpret the results of the MLP model, SHAP analysis was applied, which confirmed the predominant influence of the mass fraction of the filler on decision-making for all classes. The results of the study confirm the high effectiveness of machine learning methods for recognizing filler type in composite materials, as well as the potential of interpretable AI in materials science tasks. Full article

In the research and development system of bat-like flapping-wing flying robots, lift modeling and numerical analysis are the key theoretical basis, which will directly affect the construction of the body structure and flight control system. However, due to the complex three-dimensional flapping motion mechanism of bats and the flexible deformation characteristics of their wing membranes, the existing lift theory lacks a mature calculation method suitable for bionic flapping-wing flying robots. In this paper, the wing membrane deformation mechanism of a bat-like flapping-wing flying robot is studied, and the coupling effect of wing membrane motion and deformation on flight parameters is analyzed. A set of calculation methods for flexible morphing wing membrane lift is improved by using a quasi-steady model and the blade element method. By comparing and analyzing the theoretical calculation and experimental results under various working conditions, the error is less than 4%, which proves the effectiveness of this method. Full article

This study aims to accurately characterize the nonlinear stress–strain evolution of rocks under uniaxial compression considering crack propagation. First, the rock meso-structure was generalized into intact rock unit cells, crack propagation damage unit cells, and pore unit cells according to phenomenological theory. A mesoscopic rock stress model considering crack propagation was established based on the static equilibrium relationship of the unit cells, and the effective stress of the crack propagation damage unit cells was solved based on fracture mechanics. Then, the geometric damage theory and conservation-of-energy principle were introduced to construct the damage evolution equation for rock crack propagation. On this basis, the effective stress of the damage unit cells and the crack propagation damage equation were incorporated into the rock meso-structure static equilibrium equation, and the effect of nonlinear deformation in the soft rock compaction stage was considered to establish a rock damage constitutive model based on mesoscopic crack propagation evolution. Finally, methods for determining model parameters were proposed, and the effects of the model parameters on rock stress–strain curves were explored. The results showed that the theoretical model calculations agreed well with the experimental results, thus verifying the rationality of the damage constitutive model and the clear physical meaning of the model parameters. Full article

Accurate identification of plastic polymers is essential for effective recycling, quality control, and environmental monitoring. This study assesses how spectral derivative preprocessing affects the classification of six common plastic polymers: Polyethylene Terephthalate (PET), Polyvinyl Chloride (PVC), Polypropylene (PP), Polystyrene (PS), and both High- and Low-Density Polyethylene (HDPE and LDPE), based on Fourier Transform Infrared (FTIR) spectroscopy data acquired at a resolution of 8 ${cm}^{-1}$. Using Savitzky–Golay derivatives (orders 0, 1, and 2), five machine learning algorithms, namely Multilayer Perceptron (MLP), Extremely Randomized Trees (ET), Linear Discriminant Analysis (LDA), Support Vector Classifier (SVC), and Random Forest (RF), were tested within a strict framework involving stratified repeated cross-validation and a final hold-out test set to evaluate generalization. The first spectral derivative notably improved the model performance, especially for MLP and SVC, and increased the stability of the ET, LDA, and RF classifiers. The combination of the first derivative with the ET model provided the best results, achieving a mean F1-score of 0.99995 (±0.00033) in cross-validation and perfect classification (1.0 in Accuracy, F1-score, Cohen’s Kappa, and Matthews Correlation Coefficient) on the independent test set. LDA also performed very well, underscoring the near-linear separability of spectral data after derivative transformation. These results demonstrate the value of derivative-based preprocessing and confirm a robust method for creating high-precision, interpretable, and transferable machine learning models for automated plastic polymer identification. Full article