Modelling, Volume 6, Issue 4 (December 2025) – 55 articles

In this study, we numerically investigate the influence of thermal gradients on the growth and intensification of vortices formed within a rotating cylinder subjected to inhomogeneous cooling at the top or inhomogeneous heating at the bottom. The presence of horizontal thermal inhomogeneities at the upper and lower boundaries determines whether the vortex originates near the top or the bottom of the domain. Moreover, the magnitude of both horizontal and vertical thermal gradients plays a critical role in the vortex’s intensification, vertical stretching, and overall development. The observed phenomena are interpreted through a force balance analysis. Increasing the ambient rotation rate leads to the emergence of periodic structures, such as tilted or double vortices, which also undergo intensification and stretching as thermal gradients increase. These findings highlight the importance of thermal boundary conditions in shaping vortical structures and may contribute to a deeper understanding of the genesis, morphology, and intensification mechanisms of thermoconvective vortices. Full article

Graph learning is well suited to modeling relationships among communicating entities in network intrusion detection. However, the resulting models are frequently difficult to interpret, in contrast to many classical approaches that offer more transparent reasoning. This work integrates SHapley Additive exPlanations with temporal, edge-aware GNN based on GraphSAGE architecture to deliver an explainable, inductive intrusion detection model for NetFlow data named TE-G-SAGE. Using the NF-UNSW-NB15-v3 dataset, flow data are transformed into temporal communication graphs where flows are directed edges and endpoints are nodes. The model learns relational patterns across two-hop neighborhoods and achieves strong recall under chronological evaluation, outperforming a GCN baseline and recovering more attacks than a tuned XGBoost model. SHAP is adapted to graph inputs through a feature attribution on the two-hop computational subgraph, producing global and local explanations that align with analyst reasoning. The resulting attributions identify key discriminative features while revealing shared indicators that explain cross-class confusion. The research shows that temporal validation, inductive graph modeling, and Shapley-based attribution can be combined into a transparent, reproducible intrusion detection framework suited for operational use. Full article

Eye tracking scanpaths encode the temporal sequence and spatial distribution of eye movements, offering insights into visual attention and aesthetic perception. However, analysing scanpaths still requires substantial manual effort and specialised expertise, which limits scalability and constrains objectivity of eye tracking methods. This paper examines whether and how multimodal large language models (MLLMs) can provide objective, expert-level scanpath interpretations. We used GPT-4o as a case study to develop eye tracking scanpath analysis (ETSA) approach which integrates (1) structural information extraction to parse scanpath events, (2) knowledge base of visual-behaviour expertise, and (3) least-to-most and few-shot chain-of-thought prompt engineering to guide reasoning. We conducted two studies to evaluate the reliability and effectiveness of the approach, as well as an ablation analysis to quantify the contribution of the knowledge base and a cross-model evaluation to assess generalisability across different MLLMs. The results of repeated-measures experiment show high semantic similarity of 0.884, moderate feature-level agreement with expert scanpath interpretations (F1 = 0.476) and no significant differences from expert annotations based on the exact McNemar test (p = 0.545). Together with the ablation and cross-model findings, this study contributes a generalisable and reliable pipeline for MLLM-based scanpath interpretation, supporting efficient analysis of complex eye tracking data. Full article

Titanium alloys such as Ti-6Al-4V are widely used in aerospace and biomedical fields, but their poor wear resistance and high friction coefficient limit service performance. In this study, laser cladding with La₂O₃ addition was employed to enhance the surface properties of Ti-6Al-4V, and the underlying mechanisms were systematically investigated by combining experimental characterization with multiphysics simulations. XRD and SEM analyses revealed that La₂O₃ addition refined grains and promoted uniform phase distribution throughout the coating thickness, leading to good metallurgical bonding. The hardness was 2–3 times higher than that of the titanium alloy substrate when the content of 2–3 wt.% was of added La₂O₃, while the wear loss ratio was reduced to 0.021% and the average friction coefficient decreased to 0.421. These improvements were strongly supported by simulations: temperature field calculations demonstrated steep thermal gradients conducive to rapid solidification; velocity field analysis and recoil-pressure-driven flow revealed vigorous melt pool convection, which homogenized solute distribution and enhanced coating densification; phase evolution simulations confirmed the role of La₂O₃ in heterogeneous nucleation and dispersion strengthening. In summary, the combined results establish a mechanistic framework where thermal cycling, melt pool dynamics, and La₂O₃-induced nucleation act synergistically to optimize coating microstructure, hardness, and wear resistance. This integrated experimental–numerical approach provides not only quantitative improvements but also a generalizable strategy for tailoring surface performance in laser-based manufacturing. Full article

Common cause failure (CCF) is concealed and harmful. With the increase in the number of redundant systems in aircraft, quantifying the impact of CCF is crucial for accurately calculating system failure probabilities. However, the diverse and complex redundancy configurations prevalent in modern aircraft systems often limit the applicability and analytical efficiency of existing CCF quantification methods. To address these challenges, the applicability of three CCF modeling approaches, namely the β-factor model, the α-factor model, and the square root model is analyzed. Furthermore, a failure probability correction model is constructed to quantify CCF impacts across systems with varying redundancy levels and configurations. The effectiveness and versatility are then validated on three typical aircraft system failure cases. Further, a software for correcting the failure probability of complex systems considering CCF is developed, which is highly applicable and efficient in calculation. This study not only enriches the methodologies for system safety analysis but also significantly enhances the efficiency and accuracy of CCF quantification in aerospace engineering. Full article

The modern power system is becoming increasingly complex, and the uncertainty in the operation of each link has intensified the possibility of risks emerging. Therefore, efficient risk prediction is of great significance for maintaining the reliable operation of the entire system. In this paper, to address the uncertainty and spatiotemporal coupling in local power grids with renewable integration, an integrated “state prediction–risk assessment–early warning” framework is proposed. A spatiotemporal graph neural network is used to predict node voltage, power, and phase angles under topological constraints, where physics-aware graph attention, disturbance-enhanced temporal modeling, and prediction-smoothing constraints are jointly incorporated to improve sensitivity to renewable fluctuations and ensure stable multi-step forecasting. Furthermore, voltage deviation, power fluctuation, and phase-angle variation are quantified to compute a composite risk index via normalized softmax weighting, with factor contributions enhancing interpretability. Test results on the IEEE 33-bus system under diverse disturbances show improved accuracy and stability over baselines, showing consistently lower MAE/RMSE than three baselines across all disturbance scenarios while pinpointing high-risk nodes and causes, highlighting good engineering potential. Full article

In natural gas gathering and transportation projects, efficient gas-liquid separation equipment is crucial to ensuring the stable operation of subsequent processes. Conventional separation units often have problems such as low efficiency, high energy consumption and poor resistance to load fluctuations when dealing with foam-containing gas-liquid mixtures. For this purpose, numerical simulation and structural optimization of multi-stage foam separation units were carried out in this study. Based on FLUENT software fluid analysis software, a three-dimensional, multi-physics coupled model incorporating cyclonic defoaming components and axial-flow separation tubes was developed. The volume of fluid (VOF) multiphase flow model was used to capture the dynamic characteristics of the gas-liquid interface, and the population balance model was used to simulate the coalescence and fragmentation of the foam. The results show that in the non-working fluid stage, the optimal operating pressure is 5.0–5.5 MPa, and the droplet concentration should be maintained below 50 × 10⁻⁵. The system performance during the working fluid stage is significantly influenced by foam size. The efficiency of millimeter-sized foams is stable above 88% in the 5.0–6.0 MPa range, while the efficiency of micrometer-sized foams is optimal in the 5.3–5.7 MPa range. It is recommended to control the foam proportion below 35% and add a pre-defoaming unit to improve overall performance. Full article

The current contact of pyro-breakers must rapidly interrupt current when the superconducting magnet loses its superconductivity. To enhance the microsecond-scale current-breaking capability of pyro-breakers in nuclear fusion devices, this study investigates the impact of current contact notch structures on dynamic fracture behavior. Through multi-physics field modeling and controlled explosive testing, it is revealed for the first time that the rectangular-notch structure demonstrates enhanced fracture performance relative to the V-notch configuration under explosive impact loading conditions, achieving a 27.3% reduction in fracture initiation time alongside a 47.5% increase in crack propagation width. These findings provide a robust theoretical basis for designing pyro-breakers with enhanced fast-break capabilities in fusion devices. Full article

Accurate short-term (10–20 days) natural gas load forecasting is crucial for the “tactical planning” of gas utilities, yet it faces significant challenges from high volatility, strong noise, and the high-dimensional multicollinearity of influencing factors. To address these issues, this paper proposes a novel hybrid forecasting framework: PCCA-ISSA-GRU. The framework first employs Principal Component Correlation Analysis (PCCA), which improves upon traditional PCA by incorporating correlation analysis to effectively select orthogonal features most relevant to the load, resolving multicollinearity. Concurrently, an Improved Singular Spectrum Analysis utilizes statistical criteria (skewness and kurtosis) to adaptively separate signals from Gaussian noise, denoising the historical load sequence. Finally, the dually optimized data is fed into a Gated Recurrent Unit (GRU) neural network for prediction. Validated on real-world data from a large city in Northern China, the PCCA-ISSA-GRU model demonstrated superior performance. For a 20-day forecast horizon, it achieved a Mean Absolute Percentage Error (MAPE) of 6.09%. Results show its accuracy is not only significantly better than single models (BPNN, LSTM, GRU) and classic hybrids (ARIMA-ANN), but also outperforms the state-of-the-art (SOTA) model, Informer, within the 10–20 days tactical window. This superiority was confirmed to be statistically significant by the Diebold–Mariano test (p < 0.05). More importantly, the model exhibited exceptional robustness, with its error increase during extreme weather scenarios (e.g., cold waves, rapid temperature changes) being substantially lower (+56.7%) than that of Informer (+109.2%). The PCCA-ISSA-GRU framework provides a high-precision, highly robust, and cost-effective solution for urban gas short-term load forecasting, offering significant practical value for critical operational decisions and high-risk scenarios. Full article

The translational and rotational dynamics of quadrotor UAVs are commonly described by mathematical modeling where aerodynamic and inertial parameters are involved. Therefore, the importance of having accurate parameters in the model is critical for the correct performance of the UAV. In this paper, Artificial Neural Networks (ANNs) are used to estimate the aerodynamic and inertial parameters corresponding to the mathematical model of a quadrotor. Thrust and torque coefficients from the rotor models and the quadrotor inertia matrix are estimated by proposing and training two different ANN models implementing the back-propagation algorithm, using both experimental and simulation data. The estimated parameters are then compared with the reference parameters by means of quadrotor attitude simulations, showing high accuracy in their behavior. The results have shown that the proposed ANN models can accurately estimate both the aerodynamic and inertial parameters of a quadrotor UAV model using both experimental and simulation data, thus contributing to increasing the tools available for parameter estimation. Full article

This study investigates the optimal operation of distribution systems incorporating Photovoltaic (PV) units, Electric Vehicle Charging Stations (EVCSs), and DSTATCOM devices using the Starfish Optimization Algorithm (SFOA). The main goal of the SFOA is to minimize a combined function that encompasses three key objectives: reducing system losses, increasing PV capacity, and enhancing EVCS power. By applying the SFOA within a multi-objective optimization framework, the optimal locations and sizes of PV units, EVCSs, and DSTATCOMs are identified to meet these objectives. This study analyzes and compares several case studies with different numbers of EVCSs, focusing on the operation of a modified 51-bus distribution system over 24 h. Results show that PV hosting energy increases to 21.73, 23.83, and 29.22 MWh for cases with 1, 2, and 3 EVCSs, respectively. EVCS energy also rises to 12.41, 19.50, and 37.23 MWh for the same cases. The corresponding optimized DSTATCOM reactive powers are 11.02, 12.02, and 13.74 MVarh. Throughout all cases, system constraints—such as voltage limits, utility current, and power flow equations—remain within acceptable ranges. The findings demonstrate the SFOA’s effectiveness in optimizing distribution systems with various devices, ensuring efficient operation and meeting all key objectives while adhering to system constraints. Full article

High-space industrial facilities often store substantial quantities of flammable volatile organic compounds (VOCs), posing significant fire and explosion hazards. This study employed computational fluid dynamics (CFD) to investigate the migration and diffusion characteristics of VOCs in a semi-enclosed, high-space wood chip fuel storage shed. A three-dimensional transient numerical model was developed based on a real-scale industrial prototype, incorporating the Realizable $k-\epsilon$ turbulence model with species transport equations. Validation using experimental data demonstrated good agreement between the model and experimental results, with a maximum relative error of 5.0%. A systematic assessment of key parameters was conducted, including time, ambient temperature, relative humidity, wood chip stack height, and VOCs type. Evaluation metrics comprised the surface-average mass fraction and the proportion of areas exceeding 5% of the lower explosive limit (LEL). The results show that peak concentrations occurred at 25~27 min. The system reaches quasi-steady state after 60 min. At 300~304 K, the lowest peak mass fractions are observed (0.31% and 0.43% at 19 m), yet the area exceeding 5% LEL was the largest. Moderate humidity (40~60%) reduces peaks by 0.06~0.11%. A stacking height of 7.5 m reduces peak values to 0.21% (left) and 0.28% (right), while a 10 m height increases the hazardous area to 48.87%. Low-polarity VOCs (C₁₀H₁₆) spread widely (34.10% exceeding 5% LEL area), whereas polar VOCs (C₁₅H₂₆O) accumulated locally (4.48%). These findings provide theoretical guidance for VOC hazard control and ventilation optimization in high-space biomass fuel storage facilities. Full article

A series of machine learning models have been proposed in the past decades, but it remains undetermined which is optimal for specific applications. Establishing mathematical prediction models for dam deformation and structural health monitoring based on environmental factors is crucial to dam safety assessment. This paper takes Zhexi Dam, a concrete gravity-type dam in China, as an example to conduct a comparative study on the performance of deformation prediction models. The physical factors that cause dam deformation include the air temperature, reservoir water temperature, reservoir water level, and dam aging. The correlations between environmental factors and dam deformation are evaluated by maximum information coefficient (MIC) and Pearson, Kendall, and Spearman correlation coefficients. The monitoring data reveal that the deformation has a high correlation with environmental factors. A number of the most representative monitoring points from hundreds of monitoring points are selected for modeling. For comparison, seven modeling methods, i.e., multiple linear regression (MLR), gradient boosting decision tree (GBDT), random forest (RF), support vector machine (SVM), and long short-term memory network (LSTM), weighted average model (WAM) of the above five algorithms, and Transformer-based neural network, are introduced to establish dam deformation prediction models. The experimental results indicate that both the weighted average model and the Transformer-based neural network achieve consistently high accuracy, showing strong agreement with the monitoring data generally. However, in scenarios involving small sample sizes, the SVM model demonstrates relatively superior predictive performance compared to the other models. Full article

Agriculture is a major source of greenhouse gas emissions, yet predicting temperature increases associated with specific CO₂ sources remains challenging due to the heterogeneity of agri-environmental systems. In response, this study presents a machine learning framework that adopts an agri-food system boundary (production to retail) and combines systematic model benchmarking, interpretability, and a multi-scale perspective. Seven regression models, including tree ensembles and deep learning architectures, are evaluated on a harmonized dataset covering 236 countries over the 1990–2020 period to forecast annual temperature increases. Results show that gradient-boosted decision trees consistently outperform deep learning models in predictive accuracy and offer more stable feature attributions. Interpretability analysis reveals that spatio-temporal variables are the dominant drivers of global temperature variation, while environmental and sector-specific factors play more localized roles. A country-level case study on Iran illustrates how the framework captures national deviations from global patterns, highlighting intensive rice cultivation and on-farm energy use as key influential factors. By integrating high-performance predictions with interpretable insights, the proposed framework supports the design of both global and country-specific climate mitigation strategies. Full article

This paper presents the results of a numerical simulation of oil displacement from models of terrigenous and cavernous-fractured media using solutions of the anionic surfactant (sodium laureth sulfate). The surfactant concentration was varied from 0 to 0.1 wt.%. The simulations employed a mathematical model for the flow of immiscible liquids based on the VOF method. The model incorporated experimentally measured interfacial tension coefficients and wettability parameters for the surfactant solutions. The results demonstrate that increasing the surfactant concentration enhances the oil displacement coefficient: by 15% for the terrigenous model and by 19% for the cavernous-fractured model compared to water flooding (at 0 wt.% surfactant), achieving a maximum at a concentration of 0.1 wt.%. The influence of potential mechanisms leading to the improved oil displacement coefficient during surfactant solution injection was investigated. It was established that at a fixed displacement rate, the addition of the surfactant causes a local increase in the generalized capillary number by a factor of approximately 3.7. This is identified as the primary mechanism for the observed enhancement of the oil displacement coefficient in this case. The data obtained in this study can be used for further improvement of surfactant flooding technologies for enhanced oil recovery. Full article

The performance of thermoplastic matrix composites for linerless Type V cryotanks is evaluated via a partially coupled, multiscale computational workflow with the objective of assessing the choice of thermoplastic matrix material under realistic conditions. Atomistic molecular dynamics simulations provide temperature-dependent stiffness, thermal expansion, and yield strength data for six candidate thermoplastics. These inputs feed into a recursive micromechanics model that simulates a stress-free cooldown to liquid hydrogen temperature, followed by biaxial hoop to longitudinal loading representative of a cylindrical tank’s acreage. Progressive damage analyses predict the onset of matrix microcracking and ultimate burst behavior across four industry-relevant layups. Results highlight that [55/5/−55/−5] Double-Double or [0/±30/±60]ₛ layup architectures with low-melt poly(aryl ether ketone) or poly(ether ketone ketone) matrices deliver superior microcrack resistance, illustrating the power of this framework to guide material and layup selection for leak-resistant thermoplastic composite cryotanks. Full article

The excellent thermal performance, quiet operation, and fuel flexibility of free-piston Stirling machines enable their broad application potential in sectors such as aerospace, distributed power generation, and industrial waste heat utilization. The impact of structural parameters on the output characteristics of the free-piston Stirling engine was investigated using a parametric MATLAB model based on an isothermal thermodynamic approach. Parameters such as the dead volume ratios (χ_H, χ_K, χ_R), temperature ratio τ, sweep volume ratio k, piston phase angle a_dr, and minimum pressure angle θ were evaluated for their effects on the dimensionless power Z. The results indicate that the dead volume ratio in the cold space χ_K has the most significant influence on system performance, followed by the hot space χ_H, while the regenerator χ_R exhibits a comparatively weaker effect. All three parameters demonstrate the existence of optimal design intervals. The dimensionless power Z decreases monotonically with increasing dead volume ratio. Moreover, this decline is intensified at higher temperature ratios τ, indicating that the influence of dead volume becomes more significant under larger τ values. The interaction between these parameters can be described by $Z=0.0037{\tau }^2-0.0045\tau +0.0021$. An excessively large sweep volume ratio k tends to degrade the system’s output performance. An empirical correlation between k and the dimensionless power can be established as follows $Z=1.53(1-e^{-3.37k})+0.01$. A moderate increase in the piston phase angle a_dr and a reduction in the minimum pressure angle θ contribute to improved system performance by enlarging the p-v diagram area and enhancing the utilization of gas expansion. The relationship between a_dr and the dimensionless power Z follows a linear trend, expressed as $Z=0.341a_{dr}-0.2104$. A well-defined functional relationship exists between the minimum pressure angle θ and the dimensionless power output Z, which can be expressed as $Z=2.18\times {10}^{-4}{\theta }^2-0.0261\theta +0.7065$. A coupling regulation mechanism and design strategy have been developed to facilitate the coordinated optimization of multiple parameters in free-piston Stirling engines, which delivers theoretical guidance that is expected to support the engineering implementation of next-generation, high-performance Stirling technologies. Full article

The release of industrial wastewater containing synthetic dyes poses a major environmental issue because of their toxicity and persistence. Among treatment options, natural materials, specifically chitosan–polyvinyl alcohol (chitosan–PVA) hydrogel, have shown high effectiveness in dye removal due to their abundant functional groups and proven adsorption capacity. However, optimizing these systems experimentally is often time-consuming and requires many resources. This study introduces an artificial neural network (ANN) model to predict the adsorption capacity ($q_e$) and the time needed to reach equilibrium during the removal of tartrazine dye using chitosan–PVA hydrogel beads of different mean sizes, categorized as small, medium and large (2.1, 2.5, and 3.2 mm, respectively) at temperatures of 10, 30, and 50 °C The ANN model was compared with traditional kinetic models: pseudo-first-order, pseudo-second-order, and Elovich. Results showed that the ANN outperformed conventional models in predicting $q_e$ and equilibrium time, especially for small beads at 10 °C, where it predicted $q_e$ = 945 mg/g in 40 h with an $R^2$ of $0.9428$. Across all conditions, the ANN achieved strong correlation coefficients ($R^2>0.94$) and significantly shortened prediction times. Although the pseudo-second-order model achieved high $R^2$ values (up to $0.9929$), it took over 72 h to reach equilibrium prediction. These results demonstrate that ANN-based modeling can reduce experimental effort by up to 50% in prediction time while maintaining high predictive accuracy ($R^2>0.94$), offering a sustainable and efficient approach for designing wastewater treatment processes. Full article

Winding numbers are key indices in the depiction, modelling, and testing of dynamical processes. They capture phase progression on closed curves and are robust for quasiperiodic dynamics, but their status for chaotic Poincaré sections is unclear. This study tests whether any non-trivial winding-type index can be extracted from chaotic Poincaré maps using three approaches: (i) phase-angle analysis, (ii) Kabsch optimal-rotation estimation, and (iii) local turning-angle averaging. To benchmark feasibility and error, we compare four systems: the standard circle map, the same circle map embedded on two planar fractal curves (Koch snowflake and Hilbert curve), a quasiperiodic Duffing–van der Pol (DVP) Poincaré map, and a chaotic DVP Poincaré map. For the quasiperiodic map, all methods yield consistent, accurate winding numbers. For the transitional systems (circle map and its fractal embeddings), indices remain non-trivial but more deviated. In stark contrast, chaotic Poincaré maps produce only trivial indices across all methods. These results indicate a crucial fact about the modelling of chaotic Poincaré maps. That is, although being fractal, they are not merely chaotic maps on fractal curves; rather, they reflect a tighter coupling of geometry and dynamics. Practically, the recoverability of a non-trivial winding index offers a simple diagnostic to distinguish quasiperiodicity from chaos in Poincaré data or corresponding models. The constructed chaotic-map-on-fractal systems also act as test-bed models that bridge ideal one-dimensional mappings and realistic two-dimensional Poincaré sections. Full article

This paper addresses the issue that traditional magnetorheological (MR) dampers have limited improvements in magnetic field utilization and damping channel length in confined spaces. It proposes an annular MR damper with an annular cylinder for propeller shafting. The piston head forms damping gaps with the cylinder’s inner and outer walls. This doubles the damping channel length without increasing axial size. The paper explains its working principle, completes the magnetic circuit design and damping force modeling, and utilizes COMSOL 5.6 Multiphysics to construct a magneto-fluid coupling model for analysis. Results show that, under 10 mm amplitude, 1 Hz sinusoidal excitation, and 2.0 A current, the damper outputs a damping force of 67.65 kN, with a damping adjustable coefficient of 10.87. Its force-displacement curve has a full hysteresis loop, showing excellent energy dissipation. The study proves the annular structure boosts the damper’s performance, offering a new way to achieve high damping force and a wide dynamic range in a compact space. Full article

This paper explores methods of simulating the behaviour of building structures under progressive collapse conditions through alternative models of different levels of structural idealization. Such models have been applied in many previous studies, but there is insufficient information regarding their reliability and their ability to represent actual structural behaviour as the level of idealization is reduced. To address this, the study adopts the alternative load path method through the well-established concept of notional column removal, performed via nonlinear static analyses of models with different levels of structural idealization. The focus is on the interaction between the directly affected structural members and the surrounding structure, which is shown to significantly influence the overall response under progressive collapse. The results demonstrate that this interaction depends on multiple factors and cannot be reliably captured when the surrounding structure is not explicitly modelled. Building on this finding, the study systematically evaluates how reduced models can be enhanced to better represent these interactions and proposes strategies for defining boundary conditions that preserve global structural behaviour. Overall, the study advances understanding of model idealization effects and provides practical guidance for developing efficient reduced models for progressive collapse simulations without compromising essential aspects of structural response. Full article

Agent-based models (ABMs) in transport represent a paradigm shift from traditional aggregate and equilibrium-based approaches. By modeling individual behaviors of a heterogeneous population, an ABM offers a more realistic representation of urban phenomena and extends sensitivity to different policy interventions. Despite this, ABM implementation faces several challenges such as limited reproducibility, uneven global implementation, and high technical and financial costs, particularly relevant in the Global South. The proposed framework addresses these gaps by implementing a modular, transparent, publicly shared data-driven approach, reducing hierarchies and relationships definitions while ensuring reproducibility. Utilizing nationally available data to generate a synthetic population, activity plans, multimodal network and agent simulations in MATSim, the framework was applied in the Metropolitan Area of Fortaleza, a region with approximately 4 million people in Brazil. Despite inherent data limitations characteristic of developing contexts, the framework demonstrated performance compatible with strategic planning applications. Traffic assignment validation showed a mean absolute error of 301 vehicles during morning peak hours and 423 vehicles for the 24 h period, which are acceptable for scenario-based policy analysis. Beyond the potential to democratize access to robust urban planning models in similar data-constrained scenarios worldwide, this study presents pathways to foster national dialogue toward improved data collection practices for disaggregated transport model implementation. Full article

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