Modelling

Lithology identification is a key task in petroleum geological exploration and development, essential for evaluating sweet spots and characterizing reservoirs. A significant challenge in lithology identification is the insufficient accuracy of traditional machine learning methods due to the uneven distribution of geological data categories. To address this, we propose a novel lithology identification framework combining a denoising diffusion model with auxiliary classification, a neural network with channel attention mechanisms, and a bidirectional Gated Recurrent Unit (GRU). The proposed framework first employs the Auxiliary Classification Denoising Diffusion Probabilistic Model (A-CDPM) to generate high-quality well log data, effectively balancing the data classes. Secondly, it utilizes a multi-scale convolutional model with channel attention mechanisms and a Bidirectional GRU classification model, which automatically adjusts feature weights and effectively integrates information from different well log data. Experimental results demonstrate that our method significantly improves lithology identification accuracy, achieving 86.66% on datasets from the Hugoton and Panoma fields in Kansas, USA. Compared to traditional methods, this framework substantially enhances recognition precision, providing a novel and effective solution for lithology identification in petroleum geological exploration. Full article

Reliable fault detection in brushless DC motors is challenging owing to environmental complexity and high equipment costs. To address these challenges, we propose an effective and cost-effective approach using an optimized end-to-end one-dimensional convolutional neural network. Specifically, a real experimental platform simulating bearing and eccentricity faults was developed. Statistical t-tests indicated that three-axis accelerometer signals from a low-cost inertial measurement unit provided sufficient fault information for the present diagnosis task. Unlike traditional methods such as support vector machines, multilayer neural networks, and random forests, which rely on manual feature extraction, our model learns directly from raw waveforms and can handle signal drift. Under the present controlled experimental setting and the leave-one-day-out evaluation protocol, the model achieved 100.00% average window-level classification accuracy, considerably outperforming traditional methods, the performances of which declined to 67.95–71.37% under environmental shifts. Moreover, with an inference time of only 0.96 ms, 32 times faster than that of random forests, this approach is well suited for real-time embedded monitoring. The proposed method demonstrates strong potential for cost-efficient and robust fault diagnosis under the present experimental setting. Full article

This study presents a comparative computational fluid dynamics (CFD) investigation of the aerodynamic performance of four simplified crossover/sports utility vehicle (SUV)-type vehicle body configurations. The models were developed with systematic geometric variations, including front face inclination, roof spoiler length, roof spoiler slotting, and rear underbody diffuser integration. Steady-state Reynolds-averaged Navier–Stokes (RANS) simulations using the k–ω SST turbulence model were conducted in ANSYS Fluent to evaluate key aerodynamic parameters, including the drag coefficient, drag force, pressure distribution, velocity field, and modeled turbulence kinetic energy. The results indicate that the baseline configuration exhibits the highest drag due to early flow separation and poor rear pressure recovery. Progressive geometric modifications led to improved aerodynamic performance, with the configuration incorporating a slotted roof spoiler and rear diffuser achieving the lowest drag coefficient, corresponding to an approximate 13% reduction compared to the baseline model. The findings demonstrate that coordinated front- and rear-end design modifications play a critical role in reducing wake intensity and enhancing aerodynamic efficiency. This study provides insight into effective drag reduction strategies for crossover-type vehicles and highlights the importance of integrated aerodynamic design approaches. Full article

Hydraulic fracture geometry is of great importance for evaluating stimulation effectiveness and supporting the efficient development of unconventional oil and gas reservoirs, and it can be estimated from field shut-in water hammer signals. However, field signals are commonly characterized by strong noise, pronounced non-stationarity, strong dependence on manual extraction of effective response segments, and limited automation in inversion analysis. To address these issues, this study develops an integrated automated interpretation framework for shut-in water hammer analysis, which combines an adaptive shape-preserving Kalman filter for non-stationary signal denoising, an automatic response segment identification method, and a particle swarm optimization-based inversion strategy for fracture geometry estimation. The framework is validated using field high-frequency pressure data from hydraulically fractured wells. The results show that the proposed denoising method improves the signal-to-noise ratio from 11.99 dB to 25.05 dB while preserving key transient features. The response segments can be extracted efficiently, with runtimes of 0.84–1.22 s and onset errors within 0–5 s. For a representative fracturing stage, the relative errors of the inverted fracture half-length and fracture height are 6.21% and 3.04%, respectively. The proposed framework provides a low-cost and field-applicable tool for fracture evaluation and engineering decision-making. Full article

Poly(ethylene terephthalate) (PET) composites reinforced with reduced graphene oxide (rGO) were investigated in order to elucidate the influence of nanofiller concentration and compatibilization on the viscoelastic relaxation behavior across the glass transition. Composites containing 0.1 and 0.5 wt% rGO were prepared by melt blending, and selected systems incorporated 5 wt% of an ionomeric polyester (PETi) as compatibilizer to enhance interfacial adhesion. The thermomechanical response was characterized using dynamic mechanical analysis (DMA) as a function of temperature. Experimental results revealed a strong dependence of stiffness, damping, and glass transition behavior on filler concentration and interfacial interactions. While low rGO loading produced minor changes, the incorporation of 0.5 wt% rGO significantly increased the glassy modulus and shifted the glass transition temperature, indicating restricted segmental mobility. Compatibilized systems exhibited further stiffness enhancement and modified relaxation dynamics due to improved stress transfer and interphase development. To capture the distributed nature of the relaxation processes, the glass transition region was modeled using a fractional Zener model (FZM) with two spring-pot elements within a cooperative relaxation framework. The model successfully reproduced the experimental $E^{\prime }$ and $tan\delta$ curves and revealed systematic variations in the fractional exponents and cooperative parameters. The results demonstrate that the introduction of rGO and compatibilizer progressively transforms the relaxation spectrum of PET from a relatively uniform segmental process into a heterogeneous, interfacially mediated viscoelastic response that is naturally described by fractional rheology. Full article

To improve the assembly efficiency and productivity of complex aircraft components, the optimization of an assembly line was investigated in this study. A hierarchical hybrid multi-objective optimization algorithm (HHMOA) was proposed using an improved non-dominated sorting genetic algorithm II and an enhanced longest processing time algorithm. The algorithm incorporates a two-layer framework for global–local optimization; an information entropy-based problem formulation with three objectives, including line balance rate, load balance index and assembly complexity smoothness index; and a hybrid initialization strategy for high-quality initial solutions. Based on the assembly line datasets of different scales, the algorithm performance was verified by comparing the hypervolume and the calculation efficiency using HHMOA and three benchmark algorithms, and the sensitivity analyses verified the algorithm robustness. For an actual aircraft component assembly line, the optimizations carried out with the given process time, number of workstations and precedence relationships indicate that the balance rate of the optimized line increased 72%, and the load balance index and the assembly complexity smoothing index were reduced by 80.3% and 92% respectively, which proved the reliability of the hybrid algorithm in optimizing the aircraft component assembly line. Finally, the optimization analyses with various workstation numbers and assembly process times suggest that reducing the workstations and adopting robotic automated processing can improve the aircraft component assembly line. Full article

A significant number of regional boats are used in the Brazilian Amazon to perform a range of social activities. However, the estimation of their propulsion parameters still requires exploring technically supported methods if the efficiency and sustainability of inland navigation is to be optimized. This study explores various approaches for estimating the total resistance and effective propulsive power required by regional boats. The research examines the real case of a rabeta, a regional boat commonly used in the Brazilian Amazon, by analyzing the applicability at full scale of two approaches: the conventional Mercier–Savitsky pre-planing method and a multiphase computational fluid dynamics (CFD) approach. Using experimental data, such as boat speed and the patterns of boat-generated waves, computational analysis and the comparison of results, respectively, were carried out. It was found that for the case considered, the CFD results underpredicted the conventional approach in less than 10% for the minimum and maximum drafts considered, suggesting that both approaches are useful for estimating the effective power of artisanal boats. However, the use of CFD has the potential to visualize a greater number of parameters, such as the generated waves during vessel motion, which can facilitate the optimization of the hydrodynamics of boats, thus contributing to the sustainability of inland navigation in the region. The procedure employed in this study can be further extended to estimate the propulsive parameters of other regional vessels in the Amazon and similar areas. Full article

Accurate detection of cardboard boxes is essential to mobile manipulators to perform pick-and-place operations in warehouses. Conventional object detection methods like YOLOv11 struggle in low-texture and occluded environments. This paper presents YOLO-REFB, a novel object detection framework for real-time cardboard box detection in robotic manipulation using a dual-arm mobile robot (DAMR) operating in indoor warehouse environments. The proposed approach enhances the network by integrating the Rectangular Edge Fusion Block (REFB) into the YOLOv11 architecture; it focuses on learning the geometric and structural features of cardboard boxes. Enhanced edge information extraction and feature fusion improve training stability and localization accuracy. A custom dataset of 3501 annotated images, collected under varied conditions, was utilized. The images were randomly assigned to training and validation sets while keeping an 80:20 ratio. They were manually annotated and trained using Roboflow software, ensuring precise alignment of bounding boxes with cardboard box edges for accurate comparison with existing YOLO models. The model outperformed existing YOLO variants (YOLOv8n and YOLOv5n) in terms of precision (89.29%), recall (83.95%), and F1-score (86.54%). YOLO-REFB achieved improved localization metrics, including mean Average Precision (mAP)@0.5 (91.68%) and mAP@0.5:0.95 (68.61%). The inclusion of REFB was essential to performance gains, enabling effective detection of objects in challenging environments. Future developments may include 3D pose estimation and multi-object grasp planning for advanced robotic manipulation. Full article

Self-contained local forecasting of wind and solar series can improve operational planning of wind farms and photovoltaic (PV) plant day-cycles in addition to numerical models, which are mostly behind time due to high simulation costs. Unstable electricity production requires balancing the availability of renewable energy (RE) with unpredictable user consumption to achieve effective usage. Artificial intelligence (AI) predictive modelling can minimise the intermittent uncertainty in wind and solar resources by trying to eliminate specific problems in RE-detached system reliability and optimal utilisation. The proposed 24 h day-training and prediction scheme comprises the starting detection and the following similarity re-assessment of sampling day-series intervals. Two-point professional weather stations record standard meteorological variables, of which the most relevant are selected as optimal model inputs. Automatic two-layer altitude observation captures key relationships between hill- and lowland-level data, which comply with pattern progress. New biologically inspired differential learning (DfL) is designed and developed to integrate adaptive neurocomputing (evolving node tree components) with customised numerical procedures of operator calculus (OC) based on derivative transforms. DfL enables the representation of uncertain dynamics related to local weather patterns. Angular and frequency data (wind azimuth, temperature, irradiation) are processed together with the amplitudes to solve simple 2-variable partial differential equations (PDEs) in binomial nodes. Differentiated data provide the fruitful information necessary to model upcoming changes in mid-term day horizons. Additional PDE components in periodic form improve the modelling of hidden complex patterns in cycle data. The DfL efficiency was proved in statistical experiments, compared to a variety of elaborated AI techniques, enhanced by selective difference input preprocessing. Successful LSTM-deep and stochastic tree learning shows little inferior model performances, notably in day-ahead estimation of chaotic 24 h wind series, and slightly better approximation of alterative 8 h solar cycles. Free parametric C++ software with the applied archive data is available for additional comparative and reproducible experiments. Full article

Air-to-air heat pumps are a key technology for improving energy efficiency and reducing carbon emissions in residential buildings, yet their optimal control remains challenging under real-world conditions. This study evaluates the performance of a hybrid physical–LSTM model for controlling an air-to-air heat pump in a residential building in Zadar, Croatia. The hybrid framework integrates a first-order energy balance model of the building envelope with LSTM-based temperature correction using adaptive weighting. The physical model was calibrated and validated against 52,128 real IoT measurements collected during the 2024/2025 heating season, achieving high accuracy (RMSE ≈ 0.076 °C). Rolling one-day and continuous multi-day closed-loop simulations (up to 15 days) show that the hybrid model yields slightly lower RMSE in long-term runs compared to the pure physical model. However, this apparent statistical improvement is accompanied by systematic underestimation of indoor temperature and significantly higher simulated energy consumption. The results indicate that the observed effect originates from an implicit virtual heat flux introduced by the LSTM correction, which affects thermodynamic consistency in closed-loop operation. The findings highlight that short-term error metrics such as RMSE alone are insufficient for evaluating hybrid models intended for model predictive control (MPC). The main contribution of this study is the explicit demonstration and quantification of an implicit virtual heat flux generated by the LSTM correction in closed-loop multi-day operation, which leads to misleading statistical improvements while causing significant thermodynamic inconsistency and energy overconsumption. In 15-day continuous simulations the hybrid model (ω = 0.05–0.10) caused an indoor temperature underestimation of 1.25–1.31 °C and increased simulated electricity consumption by more than 300% (316 kWh vs. 72 kWh) compared to the physical model. These results have direct implications for the development of reliable digital twins and model predictive control strategies in residential HVAC systems. Full article

Bird strike events on rotating jet engine fan blades pose significant risks to aviation safety, yet high-fidelity numerical simulations remain computationally expensive, limiting their use in parametric design studies. This study develops a physics-guided machine learning surrogate framework for predicting bird strike response on rotating Ti-6Al-4V fan blades, systematically comparing Lagrangian (gelatin-based, Mooney–Rivlin) and Smoothed Particle Hydrodynamics (SPH, water-like) formulations. A total of 100 explicit dynamic simulations were conducted in ANSYS LS-DYNA (R2) (50 per formulation), varying bird impact velocity and blade angular speed. Random Forest, Support Vector Regression, Polynomial Regression, and XGBoost regression models were trained and evaluated using five-fold cross-validation. Results demonstrate that SPH-based surrogates achieve superior predictive accuracy, with Random Forest yielding R² = 0.9938 for maximum deformation and R² = 0.9962 for total energy dissipation. In contrast, Lagrangian-based stress surrogates exhibited severe performance degradation (R² = 0.24) due to mesh-dependent numerical noise. The trained surrogates achieved computational speed-up factors of 10⁴–10⁵ relative to direct simulation. These findings establish that surrogate model reliability is fundamentally governed by the numerical quality of the training data, providing guidance for integrating machine learning with impact simulation workflows in aero-engine blade design. Full article

To address the insufficient parameter estimation accuracy and poor filtering convergence caused by noise in radar trajectory measurement data, this paper proposes a joint framework combining SW-STPSO adaptive wavelet denoising and an improved Unscented Kalman Filter (UKF). First, SW-STPSO preprocesses noisy data using a sliding-window strategy and improved particle swarm optimization to adapt wavelet parameters to local noise characteristics. Then, the improved UKF adopts exponential-decay adaptive Q adjustment and covariance matrix positive-definite regularization to achieve high-precision estimation of ballistic parameters, including position, velocity, and ballistic coefficient. Simulation results show that: (1) SW-STPSO denoising improves subsequent parameter-estimation accuracy by more than 60% compared with the case without denoising; (2) the improved UKF achieves 37% faster convergence and 42% higher stability than the traditional UKF; and (3) the joint scheme reduces the position RMSE, velocity RMSE, and ballistic-coefficient RMSE to 0.92 m, 0.256 m/s, and 0.023 m²/kg, respectively. These results indicate that the proposed method is effective for radar trajectory data processing under the adopted simulation conditions. Full article

Cavitation damage poses a serious threat to the reliability of morning-glory spillways. This study aims to develop a reliability framework for predicting cavitation damage probability under uncertain operational conditions for the Haraz Dam spillway. Cavitation analysis in such structures exhibits inherent nonlinearity and uncertainty, complicating accurate damage prediction. This study incorporates model uncertainties to assess cavitation responses at multiple points on the Haraz Dam morning-glory spillway. Three-dimensional flow simulations were performed using Computational Fluid Dynamics (CFD) and validated against an experimental model from the Iran Water Research Institute, showing satisfactory agreement. Statistical parameters and probability density functions (PDFs) for key uncertainties were determined using the Shapiro–Wilk test. A total of 35 simulation runs, designed via the Central Composite Design (CCD) method, were conducted using Latin Hypercube Sampling (LHS). These simulations incorporated inter-uncertainty correlations and predicted cavitation damage responses at ten critical spillway locations through Response Surface Methodology (RSM). Both linear and second-order response functions were formulated based on interactions among model uncertainties. The results indicated a strong correlation (R² > 0.95) between numerical model outputs and RSM predictions, with the maximum RSM errors remaining within acceptable thresholds. Among the uncertainty factors, the inflow velocity demonstrated the highest contribution (>50%) to cavitation damage responses. These outcomes advance the understanding of cavitation mechanisms and provide a reliable methodology for evaluating damage risks in morning-glory spillways under uncertain operational conditions. Full article

Reliable short-term air traffic forecasts are important for operational planning in national airspace systems. This study develops a transparent forecasting framework for Türkiye’s monthly aircraft movements using publicly available data from the General Directorate of State Airports Authority (DHMİ) for 2018–2025. Because DHMİ releases may follow cumulative within-year reporting, month-specific increments are reconstructed through within-year differencing and checked through simple audit procedures. The empirical analysis compares seasonal naïve, ETS, and a constrained SARIMA family under leakage-free evaluation, combining a strict 2025 holdout with expanding-window rolling-origin validation. Forecast performance is assessed using standard accuracy metrics and complemented by Diebold–Mariano comparisons, which are interpreted cautiously, given the short holdout length. To examine instability around the pandemic period, this study also reports structural-break and stability diagnostics as supportive evidence rather than definitive identification. Uncertainty is evaluated through backtested 80% and 95% prediction intervals, comparing nominal SARIMA intervals, parametric bootstrap, split conformal prediction, and adaptive conformal inference (ACI). The results show that SARIMA provides the strongest point-forecast performance among the benchmarked models, while adaptive conformal calibration offers a useful balance between empirical coverage and interval width under changing conditions. Overall, this study provides a reproducible and operationally interpretable baseline for national air traffic forecasting in Türkiye and a clear benchmark for future multivariate extensions. Full article

To precisely evaluate the fatigue hot-spot stress concentration factor (SCF) of welded tubular joints and verify the accuracy of existing methods, this research selects Y-type tubular joints as the research subject. The dihedral angle formula is re-derived, and the dihedral angles corresponding to each polar angle along the intersection line are calculated using MATLAB R2018a (MathWorks Inc., Natick, MA, USA). After determining the geometric parameters of the weld profile in accordance with AWS specifications, finite element models named “AWS-max” and “AWS-min” are established in ANSYS 2022 R1 (ANSYS Inc., Canonsburg, PA, USA). These models meet the maximum and minimum allowable weld sizes respectively, and a novel modeling approach is proposed. Tests on tubular joints under axial tension loading are conducted, and the SCF is obtained through the surface stress interpolation method. Comparative analyses are carried out among the SCF from the established “AWS-max” and “AWS-min” weld models, the non-weld model, and the test results of the tubular joints. The results indicate that the weld geometric size has a significant impact on SCF: a larger weld cross-section results in a lower SCF. For the AWS maximum weld model, the SCF of the chord ranges from 4.21 to 5.42, and that of the brace ranges from 1.71 to 5.33; for the AWS minimum weld model, the chord SCF is 4.41–5.73, and the brace SCF is 2.11–5.79. The numerical results are in good accordance with the experimental data, while the non-weld model produces obviously conservative results with inconsistent distribution laws. The calculated dihedral angles obtained by the proposed method are highly consistent with the AWS standard. The modeling method is characterized by reliable accuracy and strong engineering applicability, and can be extended to the SCF calculation and fatigue evaluation of various tubular joints. Full article

Trajectory planning in dynamic multi-vehicle interaction environments faces three critical challenges, including the difficulty of quantifying spatial risk distributions, the complexity of characterizing behavioral uncertainty arising from the multimodal maneuvers of surrounding vehicles, and the challenge of simultaneously optimizing multiple competing objectives such as safety, efficiency, comfort, and energy consumption. To address these challenges, this paper proposes an Improved Driving Risk Field-based Multi-objective Trajectory Optimization (IDRF-MTO) method. First, a joint spatiotemporal social attention mechanism achieves unified modeling of spatial interactions, temporal dependencies, and spatiotemporal coupling, combined with a lateral–longitudinal intent strategy for multimodal trajectory prediction. Second, an improved dynamic risk field model is constructed comprising three components: a vehicle risk field that incorporates spatial orientation and motion direction factors for anisotropic risk representation, along with a collision tendency factor that converts objective risk into effective risk; a predicted trajectory risk field that achieves anticipatory quantification of future risk from surrounding vehicles through confidence-weighted fusion; and a driving environment risk field that encapsulates road geometry, static obstacles, and environmental conditions. Finally, a multi-objective cost function embedding risk field gradients is formulated, and multi-objective coordinated optimization is realized through a three-dimensional spatiotemporal situation graph with adaptive safety sampling. Simulation results demonstrate that the proposed method enhances safety while simultaneously improving comfort and efficiency and reducing energy consumption, exhibiting excellent planning performance in complex dynamic environments. Full article

The condition assessment of alkali-silica reaction (ASR)-damaged concrete structures necessitates accurate reproduction of ASR expansion progression and its induced load effects across time and spatial dimensions. To address this challenge, a time-dependent free ASR expansion model was developed based on experimental measurements. A user subroutine incorporating stress-dependent behavior for restrained ASR expansion evolution was implemented on the ABAQUS platform and validated through simulation of ASR expansion in specimens under external loading and internal reinforcement restraint. Finite element analyses of the reinforced concrete specimens revealed distinct variations in ASR expansion between the surface and interior zones of concrete members. The assumption that surface ASR expansion strain equals steel rebar strain leads to significant overestimation of actual rebar stress and strain conditions. Additionally, based on the validated finite element model, the influence of elastic modulus, creep, stress-dependent function, steel plate thickness, and reinforcement ratio on the ASR expansion was investigated. For the reinforced concrete specimens, the stress variation over the cross-section is considerably reduced when creep is considered, while the concrete strain at the surface is only slightly influenced by creep. Full article

Congestion in urban transportation is a significant challenge, often exacerbated by increasing private vehicle use and limitations in public transport. This study introduces a two-stage approach combining multi-criteria assessment and traffic simulation to examine current conditions and propose improvements. Initially, data on five primary and twenty-one secondary factors affecting public transport choice are assessed using the Best–Worst Method (BWM). The findings reveal that convenience is prioritized by working professionals, while travel cost is most important to students. A baseline simulation model is established using a case study at Kaset Intersection in Bangkok. Incorporating weighted preferences into the simulation aims to enhance public transport and encourage private car users to switch modes through potential traffic management policies. Additionally, a micro-simulation assesses the impacts of decreased traffic density, revealing that a reduction in traffic density can shorten overall travel time by about 2.04 s, based on regression analysis. The results suggest policies to improve public transport, reduce traffic density, and enhance urban transport system performance. Full article

The integration of automated learning and video analysis enables the development of intelligent systems that can operate effectively in uncertain scenarios. These systems can autonomously identify dominant motion dynamics, depending on the theoretical framework used for representation and the learning process used for pattern identification. Current literature offers a state-based approach to describe the key temporal and spatial relationships required to understand motion dynamics. An important aspect of this approach is determining when the number of positively learned rules from a given information source is sufficient to detect dominant motion in automatic surveillance scenarios. This is crucial, as it affects both the variability of movements that monitored subjects can exhibit within the camera’s field of view and the resources needed for effective implementation. This study addresses these gaps through a grammar-based sufficiency criterion, which posits that learning is complete when production rule growth stabilizes, under the assumption of system stationarity. The stability criterion evaluates whether the most probable rules are learned over time, and whenever a high-growth rule is added, it is used to update the criterion. We outline several benefits of having a formal criterion for determining when a symbolic surveillance system has a robust model that explains the observed motion dynamics. Our hypothesis is that a correct model can consistently account for the majority of motion dynamics over time in an automated learning process. The proposed approach is evaluated by modeling motion dynamics in several scenarios using the SEQUITUR algorithm as input and computing the probability of stability along the learning curve, which indicates when the model reaches a steady state of consistent learning. Experimental validation was conducted in real-world scenarios under varying acquisition conditions. The results show that the proposed method achieves robust modeling performance, with accuracy values ranging from 83.56% to 95.92% in dynamic environments. Full article