Appl. Mech., Volume 7, Issue 1 (March 2026) – 25 articles

A sucker rod pump is an artificial lift system widely used in oil wells to extract crude oil from deep underground. Due to the clearance between the barrel and the pump plunger, a phenomenon termed slippage occurs in which the annulus column of oil returns to the pump chamber due to the plunger motion and the pressure difference at the two ends of the plunger. Although it is important to maintain the clearance for lubrication between the plunger and the pump barrel in order to prevent excessive wear and tear along with galling, excessive clearance can also be a primary factor in the reduction of oil well production and must be managed. In this research, after briefly reviewing the Couette and Poiseuille flows within the annulus region, the relaxation time for the transients, and the eccentricity effects, we focus on the derivation of important system parameters such the effective mass, stiffness, and damping ratio based on the measurements of the sucker rod displacement and the pressures or loads. Analysis of experimental measurement data can provide better understanding of the sucker rod pump system parameters, helping to quantify and manage the so-called slippage issues. Full article

Wind energy has become an essential resource for the development and diversification of the energy sector in México and worldwide. In this context, the mechanical design of turbine blades has emerged as a priority research topic, given its impact on performance and viability. The present research evaluates the aero-structural response of multiple lay-up configurations of a 6 m blade by coupling computational fluid dynamics (CFD) and finite element analysis (FEA). The fluid–structure interaction (FSI) was simulated in ANSYS, a commercial software chosen for its capacity for multivariable analysis. The nominal operating conditions included a wind speed of 10.5 m/s and a rotational speed of 100 rpm, leading to a theoretical power output of 6591 W. For the proposed lay-up configurations, the Tsai-Wu and Puck (Global IRF) criteria were estimated and remained below the critical threshold of 1.0, indicating no risk of structural failure. However, some carbon fiber/epoxy layers, including unidirectional layers in the spar caps and bidirectional layers in the structural shear web, may present failure risks under extreme loading conditions. This applies to configurations with the lowest number of layers in the mid-span spar caps; this fact is reinforced by the main effects analysis. The results emphasize the relevance of conducting comprehensive composite failure evaluations to optimize material selection and structural design, even for small-scale blades. Full article

In many densely populated towns and semi-urban areas, masonry buildings often stand close to busy roads, exposing them to blasts from improvised explosives or other localized sources. Such structures are rarely designed to resist sudden explosive forces, making severe damage or even progressive collapse likely. Even moderate-intensity blasts can weaken walls, endanger occupants, and cause significant property loss. Unlike reinforced concrete, masonry is highly susceptible to explosive impact. Therefore, understanding how these buildings behave under blast loads and developing affordable protection methods is crucial. Low-rise unreinforced masonry (URM) structures, usually up to about 13 m in height (roughly 2–4 stories), common in villages, semi-urban regions, and conflict-prone zones, are particularly at risk. In many areas, these poorly constructed buildings lack proper engineering design and are therefore highly vulnerable to blast damage. Non-load-bearing internal dividers and perimeter enclosures are especially prone to lateral displacement, which can initiate instability and, in severe cases, lead to overall structural failure. This research focuses on reducing catastrophic damage in URM walls when exposed to close-proximity blast forces using concrete-based protective coatings, both with and without embedded steel-welded wire mesh. The study references a previously tested laterally supported clay brick wall built with cement–sand mortar as the baseline model, with its behavior validated against experimental findings from existing literature. Two blast cases were considered corresponding to scaled stand-off distances of 2.19 m/kg^1/3 and 1.83 m/kg^1/3, representing moderate flexural-shear cracking and full structural failure, respectively. To replicate the observed behavior, a comprehensive 3D numerical simulation was developed using the ABAQUS/Explicit 2020 solver. The model’s predictions were benchmarked and verified through comparison with reported test data. While both blast intensities were used to confirm computational accuracy, the effectiveness of UHPC and UHPFRC protective coatings with and without embedded wire mesh was specifically evaluated under the more severe collapse scenario (Z = 1.83 m/kg^1/3). Results indicated that at a scaled distance of 1.83 m/kg^1/3, the uncoated URM wall could not withstand the blast because of poor tensile and bending capacity. In contrast, the UHPC- and UHPFRC-coatings provided improved confinement and better stress distribution. When welded wire mesh was embedded, crack control improved further, the interface bond strengthened, and a larger portion of blast energy was absorbed and dissipated. Full article

Fatigue failure constitutes an issue that cannot be ignored when designing welded steel structures due to the initiation of cracks at weld toes and defects under cyclic loading conditions. Traditional methods, such as the notch stress approach, estimate fatigue life by modeling local stress distributions using idealized weld geometries. While these methods are widely accepted in design codes, they can be limited by complexity and reduced accuracy in real-world applications. This study explores the use of artificial neural networks (ANNs) to enhance fatigue life prediction through data-driven modeling. The proposed method involves training an ANN using synthetic data generated through finite element simulations of S355 steel weldments under various loading histories, rates, and frequencies. The objective is to capture the influence of local geometric and stress features without relying solely on assumptions used in conventional approaches. The FEM-based training data incorporate both classical experimental findings and validated modeling practices. While performance evaluation of the ANN model is reserved for future work, this study lays the groundwork for replacing or supplementing the notch stress approach with a more adaptable and efficient predictive tool. The integration of machine learning into fatigue assessment has the potential to improve reliability, reduce computational burden, and support more informed maintenance and design decisions. Full article

This study proposes two novel stiffened panel configurations, designated X and X-30, manufactured from the conventional aerospace alloy Al 2024-T3 to enhance the critical buckling resistance under in-plane compression. Their performance was evaluated against traditional T-, I-, L-, and Omega-type stiffeners, as well as a newly introduced Y-panel found in the literature. Initial results show that both proposed designs achieve 80–200% higher buckling capacity than conventional panels, with only a 4.54% difference between the X and X-30 configurations. A weight-constrained optimization was then conducted using a Box–Behnken design of experiments combined with a multi-objective genetic algorithm in Ansys DesignXplorer. After correcting inconsistencies in the initial optimization ranges, the prediction error in the optimized buckling values was reduced to 4%. The optimized X panel attained the highest critical buckling load of 2920 kN, followed by the X-30 panel with 2744.3 kN, corresponding to a 114–258% improvement over traditional stiffener geometries. A Pearson correlation matrix further suggested that, for all the stiffened panels except Omega, the base plate showed a strong correlation with the critical buckling load, typically ranging from 0.83 to 0.99. In contrast, for the X-30 panel, the lower base part also showed a strong correlation of 0.93. Full article

This study evaluates bamboo–coir hybrid composite panels developed for formwork applications using an 80:20 fiber–matrix ratio and a 50:50 bamboo-to-coir distribution. The novelty of this study lies in the combined assessment of formwork-relevant mechanical performance, Mode I and Mode II fracture behavior, finite element validation and post-fracture microstructural correlation for a high fiber volume fraction natural fiber hybrid panel. Mechanical, durability, fracture, numerical and microstructural investigations were performed and benchmarked against 10 mm thick construction-grade plywood. The hybrid panels exhibited a density of 805 ± 10.84 kg/m³, which is within 0.7% of plywood, a tensile strength of 50.20 ± 2.85 MPa, representing an increase of 41.8% over plywood, and a flexural strength of 38.60 ± 2.10 MPa, corresponding to an increase of 12.9% as compared to plywood. The impact energy absorption of hybrid panels was 7.85 ± 0.62 J, which is 26.6% greater than plywood. Mode I fracture testing yielded a fracture toughness of 456.65 ± 15.42 J/m², corresponding to an increase of 9.3% over plywood, while Mode II fracture toughness yielded a value of 792.42 ± 30.18 J/m², representing an increase of 13.7% over plywood. Finite element predictions deviated from experimental load–displacement responses by 5–13%. SEM observations identified fiber bridging, fiber pullout and interfacial sliding in the hybrid panels, consistent with the measured fracture energy values. The results indicate that bamboo–coir hybrid panels satisfy the mechanical and fracture performance requirements for reusable formwork systems. Full article

This paper presents the structural health monitoring (SHM) system applied to a 9 m composite outer wing box (OWB) specifically designed for a brand-new regional aircraft to detect barely visible impact damage (BVID) based on structural response data. The approach relies on different technologies to offer multilevel diagnosis, including impact detection as well as disbonding identification, localization, and sizing. The use of different sensing techniques based on piezoelectric transducers and distributed fiber optic sensors deployed all over wing structures is explored. Different features are simultaneously extracted from the propagating waves and from light scattering, able to detect low-energy BVID impact. In addition, the combined use of static and dynamic interrogation allows the estimation of the delamination surface after impact with good accuracy. The final test results on the OWB provided effectiveness in detecting, localizing, and tracking impact damage in the composite structure, ensuring long-term reliability and safety, as well as characterizing barely visible damage by a fully integrated onboard SHM system. Full article

This work reports the elaboration and testing of polydimethylsiloxane/titanium dioxide (PDMS/TiO₂) polymer nanocomposites, focusing on producing and combining TiO₂ nanoparticles with a polymer matrix through an ex situ route. By mixing the inherent flexibility of PDMS with the unique properties of nanoparticles, the nanocomposites aim to enhance mechanical stability, optical response, and photocatalytic activity. X-ray diffraction (XRD) confirmed the successful incorporation of TiO₂ into the PDMS matrix. UV–visible spectroscopy monitored photocatalytic performance using metronidazole as a model pollutant under 365 nm irradiation. Kinetic analysis revealed degradation and showed that the reaction rate constant (k) increased with TiO₂ loading, reaching a maximum of 0.0019 min⁻¹ for the 6 wt.% composite. These findings indicate that while the reaction kinetics are slower than those of free powders, the PDMS/TiO₂ nanocomposites provide a viable, recoverable, and flexible solution for environmental remediation applications. Future efforts will target improved durability, broadened visible light absorption, and process optimization for scalable fabrication. Full article

Non-crimp fabric (NCF) composites are increasingly adopted for structural components due to their high mechanical performance and processability. Like other fibre-reinforced plastics, NCFs remain vulnerable to in-service damage from tool drops or unintended collisions, which can substantially reduce load-bearing capacity. This study aimed to develop a validated numerical model capable of simulating damage initiation and post-impact behaviour through an integrated experimental–numerical approach. The mechanical properties of a representative unidirectional NCF composite were first experimentally established. Then, tubular NCF subcomponents were fabricated and tested under a two-phase loading protocol. In the first phase, damage was introduced using quasi-static indentation or controlled low-velocity impact. In the second phase, the residual load-bearing capacity of the damaged subcomponents was assessed under four-point bending. To support the research objective, a finite element model was developed in LS-DYNA to simulate both phases, using the MAT_ENHANCED_COMPOSITE_DAMAGE (MAT54) material formulation. Non-measurable input parameters, including stress limit factors and erosion strain thresholds, were calibrated via parameter estimation, sensitivity analysis, and iterative refinement. The final model showed close agreement with experiments in predicted damage location, deformation mode, and residual strength. X-ray computed tomography was used to validate delamination predictions. The findings support the development of reliable and cost-effective numerical tools for damage assessment in advanced composite structures. Full article

The dynamic behavior of the DS306 detacher, a critical component in industrial fiber processing lines, plays a decisive role in maintenance performance and overall operational reliability. This study introduces a strengthened preventive maintenance strategy that leverages vibration analysis and dynamic modeling with a strong emphasis on early fault anticipation. A detailed numerical finite element model of the detacher was developed to determine its natural frequencies, critical modes, and dynamic response under real operating conditions. Experimental vibration measurements were conducted to validate the numerical model and identify characteristic frequencies associated with imbalance and wear. The results show that the proposed predictive framework not only reproduces the machine’s dynamic behavior with high accuracy but also anticipates mechanical degradation trends well before the occurrence of critical failures. This early-warning capability allows maintenance teams to plan interventions proactively, significantly reducing unexpected downtime, avoiding cascading damage, and improving long-term equipment availability. Overall, the study provides a robust and practical methodology for dynamic diagnosis, fault prediction, and optimized preventive maintenance in industrial rotating machinery. Full article

The Discrete Element Method is widely used in applied mechanics, particularly in situations where material continuity breaks down (fracturing, crushing, friction, granular flow) and classical rheological models fail (phase transition between solid and granular). In this study, the Discrete Element Method was employed to simulate stick–slip cycles, i.e., numerical earthquakes. At 2000 selected, regularly spaced time checkpoints, parameters describing the average state of all particles forming the numerical fault were recorded. These parameters were related to the average velocity of the particles and were treated as the numerical equivalent of (pseudo) Acoustic Emission. The collected datasets were used to train the Random Forest and Deep Learning models that successfully predicted the time to failure. SHapley Additive exPlanations (SHAP) was used to quantify the contribution of individual physical parameters of the particles to the prediction results. The main novelty of this study was the prediction of time to failure for entire event sequences. Using only instantaneous particle velocity statistics and without using information about the history of previous events, coefficients of determination in the range R² = 0.81–0.96 were obtained. Full article

Traditional expansion joints in bridge structures are prone to durability problems, such as leakage, corrosion, and high maintenance demands, which can significantly reduce service life. To overcome these limitations, ultra-high-performance concrete (UHPC) link slabs have emerged as an effective jointless solution; however, their mechanical performance and sensitivity to key design and modeling parameters are not yet fully understood. This study presents a nonlinear finite element investigation of UHPC link slabs using the Concrete Damaged Plasticity (CDP) model in ABAQUS. A baseline model, validated against the experimental results, was established with a link slab length of 1100 mm and representative material and detailing properties. A systematic sensitivity analysis was then performed by varying five geometrical parameters (link slab length and thickness, debonding length, reinforcement diameter, and reinforcement spacing) and five numerical/material parameters (non-debonding and debonding interface friction coefficient, UHPC and normal concrete compressive strength, and steel yield strength). For each case, the load–displacement response was examined through initial stiffness (K₀), yield and peak load–deformation values (P_y, Δ_y and P_u, Δ_u), and ductility ratio (μ). The results highlight the dominant role of reinforcement detailing; larger bar diameters and closer spacing substantially increased stiffness and strength while maintaining ductility. Debonding length emerged as a critical tuning parameter, with longer debonding improving ductility but slightly reducing strength. Slab thickness primarily influenced stiffness, whereas overall length showed minor effects on peak capacity. On the numerical side, steel yield strength proved to be the most influential input, affecting all response measures, while the non-debonding interface friction coefficient strongly governed yield capacity. Variations in the debonding friction coefficient, UHPC compressive strength, and normal concrete strength exhibited secondary influence within the tested ranges. Overall, the findings provide practical guidance for both the designing and detailing of UHPC link slabs and the calibration of FEM (finite element modeling) models. By clarifying which parameters most strongly govern stiffness, strength, and ductility, this study supports more reliable structural design and efficient numerical modeling of UHPC link slabs in accelerated bridge construction applications. Full article

Recent advances in nanotechnology have highlighted the transformative potential of carbon-based nanomaterials, such as carbon nanofibers, carbon nanotubes, and graphene, in cementitious systems. These materials have shown a remarkable ability to enhance the mechanical strength, fracture toughness, and overall functional performance of cementitious composites. Their nanoscale dimensions and exceptional intrinsic properties allow for effective stress bridging, crack arrest, and matrix densification. Despite these promising features, the current understanding remains limited, particularly regarding their application to concrete. Furthermore, literature lacks systematic, parallel evaluations of their respective effectiveness in improving both mechanical performance and long-term durability, as well as their potential to impart true multifunctionality to concrete structures. It is worth noting that significant and statistically significant improvements in fracture behavior were observed at specific nanofiller concentrations, suggesting strong potential for the material system in next-generation innovative infrastructure applications. Experimental results demonstrated that both CNTs and GNPs significantly enhanced the mechanical performance of concrete, with flexural strength increases of approximately 49% and 38%, and compressive strength improvements of 22% and 47%, respectively, at optimum contents of 0.6 wt.% CNTs and 0.8 wt.% GNPs. SEM analyses confirmed improved matrix densification and interfacial bonding at these concentrations, while higher dosages led to agglomeration and reduced performance. This gap highlights the need for targeted experimental studies to elucidate the structure-property relationships governing these advanced materials. Full article

In this study, the influence of nanochitosan and kenaf fibers on the tensile strength, elastic modulus, and impact strength of polylactic acid (PLA)/natural rubber (Standard Malaysian Rubber, grade 20—SMR20) biocomposites was investigated experimentally using Response Surface Methodology (RSM). The independent variables included the weight percentage of nanochitosan (2, 4, and 6 wt%), kenaf fibers (5, 10, and 15 wt%), and SMR20 natural rubber (10, 20, and 30 wt%). Composite samples were prepared by melt mixing in an internal mixer and subsequently fabricated into test samples using hot compression molding in accordance with relevant standards. Tensile tests were conducted to evaluate tensile strength and elastic modulus, while Charpy impact tests were performed to assess impact strength. The results revealed that increasing nanochitosan content up to 4 wt% enhanced tensile strength, elastic modulus, and impact strength by 39%, 22%, and 27%, respectively; however, further addition (6 wt%) led to a decline in these properties due to nanoparticle agglomeration. Increasing kenaf fiber content to 15 wt% improved tensile strength, elastic modulus, and impact strength by 44%, 26%, and 37%, respectively, demonstrating their effective reinforcing role. The incorporation of SMR20 natural rubber significantly increased impact strength by 59% (at 30 wt%), while causing a reduction of 17% in tensile strength and 20% in elastic modulus, consistent with its elastomeric nature. Furthermore, field emission scanning electron microscopy (FESEM) was employed to examine the dispersion of nanochitosan and kenaf fibers within the PLA/SMR20 matrix, providing insights into the interfacial adhesion and failure mechanisms. The findings highlight the potential of optimizing natural filler and rubber content to tailor the mechanical performance of sustainable PLA-based biocomposites. Full article

This paper presents a systematic review conducted under the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, analyzing 286 scientific articles focused on vibration-based predictive maintenance strategies for wind turbines within the context of advanced Prognostics and Health Management (PHM). The review combines international standards (ISO 10816, ISO 13373, and IEC 61400) with recent developments in sensing technologies, including piezoelectric accelerometers, microelectromechanical systems (MEMS), and fiber Bragg grating (FBG) sensors. Classical signal processing techniques, such as the Fast Fourier Transform (FFT) and wavelet-based methods, are identified as key preprocessing tools for feature extraction prior to the application of machine-learning-based diagnostic algorithms. Special emphasis is placed on machine learning and deep learning techniques, including Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and autoencoders, as well as on hybrid digital twin architectures that enable accurate Remaining Useful Life (RUL) estimation and support autonomous decision-making processes. The bibliometric and case study analysis covering the period 2020–2025 reveals a strong shift toward multisource data fusion—integrating vibration, acoustic, temperature, and Supervisory Control and Data Acquisition (SCADA) data—and the adoption of cloud-based platforms for real-time monitoring, particularly in offshore wind farms where physical accessibility is constrained. The results indicate that vibration-based predictive maintenance strategies can reduce operation and maintenance costs by more than 20%, extend component service life by up to threefold, and achieve turbine availability levels between 95% and 98%. These outcomes confirm that vibration-driven PHM frameworks represent a fundamental pillar for the development of smart, sustainable, and resilient next-generation wind energy systems. Full article

This work investigates a data-driven approach for detecting structural damage in the wing of a Cessna 172 aircraft using reduced-order finite element (FE) models. This study focuses on the ability of machine learning methods to generalize across different structural conditions, aiming to support reliable Structural Health Monitoring (SHM) in aeronautical applications. The wing was first modeled in detail using the FiniteElement Method, followed by the development of a simplified FE model to reduce computational cost while maintaining accuracy. The similarity between the two models was evaluated through modal analysis and the Modal Assurance Criterion (MAC). Dynamic excitation representing turbulence effects was applied to simulate healthy and damaged conditions, producing acceleration data used to train one-dimensional and two-dimensional neural network classifiers. The 1D models processed raw vibration signals, while the 2D models used image representations of the same data. Both architectures were tested against results from the detailed FE model to assess their generalization capability. The 2D networks achieved higher classification accuracy, demonstrating improved robustness in identifying both minor and severe damage. The findings highlight the potential of combining reduced FE models with data-driven methods for efficient and accurate aircraft wing damage detection. Full article

This study investigates the dynamic behaviors of re-entrant unit-cell-shaped steel frames through numerical and experimental modal analyses. Inspired by re-entrant honeycomb structures, individual frame units were modeled to explore how natural frequencies vary with beam cross-sectional dimensions and frame angles. Twenty distinct frame models—incorporating four cross-sectional sizes (4 × 4 mm, 8 × 8 mm, 12 × 12 mm, and 16 × 16 mm) and five main frame angles (120°, 150°, 180°, 210°, and 240°)—were developed using 3D modeling and finite element analysis (FEA) tools, and the first eight natural frequencies and corresponding mode shapes were extracted for each model. The results reveal that lower modes exhibit global bending and torsional behaviors, whereas higher modes demonstrate increasingly localized deformations. It is found that the natural frequencies decrease in the straight frame configuration and increase in the hexagonal configurations, highlighting the critical influence of the frame geometry. Increasing the cross-sectional size consistently enhances the dynamic stiffness, particularly in hexagonal frames. A quadratic polynomial surface regression analysis was performed to model the relationship of the natural frequency with the cross-sectional dimension and frame angle, achieving high predictive accuracy (R² > 0.98). The experimental validation results were in good agreement with the numerical results, with discrepancies generally remaining below 7%. The developed regression model provides an efficient design tool for predicting vibrational behaviors and optimizing frame configurations without extensive simulations; furthermore, experimental modal analyses validated the numerical results, confirming the effectiveness of the model. Overall, this study provides a comprehensive understanding of the dynamic characteristics of re-entrant frame structures and proposes practical design strategies for improving vibrational performance, which is particularly relevant in applications such as machine foundations, vibration isolation systems, and aerospace structures. Full article

An explicit coupled two-dimensional (2D) hydromechanical model (HMM) that can simulate discontinuous features in the foundation, as well as the effects of grout curtains and drainage systems, is employed to evaluate the influence of key parameters such as dam height, foundation behaviour, joint patterns, joint stiffness and strength, hydraulic apertures, and grout curtain permeability. A parametric sensitive study using four gravity dams, and a real case study of an operating dam are presented. The results presented show that dam height influences the relationship between water level in the reservoir and drain discharges, with higher dams showing more pronounced curved nonlinearity. The strength properties of the concrete–rock interface are also shown to have a meaningful influence on the HM response, especially for an elastic foundation and for higher dams, showing the need to properly characterize this interface through in situ testing. The joint aperture at nominal zero stress is shown to be the parameter with the most significant effect on the HM response. The results also show that a progressive degradation scenario of the concrete–rock interface or of the grout curtain permeability is easier to identify through the hydraulic measurements than in the mechanical displacement field. Full article

Large-diameter butterfly valves are essential control components in high-flow hydraulic systems, where blade geometry directly impacts operational reliability, energy efficiency, and lifecycle cost. This study presents an integrated structural–hydraulic optimization of a DN3000 Boving butterfly valve blade rated for a maximum operating pressure of 10 bar with comparative analysis of a conventional flat blade and an optimized curved blade. The work applies a CFD–FEM framework specifically to DN3000 Southern African valves, which is rare in the literature. Numerical simulations evaluated stress distribution, deformation, pressure losses, and flow stability under design and hydrostatic test conditions. The curved blade achieved a 58.6% reduction in peak von Mises stress, a 50% reduction in weight, a 22% reduction in load loss, and a 33% reduction in actuation torque requirements, while maintaining seal integrity. Cost analysis revealed a 50% reduction in material costs and simplification of manufacturing. The results confirm that the introduction of curvature significantly improves structural strength and hydraulic efficiency, thus providing a reproducible framework for the design of lighter and more economical valves in hydropower, municipal and industrial applications. Full article

This study conducts systematic experimental and numerical investigations to address the parameter calibration issue in the discrete element model of seashells, aiming to establish a high-fidelity numerical model that accurately characterizes their macroscopic mechanical behavior, thereby providing a basis for optimizing parameters of seashell crushing equipment. Firstly, intrinsic parameters of seashells were determined through physical experiments: density of 2.2 kg/m³, Poisson’s ratio of 0.26, shear modulus of 1.57 × 10⁸ Pa, and elastic modulus of 6.5 × 10¹⁰ Pa. Subsequently, contact parameters between seashells and between seashells and 304 stainless steel, including static friction coefficient, rolling friction coefficient, and coefficient of restitution, were obtained via the inclined plane method and impact tests. The reliability of these contact parameters was validated by the angle of repose test, with a relative error of 5.1% between simulation and measured results. Based on this, using ultimate load as the response indicator, the PlackettBurman experimental design was employed to identify normal stiffness per unit area and tangential stiffness per unit area as the primary influencing parameters. The Bonding model parameters were then precisely calibrated through the steepest ascent test and design, resulting in an optimal parameter set. The error between simulation results and physical experiments was only 3.8%, demonstrating the high reliability and accuracy of the established model and parameter calibration methodology. Full article

This paper presents a unified aerodynamic design and optimization framework for morphing Blended-Wing-Body (BWB) Unmanned Aerial Vehicles (UAVs) operating in subsonic and near-transonic regimes. The proposed framework integrates parametric CAD modeling, Computational Fluid Dynamics (CFD), and surrogate-based optimization using Response Surface Methodology (RSM) to establish a generalized approach for geometry-driven aerodynamic design under multi-Mach conditions. The study integrates classical aerodynamic principles with modern surrogate-based optimization to show that adaptive morphing geometries can maintain efficiency across varied flight conditions, establishing a scalable and physically grounded framework that advances real-time, high-performance aerodynamic adaptation for next-generation BWB UAVs. The methodology formulates the optimization problem as drag minimization under constant lift and wetted-area constraints, enabling systematic sensitivity analysis of key geometric parameters, including sweep, taper, and twist across varying flow regimes. Theoretical trends are established, showing that geometric twist and taper dominate lift variations at low Mach numbers, whereas sweep angle becomes increasingly significant as compressibility effects intensify. To validate the framework, a representative BWB UAV was optimized at Mach 0.2, 0.4, and 0.8 using a parametric ANSYS Workbench environment. Results demonstrated up to a 56% improvement in lift-to-drag ratio relative to an equivalent conventional UAV and confirmed the theoretical predictions regarding the Mach-dependent aerodynamic sensitivities. The framework provides a reusable foundation for conceptual design and optimization of morphing aircraft, offering practical guidelines for multi-regime performance enhancement and early-stage design integration. Full article

This work presents an analytical homogenization model developed to predict the tensile and bending behavior of double-wall corrugated cardboard. The proposed approach replaces the complex three-dimensional geometry, composed of five paper layers, with an equivalent two-dimensional homogenized plate. Based on lamination theory and enhanced by sandwich structure theory, the model accurately captures the orthotropic behavior of the material. To achieve this objective, three configurations of double-wall corrugated cardboard were investigated: KRAFT LINER (KL), DUOSAICA (DS), and AUSTRO LINER (AL). A comprehensive experimental characterization campaign was conducted, including physical analyses (density measurement, SEM imaging, and XRD analysis) and mechanical testing (tensile tests), to determine the input parameters required for the homogenization process. The proposed model significantly reduces geometric complexity and computational cost while maintaining excellent predictive accuracy. Validation was performed by comparing the results of a 3D finite element model (ANSYS-19.2) with those obtained from the homogenized H-2D model. The differences between both approaches remained systematically below 2%, confirming the ability of the H-2D model to accurately reproduce the axial and flexural stiffnesses of double-wall corrugated cardboard. The methodology provides a reliable and efficient framework specifically dedicated to the mechanical analysis and optimization of corrugated cardboard structures used in packaging applications. Full article

This methodology investigates the determination of the fracture toughness of 3D-printed specimens under monotonic loading conditions. The application is based on the use of a Single Edge Notch Bending (SENB) specimen made by a 3D-printing process (17-4PH stainless steel). The load–displacement curves exhibited linear behavior until crack initiation, indicating that the Linear Elastic Fracture Mechanics (LEFM) can be used under a small-scale yielding assumption. This study extends a previous methodology, originally applied to a polymer, to a metal additively manufactured material. The methodology established in the paper represents a major outcome: the ability to characterize the fracture toughness of the material. This study extends our previous Digital Image Correlation-based methodology from thermoplastic polymers to 17-4PH stainless steel produced by metal additive manufacturing (ADAM). Its novelty lies in combining DIC with a finite element sub-model to evaluate fracture parameters, enabling accurate crack initiation detection in challenging metal AM specimens, and providing a methodology that can be generalized to other metals and AM processes. The aim of this study is to establish a robust DIC-based methodology for the identification of crack initiation and the determination of fracture toughness parameters (K_IC and J) in 3D-printed 17-4PH stainless steel produced by the ADAM process. Full article

The present article relates to the description of phenomenological relations of amorphous material behavior within the framework of viscoelasticity and elastic-viscoplasticity theory, as well as to the creation of its digital analog. Ultra-high-molecular-weight polyethylene (UHMWPE) is considered in the study. The model is based on the results of a series of experimental studies. Free compression of cylindrical specimens in a wide range of temperatures [−40; +80] °C and strain rates [0.1; 4] mm/min was performed. Cylindrical specimens were also used to determine the thermal expansion coefficient of the material. Dynamic mechanical analysis (DMA) was performed on rectangular specimens using a three-point bending configuration. Maxwell and Anand models were used to describe the material behavior. In the framework of the study, the temperature dependence of a number of parameters was established. This influenced the mathematical formulation of the Anand model, which was adapted by introducing the temperature dependence of the activation energy, the initial deformation resistance, and the strain rate sensitivity coefficient. Testing of the material models was carried out in the process of analyzing the deformation of a spherical bridge bearing with a multi-cycle periodic load. The load corresponded to the movement of a train on a bridge structure, without taking into account vibrations. It is shown that the viscoelastic model does not describe the behavior of the material accurately enough for a quantitative analysis of the stress–strain state of the structure. It is necessary to move on to more complex models of material behavior to minimize the discrepancy between the digital analog and the real structure; it has been established that taking into account plastic deformation while describing UHMWPE would allow this to be performed. Full article

This paper presents a numerical study of fluid–structure interaction (FSI) applied to wind turbines, combining computational fluid dynamics (CFD) and finite element analysis (FEA). The study focuses on a 3D wind turbine blade inspired by the GE 1.5XLE model. The blade features a twisted geometry with S818, S825, and S826 aerodynamic profiles, and is made of an orthotropic composite material with variable thickness and an internal spar. The fluid domain is defined by two circular sections upstream and downstream, aligned along the Z-axis. Simulations are performed under a wind speed of 12 m/s and a rotational speed of −2.22 rad/s (Tip Speed Ratio (TSR) = 8), with air modeled as an incompressible fluid at ambient temperature. On the CFD side, a periodic and symmetric modeling approach is applied, reducing the fluid domain to one-third of the full configuration by simulating flow around a single blade and extrapolating results to the remaining ones. This method achieves a 47% reduction in computation time while maintaining high accuracy in aerodynamic results. On the FEA side, spar condensation is performed by creating a superelement using the substructuring method. This strategy reduces structural computation time by 45% while preserving reliable predictions of displacements, stresses, and natural frequencies. These results confirm the effectiveness of the proposed techniques for accurate and computationally efficient aeroelastic simulations. Full article