Finite Element Analysis Simulation Research Articles

Interfacial [S─Cu─C] Bonds Induced π-d Electron Coupling Toward Modulating Charge Transfer for Efficient Solar Water Oxidation.

Regulating the interfacial electric field to achieve rapid charge transfer is crucial for superior photoelectrochemical water splitting. Herein, the ultra-thin hydrogen-substituted graphdiyne (HsGDY) is precisely assembled on the surface of CdS nanorod array (Cu-CdS-HsGDY) by an in situ polymerization strategy. The strong π-d electron coupling is aroused by the delocalized π electrons of HsGDY and the delocalized d electrons of CdS through the interfacial [S─Cu─C] bonds. The strong interfacial electric field can effectively promote the charge localization distribution and reduce the charge transfer resistance. The optimized Cu-CdS-HsGDY photoanode obtain a photocurrent density as high as 4.83 mA cm-2 at 1.23 V versus reversible hydrogen electrode in neutral electrolyte solution under AM 1.5G illumination, which is 6.8 times that of the pristine CdS. Moreover, the photoanode maintains an initial photocurrent density of 84% within 4 h without any assistance of sacrificial agents, which is a rather competitive performance of similar sulfide photoanodes. The mechanism of strong π-d electron coupling on interfacial charge transfer and surface reaction kinetics is investigated by transient spectroscopy measurements, density functional theory calculation, and finite element simulation analysis. This work provides new insights into designing a reasonable interface structure to regulate charge transfer for achieve efficient PEC water splitting.

Application of machine learning for ‘what if?’ stress analysis

Analysis of stresses in components of even modestly complex geometries often require the use of finite element analysis (FEA). For testing a large number of design options quickly, FEA can be time consuming and provides more accuracy than required. In this project, a machine learning-based system is developed to provide quick and approximate solutions to stress analysis problems in parametrised compressor disc geometries. Simple mechanics problems were completed preliminarily to test the practicality of machine learning approaches for this application. This included applying instance selection by the [Formula: see text]-medoids algorithm to a 2D FEA problem. A parametrised compressor disc geometry was designed and defined by eight dimensions. Stress fields were produced from two superimposed loading schemes: the rotational body force and the force exerted on the disc by the blades 495,338 samples of training data were collected from 4374 FEA simulations. Four networks were trained to predict stresses caused by each loading scheme in order to produce stress fields. The best network was of the structure 9/20/20/2 and used normalised training data. For the geometry tested, it predicted stresses with a root-mean-square error of 1.51%. The code took 0.7 s to run in total, from start-up to completion of a stress plot. The importance of the inputs in the training data set were scored with a feature selection algorithm to aid further optimisation of the system. The low computation time makes this system suitable for the early stages in a design process.

Simulation analysis and experimental research of friction performance between wood and cemented carbide surface with different micro-textures

ABSTRACT Reasonable micro-pit texture has been proved to be able to improve the friction characteristics between wood and cemented carbide. In this paper, the finite element simulation analysis was used to simulate the friction surface stress, the simulation model was determined, and the simulation results were combined with the friction characteristics test to study the influence of different texture types on the friction coefficient of the wood surface. The results show that the friction coefficient and the stress magnitude of friction between cemented carbide and wood surface have a good linear correlation (R 2 = 0.9414), the finite element simulation can provide a reference for the study of friction behavior in the wood cutting process. The friction coefficient can be influenced by texture parameters such as texture type, the friction length, the width of micro-texture and the angle of micro-texture. Under the condition of the same texture area occupancy, the surface friction coefficient of the micro-pit texture is the smallest; the surface friction coefficient of the micro-groove texture is the largest, and the micro-grid texture has the smaller friction coefficient with the decrease of the texture angle. This is related to the actual contact area between the texture area of the friction area and the wood surface and the main braking force generated during contact.

Bayesian mesh optimization for graph neural networks to enhance engineering performance prediction

Abstract In engineering design, surrogate models are widely employed to reduce the computational costs of simulations by approximating design variables and geometric parameters from computer-aided design (CAD) models. However, traditional surrogate models often lose critical information when simplified to lower dimensions, and face challenges in handling the complexity of 3D shapes, especially in industrial datasets. To address these limitations, we propose a Bayesian graph neural network (GNN) framework that directly learns geometric features from CAD mesh representations for accurate engineering performance prediction. Our framework leverages Bayesian optimization (BO) to dynamically determine the optimal mesh element size, significantly improving model accuracy while balancing computational efficiency. This approach addresses the challenge of mesh quality by optimizing mesh resolution, ensuring that critical geometric features are preserved in 3D deep-learning-based surrogate models. Unlike existing models that rely on static or predefined mesh resolutions, our method adapts mesh size based on the task, making it highly flexible for a wide range of engineering applications. Experimental results demonstrate that mesh quality directly impacts prediction accuracy, and the proposed BO-EI GNN model outperforms state-of-the-art models such as 3D CNN, SubdivNet, GCN, and GNN in predicting mass, rim stiffness, and disk stiffness. Our method also significantly reduces the computational costs compared to traditional optimization techniques. The proposed framework shows promising potential for application in finite element analysis (FEA) and other mesh-based simulations, enhancing the utility of surrogate models across various engineering fields.

WASTE EGGSHELL AND SAWDUST AS REINFORCEMENTS FOR SUSTAINABLE POLYESTER COMPOSITES: MECHANICAL CHARACTERIZATION AND ENVIRONMENTAL DURABILITY ASSESSMENT

This research endeavor undertakes an experimental investigation into the bending and tensile properties of polyester composites reinforced with waste eggshell powder and sawdust particles at 35 wt.%. Valorizing these waste materials as reinforcements contributes to sustainable practices, waste minimization, and environmental pollution mitigation. Composite samples were fabricated using hand lay-up and compression molding techniques. The effects of environmental exposure to water, acidic, and basic media immersion on the mechanical performance were evaluated to assess durability. Experimental characterization through tensile and three-point bending tests determined mechanical properties, stress-strain behavior, and failure mechanisms. Additionally, finite element analysis (FEA) simulations complemented the experiments by providing insights into stress distributions, deformations, and potential failure initiation sites within the composites under flexural loading. The computational models accurately captured the reinforcing effects of the waste materials and the degradation induced by environmental exposure, validating the experimental trends. Incorporating waste natural fibers significantly enhanced polyester's bending strength by 140% (sawdust) and 75% (eggshell), and tensile strength by 13% (sawdust) and 10% (eggshell). However, environmental exposure degraded properties to varying extents, with water immersion reducing tensile strength by 63% (sawdust) and 3% (eggshell), and flexural strength by 63% (sawdust) and 3% (eggshell). The acidic medium caused 58% (sawdust) and 23% (eggshell) reductions in tensile strength, and similar flexural strength degradations. Strikingly, the basic medium decreased eggshell composite tensile strength by 170% and flexural strength by 6%, while sawdust composite strengths declined by 44%.

Experimental study and finite element analysis on shear performance of offshore wind turbine jacket foundation

The repeated extrusion of sea ice leads to the continuous collapse of marine structures. In this paper, the shear test and finite element simulation analysis of jacket foundation of large offshore wind turbine are carried out. The results show that the bearing capacity of the structure under positive displacement loading is slightly larger than that under negative displacement loading, and the bearing capacity is increased by 5.3 %. The lateral support joints have compression bending and fracture failure modes, and the maximum strain is 3600 με. Finally, the failure mode, energy dissipation capacity and bearing capacity of jacket foundation under reciprocating displacement load are obtained. At the same time, the ice load action model is established, and the research results provide experimental basis for improving the shear performance of offshore wind turbine.

The Role of Thermal Analysis in Engine Fin Design: Insights and Perspectives

Objective: The study aims to address the significance of fins in enhancing the heat transfer rate of engine cylinders and highlight their applications and advancements in various fields. Methods: Thermal analysis of fins using ANSYS Workbench is an invaluable tool for engineers and researchers in determining and optimizing the heat transfer characteristics of finned structures. ANSYS Workbench provides a comprehensive platform for evaluating the performance of fins in various applications using simulations of computational fluid dynamics (CFD) and finite element analysis (FEA). With ANSYS Workbench, it is possible to assess the thermal behaviour of fins under a variety of conditions, such as variable boundary conditions and thermal loads. Some significant recent patents on Engine fin are also discussed in this review article. Results: Fins are designed to maximize contact with the surrounding air or coolant, thereby facilitating engine heat transfer to the surrounding environment. Engineers can enhance heat dissipation capabilities by considering parameters such as fin geometry, density, material selection, and manufacturing techniques while minimizing associated drawbacks. Conclusion: The numerous applications of fins highlighted in this review article will encourage researchers to find novel strategies to improve heat transfer by employing fins for use in a variety of sectors.

Enhanced high-temperature energy storage performance in all-organic dielectric films through synergistic crosslinking of chemical and physical interaction

Advanced electronic devices and energy systems urgently require high-temperature polymer dielectrics that can offer both high discharge energy density and energy storage efficiency. However, the capacitive properties of most polymers sharply deteriorate at elevated temperatures, due to the significant rise in leakage current density and energy loss. Herein, a new design approach is adopted to fabricate high-temperature polyetherimide (PEI) dielectrics with chemical and physical cooperative crosslinking networks, the dual-crosslinked PEI dielectrics are prepared through amine crosslinking agent and the electrostatic interaction of oppositely charged phenyl groups between triptycene (TE) and PEI chain segments. Benefiting from the increased crosslinking sites, the dual-crosslinked PEI films achieve the simultaneously enhancement in Tg, modulus and bandgap compared to un-crosslinked and single-crosslinked polymers. Meanwhile, the PEI with dual-crosslinked network displays higher chain packing density, effectively reducing the mean motion pathways of charge carriers and the conduction loss inside polymer dielectric. Consequently, the dual-crosslinked PEI containing 0.25 wt% TE delivers an outstanding discharge energy density of 2.69 J/cm3 and retains excellent cyclability after 100,000 charge–discharge cycles at 200 ℃. Additionally, finite element analysis and molecular dynamics simulation further confirm that less Joule heat and tighter chain structure are formed in the dual-crosslinked polymer dielectric. This research offers a novel methodology to prepare high-performance polymer dielectrics for high-temperature applications.

Optimization of Stator Structure for Improved Accuracy in Variable Reluctance Resolvers Using Advanced Machine Learning Techniques

This study presents an optimized design for a Segmented Sinusoidal Parameter Winding with Magnetic Wedge Variable Reluctance Resolver (SSPWMW-VRR), addressing challenges like winding asymmetry and harmonic distortion in conventional designs. By integrating particle swarm optimization (PSO) for winding design, magnetic equivalent circuit (MEC) analysis for leakage flux, and machine learning techniques (XGBoost and Multi-Layer Perceptron), the stator slot shape was fine-tuned for improved accuracy. XGBoost outperformed MLP in prediction accuracy with a mean absolute error (MAE) of 0.1172. Finite element analysis (FEA) simulations and experimental validation demonstrated a reduction in position errors from ±30′ in conventional VRRs to ±5′ in the optimized design, along with significant harmonic reduction.

A comprehensive study of AFM stiffness measurements on inclined surfaces: theoretical, numerical, and experimental evaluation using a Hertz approach

Atomic Force Microscopy (AFM) is a leading nanoscale technique known for its significant advantages in the analysis of soft materials and biological samples. Traditional AFM data analysis is often based on the Hertz model, which assumes perpendicular indentation of a planar sample. However, this assumption is not always valid due to the varying geometries of soft materials, whether natural, synthetic or biological. In this study, we present a new theoretical model that incorporates correction coefficients into Hertz’s model to account for cone-like and spherical probes, and to consider local tilt at the probe-sample interface. We validate our model using finite element analysis (FEA) simulations and experimental AFM measurements on tilted polyacrylamide gels. Our results highlight the need to include local tilt at the probe-sample contact to ensure accurate AFM measurements. This represents a step forward in our understanding of the elastic properties at the surface of soft materials in the broadest sense.

Applicability evaluation technology for steel small diameter pipes containing external surface corrosion defects

With the continuous operation of oil and gas stations and petrochemical equipment, it has been found that steel small bore pipelines have different degrees of corrosion defects, which have led to pipeline cracking and medium leakage, and have increasingly attracted great attention of their oil and gas and chemical enterprises. In order to ensure the safe operation of the small diameter pipes with external surface corrosion defects, the influence law of the size of corrosion defects on the bearing capacity of the small diameter pipes were studied by finite element analysis and test simulation method, and the damage mechanism and failure form were analyzed, and the failure pressure prediction formula of the small diameter pipes with defects was fitted. It provides technical guidance for the applicability evaluation of corrosion-containing steel small diameter pipes in oil and gas chemical stations or installations.

On the Relative Effects of Wall and Intraluminal Thrombus Constitutive Material Properties in Abdominal Aortic Aneurysm Wall Stress.

An abdominal aortic aneurysm (AAA) is a dilation localized in the infrarenal segment of the abdominal aorta that can expand continuously and rupture if left untreated. Computational methods such as finite element analysis (FEA) are widely used with in silico models to calculate biomechanical predictors of rupture risk while choosing constitutive material properties for the AAA wall and intraluminal thrombus (ILT). In the present work, we investigated the effect of different constitutive material properties for the wall and ILT on 21 idealized and 10 unruptured patient-specific AAA geometries. Three material properties were used to characterize the wall and two for the ILT, leading to six material model combinations for each AAA geometry subject to appropriate boundary conditions. The results of the FEA simulations indicate significant differences in the average peak wall stress (PWS), 99th percentile wall stress (99th WS), and spatially averaged wall stress (SAWS) for all AAA geometries subject to the choice of a material model combination. Specifically, using a material model combination with a compliant ILT yielded statistically higher wall stresses compared to using a stiff ILT, irrespective of the constitutive equation used to model the AAA wall. This work provides quantitative insight into the variability of the wall stress distributions ensuing from AAA FEA modeling due to its strong dependency on population-averaged soft tissue material characterizations. This dependency leads to uncertainty about the true biomechanical state of stress of an individual AAA and the subsequent assessment of its rupture risk.

Enhanced Mechanical Properties and Degradation Control of Poly(Lactic) Acid/Hydroxyapatite/Reduced Graphene Oxide Composites for Advanced Bone Tissue Engineering Application

This study explores the enhancement of poly(lactic acid) (PLA) matrix using calcium hydroxyapatite (cHAP) and reduced graphene oxide (rGO) for developing composite scaffolds aimed at bone regeneration applications. The PLA composites were fabricated through solvent evaporation and melt extrusion and characterized by various techniques, including thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and mechanical testing. The incorporation of cHAP and rGO significantly improved the thermal, mechanical, and morphological properties of the PLA matrix. Mechanical testing revealed that adding 10% cHAP and varying amounts of rGO (0.1%, 0.3%, 0.5%) enhanced tensile and compressive strengths, with the highest improvements observed at 0.5% rGO content. Thermal analysis showed increased thermal stability with higher degradation temperatures for the composites. Spectroscopic analyses confirmed the effective integration of cHAP and rGO into the PLA matrix with characteristic peaks of the fillers identified in the composite spectra. In vitro, degraded action tests in phosphate-buffered saline (PBS) at pH 7.4 over 12 months indicated that composites with higher rGO content exhibited lower mass loss and better mechanical stability. Furthermore, finite element analysis (FEA) simulations were performed to validate the experimental results, demonstrating a strong correlation between simulated and experimental compressive strengths. This novel approach demonstrates the potential of PLA/cHAP/rGO composites in creating effective and biocompatible scaffolds for tissue engineering, providing a comprehensive analysis of the synergistic effects of cHAP and rGO on the PLA matrix and offering a promising material for bone regeneration applications.

Four ribbons of double-layer graphene suspending masses for NEMS applications

Graphene ribbons with a suspended proof mass for nanomechanical systems have been rarely studied. Here, we report three types of nanomechanical devices consisting of graphene ribbons (two ribbons, four ribbons-cross and four ribbons-parallel) with suspended Si proof masses and studied their mechanical properties. The resonance frequencies and built-in stresses of three types of devices ranged from tens of kHz to hundreds of kHz, and from 82.61 MPa to 545.73 MPa, respectively, both of which decrease with the increase of the size of proof mass. The devices with four graphene ribbons featured higher resonance frequencies and spring constants, but lower built-in stresses than two ribbon devices under otherwise identical conditions. The Young’s modulus and fracture strain of double-layer graphene were measured to be 0.34 TPa and 1.13% respectively, by using the experimental data and finite element analysis (FEA) simulations. Our studies would lay the foundation for understanding of mechanical properties of graphene ribbons with a suspended proof mass and their potential applications in nanoelectromechanical systems.

ViscoNet: A lightweight FEA surrogate model for polymer nanocomposites viscoelastic response prediction

Polymer-based nanocomposites (PNCs) are formed by dispersing nanoparticles (NPs) within a polymer matrix, which creates polymer interphase regions that drive property enhancement. However, data-driven PNC design is challenging due to limited data. To address the challenge, we present ViscoNet, a surrogate model for finite element analysis (FEA) simulations of PNC viscoelastic (VE) response. ViscoNet leverages pre-training and finetuning to accelerate predicting VE response of a new PNC system. By predicting the entire VE response, ViscoNet surpasses previous scalar-based surrogate models for FEA simulation, offering better fidelity and efficiency. We explore ViscoNet's effectiveness through generalization tasks, both within thermoplastics and from thermoplastics to thermosets, reporting a mean absolute percentage error (MAPE) of < 5 % for rubbery modulus and < 1 % for glassy modulus in all cases and 1.22 % on tan δ peak height prediction. With only 500 FEA simulations for finetuning, ViscoNet can generate over 20k VE responses within 2 min with 1 CPU, compared to 97 days with 4 CPUs via FEA simulations.

Explainable machine learning models for predicting the ultimate bending capacity of slotted perforated cold-formed steel beams under distortional buckling

This study develops explainable machine learning (ML) models to predict the ultimate bending capacity of cold-formed steel (CFS) beams with staggered slotted perforations, focusing on distortional buckling behavior. Utilizing a dataset from 432 non-linear finite element analysis simulations of CFS Lipped channels, ten ML algorithms, including four basic and six ensemble models, were evaluated. Ensemble models, specifically CatBoost and XGBoost, demonstrated superior accuracy, with test-set performances reaching a coefficient of determination (R2) of 99.9%, outperforming traditional analytical methods such as the Direct Strength Method (DSM). SHapley Additive Explanations (SHAP) were applied to highlight how features like plate thickness and root radius critically influence predictions. The findings underscore the enhanced predictive capabilities of ML models for structural performance, suggesting a significant potential to refine traditional design methodologies and optimize CFS beam designs.

Novel Soft Gripper With Adjustable Performance Based on Blade Flexures

Abstract The design of soft grippers is challenging as the target objects to be handled involve a wide variety of sizes, shapes, and softness. Most grippers reported present drawbacks, e.g., complex control strategies for stiffness and force variation, inadequate adaptability to target shape variability, and complicated adaptation to manipulators, which in turn limit their implementation in applications such as harvesting. This paper presents a novel overlay soft gripper based on an assembling mechanism and passive soft structures, enabling the modification of grip size, orientation, and gripping force. This gripper can be assembled over a commercial robot-gripper taking the closing motion of this as the input to perform its closing motion. Input and output parameters are related by means of the displacement transmission mechanism which converts the closing motion of the robot-gripper into the closing motion of the overlay soft gripper. The gripping force can be modified through the change of stiffness, via adjustment of effective length using contact elements, of internal blade flexures. The displacement transmission mechanism works in two modes: as rigid-body mechanism without the contact elements and as rigid-body/flexible mechanism with the contact elements. The relationships between input and output parameters are obtained analytically for the case of rigid-body mechanism, and through finite element analysis simulations for the case of rigid-body/flexible mechanism. Relationships between input and output parameters are approximated with polynomial surfaces. Finally, physical prototypes are manufactured and assembled on their respective robot-grippers to qualitatively demonstrate their performance.

Advanced optimization of elastic sheets for solar parabolic trough concentrators: integrating particle swarm optimization and genetic algorithms

Abstract The fabrication of solar parabolic trough concentrators using flat elastic sheets presents a straightforward and cost-effective method. This paper introduces an optimization technique centered on stiffness adjustment, harnessing elastic buckling to attain precise parabolic shapes in these concentrators. Through an enhanced Particle Swarm Optimization-Genetic Algorithm (PSO-GA), strategically punched holes are optimized on the flat sheet, allowing for the attainment of perfect parabolic shapes by controlling the chord length with a positional rod or cable. The efficacy of this approach is showcased not only through interactive finite element analysis and ray tracing software simulations but also via experimental sunlight concentration. A geometric concentration ratio of up to 145.16 is achieved, underscoring the effectiveness of this innovative concept. This approach facilitates the simple fabrication and transportation of flat mirror elements to field sites, where they can be assembled into parabolic trough concentrators, offering potential cost reductions and highly efficient solar energy solutions.

A Novel Caterpillar-Inspired Vascular Interventional Robot Navigated by Magnetic Sinusoidal Mechanism

Magnetic soft continuum robots (MSCRs) hold significant potential in fulfilling the requirements of vascular interventional robots, enabling safe access to difficult-to-reach areas with enhanced active maneuverability, shape morphing capabilities, and stiffness variability. Their primary advantage lies in their tether-less actuation mechanism that can safely adapt to complex vessel structures. Existing commercial MSCRs primarily employ a magnetic-pull strategy, which suffers from insufficient driving force and a single actuation strategy, limiting their clinical applicability. Inspired by the inchworm crawling locomotion gait, we herein present a novel MSCR that integrates a magnetic sinusoidal actuation mechanism with adjustable frequency and kirigami structures. The developed MSCRs consist of two permanent magnets connected by a micro-spring, which is coated with a silicone membrane featuring a specific notch array. This design enables bio-inspired crawling with controllable velocity and active maneuverability. An analytical model of the magnetic torque and finite element analysis (FEA) simulations of the MSCRs has been constructed. Additionally, the prototype has been validated through two-dimensional in-vitro tracking experiments with actuation frequencies ranging from 1 to 10 Hz. Its stride efficiency has also been verified in a three-dimensional (3D) coronary artery phantom. Diametrically magnetized spherical chain tip enhances active steerability. Kirigami skin is coated over the novel guidewire and catheter, not only providing proximal anchorage for improved stride efficiency but also serving similar function as a cutting balloon. Under the actuation of an external magnetic field, the proposed MSCRs demonstrate the ability to traverse bifurcations and tortuous paths, indicating their potential for dexterous flexibility in pathological vessels.

Finite Element Analysis and Computational Fluid Dynamics for the Flow Control of a Non-Return Multi-Door Reflux Valve

This paper presents a comprehensive analysis of a multi-door check valve using computational fluid dynamics (CFD) and finite element analysis (FEA) to evaluate flow performance under pressure test conditions, with an emphasis on its ability to prevent backflow. Check valves are essential components in various industries, ensuring fluid flow in one direction only while preventing reverse flow. The non-return multi-door reflux valve is increasingly preferred due to its superior backflow prevention, fluid control, and effective flow regulation. Rigorous testing under varying pressure conditions is essential to ensure that these valves perform optimally. This study uses CFD and FEA simulations to evaluate the structural integrity and flow characteristics of the valve, including pressure drop, flow velocity, backflow prevention effectiveness, and flow coefficient. A high-fidelity 3D model was created to simulate the valve’s behavior under various conditions, analyzing the effects of parameters such as the number of doors, their orientation, geometry, and operating conditions. The CFD results demonstrated a significant reduction in backflow and pressure drop across the valve. However, localized turbulence and flow separation near the valve doors, particularly under partially open conditions, have raised concerns about potential wear. The velocity profiles indicated a uniform distribution at full opening with laminar velocity profiles and minimal resistance to flow. The results of the FEA showed that the stresses induced by the fluid forces were below critical levels, with the highest stress concentrations observed around the hinge points of the valve doors. Although the valve structure remained intact under normal operating conditions, some areas may have required reinforcement to ensure long-term durability. Combined CFD and FEA analyses demonstrated that the valve effectively preserves system integrity, prevents backflow, and maintains consistent performance under various pressure and flow conditions. These findings provide valuable insights into design improvements, performance optimization, and enhancing the efficiency and reliability of reflux valve systems in industrial applications.