Finite Element Simulation Research Articles (Page 1)

Phased array technology involves the coordinated control of multiple elements to steer and focus elastic, electromagnetic, light, seismic, and radio waves in a specific location or direction. In structural integrity applications, it enables the precise inspection of materials and the identification of flaws/defects in structures. In this paper, we proposes a novel phased array method based on the steering and focusing of thermal waves, not previously explored for applications in NDT, named Phased-Array Thermography (PAT). This new three-dimensional approach aims to overcome the main limitations of most of the Active Infrared Thermography (IRT) methods that uniformly heat the component surface and generate a normal temperature gradient, resulting in lack of control in the gradient direction and, ultimately, limiting the identification capabilities of IRT. PAT leverages an array of heating elements to precisely steer and control the thermal wavefront. A closed-form analytical solution of the thermal wave propagation is derived and validated against numerical simulations. Then, the accuracy of proposed method is assessed via thermal Finite Element (FE) simulations of an aluminium plate by comparing PAT with a commonly used IRT technique such as the Pulsed Thermography (PT). Finally, experimental analyses of an aluminium plate with flat bottom holes and a composite plate with impact damage are performed to validate the proposed methodology. This novel approach to thermal wavefront steering via phased array technology introduces a previously unexplored mechanism for controlled heat wavefront, with transformative potential for non-destructive evaluation, structural health monitoring, and adaptive manufacturing systems.

Abstract This work investigates how the size and spatial distribution of cells influence the apparent Poisson's ratio of ordered and unordered (stochastic) flexible cellular structures at constant relative densities. A unifying semi-empirical model that captures the structures' mechanical behavior is first developed. The model assumes that adding cells with various diameters alters the apparent Poisson's ratio in an exponentially decaying or growing fashion. The model is parameterized by combinations of cell volume fractions that capture cell morphology and response, an exponential constant, and the apparent Poisson's ratio of a structure with equal cell sizes for a structure class. To validate the model, flexible structures with ordered and unordered cell arrangements are designed, manufactured, and characterized using experimental full-field measurements and finite element simulations. Experimental results validate that the apparent Poisson's ratios and elastic moduli of the ordered structures decrease with an increase in the cell size distribution in the linear deformation regime. Unordered structures show smaller systematic changes in apparent mechanical parameters with a change in cell size distribution. Full-field deformation analyses are performed at various length scales to discuss the contributions of the model parameters and structural deformation mechanisms. Experimental and finite element data are used to fit models for known cell volume fraction functions. Numerical simulations are then performed using the model to study the influence of key model parameters on the apparent Poisson's ratio behaviors in broad classes of cellular solids.

Aluminium alloy composites are extensively utilised in the aerospace, automobile, and marine industries due to their lightweight structure and high strength-to-density ratio. However, there remains significant potential to further improve these composites for advanced applications by enhancing their strength-to-weight ratio, corrosion resistance, wear resistance, and temperature performance. This study investigates the mechanical and tribological properties of AA7076 alloy reinforced with varying concentrations (1.0 and 1.5 wt.%) of perlite nanoclay. These composites were synthesized using a motorised stir casting process and characterised through tensile, wear, and hardness tests. Results showed that 1.5 wt. % perlite nanoclay composite exhibited the most significant improvements, with hardness, tensile strength, and wear resistance increasing by 32%, 38%, and 59%, respectively, compared to the base AA7076 alloy. Finite element simulations in ANSYS Workbench predicted tensile strengths in close agreement (within 5 – 8%) with experimental results, validating the strengthening effects of nanoclay. The enhancements are attributed to the homogeneous dispersion of nanoclay particles, strong interfacial bonding, and their role in restricting dislocation motion. These findings establish perlite nanoclay as a cost-effective and sustainable reinforcement for aluminium alloy, well-suited for demanding applications in automotive, aerospace, and marine industries, offering a promising combination of lightweight design and superior performance.

Concrete expanded-plate piles (CEP piles) are novel variable-section piles that offer broader applicability, greater bearing capacity, and superior economic benefits compared to conventional straight-shaft piles. Their increasing use in construction projects underscores these advantages. While previous studies have demonstrated the favourable bearing performance of CEP monopiles, the influence of pile spacing on the performance of CEP double piles remains unexplored. This study combines laboratory-scale unitary soil tests with ANSYS Workbench 2022R1 finite element simulations to investigate the effects of pile spacing on the bearing behaviour and soil failure mechanisms of CEP double piles. An optimal pile spacing range is proposed, and the compressive bearing capacity formula is modified accordingly. These findings establish a theoretical foundation for the development of CEP double-pile and pile group foundations, thereby supporting their wider use and promotion in geotechnical engineering.

The subsurface of cities is warming up-undergoing an underground climate change caused by subsurface urban heat islands (SUHIs). This phenomenon poses hazards while offering opportunities for sustainable urban heating. Although the morphology of urban areas above the ground is renowned for markedly influencing surface heat islands, the impact of the underground urban morphology on SUHIs remains largely unexplored. This study aims to (i) extend the definition of quantitative variables for analysing the urban morphology from the surface to the subsurface of cities and (ii) systematically examine how the underground urban morphology affects the intensity of SUHIs. Using three-dimensional (3D), time-dependent finite element simulations, we assess the role of different underground morphological features of cities in the development and intensity of SUHIs, such as heat source dimensions and distribution, green-space density and ground properties. Results indicate that the size and density of underground heat sources primarily drive the overall intensity of SUHIs, strongly depending on the presence of groundwater flow and only secondarily on the ground thermo-physical properties and the presence of green areas at the surface, whose influence substantially vary with depth. These findings enhance the understanding of the mechanisms governing SUHIs and provide insights to mitigate them globally.This article is part of the theme issue 'Urban heat spreading above and below ground'.

Persistent luminescent materials are essential for advancing energy‐efficient lighting, bioimaging, and sensing technologies. However, improving their optical performance remains a key challenge. Here, the integrating of Ag and Au plasmonic nanoparticles (NPs) on the surface of consolidated SrAl 2 O 4 :Eu 2+ ,Dy 3+ ceramics enhances both photoluminescence intensity and the persistent afterglow through increasing the light concentration and charge transfer between the NPs and the luminescent matrix. The ceramics are sintered via hot isostatic pressing, achieving >99% theoretical density with controlled surface roughness optimized for nanoparticle deposition. Functionalization of plasmonic nanostructures is achieved through solid‐state dewetting of thin metallic films followed by thermal annealing. This process generates discrete NPs with strong localized surface plasmon resonances. Finite element simulations reveal a hole‐sphere‐like growth for plasmonic NPs after the solid‐state dewetting process, as well as a stronger local electric field enhancement around NPs at the excitation wavelength, correlating with experimental results. This work demonstrates an effective route for tailoring long‐persistent phosphors and offers new perspectives for the design of advanced optoelectronic materials.

This study examines the fracture toughness of Al6061 alloy-based hybrid composites reinforced with silicon carbide particles and cenosphere microspheres. Aluminum alloy Al6061 is widely utilized in structural applications due to its balanced mechanical properties, and its hybridization with SiC and cenosphere reinforcements enhances its performance under critical loading conditions. The effect of specimen thickness on fracture toughness was examined by fabricating compact tension specimens in accordance with ASTM E399 standards, with thickness-to-width ratios ranging from 0.2 to 0.7. Controlled fatigue cracks were introduced, and both experimental testing and finite element simulations were conducted to assess the critical stress intensity factor and crack propagation behaviour across different thicknesses. Results show that the fracture toughness is constant after the B/W ratio of 0.5 and above, states as plane strain fracture toughness. The 3wt% SiC and 6wt% cenosphere in Al6061 shows the highest fracture toughness up to 15.56 MPa√m, due to the effective stress distribution and interfacial bonding. The fractography using the scanning electron microscopy reveals that particle debonding is major failure mechanism, with microcracking in 3wt% cenosphere composites and crack deflection and stress transfer at high reinforcement contents. Experimental results were well matched with the simulation model with ±10% differences, proving its validity.

Grouted sleeve connections are widely employed in the substructures of prefabricated bridges. After installation, the grout filling condition inside the sleeves cannot be directly inspected, while grouting defects may significantly compromise the mechanical performance of the piers. This study investigates the feasibility of using the non-destructive impact-echo method to detect grouting defects in sleeves. Finite element simulation was conducted to analyze the influence of the distance between the impact point and the signal acquisition point on detection accuracy, revealing that a distance of 40–60 mm yields optimal results. Experimental findings demonstrate that the method can effectively identify grouting defects in double-row sleeves, although it cannot precisely locate the defective sleeve. A novel analytical approach is proposed, using the thickness frequency and its modes of fully grouted specimens as a benchmark. By comparing thickness frequencies at different measurement points, grout quality can be intuitively evaluated. Validation using a six-sleeve model with varying grouting densities confirmed the method’s effectiveness in detecting grouting defects in non-boundary sleeves and its practical applicability in engineering.

Purpose The objective of this article is to address the issue of deviations between the actual alignment and the designed alignment of long-span continuous rigid-frame bridges during construction, which arise due to complex construction site environments and variations in material parameters, ultimately affecting the closure accuracy. To tackle this challenge, the study proposes a method based on a genetic algorithm (GA)-optimized backpropagation (BP) neural network for predicting alignment variations during bridge construction, aiming to enhance construction precision and closure quality. Design/methodology/approach This study establishes a finite element model based on an actual bridge engineering project and simulates the distribution range of material parameters using a normal distribution. Characteristic values are selected from the distribution range and input into the finite element model to calculate the corresponding deflection responses. The material parameter values and their corresponding deflection responses are used as training samples to train the GA-BP neural network model. Once trained, the model can effectively predict deflection variations during the bridge construction process. Findings The GA-BP neural network method proposed in this study demonstrates superior computational efficiency and prediction accuracy compared to traditional calculation methods and finite element simulation approaches, providing more reliable guidance for construction practices. In contrast to the conventional BP algorithm, the GA-BP algorithm significantly reduces errors in bridge alignment prediction, achieving an optimal mean absolute error (MAE) of 0.16067. This method substantially enhances the accuracy of bridge alignment control, mitigates construction risks and offers technical support for improving the precision of bridge closure. Research limitations/implications The method proposed in the article is more efficient compared to finite element model calculations, effectively meeting the real-time requirements of construction. It significantly reduces the labor costs associated with construction monitoring and mitigates construction risks. Practical implications This article introduces a machine learning-based method for predicting bridge construction deflection to ensure precise alignment control. Validated with actual measurement data, the model outperforms finite element simulations in computational efficiency and speed, meeting real-time construction needs. It provides innovative insights for advancing the digitization and intelligentization of bridge construction practices. Social implications With the rapid advancement of artificial intelligence (AI), the integration of AI with various industries has become an inevitable trend. The method proposed in this article combines machine learning with civil engineering, demonstrating the efficiency and superiority of artificial neural network algorithms. This approach highlights the potential for a more effective integration of AI and civil engineering, paving the way for innovative applications in the field. Originality/value This study represents the first application of a genetic algorithm-optimized backpropagation neural network for predicting deflection in the construction of continuous rigid-frame bridges, offering a novel approach for the intelligentization of bridge construction. The proposed method not only enhances prediction efficiency but also significantly reduces prediction errors, providing a scientific basis and technical support for alignment control during bridge construction. This approach holds substantial value for practical engineering applications.

Abstract A novel rotor support structure with actively tunable stiffness and self-healing capabilities was developed by combining the π-shaped shape memory alloy (SMA) actuators and the linear squirrel cage structure, offering promising potential for vibration control across various rotor operation scenarios. The quasi-static mechanical characteristics of the proposed support were first investigated through finite element simulations and mechanical testing. The results demonstrate that the support stiffness can be effectively tuned by varying the temperature, with the experimentally measured stiffness tunability reaching up to 24.5%. Additionally, the support exhibits a sharp decrease in stiffness under increasing external loads and is capable of generating a recovery force or displacement upon heating under loaded conditions. These features indicate strong potential for mitigating sudden rotor faults such as blade loss. A rotor test rig incorporating the designed support structure was developed and tested. The results show that by adjusting the support temperature, the dynamic characteristics of the rotor can be effectively modified. In particular, when the rotating speed is close to the resonance frequency, tuning the support stiffness can reduce the rotor vibration amplitude by 74.17%. These findings confirm that the proposed support structure can effectively broaden the operational speed range of the rotor.

To ensure the reliability of small-lot machining for thin-structured parts, an on-machine workholding state estimation method based on measured strain has been proposed. When applying this method to actual machining scenarios, it is necessary to select appropriate measuring points to achieve estimation accuracy. This paper proposes a method capable of systematically selecting measuring points for individual cases. First, finite element simulation was used to calculate strain values for each fixturing case. Moreover, the variations in strain values corresponding to the variated fixturing process were calculated using feasible varied fixturing conditions. Using the strain values corresponding to the feasible fixturing conditions, an evaluation criterion was applied to evaluate the candidate measuring points. The feasibility of the proposed criterion was investigated by estimating workpiece deformation using different measuring candidate points and comparing the accuracy of the estimations. In the investigation, the estimated workpiece deformation results were compared with the actual workpiece deformation. The comparison demonstrated that the proposed method can effectively identify appropriate strain measuring points.

This paper presents a comprehensive study of multi-degree-of-freedom (MDOF) structural vibration and projectiletarget penetration dynamics using analytical, numerical, and simulation-based approaches. For the undamped three-degree-of-freedom massspring system, equations of motion were derived and expressed in matrix form, enabling the determination of natural frequencies, mode shapes, and unknown mass parameters. This analysis verified the models validity and demonstrated the fundamental characteristics of MDOF vibration systems. In parallel, a reduced-order projectiletarget model was developed, coupling rigid-body motion with axial vibration through equivalent stiffness and damping. Analytical derivations, MATLAB simulations, and Python-based nonlinear modeling highlighted the roles of damping, elastic compression, and resistance forces in penetration dynamics. To validate these findings, finite element simulations using ANSYS/Autodyn were performed, capturing projectile deformation, energy dissipation, and penetration depth. The results collectively confirm that simplified models, when cross-validated with high-fidelity simulations, can effectively capture the essential physics of vibration and impact, offering practical insights for engineering design, vibration control, and protective structure development.

Steel energy dissipation devices are integral to seismic design, as they help reduce structural deformations during strong earthquakes by absorbing and dissipating energy through large inelastic deformations. This research provides new insights into the cyclic behavior and constitutive modeling of carbon steel SS275, a domestically manufactured material in Korea specifically used for seismic energy dissipation applications. To characterize its mechanical response, monotonic and strain-controlled cyclic loading tests are conducted on nine machined coupons. The cyclic tests are performed under constant strain amplitudes ranging from ±0.5% to ±3.0%. Experimental strain–life data obtained at these amplitudes are used to determine the Coffin–Manson parameters, while the cyclic stress–strain relationship is defined using the Ramberg–Osgood equation. Furthermore, material parameters for the Chaboche nonlinear hardening model are extracted from the experimental results and validated through finite element simulations of coupon tests in ABAQUS, ensuring close agreement with the measured cyclic response. Following the coupon-level analysis, a member-scale test is performed on a buckling-restrained brace (BRB) fabricated from SS275 steel. The calibrated Chaboche parameters are then applied in numerical simulations of the BRB, and the results are compared with experimental data to assess the model’s predictive capability for seismic performance.

Purpose This study aims to develop a robust and generalizable hybrid strategy for investigating impact behaviors of high-performance composites, significantly advancing composite impact engineering and demonstrating strong potential for applications in protective and structural systems. Design/methodology/approach With the hybrid framework that integrates experimental testing, finite element (FE) modeling and machine learning (ML), the study on the low-velocity impact behavior of Kevlar fiber-reinforced composites with thermoplastic polyurethane matrix was carried out: with the validation of the FE model by experiments, the numerical model was used to produce data to train the ML methods. Findings The best-performing Levenberg–Marquardt artificial neural network model achieved excellent agreement with FE simulation data, yielding a correlation coefficient R > 0.98 and a low mean squared error, which was also proven through experimental validation with satisfactory accuracy. Originality/value In the current work, the combined method with the FE model, experiments and ML was developed for low-velocity impact of thermoplastic composite materials. The damage process was investigated, while the accuracy of the proposed methodology was verified when compared to experimental outcomes.

Aiming at the limitations of direct drive wave energy conversion (DD-WEC), especially the poor power density, low energy conversion efficiency, and a large volume of linear generators (LGs), a novel magnetic helix hybrid excitation rotary generator (MH-HERG) with the higher power density and higher energy conversion efficiency is proposed. The proposed MH-HERG can convert linear motion into rotary motion without contact with a hybrid excitation magnetic screw (HEMS) unit, so it has high energy conversion efficiency. Furthermore, a new quasi-Halbach magnetization array is used in the proposed MH-HERG to increase its power density and allows a hybrid excitation method to be used to make the thrust adjustable to further improve power density. The analytical solution model is established to derive the calculation equations of air gap flux density, which are validated through the finite element simulation. Arc-shaped permanent magnets (PMs), instead of tile-type PMs, are designed to weaken cogging torque and harmonic content in proposed MH-HERG’s no-load back electromotive force (back-EMF), thereby improving output power quality. Finally, the prototype is built and an experiment is conducted to ascertain the effectiveness and superiority of the proposed MH-HERG which has increased power density by 4.4 times and energy conversion efficiency by 3 times compared to existing LGs.

The mechanical-magneto multicaloric effect offers an effective strategy to enhance the solid-state refrigeration performance of materials. However, most materials exhibiting this effect are alloy-based, requiring strong external fields, exhibiting limited deformation tolerance, and being prone to fatigue, which restricts the application of mechanical-magneto multicaloric effect. In this work, the styrene–ethylene–butylene–styrene (SEBS)/Gd/SEBS structure has been proposed and confirmed that the composites exhibit not only elastocaloric and magnetocaloric effects, but also a mechanical-magneto multicaloric effect, as demonstrated by a home-made measurement device. The experimental results are consistent with the finite element simulation results. It is found that SEBS/Gd/SEBS exhibits a large multicaloric response with rapid thermal conduction near room temperature, the temperature change of the mechanical-magneto multicaloric effect is 48% greater than that of the pure elastocaloric response and 173% higher than that of the pure magnetocaloric response, while the thermal conduction time is reduced to 22% of that in the elastocaloric effect of SEBS, suggesting that the mechanical-magneto multicaloric effect under mechanical–magnetic simultaneous excitation can effectively enhance the caloric response of materials. Additionally, we propose a refrigeration cycle model based on the mechanical-magneto multicaloric effect, providing guidance for the application of multicaloric effect refrigeration.

Polypropylene fibers (PPFs), characterized by their low density, cost-effectiveness, and superior corrosion resistance, can be effectively incorporated into concrete to enhance the impact resistance of wall panels. This study introduces an innovative composite wall panel utilizing polypropylene fiber-reinforced concrete (PFRC) as the core material. Initially, an experimental investigation into the mechanical properties of PFRC was conducted, and based on these results, a constitutive model for PFRC was established. Subsequently, the impact-induced mechanical behavior of the innovative composite wall panel was investigated through finite element simulations employing ABAQUS, version 2020, software. The findings indicate that polypropylene fibers significantly improve both the compressive strength and ductility of concrete, with an optimal coarse fiber content of 1%. The inclusion of glass fiber grids and polypropylene fibers reduced the number of cracks and the overall deformation of the composite wall panel. The integration of glass fiber grids coupled with fiber reinforcement resulted in 7.2% and 27.8% enhancements in impact resistance, respectively. Parametric studies demonstrated that greater concrete panel thickness effectively diminishes post-impact peak and residual displacements in composite wall systems. Furthermore, the impact resistance was found to be weaker at the panel edges and stronger at a quarter of the panel height.

Background: Ischemic heart disease remains a global health challenge characterized by irreversible cardiomyocyte loss and pathological ventricular remodeling. This study introduces a novel therapeutic strategy combining sustained chemokine delivery with structural reinforcement - stromal cell-derived factor-1 (SDF-1) loaded decellularized extracellular matrix (dECM) patch (SDF-dECM patch) designed to address both biological and biomechanical deficiencies in myocardial repair. Methods: dECM was prepared from the myocardium of the left ventricle of pig by decellularization and co-cultured with H9c2 cells and BMMSCs to observe its biosafety. Their mechanical properties were tested by uniaxial stretching. SDF-1 was loaded onto dECM to prepare SDF-dECM patch. The period and rule of SDF-1 release on SDF-dECM patch were detected by ELISA. The expression of related proteins were detected by Western blot. The finite element simulation model of SDF-dECM patch implantation after myocardial infarction was established to reveal the effect of SDF-dECM patch on mechanical support and wall stress (WS) of infarcted myocardium through computer simulation. SDF-dECM patch was implanted on the surface of myocardial infarction area in rats, and the expression of related markers and proteins in the homing process of endogenous BMMSCs were observed by immunofluorescence staining. The paracrine mechanism of BMMSCs conditioned medium was detected by ELISA. Results: The SDF-dECM patch had excellent elastic and mechanical properties, and it also demonstrated sustained chemokine delivery with linear release kinetics, maintaining therapeutic SDF-1 concentrations through 28 days without significant attenuation. Recruited BMMSCs exhibited potent paracrine activity, secreting VEGF and HGF. In vivo implementation of the SDF-dECM patch mobilized endogenous BMMSCs to the myocardial infarction and patch area. The SDF-dECM patch provided good mechanical support for the ventricular wall of the infarction area and effectively reduced the ventricular WS in the infarction area and the stress concentration at the healthy-infarction junction. This bimodal therapeutic strategy ultimately improved ejection fraction relative to untreated controls. Conclusion: This SDF-dECM patch establishes a regenerative microenvironment through sustained chemokine gradient for endogenous stem cell mobilization, and biomechanical stabilization to prevent adverse remodeling.

The combination of solar energy and radiative cooling through thermoelectric generators (TEGs) offers a promising approach for sustainable power generation. However, current thermoelectric systems that integrate solar energy and radiative cooling still face the challenge of insufficient nighttime power generation capacity. This study proposes a thermoelectric power generation system featuring a heat storage structure inspired by bionic flowers, which utilizes residual heat stored during the daytime to supply heat to the hot end of TEGs at night, thereby increasing the temperature difference at night. A numerical model was developed, and the temperature field and phase change characteristics within the heat storage structure were investigated through finite element simulation. The thermoelectric system with a heat storage structure maintains a higher nighttime voltage than the one without it. After 12 AM, the system without a heat storage structure can only sustain an output voltage of approximately 18 mV. In contrast, the system with a heat storage structure can achieve a voltage output of 42.57 mV. Even four hours later, it retained a 6.64 mV advantage. The results demonstrate that the heat storage structure significantly enhances the power generation performance at night. This research provides a potential solution to effectively mitigate the issue of insufficient nighttime power generation capacity in thermoelectric power generation systems.

The world is moving towards the renewable energy generation and utilization. The machinery, tools have been now updated to cope up with the environmental need including sustainability and environmental protection. From Gasoline to hybrid and Electric Vehicles have also been introduced. The material of the vehicles has also been made light weighted so that the efficiency of the vehicle can also be enhanced. On the other side if the vehicle body gets dents, scratches or even bumps so they need to be either repair and mostly new parts are installed like fender, bumper or bonnets and hoods. In this regards a dent remover is designed with the facility to remove the dent even without the removal of the paint that is a paintless dent remover. This Paintless dent remover is also equipped with the ability to work on different angles which another dent remover could not with in the facility. The angular dent removing facility provide a wider span of work and better efficiency respectively. Finite Element Analysis and simulation is also necessary for a successful design, so it is also done and from FEAit was found that the dent remover can easily pulled a dent with a maximum force of 100 kg. After successful simulation and designing the Dent remover is also put forward for fabrication so that it may give a large range of dent removing facility in real time.