Combined Finite Element Method Research Articles

Mesoscopic Modelling of Li Deposition in Sulfide-Based Solid Electrolyte Using Two-Dimensional Artificial Polycrystal Structure

Abstract Understanding Li nucleation and growth mechanism during charging in solid electrolytes (SEs) is essential in development of all-solid-state batteries with metallic Li, because the Li dendrite could cause internal short-circuit by penetration of SE. However, it is still under debate how the factors including degradation of SE layer, the stacking pressure, and the microstructure affect the Li nucleation and growth in SE. In this study, the coupled current-deposition-stress models using the two-dimensional artificial SE structures have been developed by combination of finite element method and Monte Carlo simulations. The model assumed that Li flux on the SE grain induces an eigen strain in the deposited Li region, and Li grows into the SE layer by breaking grain boundary (GB). Degradation of SE was modelled as the decrease of fracture strength of GB using a coefficient. The effects of these microstructure and operation factors on GB fracture and Li deposition have been evaluated and discussed.

Investigating the Tribological Properties of the High-Hardness, Low-Young's-Modulus 60NiTi Alloy: A Combined FEM Simulation and Experimental Study

ABSTRACT This study investigates the tribological properties of the 60NiTi alloy, known for its high hardness and low Young's modulus, using a combination of finite element method (FEM) simulations and experimental analysis. A discrete modified Archard model was employed to establish a FEM of a 60NiTi plate paired with a Si3N4 ball, with CSS-42L bearing steel serving as the comparison material. The analysis focused on stress distribution, wear depth, and wear rate to assess the suitability of 60NiTi as a bearing material. The FEM results indicated that, under a 30N load, the peak stress in 60NiTi was reduced by 22.7% compared to CSS-42L, demonstrating the alloy's superior load-bearing capacity. Post-experimentation, both surface and subsurface hardness of the 60NiTi alloy increased, aligning with the stress distribution predicted by FEM, thereby confirming the model's accuracy and reliability. This study highlights the potential of 60NiTi alloy for applications requiring robust wear resistance and stress management under varying load conditions.

A combined mixed finite element method and discontinuous Galerkin method for hybrid-dimensional fracture models of two-phase flow

In this paper, a combined numerical method consisting of the mixed finite element method (MFE) for the pressure equation and the discontinuous Galerkin (DG) method for the saturation equation is proposed to solve hybrid-dimensional fracture models of incompressible two-phase flow in porous media. The hybrid-dimensional fracture models treat fractures as (d−1)-dimensional interfaces immersed in d-dimensional matrix domains and take fluid exchange between fractures and surrounding matrix into account. Fully implicit approximation schemes combining the MFE-DG method with the backward Euler time discretization for the models with both a single fracture and an intersecting fractures network are all formulated successfully. The stability of the discrete solution is analyzed, and optimal error estimates in H(div)-norm for the velocity and in L2-norm for the pressure are derived, as well as in the discrete H1-norm for the saturation. Numerical experiments with a single fracture and a T-junction intersecting fractures network are conducted to verify the accuracy of our theoretical analysis.

A novel Bayesian updating finite element model using enhanced unscented Kalman filter for time-dependent estimation of rockfill dam structure deformation

Due to the time-dependent effect of rockfill dams, the conventional time-invariant finite element method (FEM) can hardly meet practical engineering requirements. This paper proposes an updating Bayesian FEM method for accurate long-term deformation analysis. A combined FEM model is introduced accounting for both instantaneous and creep behaviors. The FEM model is then updated using a Bayesian algorithm, unscented Kalman filter (UKF). The UKF calibrates the prior FEM predictions by incorporating real-time measurement data, thus iteratively reducing discrepancies between model predictions and actual observations. To further enhance the algorithm accuracy, a power-law-based fading memory factor is proposed to mitigate measurement noise in standard UKF. For parameter identification, a slice approach of the high-dimensional covariance confidence ellipsoid is developed. The methodology is validated in Qingyuan rockfill dam, in Guangdong province, China. Results show that the updated FEM is more consistent with the actual monitoring data. The fading memory improves standard UKF performance with a lower relative root-mean-square error (RRMSE). Additionally, the slice method reveals that a specific three-parameter configuration behaves better than the others. The proposed approach can also be extended to other fields including slope and tunneling.

Flow behaviors of various metals under tensile load

Abstract The influence of flow behaviors on the results of finite element (FE) analyses of metal-forming processes is dominant, even though it varies with strain hardening and softening phenomena. However, obtaining a flow curve with sufficient accuracy is not easy, particularly at large strains. Among various well-known methods of obtaining flow curves, tensile testing-based method is effective to obtain flow curves at large strains at room temperature. It is believed that the strain hardening behavior at large strains during the tensile test was not fully known. In this study, the flow curves, previously obtained using the combined tensile test and finite element method (FEM) approach, are classified as four categories, including monotonic strain hardening function, double-curvature strain hardening function, strain hardening-perfectly-plastic function, and perfectly-plastic-strain softening function. The tensile test characteristics of each type are investigated using its FE predictions, revealing that the specific type of a material can be identified by the tensile test.

Dynamic propagation of tensile and shear fractures induced by impact load in rock based on the dual bilinear cohesive zone model

PurposeThe purpose of this study is to simulate the tensile and shear types of fractures using the mixed fracture criteria considering the energy evolution based on the dual bilinear cohesive zone model and investigate the dynamic propagation of tensile and shear fractures induced by an impact load in rock. The propagation of tension and shear at different scales induced by the impact load is also an important aspect of this study.Design/methodology/approachIn this study, based on the well-developed dual bilinear cohesive zone model and combined finite element-discrete element method, the dynamic propagation of tensile and shear fractures induced by the impact load in rock is investigated. Some key technologies, such as the governing partial differential equations, fracture criteria, numerical discretisation and detection and separation, are introduced to form the global algorithm and procedure. By comparing with the tensile and shear fractures induced by the impact load in rock disc in typical experiments, the effectiveness and reliability of the proposed method are well verified.FindingsThe dynamic propagation of tensile and shear fractures in the laboratory- and engineering-scale rock disc and rock strata are derived. The influence of mesh sensitivity, impact load velocities and load positions are investigated. The larger load velocities may induce larger fracture width and entire failure. When the impact load is applied near the left support constraint boundary, concentrated shear fractures appear around the loading region, as well as induced shear fracture band, which may induce local instability. The proposed method shows good applicability in studying the propagation of tensile and shear fractures under impact loads.Originality/valueThe proposed method can identify fracture propagation via the stress and energy evolution of rock masses under the impact load, which has potential to be extended into the investigation of the mixed fractures and disturbance of in-situ stresses during dynamic strata mining in deep energy development.

Few-mode optical fiber-based flexible pressure feedback tentacle doped with fluorescence temperature pointer

Abstract In this paper, a flexible fiber pressure feedback whisker is proposed, which consists of a water droplet shaped polydimethylsiloxane (PDMS) elastomer with an embedded balloon shaped few mode fiber. The mechanical sensing performance of the device was analyzed and optimized using a combination of finite element method and beam propagation method (BPM). The built-in cladding corroded few-mode fiber increases pressure sensitivity by more than four times. The collection efficiency of fluorescence signal is improved by cladding corrosion. The PDMS elastomer was doped with upconversion nanoparticles NaYF4:Yb, Er@NaYF4 in order to achieve temperature measurement by fluorescence intensity ratio technology. The combination of fluorescence signal and interference spectrum can not only achieve real-time and accurate pressure detection at different temperatures, but also incorporate fluorescent materials into flexible bionic skin for temperature self-compensation, which has potential application value for the development of bionic fiber micro-nano sensing and control devices.

Computational and Experimental Characterization of Aligned Collagen across Varied Crosslinking Degrees.

Collagen-based scaffolds have been widely used in tissue engineering. The alignment of collagen fibers and the degree of crosslinking in engineering tissue scaffolds significantly affect cell activity and scaffold stability. Changes in microarchitecture and crosslinking degree also impact the mechanical properties of collagen scaffolds. A clear understanding of the effects of collagen alignment and crosslinking degrees can help properly control these critical parameters for fabricating collagen scaffolds with desired mechanical properties. In this study, combined uniaxial mechanical testing and finite element method (FEM) were used to quantify the effects of fiber alignment and crosslinking degree on the mechanical properties of collagen threads. We have fabricated electrochemically aligned collagen (ELAC) and compared it with randomly distributed collagen at varying crosslinking degrees, which depend on genipin concentrations of 0.1% or 2% for crosslinking durations of 1, 4, and 24 h. Our results indicate that aligned collagen fibers and higher crosslinking degree contribute to a larger Young's modulus. Specifically, aligned fiber structure, compared to random collagen, significantly increases Young's modulus by 112.7% at a 25% crosslinking degree (0.1% (4 h), i.e., 0.1% genipin concentration with a crosslinking duration of 4 h). Moreover, the ELAC Young's modulus increased by 90.3% as the crosslinking degree doubled by changing the genipin concentration from 0.1% to 2% with the same 4 h crosslinking duration. Furthermore, verified computational models can predict mechanical properties based on specific crosslinking degrees and fiber alignments, which facilitate the controlled fabrication of collagen threads. This combined experimental and computational approach provides a systematic understanding of the interplay among fiber alignment, crosslinking parameters, and mechanical performance of collagen scaffolds. This work will enable the precise fabrication of collagen threads for desired tissue engineering performance, potentially advancing tissue engineering applications.

Initiation and arrest of cracks from corners in multi-chip semiconductor devices

A contemporary semiconductor device often contains multiple chips. Corners of the chips concentrate stress, and are principal sites to initiate failure. Here we propose to characterize the corners using a double cantilever beam, in which two silicon beams sandwich a row of chips. As the two beams are pulled open, a crack initiates at the corner of a chip, and runs unstably on the interface between the chip and a beam. The crack may or may not arrest, depending on various experimental conditions. We calculate energy release rate as a function of crack length by using a combination of finite element method and an analytical solution of the singular field around a corner. At a fixed applied displacement, the energy release rate is low for a short crack, peaks for a crack of intermediate length, and drops for a long crack. This non-monotonic behavior explains how a crack initiates, grows unstably, and possibly arrests. If the crack does arrest, as the two beams open further, the crack grows stably. We relate the initiation and arrest of the crack to machine compliance, specimen geometry, and flaw size. The force at which the crack initiates can be used to characterize the manufacturing process, whereas the stable growth of the crack can be used to measure interfacial toughness. It is hoped that this work will aid the development of multi-chip semiconductor devices.

Blind prediction of fracture in an additively manufactured geomaterial using the finite discrete element method

Modeling fracture and damage processes is a challenging, yet critical capability for promoting safe and sustainable design in geotechnical, energy, and hydrological applications. Numerical methods capable of modeling fracture processes are rarely asked to make blind predictions against experimental observations. Here, we report results from a numerical study of blind fracture prediction in an additively manufactured, three-point bend specimen. We record a number of physical mechanisms associated with the fracture of the specimen and make a blind prediction against experimental results that were unknown when the numerical study was performed. We then compare results from our model to the blind data set. We find that the combined finite discrete element method performs exceptionally well in the simulation and prediction of fracture processes.

Revealling pore microstructure impacts on the compressive strength of porous proppant based on finite and discrete element method

Ceramic spheres, typically with a particle diameter of less than 0.8 mm, are frequently utilized as a critical proppant material in hydraulic fracturing for petroleum and natural gas extraction. Porous ceramic spheres with artificial inherent pores are an important type of lightweight proppant, enabling their transport to distant fracture extremities and enhancing fracture conductivity. However, the focus frequently gravitates towards the low-density advantage, often overlooking the pore geometry impacts on compressive strength by traditional strength evaluation. This paper numerically bypasses such limitations by using a combined finite and discrete element method (FDEM) considering experimental results. The mesh size of the model undergoes validation, followed by the calibration of cohesive element parameters via the single particle compression test. The stimulation elucidates that proppants with a smaller pore size (40 µm) manifest crack propagation evolution at a more rapid pace in comparison to their larger-pore counterparts, though the influence of pore diameter on overall strength is subtle. The inception of pores not only alters the trajectory of crack progression but also, with an increase in porosity, leads to a discernible decline in proppant compressive strength. Intriguingly, upon crossing a porosity threshold of 10 %, the decrement in strength becomes more gradual. A denser congregation of pores accelerates crack propagation, undermining proppant robustness, suggesting that under analogous conditions, hollow proppants might not match the strength of their porous counterparts. This exploration elucidates the underlying mechanisms of proppant failure from a microstructural perspective, furnishing pivotal insights that may guide future refinements in the architectural design of porous proppant.

Impact of Solder Voids on IGBT Thermal Behavior: A Multi-Methodological Approach

This study investigates the thermal behavior of Insulated Gate Bipolar Transistors (IGBTs) with a focus on the influence of solder voids within the device. Utilizing a combination of Finite Element Method (FEM) simulations, X-ray imaging, and SEM-EDX analysis, we accurately modeled the internal structure of IGBTs to assess the impact of void characteristics on thermal resistance. The findings reveal that the presence and characteristics of solder voids—particularly their size, number, and distribution—significantly affect the thermal resistance of IGBT devices. Experimental measurements validate the FEM model’s accuracy, confirming that voids disrupt the heat flow path, which can lead to increased thermal resistance and potential device failure. Five regression models, including Gaussian process regression (GPR) and neural networks, were employed to predict the thermal resistance based on void characteristics, with the GPR models demonstrating superior performance. The optimal GPR RQ model consistently provided accurate predictions with an RMSE of 0.0050 and R2 of 0.9728. Using the void percentage as the only input parameter for the regression models significantly impacted the prediction accuracy, showing the importance of the void extraction method. This study shows the necessity of minimizing solder voids and offers a robust methodological framework for a better prediction of the reliability of IGBTs.

Effects of Hard-to-Measure Material Parameters on Clinching Joint Geometries Using Combined Finite Element Method and Machine Learning

<div>In this article, we investigated the effects of material parameters on the clinching joint geometry using finite element model (FEM) simulation and machine learning-based metamodels. The FEM described in this study was first developed to reproduce the shape of clinching joints between two AA5052 aluminum alloy sheets. Neural network metamodels were then used to investigate the relation between material parameters and joint geometry as predicted by FEM. By interpreting the data-driven metamodels using explainable machine learning techniques, the effects of the hard-to-measure material parameters during the clinching are studied. It is demonstrated that the friction between the two metal sheets and the flow stress of the material at high (up to 100%) plastic strain are the most influential factors on the interlock and the neck thickness of the clinching joints. However, their dependence on the material parameters is found to be opposite. First, while the friction between the two metal sheets promotes the formation of the interlock, it reduces the neck thickness and thus increases the risk of breaking in this region. Second, it is easier to form the interlock if the deformed material exhibits small flow stress at high plastic strain, but the neck thickness tends to be thinner in this case. The identified material parameters help to significantly reduce the relative error between the simulated results and the experimental results, not only in the configurations from which they are identified but also in a new configuration. This methodology shows its potential in the cases where material parameters are not available or difficult to measure.</div>

3D bi-stable negative stiffness mechanical metamaterials for bandgap tuning

A recent topic of interest in dynamics research is bi-stable negative stiffness (NS) mechanical metamaterials that allow for the efficient control of wave propagation and bandgap (BG) tuning. In this study, a three-dimensional bi-stable NS mechanical metamaterial based on fan-shaped inclined beams was developed. It has BGs in multiple directions as well as significant BG tuning capability in specific direction, and the ability to design for multiple geometrical parameters. First, the requirements for NS mechanical metamaterials to achieve bi-stable properties were theoretically investigated. Subsequently, the deformation process of the unit cell of the metamaterial under uniaxial compression and the band structure and vibrational properties of the metamaterial under different steady states were analyzed through a combination of finite element method and experiments. The results showed that the BG range of the bi-stable NS metamaterials in the vertical direction changed with the switching of the steady state, whereas the out-of-plane BG in the horizontal direction remained constant. Therefore, this bi-stable NS mechanical metamaterial could realize modulation of the BG as well as control of wave propagation in multiple directions. In addition, bi-stable NS metamaterials with different angles exhibited different BG ranges. Finally, the vibrational transmittances of the metamaterials were investigated to verify the accuracy of the BG range.

Effects of Geo-Stress and Natural Fracture Strength on Hydraulic Fracture Propagation in a Deep Fractured Tight Sandstone Reservoir.

Hydraulic fracturing technology has been widely used in the tight reservoir reconstruction. Unfortunately, with the deepening of mining depth and the increase of geo-stress, the propagation mechanism of medium-pressure fractures in the reservoir is significantly different from that of conventional shallow reservoirs. Based on the combined finite discrete element method, this paper conducts numerical simulation research on deep tight sandstone reservoirs in the west. The discrete fracture network modeling method is used to establish a tight sandstone reservoir model with natural bedding, and the influence of geo-stress difference and natural fracture strength on hydraulic fracture propagation law in a high geo-stress environment is discussed in detail. The results show that the difference between geo-stress and the strength of natural fractures has a significant effect on the shape and expansion of hydraulic fractures under the high geo-stress conditions. The greater the difference in ground stress, the more obvious the tendency of the main fractures of the reservoir, and the shorter the branch fractures. With the increase of natural fracture strength, the changes in propagation pressure, fracture length, area, and width, which can be fitted with a linear function with a goodness of fit as high as 0.99. In addition, the morphological results of hydraulic fractures in the simulation are not only affected by the constitutive parameters of the model but also may be affected by the randomness of the natural fracture network, thus, showing a certain degree of dispersion. Therefore, it is extremely necessary to build a reservoir fracturing model in a specific area based on more detailed geological monitoring data to guide actual construction. The above achievements have certain reference significance for the field operation of deep tight sandstone reservoirs.

Performance Evaluation of Tunnel Support System in Jointed Rock Mass by Numerical Simulation: A Case Study

In this study, a numerical verification of the performance of preliminary tunnel support systems suggested by rock mass classification methods is performed. The Rock Mass Rating (RMR) method and the Quality Index (Q Barton) show a succession of a moderately jointed and jointed quality of Kherrata Tunnel 3. The numerical assessment combining the Finite Element Method (FEM) with the Discrete Fracture Network (DFN) models; involves the jointed area of the tunnel, in which the Voronoi model represents the joints distribution. During numerical simulations, particular attention focused on the evolution of the total displacement and the extension of the plastic zone. In addition, a parametric study was carried out to study the effect of the length and spacing of rock bolts on tunnel deformation. Results showed that the combined FEM and DFN models represent a solution for the stability analysis of underground excavations. The Voronoi model is suitable for representing the global distribution of joints in the rock mass. The parametric study carried out revealed pertinent conclusions. Thus, increasing the length of bolts (4 m to 6 m) decreases the total displacement of the tunnel; and reduces the extension of the plastic zone. However, the spacing between rock bolts does not improve the stability. The empirical method remains a reliable tool for the recommendations of preliminary support systems.

CRACK EFFECT ON THE NATURAL FREQUENCY OF COMPOSITE BEAM: FINITE ELEMENT ANALYSIS WITH EXPERIMENTAL STUDY

Cracks in structural elements can significantly affect their dynamic behaviour and lead to detrimental consequences. This study investigates the influence of cracks on the natural frequency of a laminated beam under free vibration, employing a combination of finite element method (FEM) analysis and experimental work. The numerical computation is done using ABAQUS simulation software by developing a comprehensive FEM model that accurately represents the laminated beam's mechanical behaviour, considering material anisotropy, geometry and crack propagation. The finite element model is validated through experimental tests conducted on both intact and cracked laminated beam specimens. The experimental setup utilizes Fast Fourier Transform (FFT) analyzer high-precision measurement tool to obtain accurate natural frequency values. The parametric study is conducted to analyze the impact of crack location, size, and orientation on the laminated beam's natural frequency. The FEM simulations reveal that the presence of cracks leads to a reduction in natural frequency, indicating a decrease in structural stiffness. The extent of frequency reduction is found to be influenced by crack characteristics and their proximity to critical locations within the beam. The experimental investigations corroborate the FEM findings, highlighting the consistency between numerical predictions and actual measurements.

The Temperature Field Prediction and Estimation of Ti-Al Alloy Twin-Wire Plasma Arc Additive Manufacturing Using a One-Dimensional Convolution Neural Network

Plasma arc deposition as an additive manufacturing technology has unique advantages for producing parts with complex shapes through layer-by-layer deposition. It is critical to predict and control the temperature field during the production process due to the temperature distribution and gradients determining the properties and performance of the part. Numerical simulation approaches, such as the finite element method, which provides a large amount of data for machine learning modeling, thus reducing the overhead of experimental measurements, are widely used in machine learning. In this paper, we propose a neural network combined finite element method and process prediction workflow. A one-dimensional convolutional neural network model for predicting 2D temperature distribution is developed by training the collected data on the planar temperature field of titanium–aluminum twin-wire plasma arc additive manufacturing and the finite element method. The results show that the predicted temperature mean square error is only 0.5, with less than a 20 °C error in peak temperature and a relative error below 1%. The proposed transfer learning method achieves the same training loss and is 500 iterations faster than basic training, which improves the training speed by 25%. The current study confirms the accurate performance of the ML model and the effectiveness of the optimization method.

Моделирование аварийного столкновения грузового локомотива с полувагоном в порожнем и груженом состоянии

The article presents the results received under numerical simulation of a collision of a freight rolling stock with an empty and loaded gondola car, and allowing to evaluate the efficiency of a locomotive passive safety system (SPS). Two methods have been used for the simulation: finite-element method (FEM) and discrete element method (DEM). The combination of FEM and DEM makes it possible to evaluate the influence of bulk material at the moment of collision in the body of a gondola car upon inevitable plastic deformations of the driver’s cabin frame and upon the destruction of the glazing area of the cabin. On the results of the numerical simulation the absorption of the kinetic energy shock due to the passive safety system has been determined, as well as changes of accelerations in the places of the driver’s seats settings.

Optimization and realization of a space limited sens-PEH for smart floor applications

IoT and smart home applications demand for simultaneous sensing and energy independence. Here, a screen printable P(VDF-TrFE) cantilever based smart floor design, capable of sensing (sens) and acting as a piezoelectric energy harvester (PEH) is introduced. In a height of only 1 mm, which is well-suited for indoor floor applications, a sens-PEH cantilever optimization is performed by finite element method (FEM) simulations and experimentally validated; bending cantilevers perform much better compared to normal compressions modes with apparent sensitivities to be as high as 98 nC/N for 3.6 mm long cantilevers. This is more than three orders of magnitude larger compared to conventional flat P(VDF-TrFE) film sensors. When energy harvesting is of interest, the simulations show that short cantilevers provide the most energy. Using multilayered screen-printed devices and an active area of 8 cm2, such harvesters provide up to 18.4 µJ of step energy in the experiment, from which 6 µJ is storable in a capacitor. From the combined FEM and experimental results, the optimum sens-PEH geometry, either for sensing or harvesting, can be chosen and implemented in respective smart floor applications.