Simulating Current Distribution of Oxygen Evolution Reaction in Microcells Using Finite Element Method

Numerical 3-D FEM simulation of tensor model for liquid crystal displays

The thermal conductivity of emulsion-polymerized styrene–butadiene rubber (ESBR) composites filled with carbon nanotubes (CNTs), zinc oxide (ZnO) and alumina (Al2O3) was predicted using the finite element method (FEM). Two-dimensional (2D) FEM models, which involved the effects of aspect ratio (AR), shape, orientation and thermal conductivity anisotropy (TCA) of the CNTs, and interfacial thermal resistance (ITR), were used to simulate the microstructure of CNT filled ESBR composites. Also, 2D and three-dimensional (3D) FEM models were developed to simulate the microstructure of Al2O3 or ZnO filled ESBR composites. An increase in the thermal conductivity with increasing Al2O3 or ZnO loadings was predicted by the FEM. The orientation angle (OA) of the CNTs and the ITR strongly affect the thermal conductivity as predicted by the FEM. The TCA of the CNTs also has a prominent effect on the thermal conductivity when CNTs have a relatively small OA. At a given filler loading, the thermal conductivity increased with the increasing intrinsic thermal conductivity of the filler over a certain range for a particular shape of filler. The thermal conductivities predicted by the FEM were compared with those predicted by Agari's models and the experimental results. The trends of the thermal conductivity predicted by the FEM agreed with the experimental data. The thermal conductivity of the ESBR composites predicted by 2D and 3D spherical particle filler (SPF) FEM models as a function of ZnO and Al2O3 loading showed that the 3D SPF FEM model agreed well with the experimental results at low loadings (not higher than 20 phr), while the 2D SPF FEM model agreed well with the experimental results at high loadings (higher than 80 phr). In addition to being used for the analysis of existing composites, the proposed FEM models are useful for the design and optimization of new composite materials, and are expected to provide a more insightful understanding into the thermal conductivity of polymeric composites.

P097 Direct measurement of electric fields in human and monkeys during transcranial electric stimulation

The integration of Finite Element Method (FEM) simulations with laser-based techniques has significantly advanced acoustic research by enhancing wave measurement, analysis, and prediction in complex solid media. This review examines the role of the FEM in laser-based acoustics for wave propagation, defect detection, biomedical diagnostics, and engineering applications. FEM models simulate ultrasonic wave generation and propagation in single-layer and multilayered structures, while laser-based experimental techniques provide high-resolution validation, improving modeling accuracy. The synergy between laser-generated ultrasonic waves and FEM simulations enhances defect detection and material integrity assessment, making them invaluable for non-destructive evaluation. In biomedical applications, the FEM aids in tissue characterization and disease detection, while in engineering, its integration with laser-based methods contributes to noise reduction and vibration control. Furthermore, this review provides a comprehensive synthesis of FEM simulations and experimental validation while also highlighting the emerging role of artificial intelligence and machine learning in optimizing FEM models and improving computational efficiency, which has not been addressed in previous studies. Key advancements, challenges, and future research directions in laser-based acoustic applications are discussed.

Modelling the behaviour of Pneumatic Artificial Muscle (PAM) has proven difficult due to its highly complicated structure, nonlinear nature of rubbery material, and air compressibility. To overcome these limitations, a FEM (Finite Element Method) model using Abaqus and CATIA is derived for the quantitative analysis on the impact of different factors on the pulling force of PAM. In the Abaqus a two parameter Mooney–Rivlin model is utilized to consider the hyper-elastic nature of flexible material. Then both Abaqus and CATIA are used in the parametric design of a 3-Dimensional model of PAM. Furthermore, the FEM model is employed to predict the static force exerted by PAM and the results show that the model is promising. The FEM model produces closer results to the test data for the typical PAM. Nonlinear behaviour of PAM is found to be obvious with an increase in both the contraction and the air pressure, different from the linear curves obtained by the fundamental geometrical model. Nonlinear changes in the PAM force are also observed in the numerical study on the effect of structural factors including initial braid angle, initial diameter, initial wall thickness, and flexible material. Besides, these phenomena can be explained by a connection between mechanical and morphological behaviour of PAMs with the FEM model. Generally, this modelling approach is more accurate compared to the fundamental theoretical model and more cost competitive compared to the empirical methods.

Electrothermal purification is an effective method for achieving over 99.9 pct purity for graphite particles with minimal environmental impact. However, the lack of a suitable heat and electric resistivity model has hindered the scaling up of electrically heated fluidized bed (EHFB) reactors for graphite purification. In this study, three commercial natural graphite flake populations were tested in a bench-scale fluidized bed reactor at temperatures of up to 1000 °C. The experiments varied key parameters, including the graphite particle size, particle bed temperature, fluidization index, and electrode depth within the particle bed. Controlling the fluidized bed reactor at high temperatures requires an understanding of bed resistivity and how current flows throughout the EHFB system. The results show that fluidized bed resistivity decreases with temperature, with a diminished effect at higher temperatures. Smaller particles exhibit a higher resistance, likely due to a larger number of contact points required to pass current between the electrodes. In this study, a Finite Difference Method (FDM) model was developed using Visual Basic for Applications (VBA) in Excel®. Additionally, a Finite Element Method (FEM) model was created using COMSOL Multiphysics®. The FDM model assumes the current flows only radially, whereas the FEM model accounts for both radial and vertical current flow. The FDM model was validated against experimental data. Additionally, the FDM model was verified through a comparison with the FEM model. The FDM model showed good agreement with experimental resistance data and moderate agreement with power consumption, while the FEM model provided more accurate predictions by accounting for a detailed geometry and heat loss mechanisms. Achieving a uniform temperature distribution within the fluidized bed is influenced by the electrode’s contact area. Deeper immersion of the electrode enhances thermal uniformity and provides results that more closely match experimental observations.

The aim of this work was to develop a parameterized analytical and FEM (Finite Element Method) model of a switched reluctance motor. The developed analytical model was used to assess the performance of these types of motors, and is determined to be a tool for comparing and evaluating switched reluctance motors of various designs. The aim of the work was also the systematization of knowledge related to the operation, structure and methods of determining the static electromagnetic torque generated by switched reluctance motors. The FEM model (ANSYS) was, also, made in order to verify physical phenomena occurring during operation of these motors. Four laboratory tests were executed as part of the work prototypes of switched reluctance motors built at Warsaw University of Technology. A parameterized analytical model was developed and implemented in MATLAB. The model operation tests were conducted and as a result, the characteristics describing the dependence of the electromagnetic torque as a function of the current and the position of the rotor were obtained. The model was validated by confronting the analytical calculations supported by FEM simulation results with the measurement results. The usefulness of the results obtained with the use of the procured models was assessed.

Adaptive Gaussian process regression as an alternative to FEM for prediction of stress intensity factor to assess fatigue degradation in offshore pipeline

Slope stability in the Himalayas is a critical issue due to the region's unique geological and tectonic characteristics. The rocks of Himalayas are fragile and weak due to complex deformational structures causing the instability of slopes which are common, posing significant risks to infrastructure and communities. The central Nepal is characterized by similar problems and while constructing the engineering structures, challenges might be encountered. This study is aimed at modelling slope stability along the Malekhu-Dhadingbeshi road section of NH43 lying at the central Nepal lesser Himalaya using Kinematic analysis and Finite Element Method (FEM) modelling. The FEM modelling was conducted using two different computer programs. It was carried out at six different potential slopes with multifaceted geometry composed of slightly to moderately weathered quartzites and phyllites. The potential slope sections were identified using satellite imagery and field observations with key indicators including the condition of the slope toe, the presence of dislodged rocks and the formation of tension cracks. Their geometries were then created from the survey data obtained from topographic survey in the field. Input parameters for the modeling, such as cohesion and friction angle were calculated using the Hoek-Brown criterion while unit weight, elastic modulus and uniaxial compressive strength of rocks were determined from laboratory tests. The modelling was carried out under static condition provided there are no external influences from seismic activity or water forces. Out of six identified slopes sections, three of them were found to be critical during kinematic analysis, which were further modelled using FEM. Results from FEM revealed the non-significant total displacement at all of the three slope sections with higher critical Strength Reduction Factor (SRF) making the slopes stable. The findings also highlight the strength of the slopes under current conditions, providing valuable information for infrastructure planning in the region.

Prediction of the interpass temperature of a wire arc additive manufactured wall: FEM simulations and artificial neural network

Prediction of the deformation characteristics is an important step to understand the workability of alloys during imposing large strains. In this research, severe plastic deformation of Sn-5Sb solder alloy was carried out under different t deformation conditions, including the temperature range of 298, 330, 36, 400 K and die designs. The current study applies an experimentally validated finite element method (FEM) to establish a model for predicting workability in equal channel angular pressing (ECAP). To do this object, two ECAP dies were prepared with channel angle of 90 and the outer corner angle of 30O with and without choked angle in outlet channel. Angularly pressed Sn-5Sb solder alloy were utilized for validating the proposed FEM model. Different parameters such as die angles (angle between the channels and the outer corner angle), pressing temperature and the die outlet channel geometry were studied using FEM simulation. In conclusion, experimentally verified numerical data were successfully used for proficient die design and process determination in the ECAP of tin alloy. The obtained results of hybrid FEM model were in acceptable conformity with experimental measurements.

Background: Accurate registration for surgical navigation of laparoscopic surgery is highly challenging due to vessel deformation. Here, we describe the design of a deformable model with improved matching accuracy by applying the finite element method (FEM).Material and methods: ANSYS software was used to simulate an FEM model of the vessel after pull-up based on laparoscopic gastrectomy requirements. The central line of the FEM model and the central line of the ground truth were drawn and compared. Based on the material and parameters determined from the animal experiment, a perigastric vessel FEM model of a gastric cancer patient was created, and its accuracy in a laparoscopic gastrectomy surgical scene was evaluated.Results: In the animal experiment, the FEM model created with Ogden foam material exhibited better results. The average distance between the two central lines was 6.5mm, and the average distance between their closest points was 3.8 mm. In the laparoscopic gastrectomy surgical scene, the FEM model and the true artery deformation demonstrated good coincidence.Conclusion: In this study, a deformable vessel model based on FEM was constructed using preoperative CT images to improve matching accuracy and to supply a reference for further research on deformation matching to facilitate laparoscopic gastrectomy navigation.

Abstract. This study investigated the application of the finite element method for modelling complex physical processes in real time. The aim of the research was to determine the effectiveness of this method when combined with distributed computing. An integrated model was developed, combining classical numerical analysis with modern distributed system technologies to ensure high accuracy and computational efficiency. It was established that the finite element method had traditionally been used for modelling heat transfer, deformations, and electromagnetic phenomena. However, modern requirements for monitoring and control created the need to adapt this method for distributed computing. Algorithms for the efficient distribution of computational tasks were developed, allowing data processing delays to be minimised. Experimental simulations showed that the use of distributed computing reduced calculation time by almost 14.5 times – from 420 seconds on a single node to 29 seconds on 16 nodes – while maintaining a relative error of 2-4%. More than 50 test runs were conducted, confirming the system’s operational stability. The use of an adaptive integration step reduced computation time by 15% compared to a fixed step, demonstrating the effectiveness of load distribution optimisation. The obtained results confirmed the high potential of this method for solving real engineering problems, where speed and accuracy of calculations were crucial. The proposed methodology was recommended for use in industrial processes, monitoring, and control systems, as it provided fast and accurate modelling of complex engineering tasks with high scalability

A method utilizing variable depth increments during incremental forming was proposed and then optimized based on numerical simulation and intelligent algorithm. Initially, a finite element method (FEM) model was set up and then experimentally verified. And the relation between depth increment and the minimum thickness tmin as well as its location was analyzed through the FEM model. Afterwards, the variation of depth increments was defined. The designed part was divided into three areas according to the main deformation mechanism, with Di (i=1, 2) representing the two dividing locations. And three different values of depth increment, Δzi (i=1, 2, 3) were utilized for the three areas, respectively. Additionally, an orthogonal test was established to research the relation between the five process parameters (D and Δz) and tmin as well as its location. The result shows that Δz2 has the most significant influence on the thickness distribution for the corresponding area is the largest one. Finally, a single evaluating indicator, taking into account of both tmin and its location, was formatted with a linear weighted model. And the process parameters were optimized through a genetic algorithm integrated with an artificial neural network based on the evaluating index. The result shows that the proposed algorithm is satisfactory for the optimization of variable depth increment.

Infrared thermography has already been proven to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity, and low cost. However, the thorniest issue for the wider application of IRT is quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (GRUs) in composite material samples via pulsed thermography (PT). Finite Element Method (FEM) modeling provides the economic examination of the response pulsed thermography. In this work, Carbon Fiber Reinforced Polymer (CFRP) specimens embedded with flat bottom holes are stimulated by a FEM modeling (COMSOL) with precisely controlled depth and geometrics of the defects. The GRU model automatically quantified the depth of defects presented in the stimulated CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions.