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The pitting mechanism of 7 series high-strength aluminum (Al) alloys (7xxx) is investigated experimentally and numerically (finite element method, FEM) by comparing the characteristics of passive films and pitting kinetics of two types of 7xxx Al alloys. The point defect model (PDM) is used to evaluate the passive film properties in the two varieties of 7xxx Al alloy, and the FEM is employed to calculate the point defect diffusion process in detail. The results reveal that the two 7xxx Al alloys exhibit various microstructures, particularly the 7xxx-1, characterized by a robust (111) fiber texture and a lower proportion of low-angle grain boundaries than the 7xxx-2. The diffusivity D of point defects in 7xxx-1 (3.85 × 10−18–52.74 × 10−18 cm2 s−1) is lower than that in 7xxx-2 (5.47 × 10−18–66.61 × 10−18 cm2 s−1) and the diffusion duration of 7xxx-1 is longer than 7xxx-2, which is closely associated with the rupture of passive film. Additionally, FEM proved that the initial shapes of pits dictate the progression of pitting, manifesting that the shallow dish shape grows laterally while the deep hole shape grows longitudinally.

This study intends to understand the fundamental characteristics of hybrid nanofluids (HNF) in a porous cavity using computational modeling, which will aid in developing HNF within hydrodynamics and enable adaptability. The present research investigates the enhancement of the transmission of heat in a double lid-driven trapezoidal porous cavity containing a heated block, utilizing a HNF composed of aluminum oxide/copper-water (Al2O3/Cu-H2O) employing the Finite Element Method (FEM) based on Galerkin weighted residual (GWR). Additionally, the Darcy-Brinkman-Forchheimer comprehensive approach has been used to demonstrate fluid flow in the porous media. The governing parameters such as the Darcy number (10−4 ≤ Da ≤ 10−2), the porosity of the porous medium (0.6 ≤ ε ≤ 0.9), inclined angle (π/16 ≤ ϕ ≤ π/3), and solid volume fraction (0.001 ≤ δ ≤ 0.05) have been chosen to evaluate the impact within dimensionless time (0.1 ≤ τ ≤ 1). The simulation outcomes of flow and thermal fields are shown through streamlines, isotherms, average heat transfer rate at the hot surfaces, and average temperature inside the cavity. The outcomes of this study show that the HT rate rises by 41.02% when increasing the solid volume fraction (svf) from 0.1% to 5%. Also, it has been found that if the inclined angle is enhanced from π/16 to π/3 at non-dimensional time τ = 0.5, the average HT augmented by 63.16%.

This paper presents the design and simulation of a surface plasmon resonance (SPR) biosensor using a Platinum diselenide (PtSe2) and Blue Phosphorus/tungsten disulfide (BlueP/WS2) heterostructure for biosensing protocols. The simulation is done by using a finite element method (FEM) based COMSOL Multiphysics software. The performance of the SPR biosensor is then optimized for obtaining maximum sensitivity, quality factor, detection accuracy, and low limit of detection (LOD). The SPR biosensor demonstrates a maximum sensitivity of 234 deg/RIU, suggesting its ability to detect minute refractive index changes with remarkable precision. Furthermore, a quality factor of 390 RIU−1 demonstrates the biosensor's capacity to detect tiny fluctuations in target analyte concentration. The achieved detection accuracy of 7.8 deg−1 presents the biosensor's ability to detect target biomolecule solutions in the desired RI range. The remarkably low LOD of 4.26 × 10−6 ensures early and accurate detection. The significance of this research lies in five layered hetero-structure based combinations of BK7 prism, gold, PtSe2, BlueP/WS2 and sensing medium respectively. The introduction of transition metal dichalcogenides (TMDC) material of PtSe2 with a hybrid 2D nanomaterials heterostructure of BlueP and TMDCs offers a rapid, sensitive, label-free and reliable platform for early detection. Additionally, the FEM method allows for the investigation of physical phenomena as part of the work. In summary, the proposed senor outcomes effectively demonstrate the speedy capability of detecting any pathogens or analytes in the RI range of 1.330–1.350 with remarkable sensitivity and accuracy. The rapid detection without giving false results is the benefit of the proposed sensor structure.

Electromagnetic vibrational harvesters are low-cost devices featuring high-power densities and robust structures, often used for capturing the energy of environmental vibrations (civil infrastructures, transportation, human motion, etc.,). Based on Faraday’s law, energy generation relies on the modification of the magnetic field distribution within a magnetic element caused by mechanical vibrations inducing an electromotive force (EMF) in a pick-up coil. However, the practical implementation of this type of vibrational harvester is currently limited due to the reduced generated power under low-frequency vibrations. In this work, an electromagnetic vibrational harvester is experimentally characterized and analyzed employing magnetic circuit analysis. The harvester consists of a ferromagnetic U-shaped cantilever, a NdFeB magnet and a ferrite magnet used as “magnetic tip mass” to enhance the magnetic flux changes under vibrations of frequency

Experimental studies have shown that ultrasonic cavitation can reversibly open the blood–brain barrier (BBB) to assist drug delivery. Nevertheless, the majority of the present study focused on experimental aspects of BBB opening. In this study, we developed a three-bubble-liquid-solid model to investigate the dynamic behavior of multiple bubbles within the blood vessels, and elucidate the physical mechanism of drug molecules through endothelial cells under ultrasonic cavitation excitation. The results showed that the large bubbles have a significant inhibitory effect on the movement of small bubbles, and the vibration morphology of intravascular microbubbles was affected by the acoustic parameters, microbubble size, and the distance between the microbubbles. The ultrasonic cavitation can significantly enhance the unidirectional flux of drug molecules, and the unidirectional flux growth rate of the wall can reach more than 5 %. Microjets and shock waves emitted from microbubbles generate different stress distribution patterns on the vascular wall, which in turn affects the pore size of the vessel wall and the permeability of drug molecules. The vibration morphology of microbubbles is related to the concentration, arrangement and scale of microbubbles, and the drug permeation impact can be enhanced by optimizing bubble size and acoustic parameters. The results offer an extensive depiction of the factors influencing the blood–brain barrier opening through ultrasonic cavitation, and the model may provide a potential technique to actively regulate the penetration capacity of drugs through endothelial layer of the neurovascular system by regulating BBB opening.

Integration of steel and concrete benefits in square concrete-filled tube columns (SCFTs) makes them be utilized extensively worldwide. To evaluate the seismic behavior of special moment frames having SCFT columns, an accurate macro-modeling tool with little complexity is necessary. Although the fiber section can consider both linear and nonlinear behavior of SCFT, it fails to allow for deterioration, which is vital in assessing a seismic resisting system. This study proposes a new deterioration tool based on the Ibarra-Krawinkler model for compact SCFT columns using artificial neural networks (ANNs) through numerical simulation. Initially, 96 specimens with variations in geometry, material, and axial loading conditions were simulated in ABAQUS and then were analyzed using displacement control loading protocol to obtain hysteretic curves, which were used to prepare the data sets for training, testing, and validating the neural networks. Finally, the performance of ANN models was evaluated by statistical relations such as regression and mean square error. Through FEMA 356 and Ibarra-Krawinkler approaches, parameters defining linear and nonlinear phases of SCFTs behavior were derived. By employing efficiently trained neural networks, equations predicting the parameters of the deterioration model were established, and the obtained results show the accuracy and efficiency of the proposed model.

Total knee arthroplasty (TKA) is a cornerstone in addressing knee joint disorders, significantly enhancing patients' quality of life. However, despite technological advancements, a comprehensive understanding of the dynamic stresses experienced by knee prostheses during daily activities, particularly under rehabilitation interventions, remains elusive. This study aims to bridge this gap by employing numerical simulations and finite element analysis to elucidate these dynamic stresses and their interaction with rehabilitation protocols.A real-life knee replacement prosthesis model was meticulously constructed through coordinate measuring and 3D scanning, facilitating detailed finite element analysis in ANSYS Workbench version 17.1. Two distinct boundary conditions and loading scenarios were applied, with comparisons made between linear and nonlinear material assumptions. The simulation results using these different boundary condition methods revealed minimal differences. Specifically, at a knee angle of 0°, the relative stress error rate between the two boundary condition types was approximately 1 % (1.11 MPa and 1.099 MPa, respectively). At 15° and 90°, the error rates were 1.9 % and 0.56 %, respectively (10.275 MPa and 10.078 MPa at 15°; 10.275 MPa and 10.078 MPa at 90°). Given these minimal differences, the first type of boundary condition was adopted for the subsequent scenarios to enhance convergence efficiency in the analysis.Moreover, comparative analyses between linear and nonlinear material behaviors demonstrated acceptable agreement, offering insights into potential efficiency gains in simulation methodologies. Building on this foundation, an optimized tibial model was proposed, incorporating geometric alterations to the tray. Quantitative assessments revealed significant reductions, with von Mises stress decreasing by 23.35 % and equivalent strain by 17 % at a knee angle of 140°. Further evaluations at varying angles, including 60°, consistently showed positive influences on stress and strain. These quantitative findings not only contribute valuable insights into the mechanical behavior of knee prostheses but also provide tangible evidence for the efficacy of linear material behavior assumptions. The proposed optimized model exhibits promising potential for enhancing the design and performance of knee prostheses, particularly under critical loading conditions.In conclusion, these results underscore the importance of a nuanced understanding of knee prosthesis behavior during rehabilitation, offering a quantitative foundation for refining existing designs and informing the development of next-generation prostheses.

This paper presents a comprehensive investigation into the structural response of curved composite beams enhanced with carbon nanotube (CNT). Employing a multiscale framework, our analysis leverages the finite element method (FEM) to account for both bending and shear deformations across six degrees of freedom. The inquiry encompasses diverse mechanical, geometrical, and boundary configurations to assess these composite beams' natural vibration features. Moreover, we introduce a multilayer perceptron (MLP) neural network architecture designed to forecast such beams' dimensionless first natural frequency. Trained on a meticulously curated dataset derived from FEM simulations, the neural network model exhibits promising predictive capabilities concerning the free vibration frequency. To ascertain the efficacy and precision of our proposed methodology, we conduct a comparative analysis between FEM results and employ statistical metrics to evaluate the neural network's predictive performance. The findings of this study reveal an impressive predictive accuracy of over 95 % with regards to the initial natural frequency of the composite beams, thereby emphasizing the potential effectiveness of neural network methodologies in engineering analyses. This study significantly contributes to advancing our comprehension of the vibrational dynamics inherent in carbon nanotube-reinforced composite beams, while concurrently underscoring the potential efficacy of neural networks in forecasting their dynamic attributes.

This study investigates the effects of laser shock peening (LSP) on the microstructural evolution and surface hardening of MAR-M247 nickel-based alloy. Experimental and numerical simulation methods are employed to analyze the influence of pulse power density and spot size. The results demonstrate that LSP significantly enhances surface hardness and yield strength. As the pulse power density increases, the magnitude and depth of plastic deformation increase. Higher power densities can implant deeper and greater compressive residual stress (CRS), but the corresponding tensile residual stress (TRS) also increases. Reducing both power density and spot size decreases the amount of plastic deformation of the surface. However, decreasing the spot size results in a decrease in the depth of the CRS. On the other side, based on the analysis of the microstructure, during the process of LSP, many dislocations accumulate in the γ matrix and evolve into subgrain boundaries, resulting in grain refinement. Additionally, LSP induces the precipitation of nanoscale carbides. This study tries to establish the influence law and action mechanism between LSP process-material property-microscopic evolution, which provides theoretical and applied data support for engineering applications.

Thus far, Mo/Ag laminated metal matrix composites (LMMCs) have been widely used as interconnectors in solar cells. In order to enhance the connection strength between Mo–Ag LMMCs and solar cells, a multi-scale simulation technique (MSM) was utilised to simulate the parallel gap resistance welding (PGRW) process. This method involved molecular dynamics (MD) simulation and the finite element method (FEM). The structure of the Mo–Ag diffusion layer was examined by high-resolution transmission electron microscopy (HRTEM), which revealed a thickness of around 5 nm. It exhibited a wedge-shaped configuration that augmented the bonding between Mo and Ag. The characteristics of the Mo–Ag diffusion layer were computed using molecular dynamics (MD), which has a substantial influence on the properties of the interconnectors. The FEM model was provided with these characteristics, and a total of nine sets of orthogonal simulation experiments were devised. The findings indicate that when a voltage of 0.5 V is applied, the distance between the electrodes is 0.15 mm, and the pressure on the electrodes is 8.89 N, the temperature at the interface of the workpiece can reach 940 °C. Furthermore, the internal stress (90 MPa) remains below the material's yield strength (430 MPa). In addition, based on the specifications of nine sets of orthogonal trials, the interconnectors were welded to the solar cells, and further 45° tensile tests were performed on the resulting joints. According to the simulation, the connection strength can achieve a maximum value of 558 gf (5.47 N) using the optimal parameters. Meanwhile, the findings from electron backscatter diffraction (EBSD) revealed that the primary process involved in the connection mechanism of PGRW is the mutual diffusion and recrystallization that occur due to electrode pressure and resistance heat.

The Permanent Magnet Vernier Machine (PMVM) is favored for wave energy conversion due to its high torque production at low speeds and straightforward design. This study introduces the innovative Dual Stator Linear Permanent Magnet Vernier Machine (DSLPMVM) with concentrated winding. In this design, permanent magnets (PMs) are strategically placed on both the stator and a segmented translator, enhancing system reliability. The stator utilizes a simple PM layout, while the translator features a Halbach array. This research advances the DSLPMVM’s performance through the optimal placement and orientation of magnets, alongside a meticulous selection of tooth types. The paper rigorously evaluated all possible magnetic orientations and eight distinct stator tooth models. Detailed comparisons of flux linkage, back electromotive force (back-EMF), inductance, efficiency, power factor (PF), and force were conducted to identify the most effective designs.

This investigation's primary objective is to analyze the fluid dynamics and heat transfer enhancement in an irregular enclosure. Several physical phenomena such as Alumina-water nanofluids, magnetic field, non-uniform heating and amplitude of non-uniform heating are taken into account. A thorough investigation using machine learning algorithms (MLAs) and computational fluid dynamics (CFD) is carried out. Initially, for CFD phase the coupled non-linear dimensionless governing equations of the considered problem are solved numerically by adapting a Finite Element method (FEM) using appropriate boundary conditions. The analysis considers a range of physical parameters, such as the Hartmann number (0–20), nanoparticle volume friction (0–0.04), Ryleigh number (103-105), offset temperature (0.1–1), frequency (1-10) and non-uniform heating amplitude (0.1–1). The numerical results are explicated in the form of streamlines, isotherms, heatlines structures. The impact of these factors on entropy generation is also explored. In addition, three popular MLAs, Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine (SVM) were used to save the computational costs. The finding explore Hartmann number and nanoparticle volume friction has decreasing trend on total entropy production and heat transfer rate. The entropy production decreases up to 42.02 % for Hartman number and for nanoparticles it decreases 12.61 %. However, the non-uniform heating amplitude has opposite effect on irreversibility factor. Also, compared to other MLAs, the ANN model demonstrates more accurate predictions. The objective is to propose novel concept for enhancing the performance of cooling systems and optimizing energy utilization across diverse technical domain.

In this study, an external semi-elliptical crack was modeled in a 3D API 5 L X52 pipeline with stress corrosion cracking (SCC). To make the crack, the finite element method (FEM) was used to obtain the failure pressure (Pf) and its mechanical behavior. The longitudinal crack had a constant length (2c) and varied with depth (a). The properties of X52 steel subjected to SCC using the slow strain rate technique (SSRT) were considered. True stress-strain curves were obtained in air and in an NS4 solution with pHs of 3.5 and 9.5 at temperatures of 25 and 50 °C. According to the SCC index calculated from the mechanical properties of the SSRT, the X52 steel is susceptible to SCC at pH 3.5 and 50 °C. The mechanical properties of the tensile test using the stress-strain curves decreased as the pH and temperature changed, compared to those carried out at room temperature. This was due to the corrosion and hydrogen embrittlement produced by the solution on the X52 steel. The failure pressure is sensitive to the stress-strain curve; if the stress-strain curve decreases, the failure pressure also decreases. Corrosion defects, such as longitudinal cracks in X52 steel, which could be susceptible to SCC in an NS4 solution, decrease the failure pressure (Pf) and its capacity to withstand pressures of up to 75 % in pipe-grade steel that transports hydrocarbons.

This work presents a new approach for performing a parametric study and examining nonlinear material behaviours of a coupled thermal-mechanical model of a Power Electronics Module (PEM) by integrating the Finite Element Method (ANSYS-FEM) with Parametric Model Order Reduction (pMOR). The considered coupling method solves the thermal and structural models concurrently compared to the widely practised sequential coupling method. Instead of constant parameter values, which are generally regarded for pMOR studies, the temperature-dependent material properties of the wire material have been parametrised in the work using the pMOR method. A generalised 2D model has been regarded here for thermal-mechanical analysis with the pMOR approach, parametrising temperature-dependent coefficient of thermal expansion (CTE) and Young’s modulus (E) of the wire material to explore their impact on wire bonds. The matrix interpolation method has been applied here for the pMOR study, and PRIMA, a Krylov subspace-based model order reduction (MOR) technique, has been exercised for local model order reductions. A new efficient process of matrix interpolation, based on the Lagrange interpolation technique, has been developed to implement matrix interpolation in the parametric reduced order model (pROM). The local reduced order models (ROMs) have a degree of freedom (DOF) of just 8, compared to the full-order models’ (FOMs) of 50,602. The pROM provides an excellent solution and reduces computational time by 84% for the presented case.

Refractory metals that can withstand at high temperatures and harsh conditions are of utmost importance for solar-thermal and energy storage applications. Thin films of TiN have been deposited using cathodic vacuum arc deposition at relatively low temperatures ∼300 °C using the substrate bias ∼ −60 V. The nanomechanical properties of these films were investigated using nanoindentation and the spatial fluctuations were observed. The nanoindentation results were simulated using finite element method through Johnson-Cook model. A parametric study was conducted, and 16 different models were simulated to predict the hardening modulus, hardening exponent, and yield stress of the deposited film. The predicted values of elastic modulus, yield stress, hardening modulus and hardening exponent as 246 GPa, 2500 MPa, 25000 MPa and 0.1 respectively are found to satisfactorily explain the experimental load-indentation curves. We have found the local nitridation plays an important role on nanomechanical properties of TiN thin films and confirms that the nitrogen deficient regions are ductile with low yield stress and hardening modulus. This study further opens the opportunities of modelling the nanoscale system using FEM analysis.

To enhance the energy absorption properties of the energy-absorbing structure, carbon fiber-reinforced polymer (CFRPs) with higher specific energy absorption and porous material aluminum foam with better compressive characteristics were organically combined, and a lighter aluminum foam winding carbon fiber-reinforced polymer structure (CFRP-FA-FW) was designed. Through quasi-static compression testing, the deformation mode and energy absorption properties of CFRP-FA-FW under axial load were examined. The energy absorption and specific energy absorption of CFRP-FA-FW are both increased by 113.55 % and 60.73 %, respectively, compared to the simple composite structure CFRP-FA. Finite element simulation was used for the parametric analysis of the CFRP-FA-FW structure to assess the effects of the relative density of the aluminum foam, the fiber lay-up angle, and the thickness. The results reveal that the change in the relative density of aluminum foam has little impact on the failure deformation mode of CFRP-FA-FW under axial load; the structure has a higher energy absorption capacity and a smoother energy absorption process when the fiber lay-up angle is [0°/90°]ns and [45°]ns; the energy absorption capacity of CFRP-FA-FW is significantly improved by increasing the thickness of the carbon fiber lay-up, and the procedure is also more efficient.

A transient transport flow model is adopted in a renal bifurcated artery with different stenosis cases to examine the flow phenomena when the non-Newtonian blood flow regime within the three-dimensional space domain is chosen as a pulsatile nature. The nonlinear governing equations are solved using the Finite Element Method with suitable initial-boundary conditions. During numerical computation, distinct hemodynamic parameters including Wall shear stress, Reynolds number, power law index are evaluated based on different stenosis severities (0%, 25%, and 50%) and stenosis lengths (0mm, 0.5mm, and 1mm). For 0%, 25%, and 50% stenosis, the approximate values of velocity and pressure profiles are estimated at t = 5s at various positions, and achieved the highest velocities that were around 0.07m/s, 0.08m/s, and 0.13m/s respectively. The unsteady response of the stress due to stenosis-induced shear on the outside wall is described as a result of stenosis at t = 0.1s, 0.2s, 5s. The flow is severely restricted by constriction, which increases wall movement and produces stress, depending on the amount of stenosis. As pulsatile flow time increases due to the presence of stenosis, the wall shear stress decreases gradually. The impact of Reynolds numbers on pressure profiles and velocity profiles are displayed for Re = 100, 300, 500, and 700, and found that both velocity and pressure outlines increase significantly. In order to identify the flow phenomena, the influences of the power law index for n = 0.1, 0.3568, 0.5 are presented on the velocity and pressure profiles. Shear-thinning flow occurs for 0

In this work, a workflow has been developed for the generation of surrogate metamodels to predict and evaluate failure with a confidence above 95 % in initial service conditions of high-performance cylindrical vessels manufactured in composites by Roll Wrapping technology. Currently, there is no specific testing standardization for this type of vessel and to fill this gap probabilistic numerical models were developed, performed by the Finite Element Method, fed with the material characteristics obtained experimentally by 2D digital image correlation from flat specimens. From the initial numerical model, a surrogate metamodel was generated by stochastic approximations. Once the metamodels were obtained by robust engineering, an experimental ring-ring tensile test was developed under service conditions and deformations were measured by high-precision 3D digital image correlation. Parametric and robust tests showed that the results of the metamodel did not show statistically significant differences, with errors in the rupture part of less than 2 % with respect to the results obtained in the test, being proposed as a basis for new test procedures.

Background and objective: In early breast cancer diagnosis, tumor size is key to improving the patient's survival chances. It helps doctors to determine the adequate treatment for each case. Surface thermography presents encouraging results to detect thermal patterns of breast tumor abnormality. However, the early and accurate estimation of tumor size based on temperature characteristics is quite challenging due to unavailability of labeled clinical data. This work proposes a Feed-Forward Deep Neural Network (FF-DNN) for an inverse estimation of breast tumor size using thermographic data. Methods: A 3D breast model was created by the COMSOL Multiphysics software incorporating tumors in the gland and covered by fat, muscle, and skin layers. Several tumor configurations are included to generate a large amount of training data. An initial thermal data analysis was performed to affirm the influence of breast tumor size on the skin surface temperature. Then, 1400 different cases were prepared, and the relevant features were extracted to train the deep learning model. The coefficient of determination (R2) and the Mean Square Error (MSE) were used as metrics for evaluating the predictive model. Results: The analysis of the normalized temperature variations demonstrated the influence of tumor size on the surface temperature of the breast. Thus, the comparison between the FF-DNN against the Convolutional Neural Network (CNN) showed the reliability of the proposed approach. As result, the prediction accuracy indicates the capacity of the proposed FF-DNN model to estimate tumor size from the provided relevant features with an MSE value of 0.194 and an R2 value of 0.998. Conclusion: The findings of this study indicate that surface thermography, when paired with deep learning, holds promise as a valuable diagnostic tool to improve the prognosis of breast cancer.

Stator asymmetric-paths winding of variable speed pump storage (VSPS) generator-motor can solve the problem of mismatch of unit speed, capacity and outlet voltage. However, different winding connection schemes will lead to the change of magnetomotive force (MMF) asymmetry of each branch, and then affect the electromagnetic force of generator-motor. In order to explore the effect of this winding type on the electromagnetic force, stator asymmetric-paths winding's MMF and electromagnetic force of 336 MW VSPS generator-motor is derived. Waveforms and harmonic contents of the stator asymmetric-paths winding MMF of the equal-pitch and unequal-pitch schemes are analyzed. Under the consideration constraints of factors such as minimizing MMF asymmetry and electromagnetic vibration, a reasonable asymmetric-paths winding scheme with unequal pitch is determined It is concluded that asymmetric-paths winding scheme with pitch of 14-15-16 can effectively weaken the low-order MMF harmonics and reduce the electromagnetic force under the limitation of asymmetric-paths winding form. The finite element model of VSPS generator-motor is further developed, and the electromagnetic force amplitude and spatial and temporal distribution characteristics under 6 winding schemes are calculated to verify the accuracy of the calculation model. The results of the study can provide technical support for the development of localized VSPS generator-motor.

The mechanical behavior of metal foams under impact loading depends on multiple and complex parameters like impact velocity, strain-rate, local plastic deformation, oscillating and micro-inertial effects, etc. The prediction of the behavior of metal foams that are subject to impact loads is still challenging and engineering application of these materials typically requires time-consuming experimental tests. Numerical models based on the finite element method (FEM) can contribute to minimizing the experimentation effort. Realistic FEM models were built that account both for the macro- and micro-scopic characteristics of the porous material, explain the acting mechanisms that take place during impact, and study the yield properties as well as the energy absorption during the impact of closed-cell aluminum foams. The simulation results are compared with the ones derived from respective experimental uniaxial tests. Two different modeling approaches were applied thus creating two models. The first model relies on a cell-based method where the initial geometry of the foam was generated based on the Voronoi tessellation algorithm and the second one relies on the isotropic, strain-hardening, and continuum-based model developed by Deshpande-Fleck. The outcome of the investigation sheds light on the metal foam behavior under impact by explaining macro- and micro-structural phenomena that develop during impact.

The geoelectric fields induced during geomagnetic disturbances (GMD) may affect the normal operation of technological systems at the Earth’s surface. The non-uniform Earth conductivity structure, especially large lateral conductivity contrasts, can lead to the enhancement of geoelectric fields near boundaries such as a coastline. In this paper, we give an overview of the applicability of a two-dimensional finite element method (2D-FEM) for studying lateral variations of the Earth’s conductivity, which can be considered as one of magnetotelluric sounding forward modelling methods, including both the E and the H polarization cases. Then the spatial distribution of the geoelectric field near a continent-ocean boundary is calculated with different models. This is compared with analytic solutions for simple models to verify the accuracy of the FEM modelling, which is calculated by using the ANSOFT software. More realistic scenarios are then developed to emphasize the advantages of FEM. The factors influencing the coast effect are discussed. Results show that frequency, the shape of seafloor boundaries, and the depth of ocean have a significant impact on the geoelectric fields. Therefore, these factors should attract more attention when assessing risk for man-made conducting systems located within the coastal influence range.

This work deals with an investigation of negative corona discharge generated from standing wire particles in air insulation. Eight samples from different particle lengths and diameters were studied to understand the effect of particle geometries on discharge characteristics. The particle was stood artificially on the ground inside parallel plane electrodes and was energized with a positive HVDC generator. Pulse sequence analysis (PSA) technique was used to evaluate the corona discharge patterns and behaviors. Under the same applied voltage, it is concluded that an increase in particle length causes a rising trend on the average discharge current and pulse magnitude, as well as the pulses appear more frequent and fluctuate. Meanwhile, variation of the particle diameter shows that a larger particle size generates fewer pulses and a lower average discharge current. However, the pulse magnitude is higher and appears randomly with larger particle diameter. A simulation model based on Finite Element Method (FEM) was also established to explain the behaviors better. It is found that the pulse repetition rate is related to the electric field distribution and negative ions movement, while the pulse magnitude is related to ionization rate and positive ions velocity.

Recently, grid composites have drawn considerable attention in various engineering applications due to their customizable mechanical properties, stemming from a wide spectrum of available configurations. Advanced additive manufacturing techniques have made the production of these configurations more feasible. While much research has focused on optimizing for stiffness and strength, there has been a relative lack of studies targeting toughness optimization, mainly due to the intricate unpredictability arising from the complexity of crack propagation mechanisms. This study seeks to fill that gap by leveraging a hierarchical deep neural network (DNN) model, integrated with finite element method (FEM) simulations, to maximize grid composite toughness. DNN model incorporates both structural configuration and stress field data, improving prediction accuracy for unseen domains. Using a genetic algorithm informed by the DNN model, a substantial 60% increase in toughness was achieved while investigating only 3.5% of the original dataset. Subsequent analyses of the stress field and crack phase field elucidated the mechanisms contributing to this increased toughness. Finally, the optimized configuration was experimentally validated by fabricating specimens using a polyjet 3D printer. The results represent a significant advancement in maximizing energy absorption, offering a substantial contribution to the field of composite material optimization.

The goal of this paper is to analyze the thermal exhibition of transient free convective nanofluid flow in a square cavity with inclined periodic magnetic field using one-component thermally equilibrium homogeneous model. Distinct thermal settings (constant, parabolic, and sinusoidal) for the left heated wall are deliberated when the right wall of the cavity is cold and the horizontal walls are insulated. For numerical simulations, eight types of nanofluids consisting Cu, Co, Zn, and Al2O3 nanoparticles along with H2O and kerosene as base fluids have been employed. During numerical computation, the impacts of different factors (Hartmann number, Rayleigh number, nanoparticles volume fraction, period, and inclination angle) on the fluid flow and heat transfer are examined. The numerical results show that cobalt-kerosene nanofluid distributes the maximum heat transfer rate compared to other 7 types of nanofluids. The results also indicate that sinusoidal thermal scenery at the left heated wall delivers the uppermost average Nusselt number compared to the other types of thermal approaches. The optimum thermal performance is achieved at magnetic field's inclination angle δ=π3 and period number λ = 1 for the case of sinusoidal scenery.

The currently defined Safe Working Load (SWL) of the U-bolt has been determined excessively conservatively. To address this issue, this study conducted structural tests and numerical analysis on round type U-bolts of various sizes. The structural tests were conducted using a 2.5 MN actuator at the SURF R&D center, and the strain was measured through a uniaxial strain gage. The test showed the failure load greatly exceeded the design load with horizontal force. This necessitates a reevaluation and redefinition of load standards. Nonlinear numerical analysis was carried out, and these results were compared with the structural test results. When subjected to vertical loading, behavior was similar to uniaxial tension. On the other hand, using linear elastic analysis for determining SWL for horizontal loading was found to be irrational. A methodology was proposed for estimating the SWL of the U-bolt.

Microwave ablation (MWA) is a type of thermal ablation used for cancer treatment in interventional radiology. To induce localized tissue heating MWA employs electromagnetic waves within the microwave energy spectrum, which is done by the precisely designed antenna. This study substantially emphasizes the design and performance ameliorating of slot (both single and double) antennae and compares the results with conventional monopole antennae in terms of temperature distribution, specific absorption ratio (SAR), and thermal tissue damage rate. The simulation has been done in COMSOL by solving the Bioheat equation along with Maxwell electromagnetic equations using the finite element method. The simulation results reveal that the double-slot antenna has the most accurate and directional heat dissipation for liver tumors as well as the highest tissue damage rate and SAR. The highest SAR was found to be 3500 ​W/kg and 3350 ​W/kg at the implant depth of 61 ​mm and 63 ​mm for double and single-slot antennae, respectively. In addition, the fastest tissue damage occurred near the upper slot of the double-slot antenna. This study helps to understand the basic design parameters for enhancing single and double-slot antennae performance.

The LabPET II detection module serves as the fundamental component within Positron Emission Tomography (PET) scanners designed for achieving ultra-high-resolution imaging of small to medium-sized animals as well as the human brain. To ensure peak performance, it is imperative that this module operates consistently at a stable temperature. In this paper, we present an automatic and real-time method to improve the thermal control of the scanner LabPET II by evacuating the heat to reduce the overall temperature and maintain the thermal stability of the system. Based on the Thermo-Electric Cooler (TEC) process, our method applies two numerical techniques, Computational Fluid Dynamics (CFD) and Heat Transfer Analysis (HTA). The Dirichlet Boundary Conditions (DBC) method is applied for the Application-Specific Integrated Circuit (ASIC) at 25∘ C. The real measurements confirm and validate the simulation results with the Finite Element Method (FEM) based on the software COMSOL Multiphysics™. Field-Programmable Gate Array (FPGA) implementation on the Development and Education (DE1) board is performed with comparative results study. The proposed method is both simple and practical, and ensures thermal stability at 35∘C for the duration of operation of the scanner, which has a complex structure.

The image's quality is a crucial consideration in medical imaging, therefore selecting a fiber that offers more benefits is important. The primary objective of this work is to explore the imperative applications of Photonic Crystal Fiber (PCF) in medical imaging. Numerical studies utilizing the finite element method and a normal mesh have been performed on the proposed PCF with several different types of fiber materials in the core and background. More broadly in terms of wavelength, from 0.5 μm to 1.6 μm, some of the optical parameters investigated, including confinement loss, nonlinearity, effective area, and numerical aperture (NA). Changing PCF’s background material from BK7, fused silica, or ZBLAN has resulted in a shift in the parameters. Here, BK7 is discovered to be a promising possibility in medical imaging, and a thorough comparative analysis of PCF parameters has been performed. The comparison reveals that at an optical wavelength of 0.5 μm, the proposed PCF with BK7 exhibits strong nonlinearity of 119 W−1Km−1, low confinement loss of 2.97 × 10−12 dB/m, a numerical aperture of 0.15, along with a very low effective area of 4.18 × 10−12 m2. It is therefore anticipated that the suggested PCF with BK7 will be more suitable for the imaging application.

While several theoretical and experimental studies for cracks in piping exist, most pertain to pipelines, equipment, or fittings under pressure conditions or under stress corrosion conditions at welding. Element finite Method models have occasionally supplemented experimental methods, to investigate such operational fails. In this approach we explore technical options to comprehensively understand crack propagations, by first, evaluating the Stress Intensity Factor (KI) using ANSYS Parametric design language then, comparing with the Displacement Correlation Technique, for an elliptical base gas piping (20″APL Gr. B) suffering a longitudinal welding-induced crack, under a compression of 1.86 MPa. The KIvalue for an Electric Resistance Welding crack was calculated for the two-dimensional plane, for a quarter-length of propagated crack along the elliptical front. The KI value estimates are 0.94x(10)−3 MPam from ANSYS Parametric design language vs. 0.70x(10)−2 MPamfrom DCT the two methods are close less than 1. These results were compared with the theorical stress intensity factor for elliptical cracks by Broek11 Broek, D. (1984). Elementary engineering fracture mehcanics . The Hague: Martinus Nijhoff Publishers. David called elementary engineering fracture mechanics where the values were 0.5x(10)−1 MPam. We found that the proposed FEM method for estimating (KI)is the approach that is closest to the theoretical value.

Background: Electric field (E-field) modeling is a potent tool to estimate the amount of transcranial magnetic and electrical stimulation (TMS and tES, respectively) that reaches the cortex and to address the variable behavioral effects observed in the field. However, outcome measures used to quantify E-fields vary considerably and a thorough comparison is missing. Objectives: This two-part study aimed to examine the different outcome measures used to report on tES and TMS induced E-fields, including volume- and surface-level gray matter, region of interest (ROI), whole brain, geometrical, structural, and percentile-based approaches. The study aimed to guide future research in informed selection of appropriate outcome measures. Methods: Three electronic databases were searched for tES and/or TMS studies quantifying E-fields. The identified outcome measures were compared across volume- and surface-level E-field data in ten tES and TMS modalities targeting two common targets in 100 healthy individuals. Results: In the systematic review, we extracted 308 outcome measures from 202 studies that adopted either a gray matter volume-level (n = 197) or surface-level (n = 111) approach. Volume-level results focused on E-field magnitude, while surface-level data encompassed E-field magnitude (n = 64) and normal/tangential E-field components (n = 47). E-fields were extracted in ROIs, such as brain structures and shapes (spheres, hexahedra and cylinders), or the whole brain. Percentiles or mean values were mostly used to quantify E-fields. Our modeling study, which involved 1,000 E-field models and > 1,000,000 extracted E-field values, revealed that different outcome measures yielded distinct E-field values, analyzed different brain regions, and did not always exhibit strong correlations in the same within-subject E-field model. Conclusions: Outcome measure selection significantly impacts the locations and intensities of extracted E-field data in both tES and TMS E-field models. The suitability of different outcome measures depends on the target region, TMS/tES modality, individual anatomy, the analyzed E-field component and the research question. To enhance the quality, rigor, and reproducibility in the E-field modeling domain, we suggest standard reporting practices across studies and provide four recommendations.

An alternative construction methodology has been raising interest, consisting of employing fabrics as formworks. The implementation of flexible formworks allows the production of highly optimized organic structures, avoiding the use of unnecessary materials and consequently reducing construction loads, costs and waste. This study aims to integrate the experimental characterization of the mechanical behaviour of the knitted textiles used as formworks, the mechanical and deformational behaviour of the formworks while being filled with fresh mortar, and the numerical simulation of the multi-step process leading to the final structure. The integration of these different processes is essential for a sound design of this composite system as a construction strategy. To this end, this investigation identified several aspects, related to either experimental or computational mechanics that are relevant to the topic and should be further investigated, with the aim of a future integrated material and structural design approach.

Mixed convective heat transfer due to rotating surface has sustainable importance in improving cooling efficiency of thermal engineering equipment. In this investigation, hydromagnetic double diffusive mixed convection in a wavy porous cavity filled with hybrid nanofluid having rotating heat source is numerically studied. A solid cylindrical rotating heat source is positioned at the center of the wavy cavity. The fluid domain inside the cavity is heated from the rotating heat source and partially heated bottom wall. The governing partial differential equations are simulated using finite element method. The computational technique is validated performing rational comparisons. The results indicate that the pattern of streamlines circulation, isotherms and local entropy generation are significantly influenced with the rotating heat source. The flow velocity is observed increasing rapidly with cylinder rotation speed which is maximized with increasing cavity porosity and permeability but minimized with magnetic field impact and amalgamating hybrid nanoparticles. Heat transfer enhancement is found increasing by 682.07% with rotating heat source for varying Darcy number (Da = 10−4 to 10−1) in absence of magnetic field (Ha = 0) which reduces to 504.02% in presence of magnetic field (Ha = 100). Moreover, 45.62% heat transfer enhancement is achieved for varying of cavity porosity (ε = 0.3 to 0.9) which declines to 22.83% in presence of magnetic field (Ha = 100). In addition, 90.04% more heat transfer enhancement is estimated in hybrid nanofluid of 5% volume fraction than base fluid water. The entropy generations components are affected significantly with higher values of physical parameters. The Bejan number is found declining for all influential parameters studied. Accordingly, the study has significant impact on the controlling and optimizing of fluid flow and heat transfer in hybrid nanofluid filled improved designing and is applicable in improving the performance of thermal equipment such as high-performance heat exchangers, electronic device cooling, energy storage systems, thermal mixing, space thermal management, crystal growth, float glass production, solidifications, solar technologies, and reactor safety devices, etc.

It is well-known that, in thermal aging management of underground power cables, analytical Arrhenius models can include the thermal effect of a heating pipeline on the lifespan of power cables through a rise in the soil temperature and an appropriate ampacity de-rating factor. However, the thermal effect of a pavement on the lifespan of power cables installed below that pavement cannot be taken into account using any de-rating factor. Accordingly, this paper proposes FEM-based Arrhenius models to analyze the thermal effects of one heating pipeline and several different types of pavements on the thermal lifespan of an underground line of 110 kV consisting of cables with cross-linked polyethylene (XLPE) insulation. Each proposed Arrhenius model is created using both IEC 60287 and IEC TR 62095, as well as combining the traditional Arrhenius model with an appropriate FEM-based steady-state thermal model. An experimental background consisting of the existing thermal aging tests with XLPE insulation is also used. Different pavements are represented by different values of thermal and surface radiation properties, namely: thermal conductivity, solar absorptivity, and thermal emissivity. The pavement surface radiation properties are specified within the frames of the most unfavorable summer conditions, and the most common winter conditions. Moreover, different distances between the 110 kV cables and the heating pipeline are considered. Finally, it is found that: the considered thermal effects can shorten the expected total service lifespan of the 110 kV cables in the area adjacent to the heating pipeline by up to 297.5 h; the pavement surface radiation properties do not affect the consumption of total thermal lifespan; as well as the largest consumption of total thermal lifespan occurs when the 110 kV cables are at the smallest distance from the heating pipeline.

Background: Group analysis of patients with deep brain stimulation (DBS) has the potential to help understand and optimize the treatment of patients with movement disorders. Probabilistic stimulation maps (PSM) are commonly used to analyze the correlation between tissue stimulation and symptomatic effect but are applied with different methodological variations. Objective: To compute a group-specific MRI template and PSMs for investigating the impact of PSM model parameters. Methods: Improvement and occurrence of dizziness in 68 essential tremor patients implanted in caudal zona incerta were analyzed. The input data includes the best parameters for each electrode contact (screening), and the clinically used settings. Patient-specific electric field simulations (n = 488) were computed for all DBS settings. The electric fields were transformed to a group-specific MRI template for analysis and visualization. The different comparisons were based on PSMs representing occurrence (N-map), mean improvement (M-map), weighted mean improvement (wM-map), and voxel-wise t-statistics (p-map). These maps were used to investigate the impact from input data (clinical/screening settings), clustering methods, sampling resolution, and weighting function. Results: Screening or clinical settings showed the largest impacts on the PSMs. The average differences of wM-maps were 12.4 and 18.2% points for the left and right sides respectively. Extracting clusters based on wM-map or p-map showed notable variation in volumes, while positioning was similar. The impact on the PSMs was small from weighting functions, except for a clear shift in the positioning of the wM-map clusters. Conclusion: The distribution of the input data and the clustering method are most important to consider when creating PSMs for studying the relationship between anatomy and DBS outcome.

Composite helical springs (CHSs) are mainly used in transportation and aerospace fields, such as automobile suspension, railway bogie and aircraft engine systems. It has become a trend to replace the traditional metal helical springs with CHSs with the advantage of energy conservation during service and emission reduction during manufacturing. The advantages of CHSs such as low weight, high specific strength, high specific modulus, corrosion resistance, fatigue resistance and high strain energy storage capacity mean that it has great development potential. The static and dynamic performances of CHSs together determine whether they can be used on a large scale in the engineering field. The static performance of the CHS determines their service-load range and the dynamic performance determines the service life and safety performance of CHSs under dynamic load environmental conditions. Therefore, it is an important task to optimize the static and dynamic performance of CHSs. To provide a reliable reference for the development of CHSs, this review analyzes the important factors affecting the static and dynamic performance optimization of CHSs from the perspectives of theory, finite element method (FEM), and experiment. In addition, the outlook in the research of CHSs are discussed.

In this study, mixed convective hybrid-nanofluid flow in a partially heated square cavity with two rotating rough cylinders in presence of an external magnetic field is numerically investigated. A pair of rotating rough cylinders is placed at different locations inside the cavity. The cavity is permeated by an external magnetic field at different inclination angles. Maxwell's thermal conductivity model is modified incorporating Brownian motion of hybrid nanoparticles. The conservation equations of the flow and thermal fields are simulated using finite element method. The effects of different influential parameters such as cylinders rotation velocity (0 ≤ ω ≤ 50), Hartmann number (0 ≤ Ha ≤50), hybrid nanoparticle volume fraction (0% ≤ φ ≤ 5%) and magnetic inclination angle (0° ≤ α ≤ 135°) on the flow and thermal fields are explored via streamlines, isotherms and bar charts of average Nusselt number. The simulated results ensure that mixed convective flow is accelerated with cylinders rotation speed but declines with higher magnetic field strength and hybrid-nanoparticle volume fraction. Heat transfer enhancement is recorded up to 261.29% at highest rotating speed of rough cylinders (ω: 0–50, φ = 1%, Ha = 10). Enhancement of heat transfer rate is found decreasing for increasing magnetic field strength. Lowest heat transfer rate is occurred at highest magnetic field impact (Ha = 50) which is 144.62% less than that of heat transfer in absence of magnetic field (Ha = 0). Optimum heat transfer is found for 5% hybrid nanoparticle volume fraction which is 101.2% more compared to base fluid water. The presence of triangular rough components accelerates the fluid flow and heat transfer rate significantly. In addition, 48.89% more heat transfer is obtained for rotating rough cylinders with triangular components compared to smooth cylinders. Moreover, maximum heat transfer is achieved at magnetic inclination angle of 90°. It is also observed that the flow and thermal fields strongly depend on the cylinders arrangements.

The horizontal self-cleaning single-shaft kneader possesses excellent mixing performance and has many industrial applications in bulk polymerization, polycondensation and polymer devolatilization. Hence, the objective of this research is to investigate the hydrodynamics and mixing performance of different new types of self-cleaning single-shaft kneaders by using the finite element method (FEM) simulation and particle tracking technique. Both the distributive mixing process of randomly distributed material points between halves of kneaders and the local distributive mixing process of a cluster in selected positions were analyzed. The effect of kneader geometry structure on hydrodynamics and mixing performance was also analyzed. The FEM simulation were validated by using a small visual experimental device. There hardly exists the flow dead zone in the self-cleaning kneader. The area of region with high velocity magnitude increases with the number of dynamic kneading bars and decreases with the number of static kneading bars. The area with high shear rate increases with the number of both dynamic and static kneading bars due to small clearance. There exists the periodical intermeshing interaction between the static kneading bars on kneader wall and the dynamic kneading bars on rotating shaft. What counts is that the distributive mixing process and mixing efficiency can be enhanced and improved by the periodical intermeshing interaction.

The Finite Element Method (FEM) is an important numerical method to solve Partial Differential Equations (PDEs). The widely used open source FEM platform — FEniCS, supports Hierarchical Data Format 5 (HDF5), which is very effective at storing and organizing large amounts of simulation data. However, HDF5 files can become prohibitively large for certain engineering applications, such as the thermal simulation of CPUs. Thus, this paper introduces a Type-Length-Value data format (TDF) plugin for compact storage of the FEM simulation solutions. Our Type-Length-Value (TLV) encoding implementation can be readily expanded to store more generic mathematical data generated by engineering and scientific applications. The evaluation results indicate that the TDF format could lead to a ≈95% space savings in our illustrated example. Meanwhile, the read/write speed has been improved compared to HDF5 used by FEniCS.

Finite element method (FEM) simulation combined with the mesh superposition technique (MST) was adopted to investigate the flow field in a horizontal self-cleaning differential rotating twin-shaft kneader with a highly viscous Newtonian fluid. The distributive mixing process and mixing efficiency were investigated by using the particle tracking technique. The comparison of flow pattern, distributive mixing process and mixing efficiency for the opposite-rotating, co-rotating and back-rotating twin-shaft kneader was performed. For the opposite-rotating kneader, the material flow in the left and right region merges in the upper region of overlapping zone, moves towards the lower region and is split into two parts in the lower region of overlapping zone. For the co-rotating kneader, the main flow pattern that the material moves towards the right region from the left region. Besides, there exists the secondary flow and periodic cyclic vortex in the overlapping zone. For the back-rotating kneader, the material flow in the left and right region merges in the lower region of overlapping zone, moves towards the upper region and is split into two parts in the upper region of overlapping zone. The material exchange between the left and right part of flow domain in the co-rotating kneader is much more efficient than that in the opposite-rotating and back-rotating kneader. The mean length of stretch, instantaneous mixing efficiency and time averaged mixing efficiency of opposite-rotating kneader is very close to that of back-rotating kneader, which is much smaller than that of co-rotating kneader. Hence, the co-rotating kneader possesses much better mixing performance than the opposite-rotating and back-rotating kneaders.

The recent decades have seen various attempts at the numerical modelling of fibre-reinforced polymer (FRP) composites in the aerospace, auto and marine sectors due to their excellent mechanical properties. However, it is still challenging to accurately predict the failure of the composites because of their anisotropic and inhomogeneous characteristics, multiple failure modes and their interaction, especially under multiaxial loading conditions. Micromechanics-based numerical models, such as representative volume elements (RVEs), were developed to understand the progressive failure mechanisms of composites, and assessing existing failure criteria. To this aim, this review paper summarises the development of micromechanics-based RVE modelling of unidirectional (UD) FRP composites reported in the literature, with a focus on those models developed using finite element (FE) and discrete element (DE) methods. The generation of fibre spatial distribution, constitutive models of material constituents as well as periodic boundary conditions are briefly introduced. The progressive failure mechanisms of UD FRP composites simulated by RVEs under various loadings are discussed and the comparison of failure envelopes predicted by numerical results and classical failure criteria are reviewed.

The use of wind energy as an alternative source of energy to generate the electricity is increasing worldwide. The application of an axial flux permanent magnet generator for small-scale wind turbine nowadays is increasing due to innovation, new material discoveries and completion in the manufacturing technology. The axial flux machines can be constructed with single-side, double-side and multi-stage topologies and can also be constructed with or without an iron core. Nine possible patterns between the single-side and double-side topologies with iron core and with surface-mounted permanent magnet method were designed in this study with the aid of the analytical method. To obtain the most qualified generator, there are six parameters used as constraints including flux density distribution, on-load terminal voltage, voltage regulation, total harmonic distortion, output power, and efficiency and analyzed with the finite element method from Maxwell-3D software. From the comparison, the generator type IX excited with rectangular poles is proven to be the most qualified generator among other generators in this case.

This paper shows a simple quad core Photonic Crystal Fiber (PCF) temperature sensor based on Surface Plasmon Resonance (SPR) which has very high sensitivity and sensor resolution. For evaluating the sensing performance Finite Element Method (FEM) is used. In this design, gold is used as plasmonic material and the outer layer of the designed PCF has to be filled by it for making the fabrication easier than any other model. Besides, the Chloroform is used as the temperature sensitive liquid (analyte). The designed temperature sensor has the maximum wavelength sensitivities which are 7.5 nm/°C and 10.5 nm/°C for x and y-polarization respectively. The sensor shows the resolution of 13.33 × 10−3 RIU for x-polarization and 9.52 × 10−3 RIU for y-polarization. Core diameter, radius of air hole, thickness of gold and pitch are the structural parameters which are adjusted to their optimized value for getting higher sensitivity. Easy fabrication and simplicity are the major aspects of this proposed model which makes it applicable to various applications such as medical science, power plant, engineering sector, environment monitoring etc.

Ankle-foot orthoses (AFOs) are typically prescribed to improve the gait function of ambulatory children with neurological conditions such as cerebral palsy or spina bifida. Due to the excessive and repetitive loading conditions, plastic material deformation can be observed in AFOs, especially over the lateral and medial parts of the ankle, which limits the effect of AFOs in the stabilization of the ankle joint. Trimline design and severity influence the rotational stiffness of an AFO considerably. In this study, we proposed novel trimming approaches for AFOs such that the trimlines were performed on the dorsal side rather than lateral and medial sides to reduce the magnitude of peak stresses and provide a homogenous stress distribution over AFOs. We analyzed eight dorsal trimline designs having different basic geometries by using the finite element method. To objectively evaluate the stress levels, the same boundary and loading conditions were considered for all design alternatives. We found that low peak stress values were observed in the AFO models with trimline geometries of the circle, ellipse, and slot variations. The vertical elliptic trimline on the dorsal side of the AFO was the most effective to decrease the magnitude of the peak stresses. The findings of our study are expected to contribute a complementary solution to orthotists in the fabrication of AFOs with high durability.

This article introduced a double guided ring-based spiral photonic crystal fiber (S-PCF) supporting both the orbital angular momentum (OAM) and linear polarization (LP) mode simultaneously with the efficient transmission. The outcomes exhibition that the designed S-PCF have good optical features such as less confinement loss (4.05342 × 10−12 dB/m for the EH9,1 mode), better mode quality and ISO (up to 98.87% and 126 dB, respectively), supported a decent amount of modes which is numerically 64 OAM and 6 LP modes, maintaining the large refractive index differences (>10-4) of all the eigenmodes, and a relatively flat dispersion variation (0.0743 ps/km-nm for the EH1,1 mode). Also, we obtained the crosstalk less than −12 dB and the power fraction is approximately 93%-99% of the S-PCF. All the optimizing properties are obtained using the full-vector finite element method (FEM) and also the perfectly matched layer (PML) as a boundary condition. Thus, the above mentioned optimizing propagation characteristic based S-PCF has effective applications like reliable long-distance communication into the space division multiplexing (SDM) process of the large-capacity fiber communication systems.

In order to meet the requirement that the permanent magnet eddy current coupler has larger output torque and smaller eddy current loss in actual operation, the structural parameters and operation performance of axial permanent magnet eddy current coupler (APMEC) are optimized by chaos multi-objective particle swarm optimization algorithm (CMOPSO) in this paper. The model of APMEC is established by three-dimensional finite element simulation. The central composite design (CCD) method is used to select the appropriate test point, and the response value is obtained by ANSYS finite element analysis software simulation. The second-order response surface regression model of APMEC was established according to the response value. The CMOPSO is used to optimize APMEC, and the optimal combination of structural parameters is obtained. By comparing the eddy current density distribution of APMEC before and after optimization with the finite element simulation experiment, it is verified that the optimization method is feasible to optimize the structural parameters of APMEC. The optimization results show that the efficiency of the permanent magnet eddy current coupler is more than 94%, and the energy consumption is reduced to 83% of the original energy consumption.

A coupled one-component mathematical model is applied to analyze the transient natural convective hydro-thermal incidents of different nanofluids occupied with a quarter circular vicinity. A sinusoidal magnetic arena is preferred in the transverse direction to examine the magnetization phenomena. The circular arc of the geometry domain is cold and the lowest wall is heated whereas the vertical wall of the arena is kept thermally insulated. During numerical computation, herein considered Water (H2O), Kerosene (Ke), Engine Oil (EO) as base fluids and Copper (Cu), Cobalt (Co), and Iron Oxide (Fe3O4) as nanoparticles. The dimensionless transports equations are solved numerically by applying the Galerkin weighted residual based finite element method. The outcomes spectacle that heat transfer rate augmented significantly with the increasing values of the Rayleigh number (Ra), nanoparticles volume fraction (ϕ), and period of the magnetic field (λ), whereas an intensification in the Hartmann number (Ha) lessens the total heat transfer rate. For nine sorts of nanofluids, the average heat transfer rate is computed along the bottom wall, and found that the heat transfer rate of Co-Kerosene nanofluid is considerably greater than other eight sorts of nanofluids. Finally, it is observed that the Cu-Kerosene nanofluid provides the high heat transfer rate whereas Cu-EO has the low rate of heat transfer for distinct thermal boundary conditions. The numerical results could be useful in assessing heat transfer features of solar collectors, and electronic devices.