Finite Element Method Simulation Research Articles

Sensorized laparoscopic surgical grasper with integrated capacitive force sensor for robot-assisted minimally invasive surgery

Purpose This paper aims to introduce a sensorized surgical grasper with a novel flexible capacitive tactile force sensor integrated within the surgical grasper for minimally invasive surgery (MIS) and robot-assisted MIS (RMIS) procedures. Design/methodology/approach The proposed sensor offers a unique configuration of sensing electrodes with one top excitation electrode and three bottom electrodes enabling the measurement of normal and shear forces without incorporating any complex decoupling algorithms. The design of the sensor is optimized using finite-element method simulations, ensuring efficiency and reliability. Findings Experimental validation, real-time sensor response and application in lump detection through stiffness assessment demonstrate the decoupled force response (0–5 N normal range and 0–2 N shear range) with high sensitivity 0.0124/N, repeatability and hysteresis response with 5.65% and 4.7% errors respectively. Originality/value The compact design of the sensor makes it compliant with surgical graspers and therefore enhances the overall efficiency of robotic surgical procedures. The sensorized surgical grasper is fabricated using conventional machining and rapid prototyping techniques, presenting a cost-effective solution for adoption.

Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands

The accurate modeling of water and heat transport in soils is crucial for both geo-environmental and geothermal engineering. Traditional modeling methods are problematic because they require well-defined boundaries and initial conditions. Recently, physics-informed neural networks (PINNs), which incorporate partial differential equations (PDEs) to solve forward and inverse problems, have attracted increasing attention in machine learning research. In this study, we applied PINNs to tackle hydraulic and thermal transport coupling forward problems in silty sands. A fully connected deep neural network was utilized for training. This neural network model leverages automatic differentiation to apply the governing equations as constraints, based on the mathematical approximations established by the neural network itself. We conducted forward problems and compared the solutions derived from PINNs with those from Finite Element Method (FEM) simulations. The forward problem results demonstrate the PINNs model’s capability in predicting hydraulic transport, heat transport, and thermal–hydraulic coupling in silty sands under various boundary conditions. The PINNs exhibited great performance in simulating the thermal–hydraulic coupling problem. The accuracy of the PINNs solutions shows its potential for simulation in geotechnical engineering.

Revealing the Principles of Confining Electroplated Lithium beneath the CVD Grown Single Layer 2D Materials.

Owing to the nanoscale thickness, excellent mechanical and chemical stabilities, 2D materials including graphene and hexagonal boron nitride have emerged as promising artificial solid electrolyte interphase (SEI) candidates for lithium metal batteries. However, whether the implementation of 2D materials is beneficial to electrochemical performance remains controversial, and the key to confining the electroplated Li beneath the 2D materials remains elusive. Here, a nanocrystalline graphene (NG) film is synthesized on high-carbon Cu and the Li plating/stripping behavior on Cu grown with different 2D materials is investigated. Interestingly, in contrast to the commonly obtained Li particles on other substrates during nucleation, a smooth Li layer is obtained on NG/Cu, leading to a compact Li layer with more stable electrochemical performance. The finite element method simulations validate that the low electrical conductivity and the high density of defects on NG film drive the fast entry of electrolytes into the NG/Cu interface and promote homogenous Li nucleation. This work reveals the principles of confining electroplated Li beneath the 2D materials, and paves the way for the applications of 2D materials in artificial SEIs and anode-free lithium metal batteries.

Carbon Defects as Highly Active Sites for Gold Detection and Recovery.

The use of precious metals (PMs) in many areas, such as printed circuit boards, catalysts, and target drugs, is increasing due to their unique physical and chemical properties, but their recovery remains a great challenge in terms of zero-valent PMs as the final product. We report a highly hydrophilic carbon dot (CD) as a reductant (electron donor), in which the defects in CD served as efficient active sites for zero-valent PMs recovery with an electron-donating capacity of ~1.7 mmol g-1. The reduction of gold follows a two-step dynamic model characterized by the formation of nano-gold nuclei (initial rapid electron transfer process) followed by an Ostwald ripening process (subsequent slow process). Finite element method (FEM) simulation shows that the reaction efficiency and confinement effect of AuCl4 - ions are positively correlated with defect density, indicating that the quantitative control of carbon defect density is the key to enhancing reduction activity. Combining density functional theory (DFT) with XPS and FTIR technology, we found that the electron is transferred from CD to Au(III) via hydrogen bonding. This nano carbon material can be exploited to recover gold from e-waste water directly, with the characteristics of reducing energy consumption and avoiding environmental pollution.

Superior mechanical properties of an additively manufactured gradient lattice structure through FEM simulation and in-situ compression tests

Currently, the increasing incidence of bone defects and damage due to diseases and trauma, the treatment of bone defects usually uses autologous bone graft or allogeneic bone graft, however, the amount of autologous bone is limited, and there is also the risk of infection at the site of bone extraction, and allogeneic bone graft also faces problems such as disease transmission and immune rejection. In summary, artificial bone provides a new option for patients. This study investigates the mechanical properties of homogeneous and gradient Gyroid and Diamond porous structures, designed with average porosities of 50%, 60%, and 70%, using triply-periodic minimal surface models for artificial joints. Finite Element Method simulations and compression tests were employed to characterize the mechanical properties of Ti6Al4V porous scaffolds fabricated using the selective laser melting technique. The measured elastic modulus of the scaffolds ranged from 6.24 to 10.5GPa, meeting the requirements for orthopedic implants. Gyroid structures were shown to exhibit superior nominal stiffness compared to Diamond structures at equivalent porosity levels. Furthermore, the linear gradient porous structures demonstrated significantly higher elastic modulus, yield strength, and compressive strength compared to homogeneous porous structures, with increases of 66.9%, 20.3%, and 16.5%, respectively. The energy absorption capacity of the linear gradient porous structure was also found to be 3.3-fold greater than that of the homogeneous structure.The lightweight design of this linear gradient porous structure provides the basis for bone implants for biomedical applications.

Out-of-plane bending and shear behavior of cross-laminated bamboo and timber under four-point loading with variable spans

This paper investigates the out-of-plane four-point loading behavior of cross-laminated bamboo and timber (CLBT) with variable spans. The experimental design involved CLBT specimens with spans ranging from 1800 mm to 3600 mm, and cross-laminated timber (CLT) specimens were used as the control. Theoretical calculations and finite element simulation methods were employed to analyze the out-of-plane loading performance. The results indicated that under the same span, the measured mechanical properties of CLBT specimens were higher than those of CLT specimens. For example, the CLBT specimens with a span of 3600 mm exhibited bending strength and global bending stiffness that were approximately 190 % and 70 % higher than those of the CLT specimens, respectively. Increasing the span reduced internal shear stress in both CLBT and CLT specimens and increased their strength and global stiffness in bending. Although the CLBT floor panel was heavier than that of the CLT floor panel, it exhibited lower mid-span deflection in the serviceability limit state, indicating better performance in safety and stability compared to CLT. The results of this study offer valuable insights into the design and application of CLBT floor panels.

Research on simulation of a bullet skimming the ground by the finite element method

It is greatly complicated to research the process of bullets penetrating into the ground due to the diversity and heterogeneity of ground physical characteristics. The empirical formulas have been developed for scenarios involving deep penetration of bullets into the ground so far; however, equivalent calculations for bullets skimming the ground scenarios remain absent. This paper has established a finite element method (FEM) simulation model for deep penetration and validated it against empirical data. Leveraging this validated model, simulations have been extended to examine the bullets skimming the ground behavior of bullets. The results have delineated the impact angles at which bullets skimming the ground occur for soil with average compaction density. This methodology can be adapted to various soil types to construct a comprehensive set of parameters for bullets skimming the ground angles, thereby optimizing artillery performance.

A Design-Oriented Model for Transmission Loss Optimization in Marine DOCs

The even more restrictive regulations imposed on chemical and acoustic emissions of ships necessitate the installation of after-treatment systems onboard. The spaces onboard are limited, and the Exhaust Gas Cleaning Systems (EGCSs) have big dimensions, so an appropriate integration and optimization of EGCSs allows to save space and comply with international regulations. Moreover, in the available literature, there is a lack of guidelines about the design of integrated EGCSs. This study aims to develop an ad hoc optimization methodology that uses combined Computational Fluid Dynamics (CFD)–Finite Element Method (FEM) simulations, surrogate models, and Genetic Algorithms to optimize the acoustic properties of EGCSs while considering the limits imposed by the efficiency of chemical reactions for the abatement of NOx and SOx. The developed methodology is applied to a Diesel Oxidation Catalyst (DOC), and the obtained results lead to a system that integrates the silencing effect into the DOC.

Photocurrent Nanoscopy in the Near Field: A Comparative Study of Different Atomic Force Microscopy Tips in Relation to Their Optical Performance

Photocurrent is a critical observable in a wide range of physical processes across different length scales, serving as a valuable tool for the characterization of semiconductors or two‐dimensional materials. Recently, photocurrent mapping, particularly when combined with magnetothermal transport effects, such as the anomalous Nernst effect (ANE), has been used to image magnetic domains and domain walls. To gain access to photocurrents on the nanoscale, this effect is combined with infrared scattering‐type scanning near‐field optical microscopy, in which strong field enhancement is created at the apex of an atomic force microscopy (AFM) tip, which serves as the confined illumination source creating localized temperature gradients through light absorption in the sample, which can be exploited for ANE detection. Herein, ANE photocurrents generated in a cobalt–iron–boron channel and the optical scattering are compared between various AFM tips, revealing significantly differing behavior for different tips. To gain insight into the origin of these differences, the measurements are further compared to finite element method simulations of tips with varied tip apex radii.

Strong coupling between exciton and quasi-bound state in the continuum with polarization dependence

Exciton–polaritons which are formed by the strong coupling of photon and exciton, have aroused great interest. In this work, we study the strong coupling between the exciton of WS2 monolayer (perovskite) and quasi-bound states in the continuum (QBIC) under y(x) polarization, in the perovskite metasurface with WS2 monolayer. Here, QBIC mode is achieved by introducing the asymmetric parameter. It is found that the energy of Rabi splitting can reach 41.369 meV (235.19 meV) under y-polarization (x-polarization). The dispersion-coupled oscillation model is used to illustrate the strong coupling mechanism. Besides, the influences of the asymmetric parameter and the positions of WS2 monolayer on the Rabi splitting are also discussed, which are verified by finite element method (FEM) simulations. Compared with the previous works, our method provides a way to achieve Rabi splitting with polarization dependence.

Assessment of capacitor bank configuration of self-Excitation in induction generators utilizing finite element analysis

Self-excited induction generators (SEIGs) require carefully designed capacitor banks to maintain stable operation. This paper compares a capacitor bank design approach based on general parameters with one derived from the magnetizing reactance of three-phase induction generators. Finite element method simulations in Simulink are used to analyze and optimize the performance of these capacitor bank configurations. The results provide insights into the most effective designs for achieving reliable self-excitation in induction generators.

Implementation of Principal Component Analysis (PCA)/Singular Value Decomposition (SVD) and Neural Networks in Constructing a Reduced-Order Model for Virtual Sensing of Mechanical Stress.

This study presents the design and validation of a numerical method based on an AI-driven ROM framework for implementing stress virtual sensing. By leveraging Reduced-Order Models (ROMs), the research aims to develop a virtual stress transducer capable of the real-time monitoring of mechanical stresses in mechanical components previously analyzed with high-resolution FEM simulations under a wide range of multiple load scenarios. The ROM is constructed through neural networks trained on Finite Element Method (FEM) outputs from multiple scenarios, resulting in a simplified yet highly accurate model that can be easily implemented digitally. The ANN model achieves a prediction error of MAEtest=(0.04±0.06) MPa for the instantaneous mechanical stress predictions, evaluated over the entire range of stress values (0 to 5.32 MPa) across the component structure. The virtual sensor is capable of producing a quasi-instantaneous, detailed full stress map of the component in just 0.13 s using the ROM, for any combination of 4-load inputs, compared to the 6 min and 31 s required by the FEM. Thus, the approach significantly reduces computational complexity while maintaining a high degree of precision, enabling efficient real-time monitoring. The proposed method's effectiveness is demonstrated through rigorous ROM validation, underscoring its potential for stress control. This precise AI-driven procedure opens new horizons for predictive maintenance strategies centered on stress cycle monitoring.

Efficient Storage of Sodium Based on MnSe@MoS2 Heterostructure With "Stress-Strain Transfer" Mechanism for Sodium-Ion Batteries.

2D layered embedding materials have shown promising applications in rapidly rechargeable sodium-ion batteries (SIBs). However, the most commonly used embedding structures are susceptible to damage and collapse with increasing cycles, which in turn leads to a degradation of the overall performance of the batteries. In order to address this issue, a "stress-strain transition" mechanism is proposed to form a heterostructure by introducing pyramid-like MnSe into the MoS2 lattice to reduce the irreversible reconstruction under deep discharge. Density functional theory and Finite element method simulation reveal that the strong orbital coupling of Mn-Mo at the heterogeneous interface provides a guarantee for the directional migration of ions, alleviates the lattice expansion caused by embedding strain, and avoids irreversible structural changes during battery operation. The capacity measured at 0.1C is 612 mAh g-1, which is consistent with the theoretical prediction. The experimental results demonstrate that the capacity is maintained at 80.3% of the initial value after 3500 cycles. This work demonstrates a strategy of addressing the structural collapse of 2D layered materials and paves the way for the commercialization of SIBs.

Hardness Degradation Behaviors of Ni-Base Superalloys after Phase Coarsening

Ni-based superalloys are commonly utilized in the production of hot-end components in the aerospace, energy, and other sectors. Due to the extreme environment, phase coarsening is prone to occur within the alloys, leading to degradation in the mechanical properties and thus posing a risk to equipment safety. To investigate the influences of volume fraction of precipitation particles and phase coarsening on hardness of materials, and explore the relationship between their microstructure and macroscopic hardness, based on the finite element method simulations, a two-dimensional Vickers hardness model is constructed for testing. Results show that hardness is closely linked to the particle shape, size, and distribution features; with the increase of the volume fraction of particles or the degree of phase coarsening, the hardness of alloys gradually weakens; and when the volume fraction of particles is larger, the decrease in the hardness is more obvious along with the degree of phase coarsening. Related research makes a good reference for the design of Ni-base superalloy structures as well as the evaluation or prediction of their mechanical properties during the service.

Gold Tetrahedral Nanoframes with Mono-Rim or Dual-Rim Morphologies for Enhanced Near-Field Focusing in SERS.

This study presents a synthesis method for Au tetrahedral nanoframes (Td NFs) through a rationally designed multiple-step process, followed by an investigation of their distinctively ordered self-assembly for enhanced performance in surface-enhanced Raman spectroscopy (SERS). Two distinct Au Td NF building blocks are synthesized, exhibiting mono-rim or dual-rim morphologies. The mono-rim structure lacks intra-nanogaps, whereas the dual-rim configuration features well-defined intra-nanogaps. The non-centrosymmetric Td NFs self-assemble into a distinctive antiparallel arrangement that alternates between the tip-up and face-up orientations of the Au Td NFs. This configuration results in the formation of both triply tip-to-tip and face-to-face nanogaps. The unique zigzag pattern exhibited strong electromagnetic field enhancement and extensive spatial hot zones, significantly amplifying near-field focusing and, consequently, the SERS effect. The near-field enhancement of Au Td NF assemblies is confirmed through finite element method simulations and experimentally validated by comparing bulk SERS measurements with those of Au octahedron NF assemblies, which tend to adopt a parallel face-to-tip alignment during assembly. Owing to the complex arrangement of multiple intra-nanogaps between the internal rim-to-rim interfaces and the four exposed facets, dual-rim Td NFs exhibited single-particle SERS activity, a capability not observed in analogous Td NFs with single rims.

Finite element simulation of modal and linearized stress characteristics of key components of heat exchanger

In order to ensure higher structural stability and load safety of the heat exchanger, modal analysis of the folded flow plate assembly was carried out based on the finite element simulation method. In extreme conditions, the stress linearization and evaluation of the tube plate structure were achieved. The models of spiral baffle and traditional single arch baffle components were established, and the first 20 nature frequencies and the first six modal shapes were compared and analyzed. The strength of the tube plate structure was analyzed under the conditions of separate action of tube side pressure and shell side pressure, and the linearization results of stress were obtained through the setting of path. The results indicate that the spiral baffle component has better stability. In addition, through stress decomposition and evaluation, it can be determined that the strength under extreme working conditions meets safety requirements.

Optimization and characterization of a PCF-based SPR sensor for enhanced sensitivity and reliability in diverse chemical and biological applications

This paper presents the meticulous design and characterization of a photonic crystal fiber (PCF)-based surface plasmon resonance (SPR) sensor, analyzed using finite element method (FEM) simulations. The sensor’s superior performance is demonstrated by its exceptional sensitivity to minute changes in the refractive index (RI) of various analytes. The optimized structure reveals a peak resonance at 0.78898 µm with a maximum confinement loss of 546.34 dB/cm. Detailed investigations show resonance wavelength red shifts of up to 0.93% with a 5% increase in air hole diameter and blue shifts of up to 0.88% with a 5% decrease. Additionally, variations in the plasmonic gold layer thickness result in resonance shifts of up to 0.76% longer or 0.87% shorter wavelengths. The sensor achieves remarkable wavelength sensitivity (WS) of up to 13,000 nm/RIU and amplitude sensitivity (AS) of up to 1538.90RIU−1, underscoring its high precision in detecting analyte concentration changes. The design’s robustness against fabrication errors, evidenced by minimal variations in resonance characteristics, highlights its practical reliability. Furthermore, the use of a polynomial regression model with an R2 value near unity accurately approximates the relationship between resonance wavelength and analyte RI, ensuring precise sensing capabilities without overfitting. Comparative analysis with existing designs confirms the sensor’s superior performance, rendering it highly suitable for a wide range of applications, including biosensing of glucose, water contaminated by cholera germs, mucosa of the human intestine, important components of human blood, and detection of chemicals like acetone, ethanol, benzene, propanol, glycerol, and expired transformer oil.

Silicon Nitride-on-Insulator Photonics Polarisation Convertor

Photonic integrated circuits constitute a vital component of contemporary telecommunications systems, facilitating traffic management and reducing energy consumption. However, the integration of these components presents a significant challenge in the form of high polarization sensitivity, which has the potential to limit the overall performance of the device. The objective of this study was to develop a design method and fabrication technology for polarization converters based on silicon nitride-on-insulator. The design of the polarization converters was optimised through the utilisation of finite element method simulations, conducted using the ANSYS Lumerical software. The device features an asymmetric rib waveguide, which facilitates efficient polarisation rotation. The technological implementation comprised plasma chemical vapor deposition of silicon nitride films, three-dimensional laser lithography, and reactive ion etching. A technological assessment determined that the reproducibility tolerance was ± 60 nm. To address this limitation, a mirrored section was incorporated into the polarization converter design, thereby increasing the allowable fabrication tolerance to ± 215 nm without compromising device performance. The optimised polarization converter exhibited a high level of polarization rotation efficiency, reaching 96.3 %, and an output power of 98.32 %. The utilisation of an asymmetric rib waveguide was pivotal in attaining these outcomes, facilitating the transfer of optical power from fundamental transverse electric to fundamental transverse magnetic modes. The incorporation of a mirrored section enhanced the device's manufacturability, maintaining performance despite geometric deviations. These findings highlight the robustness of the proposed design under typical fabrication constraints. This study presents a novel design and fabrication method for silicon nitride on insulator-based polarization converters. The proposed approach improves efficiency and stability. These results provide a foundation for future advancements in integrated photonics, with potential applications in telecommunications and beyond.

Dual-arm shaping of soft objects in 3D based on visual servoing and online FEM simulations

In this work, we propose a vision-based and finite element method (FEM)-based controller to automate the 3D shaping of soft objects with dual-arm robots. Our controller relies on a data-based approach to learn how the robot’s actions result in object deformations, while also running FEM-based simulations to infer the shape of the whole body. These model-based simulations are used to generate initial shape data, allowing to extract visual features through a principal component analysis and thus estimate the interaction matrix of the object–robot system. In contrast with most existing shape servoing controllers, our new model-based approach continuously predicts the object deformations produced by the robot, which are then compared to the visually observed deformation feedback. This iterative process enables to correct the deformed mesh model before updating the interaction matrix. To validate this new control methodology, we present a detailed experimental study with a dual-arm robot and different soft objects, which showcases the performance of our automatic shaping framework.

Learning soft tissue deformation from incremental simulations.

Surgical planning for orthognathic procedures demands swift and accurate biomechanical modeling of facial soft tissues. Efficient simulations are vital in the clinical pipeline, as surgeons may iterate through multiple plans. Biomechanical simulations typically use the finite element method (FEM). Prior works divide FEM simulations into increments to enhance convergence and accuracy. However, this practice elongates simulation time, thereby impeding clinical integration. To accelerate simulations, deep learning (DL) models have been explored. Yet, previous efforts either perform simulations in a single step or neglect the temporal aspects in incrementalsimulations. This study investigates the use of spatiotemporal incremental modeling for biomechanics simulations of facial soft tissue. We implement the method using a graph neural network. Our method synergizes spatial features with temporal aggregation using DL networks trained on incremental FEM simulations from 17 subjects that underwent orthognathicsurgery. Our proposed spatiotemporal incremental method achieved a mean accuracy of 0.37 mm with a mean computation time of 1.52 s. In comparison, a spatial-only incremental method yielded a mean accuracy of 0.44 mm and a mean computation time of 1.60 s, while a spatial-only single-step method yielded a mean accuracy of 0.41 mm and a mean computation time of 0.05 s. Statistical analysis demonstrated that the spatiotemporal incremental method reduced mean errors compared to the spatial-only incremental method, emphasizing the importance of incorporating temporal information in incremental simulations. Overall, we successfully implemented spatiotemporal incremental learning tailored to simulate soft tissue deformation while substantially reducing simulation time compared toFEM.