Finite Element Model In Order Research Articles

On-line monitoring of membrane fouling based on an improved electrical measurement method.

On-line monitoring of membrane fouling is essential in the water treatment process. Drawbacks such as low-sensitivity and off-line limitations limit the application of existing methods. An on-line monitoring method based on Electrical Resistance Tomography (ERT) sensors is put forward in this paper. The Particle Swarm Optimization with Simulated Annealing (PSO-SA) algorithm is used in optimizing the topologies of finite element models in order to decrease the ill-posedness of sensitivity matrices. The deep denoising extreme learning machine with an auto-encoder model and the K-singular value decomposition algorithm are used in ERT reconstruction to improve imaging quality. The lift-wavelet is adopted in measuring the permeate flux to improve measuring accuracy. The ERT pixel values of the membrane module and the result of flux are used to analyze the fouling status. The results of membrane fouling experiments demonstrate the following: (1) Based on the local ERT pixels, the "two stage" phenomenon of membrane fouling can be observed. (2) In the early stage, the fouling distribution of the localized membrane module is consistent with its ERT pixels. (3) The deposition process of foulants for the localized membrane module is synchronized with the variation of ERT pixels. (4) The integrity of the membrane module can be detected according to the ERT pixels. Therefore, the novel method can effectively reflect the membrane fouling process, especially in the early stages of membrane fouling.

A hybrid formulation for determining the input power in a stiffened plate over a wide frequency range

A hybrid analytical/numerical formulation is presented for determining the input power in a stiffened plate over a wide frequency range. A Component Mode Synthesis (CMS) approach is used for developing the hybrid formulation. The structure is divided into a local substructure in the vicinity of the excitation and non-local substructures representing the remaining system. It is considered that the excitation is applied at a small number of discrete locations, therefore the local substructure is a small portion of the overall system. The computational efficiency originates from using an analytical approach and periodic structure theory for determining the dynamic modes and natural frequencies for the non-local substructures. Finite elements are used for determining the static modes for the non-local substructures and also for both the dynamic and static modes of the local substructure. The CMS matrices for the non-local substructures are condensed to their common interface degrees of freedom with the local substructures. In this manner, the non-local substructures are eventually represented as boundary conditions on the local substructure. Results from the hybrid formulation are compared with results from dense finite element models in order to demonstrate the validity and the efficiency of the hybrid method.

Progress in Simulation Modeling Based on the Finite Element Method for Electrical Discharge Machining

Electrical Discharge Machining (EDM) is a machining method commonly used to produce complex shapes and deep holes by eroding hard metals with an electric arc. There is a growing demand for process simulation using finite element models in order to improve the quality and efficiency of EDM, to reduce costs, to improve resource efficiency, and to facilitate its application in critical areas such as aerospace and mechanical engineering. Finite element models have greatly improved the prediction accuracy of EDM processes, simulated complex hybrid machining processes, and provided important guidance for the optimization of EDM processes. This paper systematically reviews the research progress of finite element modeling for EDM. Finite element method modeling is evaluated mainly in terms of four indicators: material removal rate, surface roughness, tool wear ratio, and recast layer thickness. Firstly, the importance and application of EDM are described, and the EDM finite element method modeling and its advantages are summarized. Then, the single-spark simulation model and the multi-spark simulation model of EDM are compared and discussed. Among the mainstream finite element models, the prediction error of the material removal rate for single-spark simulation ranges from 8.2% to 14.75%, while the prediction error of the recast layer thickness for multi-spark simulation can be as low as 1.98%. Finally, the applications of finite element modeling in EDM hybrid machining processes’ performance prediction and new material machining are summarized, and future research directions and trends in EDM finite element modeling are predicted.

Three-Dimensional Finite Element Modelling for Prediction Corrosion Induced Cracking Damaged in Reinforced Concrete Beam

The quality and condition of the reinforced concrete structure is critical, particularly in high-rise buildings where the danger of service life failure is extremely high. Usually, certain structures collapse as a result of cracks and spreading caused by corrosion. Due to this reason, there is a requirement for software program that can promptly measure and evaluate the strength, growth, and longevity of the structure. Researchers use Finite Element Modelling (FEM), a reliable approach to foresee the deterioration and fracturing of structures. The application of finite element modelling in determining the remaining strength of damaged components can provide crucial data, which is particularly relevant for engineers in real-world scenarios. Moreover, it has the ability to replicate the cracking caused by corrosion in reinforced concrete elements. A corrosion-induced cracking model is suggested to be included into a three-dimensional plane-stress finite element model. The cracking model provides an indication of the extent of corrosion-induced damage in the concrete, allowing for the study of strength and behavioural changes. The effect of corrosion-induced cracking in a thick wall cylinder model will be simulated by the finite element model in order to identify and analyse the cracks caused by corrosion in the concrete cover. A corrosion-induced cracking experiment will be conducted with two different cover thicknesses and reinforcement using the Midas FEA commercial software to create a three-dimensional model. There is a slight discrepancy between the numerical analysis modelling and the analytical approach in terms of the crack's initiation and propagation, mostly resulting from variations in modelling assumptions, methods, and parameters. This disagreement arises when comparing the numerical analysis model with the previously published work. Nevertheless, the numerical model may be used to simulate both crack propagation and the critical pressure needed to produce corrosion-induced concrete cover cracking.

Developing an artificial neural network controller for accelerating the hot deformation of the titanium aluminide TNM-B1 using reinforcement learning and finite element simulations

AbstractThis work presents a framework for interfacing a reinforcement learning algorithm with a finite element model in order to develop an artificial neural network controller. The goal of the controller is accelerating the hot compression process of the titanium aluminide TNM-B1. The reinforcement learning algorithm interacts with the finite element model by exploring different die velocities and receiving input measurements (the velocity, displacement and force of the die) while collecting rewards if a constant stress state in the workpiece is achieved. Synthetic stochastic material behavior was used to simulate the observed variations in deformation behavior of TNM-B1. The same reinforcement learning setup and reward function was able to adapt to two example finite element environments; the compression of a simple cylinder workpiece between flat dies and the compression of a more complex bone workpiece between flat dies. The performance of the controller for the bone compression environment was comparatively reduced and less consistent. In addition, training times and training instability were significantly increased. Furthermore, the results suggest that the framework can be used as a tool to find process optimizations or alternative process routes. This work demonstrates the concept and provides the groundwork and fundamentals for transferring the method to a physical setup.

Effect of Muscle Forces on Femur During Level Walking Using a Virtual Population of Older Women.

Aging is associated with a greater risk of muscle and bone disorders such as sarcopenia and osteoporosis. These conditions substantially affect one's mobility and quality of life. In the past, muscles and bones are often studied separately using generic or scaled information that are not personal-specific, nor are they representative of the large variations seen in the elderly population. Consequently, the mechanical interaction between the aged muscle and bone is not well understood, especially when carrying out daily activities. This study presents a coupling approach across the body and the organ level, using fully personal-specific musculoskeletal and finite element models in order to study femoral loading during level walking. Variations in lower limb muscle volume/force were examined using a virtual population. These muscle forces were then applied to the finite element model of the femur to study the variations in predicted strains. The study shows that effective coupling across two scales can be carried out to study the muscle-bone interaction in elderly women. The generation of a virtual population is a feasible approach to augment anatomical variations based on a small population that could mimic variations seen in a larger cohort. This is a valuable alternative to overcome the limitation or the need to collect dataset from a large population, which is both time and resource consuming.

Application of unsupervised learning for post-earthquake assessment of the Z24 benchmark bridge

Due to the significant number of ageing infrastructures, earthquake-induced damage assessment plays a fundamental role to mitigate the seismic risk, while enabling proper preventive maintenance and providing bridge serviceability and safety. In the wake of recent advancements in Artificial Intelligence and computational power, this paper presents a fully-automated Machine Learning (ML)-based technique for unsupervised damage detection in monitored reinforced concrete (RC) bridges. The innovative methodology is based on the autoencoder, a feed-forward neural network with the ability to reconstruct a given input from its low-dimensional representation. Considering this assumption, healthy acceleration sequences of user-defined length are firstly used to train the implemented ML model and specific damage-sensitive features are afterwards selected to quantify the differences between the original and the reconstructed input. Therefore, unknown testing data can be conceivably classified as damaged when the reconstruction loss notably deviates from the established reference conditions. The proposed approach is validated on the Z24 benchmark bridge by using pseudo-monitoring data stemming from a Finite Element (FE) model, properly calibrated based on the available field information. Specifically, the developed autoencoder is firstly trained with the acceleration responses simulated during operational conditions and collected by different sensors. Then, a realistic earthquake-induced damage scenario is introduced into the FE model in order to generate damaged acceleration time-histories to test the trained ML model. Results prove the capability of the proposed technique to detect seismic-induced damage, making it suitable for a prompt post-earthquake diagnosis.

Numerical Simulation of Wear in Aircraft Carbon-Carbon Composite Disk Brake​

Friction and wear are two major factors affecting the disk brake service life. Carbon-carbon composite materials have good stable friction properties, which enable them to operate as friction material in aircraft brakes application. This article discusses a wear simulation method for predicting the wear amount of c/c composite aircraft brakes under simulated operating conditions. A modified version of Archard’s wear equation is used in 2-D axisymmetric finite element model in order to predict the disk brake friction surfaces wear progression. The finite element commercial software COMSOL Multiphysics 5.5 is used to simulate wear and is presented in this paper. Wear law was implemented as a boundary ordinary differential equation (ODE) in a COMSOL Multiphysics simulation, considering wear depth (thickness) as the independent variable. Frictional heat generation is simulated as heat affects on material thermal properties and as a result surface deformation. Element removal technique is used to simulate the brake disk geometry thickness change due to the material loss during wear. Wear progression with time is presented. Wear model is verified using experimental work from literature.

Gibbs Sampler-Based Probabilistic Damage Detection of Structures Using Reduced Order Model

Vibration-based global damage detection based on updating of finite element (FE) model by targeting the modal measurements is a significant area of interest in structural health monitoring (SHM). In a typical modal testing setup, the measured mode shapes have missing components against various degrees of freedom (DOFs) due to the limitation in the number of sensors available. In this context, a novel Gibbs sampling approach is proposed for updating of FE model incorporating model reduction (MR) to facilitate the global-level detection of structural damages from incomplete modal measurements. In addition to the ease with similar sizes of analytical and experimental mode shapes, the proposed Gibbs sampling approach (for updating the reduced order FE model in the Bayesian framework) has some important advantages like: (A) no need for consideration of system mode shapes as parameters (unlike needed in the typical Gibbs sampling approach) thereby having a significant reduction in the number of parameters, (B) non-requirement of mode matching with consequent reduction in computation time to a significant extent. A generalized formulation is presented in this work providing the scope for incorporating measurements from multiple sensor setups. Moreover, formulations are adapted to incorporate multiple sets of data/measurements from each setup targeting the epistemic uncertainty. Finally, validation is carried out with both numerical (truss structure and building structure) and experimental (laboratory building structure) exercises in comparison with the typical Gibbs sampling approach having a full-sized model. The proposed approach is observed to be evolved as a computationally efficient technique with satisfactory performance in FE model updating and global damage detection.

Application of Surrogate Models to the Multiphysics Sizing of Permanent Magnet Synchronous Motors

The application of surrogate models in engineering design is becoming popular due to their mimicking capabilities. In the design of electric motor drives for example, where a control-drive circuit is connected directly to the electric motor, the finite element simulations of the integrated motor drive system can be time-consuming. A complete motor simulation can run into several hours, if not days. Therefore, the main contribution of this article resides in the effort to address the computational burden associated with the design process by replacing the finite element models with surrogate models to achieve a faster design process. Briefly, the procedure involves filling the design space with multiple motor geometries created from an 8-pole 12-slot surface-mounted permanent magnet (SPM) synchronous motor. Then, a multiphysics performance evaluation is performed over the design space for different switching frequencies over a range of dc bus voltages. The design variables of the motor, the inverter, and the calculated performance objectives are used to train a collection of surrogate models to obtain the objective functions that could potentially be used as possible replacements for the finite element models in order to reduce the solution time.

Effect of web perforations on end-two-flange web crippling behaviour of roll-formed aluminium alloy unlipped channels through experimental test, numerical simulation and deep learning

Aluminium alloy has recently become popular in New Zealand’s construction sector as a sustainable building material. However, in roll-formed aluminium alloy (RFA) channel beams, particularly those having web holes, cripples at points of concentrated or localised loading. In this paper, end-two-flange (ETF) web crippling behaviour of RFA unlipped channels having web holes was investigated using experimental tests, numerical simulations and a deep learning model referred as deep belief network (DBN). A total of 1080 data points was generated to train the DBN, using a finite element (FE) model that was validated using 30 new experimental results reported in this paper. Compared to the test data, the DBN predictions were conservative by around 6%. Using the DBN predictions, a comprehensive parametric study was undertaken which used the validated FE model in order to explore the effects of hole size, hole location, section thickness, and bearing plate on the web crippling strength of RFA channels having web holes. Web crippling strengths obtained from the DBN, tests and finite element analysis (FEA) were utilised to evaluate the performance of current design specifications from the American Iron and Steel Institute (AISI), Australian/New Zealand Standards (AS/NZS 1664; AS/NZS 4600:2018) and Eurocode (CEN 2006, 2007). It is shown that the current design rules are not reliable to predict the web crippling strength of RFA unlipped channels with web holes. As a consequence of the parametric analysis, new web crippling strength and web crippling strength reduction factor formulae for perforated RFA unlipped channels were proposed. A reliability analysis was then conducted, which confirmed that the proposed equations are capable of predicting the ETF web crippling strength of perforated RFA unlipped channels.

Design, Analysis, Test, and Model Correlation of a Flight-Like Additively Manufactured ESPA-Class Satellite

This paper presents the design, finite element analysis, dynamic testing, and model correlation of AdditiveSat, an additively manufactured Evolved Expendable Launch Vehicle (EELV) Secondary Payload Adapter (ESPA)–class spacecraft fabricated using fused filament fabrication. The AdditiveSat octagonal prism design incorporates several known additive manufacturing benefits, such as secondary structure printed directly into the primary structure and thus reducing the number of parts required to assemble the vehicle. A Pathfinder structure was printed and sine burst tested to , as well as random vibration, to demonstrate the feasibility of an additively manufactured ESPA-class structure to survive flight launch environments. A detailed model correlation effort noted several best practices for finite element models in order to generate acceptable dynamic predictions for this type of architecture. The final correlated model accurately predicts the frequency of major structural modes of the Pathfinder structure within 3% and also provides accurate predictions for high-frequency responses up to 1000 Hz.

Test based finite element analysis of wire meshes

As fish farming is becoming more and more important worldwide, this ongoing project aims at the simulation and test-based analysis of highly stressed wire contacts, as they are found in off-shore fish farm cages in order to make them more reliable. The quasi-static tensile test of a wire mesh provides data for the construction of a finite element model to get a better understanding of the behavior of high-strength stainless steel from which the cages are made. Fatigue tests provide new insights that are used for an adjustment of the finite element model in order to predict the probability of possible damage caused by heavy mechanical loads (waves, storms, predators (sharks)).

HIGH-SPEED WAVE-PIERCING CATAMARAN GLOBAL LOADS DETERMINED BY FEA AND SEA TRIALS

Wave-piercing catamaran hull forms are widely used for high-speed ferry applications due to the hull slenderness, suitable for achieving high speeds. The global loads acting on these craft are of great interest as there is limited knowledge on determining the magnitude of the loads, in particular when operating in random sea conditions. Longitudinal and transverse bending moments as well as pitch connecting moments and hull torsion loads act on the hull simultaneously. This paper investigates the estimation of these global loads from full-scale catamaran sea trials strain gauge data using finite element methods. Det Norske Veritas (DNV) load cases are applied to a finite element model in order to determine the conversion between local strain values observed during sea trials and prevailing global loads. Comparisons are thus made of global loads determined from strain data collected from sea trials with DNV global load cases. The results show that this method is relatively reliable for the prediction of hull global loads in the absence of slamming. Comparisons have been made for different heading angles. The quasi-static design loads are important during the ship design stage, as they are good proxies in wavelengths comparable to the hull length for rationally determined loads obtained from a first-principles dynamic analysis. The broad aims here are to demonstrate the use of strain sensor data obtained during sea trials for determination of global sea loads, to reconcile the loads thus determined with DNV load cases and thereby to improve the accuracy of the predicted loads used in design to increase the structural efficiency of vessel design.

Numerical study of single helical piles and helical pile groups under compressive loading in cohesive and cohesionless soils

In recent years, the use of helical piles as a deep foundation option for structures has increased dramatically because they offer definite advantages over other solutions. The present study undertook a comprehensive investigation of the axial compressive behavior of helical piles in both sandy and clayey types of soil using finite element (FE) modeling. Since the helices of helical piles are more likely to disturb the soil adjacent to the pile, first some approaches are proposed to consider the effect of soil disturbance during a helical pile installation in numerical models. The numerical models have been validated and calibrated through full-scale compressive loading results using the ABAQUS software. The validated numerical models were used to investigate the load transfer mechanism of a typical helical pile in different types of sandy and clayey soil, and the numerically obtained ultimate bearing capacities were compared with the theoretical ones. Based on the comparison, it was found that the theoretical procedure overpredicts the ultimate capacity of helical piles in medium and dense sand. Additionally, different geometrical aspects of a typical helical pile were investigated using FE modeling in order to employ perfect geometry for studying the compressive behavior of a helical pile group with a square grid arrangement. The results revealed that the shape of the block failure mechanism in clayey soil differs from that in sandy soil. The acquired shapes are used to suggest a theoretical method for calculating the ultimate bearing capacity of a helical pile group based on the block failure mechanism.

On the dynamics of twinning in magnesium micropillars

Micro-deformation testing has recently gained far-reaching scientific importance as it provides intrinsic information on the dynamics of plastic deformation which is concealed when bulk materials are tested. In this work, single-crystal Mg micropillars favorably oriented for mechanical twinning were tested in compression with concurrent scanning electron microscopy imaging. The experimental data were complemented by the finite element modeling in order to reveal the underlying physical background of the observed twinning dynamics. It was shown that the thickness of a twin should reach a critical value before triggering the nucleation of another twin to accommodate further strain. Nucleation and growth are repeated until the twins form throughout the whole micropillar, from top to bottom. Afterwards, the thickening and coalescence of all these twins take place until the entire micropillar volume is twinned. In addition, a line-by-line analysis of the scanning electron microscopy images was employed to reveal the twin lateral growth rates, which were shown to be on the order of 10−5–10−4 m/s.

An approach for predicting failure mechanism in rough surface rolling contact fatigue

A finite element model was developed to investigate the influence of near surface orthogonal shear stress (OSS) on the competitive failure mechanism between surface originated pitting (SOP) and subsurface originated spalling (SOS), which is intrinsic to rolling contact fatigue (RCF). Surface roughness in heavily loaded non-conformal contacts causes competition between SOS and SOP. In this investigation, tribo-surface roughness has been represented as sinusoidal waveform based on surface measurements of rolling element bearings. These measurements outlined the range of roughness frequency and amplitude. The effects of these surfaces on the contact were investigated and the resulting pressure distributions were used in a finite element model in order to quantify the effects of pressure distribution on near surface orthogonal shear stress concentration. The resulting pressure distributions obtained from rough surfaces were also used in a continuum damage mechanics finite element model (CDM-FEM). The results indicate that a contact with a low frequency surface roughness (pressure distribution) is more susceptible to surface failure, whereas the contact with high frequency surface roughness frequency will resist surface failure. To quantify surface originated failure for a given surface roughness, the probability of surface failure parameter (πsf), which is defined as the ratio of contacts exhibiting SOP characteristics to the total tested is proposed. The near surface stress analysis and failure mechanism results were used to establish a relation between the near surface OSS concentration and πsf. This relation is described by a 2-parameter Weibull cumulative distribution function (CDF). The results indicate that roughness frequency and half contact width are the main parameters controlling the probability of surface failure.

Finite Element Modelling and Updating of a Thin Plate Structure using Normal Mode Analysis

Structural design and analysis require accurate and precise finite element model in order to have a structure with high safety conditions such as aerospace, offshore and civil structures. However, the predicted results of the finite element model are often considerably different from the experimental results due to the modelling assumptions and simplifications on the properties and parameters of the actual structure. This paper presents the finite element modelling and updating methods on the thin plate structure. The modal parameters obtained from the plate are measured by experimental modal analysis by using an impact hammer and roving accelerometers method under free-free boundary conditions. The initial modal parameters are predicted by numerical analysis using normal mode analysis via NASTRAN SOL 103. The accuracy of the natural frequencies and the mode shapes obtained from the initial finite element model are compared with the measured counterparts. The modal based updating methods using NASTRAN SOL 200 is applied to update the properties of the plate. In conclusion, updating the material properties of the plate is successfully achieved. The total error of the initial finite element model of the plate is reduce from 45.12 percent to 6.28 percent respectively.

Three-dimensional simulation of ship bow slamming

The bow flare slamming load was studied by using the software Ls-dyna. A coupling finite element model including air, water and 3d bow was established. Flare slamming pressure was picked up from the finite element model in order to discuss the relation between flare slamming pressure and the velocity as well as the distribution rule of slamming pressure in different velocity and different water entry angle along the length and height of the ship.

A thermomechanical‐phase‐field approach for modeling of residual stresses in fusion welding

AbstractThe welding process of metallic components, such as using gas tungsten arc welding (GTAW), is often accompanied by detrimental deformations and residual stresses, which affect the strength and functionality of these welded components. In this work, a phase‐field model, usually used to track the state of the phase‐change materials, is embedded in a thermo‐elasto‐plastic finite element model in order to simulate the welding process and the residual stresses. The phase‐field method (PFM) enables us to track the melting front of the metal, due to the welding heat source, and to split the analysis domain into soft and solid regions. In this way, temperature and phase‐field‐dependent material properties can properly be assigned to different regions in the domain. The phase‐field evolution is governed by Allen‐Cahn equation and relies on specification of the free energy density function as the main driving force for the phase change. Having a coupled thermomechanical process during the welding heating‐cooling cycle, the onset of residual stresses is associated with plastic deformations and can be observed after cooling to the ambient temperature. The plasticity model considers J2 plasticity with isotropic hardening. The coupled nonlinear system of equations is solved in the open‐source FE package, FEniCS, with time discretization based on the finite difference method.