Computational Mechanics Research Articles

System reliability calculation based on the run-time analysis of ladder program

Programmable logic controller (PLC) system, a typical member in the embedded family, is now widely applied in industry. For safety critical PLC systems, reliability is of top significance. However, due to subcomponents’ temporal correlations caused by the run-time execution of embedded ladder programs, the complexity of reliability analysis is greatly increased. In this paper, we propose a novel probabilistic model to analyze reliability of PLC systems, called run-time reliability model (RRM). RRM is automatically constructed based on the structure and run-time execution of the embedded ladder program. Moreover, it is also a dynamic bayesian network (DBN) capturing full dependencies in a PLC system. Then, according to execution semantics of RRM nodes, we present customized conditional probability distribution (CPD) tables to calculate final reliability of the system, with failure probability of every referenced component as refinement. The strength of this model is that not only does it explicitly specify the correlations between run-time execution of embedded software and system components, but also it serves as a computational mechanism for probabilistic inference. Besides, the proposed approach is superior to previous works in both accuracy and efficiency. Compared to monte carlo based simulation, the average error rate of reliability values inferred from RRM model is small.

Istraživanje mehaničkog dizajna robotske hvataljke za inteligentno upravljanje pomoću jeftinog senzora

With the advent of Industry 4.0, there is a growing need for intelligent and automated robotic systems capable of performing complex tasks in the unknowen environments. This work focuses on the development of mechanical design for a robotic gripper and the implementation of intelligent manipulation for picking a target using a FANUC robot platform. The proposed method combines computational mechanics for the gripper, advanced motion control techniques, and a grasping control strategy to enable the robot arm to accurately and efficiently identify and pick a target object. To validate our approach, several experimental validations are conducted in various scenarios. It is well-acknowledged that the proposed work is feasible, effective, and applicable for a wide range of industrial applications.

Computational mechanics simulations on epithelial folding (Strengths, insights, and future challenges)

Computational mechanics simulations on epithelial folding (Strengths, insights, and future challenges)

Numerical simulation of damage to the deformed state of the coating slab of a special structure

In this paper, we strived to the goal to ensure various special structures against damage factors of high-explosive blast. Together with application of combined blast protection devices, we considered reinforcement of constructive elements and simulated the impact and interaction of detonating projectile with reinforced concrete wall. Various packages of computational mechanics which belong to ANSYS family were applied to numerical simulations, as well as the developed numerical model of reinforced media which can be effective in development of combined blast-protective structures for urban environment. Protection devices based of developed blast-protective structures combine influence of blast-absorbing multiphase media with durability of rigid reinforced base constructions, so the numerical simulation of blast wave reflection and projectile impact are also necessary for their research and development. Our computational results, achieved in this study, have shown that, owing to reinforcement, the considered coating slab retained its integrity, though some cracking and ejection of concrete really occurred.

A cognitive computational mechanism for mutual cooperation: The roles of positive expectation and social reward

A cognitive computational mechanism for mutual cooperation: The roles of positive expectation and social reward

Transitioning from global to local computational strategies during brain-machine interface learning.

When learning to use a brain-machine interface (BMI), the brain modulates neuronal activity patterns, exploring and exploiting the state space defined by their neural manifold. Neurons directly involved in BMI control (i.e., direct neurons) can display marked changes in their firing patterns during BMI learning. However, the extent of firing pattern changes in neurons not directly involved in BMI control (i.e., indirect neurons) remains unclear. To clarify this issue, we localized direct and indirect neurons to separate hemispheres in a task designed to bilaterally engage these hemispheres while animals learned to control the position of a platform with their neural signals. Animals that learned to control the platform and improve their performance in the task shifted from a global strategy, where both direct and indirect neurons modified their firing patterns, to a local strategy, where only direct neurons modified their firing rate, as animals became expert in the task. Animals that did not learn the BMI task did not shift from utilizing a global to a local strategy. These results provide important insights into what differentiates successful and unsuccessful BMI learning and the computational mechanisms adopted by the neurons.

Research on Computational Mechanics Construction of Bridge Civil Engineering Construction Based on Computer Big Data Technology

In this paper, a 3D visualization system for bridge construction management is established based on VRP virtual platform. The bridge construction monitoring data management system is designed on the platform of Access database and the object processing technology of VRP. Starting from the basic theory of construction monitoring of cable-stayed bridge, the system realizes the problem of construction error identification in the monitoring process and using the recognition results to compensate and modify the finite element model. The system mainly realizes the functions of data editing, query, data analysis, data visualization, bridge information introduction and so on. It solves the key problem of using relational database to form various reports quickly and intelligently.

Investigations of the Mass Transfer and Flow Field Disturbance Regulation of the Gas–Liquid–Solid Flow of Hydropower Stations

With the continuous depletion of fossil fuels, all countries attach importance to clean and sustainable development. The real-time state monitoring of multiphase flows is vital for enhancing hydropower station energy conversion. However, the material mass transfer mechanism and flow field disturbance regulation strategy faces significant challenges. To solve these problems, a computational fluid mechanics and discrete element method (CFD-DEM) coupling modeling and solution method based on a particle porosity model was proposed, and the mass transfer mechanism of gas–liquid–solid mixing flows was obtained under dynamic whirl intensity regulations. Combined with the user-defined function (UDF), the interphase forces and void ratios of fluids and particles were calculated to obtain the material mass transfer laws under dynamic disturbance regulations. The evolution characteristics of the particle flow pattern were tracked during the material mixing process. The results show that the mixed flow field had a high material transport efficiency under intensive whirl regulation, especially for the particle aggregation in the center of the reaction vessel. The maximum peak velocity and energy values of the particle transport process were 3.30 m/s and 0.27 × 10−3 m2·s−2. The higher whirl regulation improved the material transport process and conveying efficiency and enhanced the particle mixing effect in the reaction space. Relevant research results can provide theoretical references for material mass transfer mechanisms, dynamic regulation strategies, and particle flow pattern identifications and can also provide technical support for hydropower energy conversion.

Application of Artificial Neural Networks for Mathematical Modelling of Horizontal Jet Fires

Aim: This article focuses on the use of artificial neural networks to mathematically describe the parameters that determine the size of a jet fire flame. To teach the neural network, the results of a horizontal propane jet fire, carried out experimentally and using CFD mathematical modelling, were used. Project and methods: The main part of the work consisted of developing an artificial neural network to describe the flame length and propane-air mixing path lengths with good accuracy, depending on the relevant process parameters. Two types of data series were used to meet the stated objective. The first series of data came from field tests carried out by CNBOP-PIB and from research contained in scientific articles. The second type of data was provided by numerical calculations made by the authors. The methods of computational fluid mechanics were used to develop the numerical simulations. The ANSYS Fluent package was used for this purpose. Matlab 2022a was used to develop the artificial neural network and to verify it. Results: Using the nftool function included in Matlab 2022a, an artificial neural network was developed to determine the flame length Lflame and the length of the Slift-off mixing path as a function of the diameter of the dnozzle and the mass flux of gas leaving the nozzle. Using Pearson’s correlation coefficient, a selection was made of the best number of neurons in the hidden layer to describe the process parameters. The neural network developed allows Lflame and Slift-off values to be calculated with good accuracy. Conclusions: Artificial neural networks allow a function to be developed to describe the parameters that determine flame sizes in relation to process parameters. For this purpose, the results of the CFD simulations and the results of the jet fire experiments were combined to create a single neural network. The result is a ready-made function that can be used in programmes for the rapid determination of flame sizes. Such a function can support the process of creating scenarios in the event of an emergency. A correctly developed neural network provides opportunities for the mathematical description of jet fires wherever experimental measurements are not possible. Solution proposed by the authors does not require a large investment in ongoing calculations, as the network can be implemented in any programming language. Keywords: computational fluid mechanics, artificial neural networks, jet fire

How the eyes respond to sounds.

Eye movements have been extensively studied with respect to visual stimulation. However, we live in a multisensory world, and how the eyes are driven by other senses has been explored much less. Here, we review the evidence on how audition can trigger and drive different eye responses and which cortical and subcortical neural correlates are involved. We provide an overview on how different types of sounds, from simple tones and noise bursts to spatially localized sounds and complex linguistic stimuli, influence saccades, microsaccades, smooth pursuit, pupil dilation, and eye blinks. The reviewed evidence reveals how the auditory system interacts with the oculomotor system, both behaviorally and neurally, and how this differs from visually driven eye responses. Some evidence points to multisensory interaction, and potential multisensory integration, but the underlying computational and neural mechanisms are still unclear. While there are marked differences in how the eyes respond to auditory compared to visual stimuli, many aspects of auditory-evoked eye responses remain underexplored, and we summarize the key open questions for future research.

Fractional order study of magnetohydrodynamical time-dependent flow of Prandtl fluid

Although the thermal and flow analysis of complex mechanisms of electrochemistry, dispersion and viscoelasticity is significantly addressed in the literature but none of the studies are made through the concept of fractional differential operators with computationally stable numerical solutions. The current work is a first attempt for the fractional classification and computational analysis of the Prandtl fluid. The physical model is formulated to examine the thermal, flow and concentration transference for a naturally convective, time-dependent, incompressible, and stagnation point flow of Prandtl fluid over an infinite plate under the impacts of magnetic, chemical reaction, Soret and Dufour. The mathematical expression of the model is expressed in terms of partial differential equation coupled with Caputo's fractional temporal differential operator. The computational code based on finite difference scheme is formulated to simulate the model. The detailed analysis of the flow pattern, thermal management and concentration of the diffusive species are made computationally. It is noted that involvement of the fractional parameter is useful to control the flow motion, heat transport and concentration of the diffusive species according to the required physical system. The temperature of the system increases 64 % against the variation of radiation parameter when fractional order parameter α=0.4 then the integer order. The study will be a useful addition to non-Newtonian computational mechanics and numerical treatment of such complex models.

Neural mechanisms of face familiarity and learning in the human amygdala and hippocampus

Recognizing familiar faces and learning new faces play an important role in social cognition. However, the underlying neural computational mechanisms remain unclear. Here, we record from single neurons in the human amygdala and hippocampus and find a greater neuronal representational distance between pairs of familiar faces than unfamiliar faces, suggesting that neural representations for familiar faces are more distinct. Representational distance increases with exposures to the same identity, suggesting that neural face representations are sharpened with learning and familiarization. Furthermore, representational distance is positively correlated with visual dissimilarity between faces, and exposure to visually similar faces increases representational distance, thus sharpening neural representations. Finally, we construct a computational model that demonstrates an increase in the representational distance of artificial units with training. Together, our results suggest that the neuronal population geometry, quantified by the representational distance, encodes face familiarity, similarity, and learning, forming the basis of face recognition and memory.

Exploring the relationship between computational frameworks and neuroscience studies for sensorimotor learning and control

<p>The relationship between computational frameworks and neuroscience studies is crucial for understanding sensorimotor learning and control. Various tools and frameworks, such as Bayesian decision theory, neural dynamics framework, and state space framework, have been used to explore this relationship. Bayesian decision theory provides a mathematical framework for studying sensorimotor control and learning. It suggests that the central nervous system constructs estimate of sensorimotor transformations through internal models and represents uncertainty to respond optimally to environmental stimuli. The neural dynamics framework analyzes patterns of neural activity to understand the computational mechanisms underlying sensorimotor control and learning. The state space framework assesses the structure of learning in the state space and helps understand how the brain transforms sensory input into motor output. Computational frameworks have provided valuable insights into sensorimotor learning and control. They have been used to study the organization of motor memories based on contextual rules and the role of structural learning in the sensorimotor system. These frameworks have also been employed to investigate the neural dynamics under sensorimotor control and learning tasks, as well as the effect of explicit strategies on sensorimotor learning. The interplay between computational frameworks and neuroscience studies has enhanced our understanding of sensorimotor learning and control. Bayesian decision theory, neural dynamics framework, and state space framework have provided valuable tools for studying the computational mechanisms underlying these processes. They have helped uncover the role of contextual information, structural learning, and neural dynamics in sensorimotor control and learning. Further research should continue exploring the relationship between computational frameworks and neuroscience studies in sensorimotor learning and control. This interdisciplinary approach can lead to a better understanding of how motor skills are learned, retained, and improved through targeted interventions. Additionally, the application of computational frameworks in clinical settings may help develop more effective rehabilitation strategies for individuals with motor impairments.</p>

Model-based and model-free mechanisms in methamphetamine use disorder.

People with methamphetamine use disorder (MUD) struggle to shift their behaviour from methamphetamine-orientated habits to goal-oriented choices. The model-based/model-free framework is well suited to understand this difficulty by unpacking the computational mechanisms that support experienced-based (model-free) and goal-directed (model-based) choices. We aimed to examine whether 1) participants with MUD differed from controls on behavioural proxies and/or computational mechanisms of model-based/model-free choices; 2) model-based/model-free decision-making correlated with MUD symptoms; and 3) model-based/model-free deficits improved over six weeks in the group with MUD. Participants with MUD and controls with similar age, IQ and socioeconomic status completed the Two-Step Task at treatment commencement (MUD n = 30, Controls n = 31) and six weeks later (MUD n = 23, Controls n = 26). We examined behavioural proxies of model-based/model-free decisions using mixed logistic regression, and their underlying mechanisms using computational modelling. At a behavioural level, participants with MUD were more likely to switch their choices following rewarded actions, although this pattern improved at follow up. At a computational level, groups were similar in their use of model-based mechanisms, but participants with MUD were less likely to apply model-free mechanisms and less likely to repeat rewarded actions. We did not find evidence that individual differences in model-based or model-free parameters were associated with greater severity of methamphetamine dependence, nor did we find that group differences in computational parameters changed between baseline and follow-up assessment. Decision-making challenges in people with MUD are likely related to difficulties in pursuing choices previously associated with positive outcomes.

A computational mechanics model for producing molecular assembly using molecularly woven pantographs

A computational mechanics model for producing molecular assembly using molecularly woven pantographs

An explicit updated Lagrangian fragile points method for dynamic fracture simulations

Dynamic fracture is a prevalent phenomenon in engineering structures subjected to dynamic loads, and reliable and simple numerical simulations of such phenomenon has been an ongoing research topic of computational mechanics. Although the finite element method (FEM) has been widely used for fracture simulations, there are still challenges such as the crack introduction and the mesh distortion. In recent years, meshless methods have emerged as potential alternatives to overcome these issues. In this paper, an explicit updated Lagrangian Fragile Points Method (FPM) is proposed for dynamic fracture simulations. The FPM is a point-based discontinuous meshless method, thus it on one hand circumvents the mesh distortion, and on the other hand allows for a simple, explicit introduction of cracks. In this paper, the formulations of the explicit updated Lagrangian FPM are introduced. Then the method is applied to various dynamic fracture problems including the spalling fracture and the crack branching, and for each cases the FPM provides convincing results. This paper shows that the explicit updated Lagrangian FPM is an effective yet simple numerical tool for predicting dynamic fracture. Note that though only small deformation dynamic fracture examples are used in this paper, the proposed method is able to handle finite strain problems, verified by some simple examples, since it is developed in the updated-Lagrangian form.

Graph-based representation of history-dependent material response in the Data-Driven Computational Mechanics framework

This work develops an extension of the Data-Driven Computational Mechanics approach introduced by Kirchdoerfer and Ortiz (2016) to history-dependent behaviour. The material database is replaced by a material directed graph, where vertices represent strain–stress pairs and arcs encode thermomechanically admissible transitions between them. A graph search algorithm is used to select local material databases representative of the incremental material behaviour. This study thus gives the mathematical tools for a structured data-driven representation of the material behaviour, designed to minimise the increase of the problem dimensionality. The performances of the proposed approach applied to elastoplasticity are studied on a single-element structure and a larger truss structure. A two-step solver is proposed to address numerical accuracy issues in the latter case.

Simulation of Heat Transfer during Paraffin And Gallium Melting and Solidification Processes Utilizing Star CCm+

Energy storage systems are essential as the world works to minimize its dependency on fossil fuel energy for environmental and economic reasons. Because of their high capacity for latent heat storage. The numerical study of benchmark cases of solidification and melting was undertaken, and the results of these investigations are presented in this project. Numerical analysis is conducted to examine the brief solidification process of a pure liquid phase-change material within a rectangular enclosure when natural convection is present. The horizontal boundaries are both taken to be adiabatic, with one vertical barrier maintained at a temperature below the material's melting point and the other above. In this work, a numerical investigation of the melting of wax (namely N-octadecane) is presented. The numerical simulations of the experiments were carried out using STAR CCM+, and the results were compared with the results of other numerical simulations (FLOW 3D). The numerical simulations of gallium melting were carried out with a commercial code, STAR CCM+, which captures the solid-liquid interface with a fixed grid. This software captures the solid-liquid interface for phase change simulations in complex geometries with the enthalpy formulation technique. In addition, it demonstrates that computational fluid mechanics has reached a state of development where it permits reliable flow computations with solidification and melting. Casting with metal melts can be studied to provide information to engineers during the design process of new casting tools. The available simulation tools can also be used to predict existing casting processes and determine the origins of defects. The key findings are as follows: The interface shape during the solidification of gallium from above is always determined in large part by anisotropy in heat conductivity and interface growth morphology. Natural convection in the liquid slows down the rate of melting and adds more complexity to the morphology and transport mechanisms at the interface.

Structural integrity analysis with crack propagation for reactor pressure vessel nozzles based on XFEM

Structural integrity analysis for reactor pressure vessel (RPV) nozzles has been a vital issue due to the irradiation embrittlement and especially the discontinuity of geometry in RPV nozzles, which may cause a higher stress concentration and influence the structural integrity of RPV nozzles. For the linear elastic fracture mechanics (LEFM) analysis of the RPV nozzle, a postulated crack with a specific crack depth is presumed to evaluate the stress intensity factors (SIFs) whose crack depth does not grow during LEFM analysis. Thus, this paper would discuss the crack propagation for the postulated crack with a specific crack depth under the different reference temperature of nil-ductility transition (RTNDT), and the SIFs on the specific crack depth are also calculated. In recent research, the extended finite element method (XFEM) was developed, which is based on the enrichment solution space to simplify the computational fracture mechanics with the crack propagation of various discontinuities, and can be used to evaluate the crack propagation of RPV nozzle with a specific crack depth under RTNDT of −23.89, −10.89, 0.89, 2.69, and 2.89 °C. To investigate the crack propagation and structural integrity of RPV nozzles, the finite element analysis of Taiwan domestic boiling water reactor (BWR) nozzles containing the postulated circular corner crack with depths of one-fourth and one-tenth thickness along the 45° path from the tip of the nozzle corner is engaged. The present result shows that the crack was growing when the RTNDT was 0.89 °C and. In addition, the XFEM calculation of the SIFs for the specific crack depth is similar to the LEFM results.

A simple spectral representation of a second-order symmetric tensor and its variation

The spectral decomposition of a second-order, symmetric tensor is widely adopted in many fields of Computational Mechanics. As an example, in elasto-plasticity under large strain and rotations, given the Cauchy deformation tensor, it is a fundamental step to compute the logarithmic strain tensor.Recently, this approach has also been adopted in small-strain isotropic plasticity to reconstruct the stress tensor as a function of its eigenvalues, allowing the formulation of predictor–corrector return algorithms in the invariants space. These algorithms not only reduce the number of unknowns at the constitutive level, but also allow the correct handling of stress states in which the plastic normals are undefined, thus ensuring a better convergence with respect to the standard approach.While the eigenvalues of a symmetric, second-order tensor can be simply computed as a function of the tensor invariants, the computation of its eigenbasis can be more difficult, especially when two or more eigenvalues are coincident. Moreover, when a Newton–Raphson algorithm is adopted to solve nonlinear problems in Computational Mechanics, also the tensorial derivatives of the eigenbasis, whose computation is still more complicated, are required to assemble the tangent matrix.A simple and comprehensive method is presented, which can be adopted to compute a closed form representation of a second-order tensor, as well as their derivatives with respect to the tensor itself, allowing a simpler and numerically accurate implementation of spectral decomposition of a tensor in Computational Mechanics applications.