Computational Mechanics Research Articles

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.

Cell geometry regulates tissue fracture

In vascular plants, the epidermal surfaces of leaves and flower petals often display cells with wavy geometries forming intricate jigsaw puzzle patterns. The prevalence and diversity of these complex epidermal patterns, originating from simple polyhedral progenitor cells, suggest adaptive significance. However, despite multiple efforts to explain the evolutionary drivers behind these geometrical features, compelling validation remains elusive. Employing a multidisciplinary approach that integrates microscopic and macroscopic fracture experiments with computational fracture mechanics, we demonstrate that wavy epidermal cells toughen the plants’ protective skin. Through a multi-scale framework, we demonstrate that this energy-efficient patterning mechanism is universally applicable for toughening biological and synthetic materials. Our findings reveal a tunable structural-mechanical strategy employed in the microscale design of plants to protect them from deleterious surface fissures while facilitating and strategically directing beneficial ones. These findings hold implications for targeted plant breeding aimed at enhancing resilience in fluctuating environmental conditions. From an engineering perspective, our work highlights the sophisticated design principles the plant kingdom offers to inspire metamaterials.

Engines for predictive work extraction from memoryful quantum stochastic processes

Quantum information-processing techniques enable work extraction from a system's inherently quantum features, in addition to the classical free energy it contains. Meanwhile, the science of computational mechanics affords tools for the predictive modeling of non-Markovian classical and quantum stochastic processes. We combine tools from these two sciences to develop a technique for predictive work extraction from non-Markovian stochastic processes with quantum outputs. We demonstrate that this technique can extract more work than non-predictive quantum work extraction protocols, on the one hand, and predictive work extraction without quantum information processing, on the other. We discover a phase transition in the efficacy of memory for work extraction from quantum processes, which is without classical precedent. Our work opens up the prospect of machines that harness environmental free energy in an essentially quantum, essentially time-varying form.

Quantum computing enhanced distance-minimizing data-driven computational mechanics

The distance-minimizing data-driven computational mechanics has great potential in engineering applications by eliminating material modeling error and uncertainty. In this computational framework, the solution-seeking procedure relies on minimizing the distance between the constitutive database and the conservation law. However, the distance calculation is time-consuming and often takes up most of the computational time in the case of a huge database. In this paper, we show how to use quantum computing to enhance data-driven computational mechanics by exponentially reducing the computational complexity of distance calculation. The proposed method is not only validated on the quantum computer simulator Qiskit, but also on the real quantum computer from OriginQ. We believe that this work represents a promising step towards integrating quantum computing into data-driven computational mechanics.

An innovative quasi-bond approach to bridge continuity, anisotropic damage and macroscopic fracture of solids and structures

Spontaneous formation and evolution of discontinuities in solid media still poses one of the significant challenges in solid mechanics and computational mechanics. To facilitate numerical modeling, a new model called herein ‘quasi-bond’ model is proposed, which redefines the form of local interaction between material particles. Unlike traditional discrete methods that require direct connections between material particles, in this quasi-bond method, only one end of each bond is actually connected to a material particle, rendering great flexibility in describing bond distribution and anisotropic damage by bond breakage. Homogenization of solid body can be performed by an integral over quasi-bonds, while the process of crack nucleation, propagation and coalescence to form fracture is simulated through bond breakage. Especially, the relationship between material elastic constants and bond stiffness parameter is established in both two- and three-dimensional contexts. Furthermore, a smoothed strain-based fracture description is proposed with the ability of eliminating particle density/distribution dependence problems. Finally, several benchmark tests are conducted to demonstrate the efficiency and accuracy of the proposed approach in solving elasticity and fracture problems.

A feasibility study of goal-directed network-based real-time fMRI neurofeedback for anhedonic depression.

Anhedonia is a hallmark symptom of depression that often lacks adequate interventions. The translational gap remains in clinical treatments based on neural substrates of anhedonia. Our pilot study found that depressed individuals depended less on goal-directed (GD) reward learning (RL), with reduced reward prediction error (RPE) BOLD signal. Previous studies have found that anhedonia is related to abnormal activities and/or functional connectivities of the central executive network (CEN) and salience network (SN), both of which belong to the goal-directed system. In addition, it was found that real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF) could improve the balance between CEN and SN in healthy individuals. Therefore, we speculate that rt-fMRI NF of the CEN and SN associated with the GD system may improve depressive and/or anhedonic symptoms. Therefore, this study (1) will examine individuals with anhedonic depression using GD-RL behavioral task, combined with functional magnetic resonance imaging and computational modeling to explore the role of CEN/SN deficits in anhedonic depression; and (2) will utilize network-based rt-fMRI NF to investigate whether it is feasible to regulate the differential signals of brain CEN/SN of GD system through rt-fMRI NF to alleviate depressive and/or anhedonic symptoms. This study highlights the need to elucidate the intervention effects of rt-fMRI NF and the underlying computational network neural mechanisms.

Preface

The 2023 7th International Conference on Mechanics, Mathematics and Applied Physics (ICMMAP 2023) took place in Dalian, China during 8th to 10th September 2023. The conferences in this series started in 2017 as an academic conference and gradually expanded to include wider topic fields as well. Now the International Conference on Mechanics, Mathematics and Applied Physics has been developed into an annual international conference based in China with topics encompassing the whole area of mechanics, mathematics and applied physics.The aim of the Conference was to provide a platform for the exploration of both fundamental topics and new applications of research fields related to mechanics, mathematics and applied physics, bring different scientific communities together, and thus facilitate the contacts between science, technology and industry. All the attendees were welcomed to give comments and raise questions to the speeches and presentations during the Conference.More than 100 participants attended the Conference which consisted of ten keynote speeches, as well as plenty of oral and poster contributions. Among them, Prof. Zhenxin Liu (Dalian University of Technology, China), who is interested in the researches on Stochastic Dynamical Systems and Stochastic Differential Equations, delivered a keynote speech on The Averaging Principle for SDEs with Monotone Coefficients. And Prof. Chongjun Li (Dalian University of Technology, China) made a report on Revised SBFEM on Arbitrary Polygons and Faceted Polyhedrons with the Second Order Completeness by Elimination of Bubble Functions. He focuses his research on Numerical Approximation, Computational Geometry, Multivariate Spline Theory, and Spline Finite Elements. And the other speakers performed their speeches one by one, sharing research experience and professional insights on specific topics. It is really a great opportunity to promote academic communication and collaboration worldwide.This volume gathers papers from regular contributions of ICMMAP 2023; each contribution submitted for publication has been peer reviewed and the Editors are very grateful to the referees for their careful support in improving the original manuscripts. The manuscripts accepted for publication cover different topics of mechanics, mathematics and applied physics: Computational Mechanics, Multiphase Fluid Mechanics, Partial Differential Equation, Simulation and Monte-Carlo Methods, Nuclear Electronics, etc.It is a great honor to present this volume of Journal of Physics: Conference Series and we deeply thank the authors for their enthusiastic and high-grade contribution.Finally, we would like to thank the Conference Chair, the Publication Chair, the members of the Technical Program Committee as well as Organizing Committee, the conference secretariat and the support from Dalian Jiaotong University that allowed the success of ICMMAP 2023.The Committee of ICMMAP 2023List of Committee Member are avaible in this pdf.

A critical review of the state of computer security in the health sector

There is growing concern about IT security in the healthcare sector due to the number of cyberattacks. The objective of the review is to analyze the state of adoption of computer security in the healthcare sector and provide valuable knowledge to researchers and health organizations interested in this field of study. An exhaustive search of international and regional articles on computer security in healthcare organizations was conducted using Scopus, Dimensions, and pubMed databases. Preferred reporting items for systematic reviews and meta-analysis (PRISMA) statement was used for the selection of articles published between 2018 and 2022. The final number of articles considered is 50. The review explored approaches related to computer security types, mechanisms, and technologies. The findings reveal that blockchain is the most widely used technology to protect medical information. In addition, network, software, and hardware security approaches are employed, using mechanisms such as data encryption, authentication, and access control. Based on these findings, a perimeter security model for the protection of medical information is proposed. In conclusion, these results highlight the importance of adopting robust security measures in terms of networks, software, and hardware, as well as adopting blockchain technology to improve data security in the healthcare sector.

Meet the authors: Yuxuan Zhao, Enmeng Lu, and Yi Zeng

Yuxuan Zhao, associate professor, Enmeng Lu, research engineer, and Yi Zeng, professor and lab director, have proposed a brain-inspired bodily self-perception model based on biological findings on monkeys and humans. This model can reproduce various rubber hand illusion (RHI) experiments, which helps reveal the RHI's computational and biological mechanisms. They talk about their view of data science and research plans for brain-inspired robot self-modeling and ethical robots.