Research on the Assessment System of Computational Mechanics Courses Based on the TOPSIS Entropy Weight Model

This paper takes the assessment and evaluation of computational mechanics course as the background,and constructs a diversified course evaluation system that is student-centered and integrates both quantitative and qualitative evaluation methods.The system not only pays attention to students’practical operation and theoretical knowledge mastery but also puts special emphasis on the cultivation of students’innovative abilities.In order to realize a comprehensive and objective evaluation,the assessment and evaluation method of the entropy weight model combining TOPSIS(Technique for Order Preference by Similarity to Ideal Solution)multi-attribute decision analysis and entropy weight theory is adopted,and its validity and practicability are verified through example analysis.This method can not only comprehensively and objectively evaluate students’learning outcomes,but also provide a scientific decision-making basis for curriculum teaching reform.The implementation of this diversified course evaluation system can better reflect the comprehensive ability of students and promote the continuous improvement of teaching quality.

Computational Simulation of Aptamer-target Binding Mechanisms

Aptamers are a type of single-chain oligonucleotide that can combine with a specific target.Due to their simple preparation,easy modification,stable structure and reusability,aptamers have been widely applied as biochemical sensors for medicine,food safety and environmental monitoring.However,there is little research on aptamer-target binding mechanisms,which limits their application and development.Computational simulation has gained much attention for revealing aptamer-target binding mechanisms at the atomic level.This work summarizes the main simulation methods used in the mechanistic analysis of aptamer-target complexes,the characteristics of binding between aptamers and different targets(metal ions,small organic molecules,biomacromolecules,cells,bacteria and viruses),the types of aptamer-target interactions and the factors influencing their strength.It provides a reference for further use of simulations in understanding aptamer-target binding mechanisms.

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.

ODE CONVERSION TECHNIQUES AND THEIR APPLICATIONS IN COMPUTATIONAL MECHANICS

In this paper,a number of ordinary differential equation(ODE)conversion techniques for trans- formation of nonstandard ODE boundary value problems into standard forms are summarised,together with their applications to a variety of boundary value problems in computational solid mechanics,such as eigenvalue problem,geometrical and material nonlinear problem,elastic contact problem and optimal design problems through some simple and representative examples,The advantage of such approach is that various ODE bounda- ry value problems in computational mechanics can be solved effectively in a unified manner by invoking a stand- ard ODE solver.

Micro air vehicle-motivated computational biomechanics in bio-flights:aerodynamics,flight dynamics and maneuvering stability

Aiming at developing an effective tool to unveil key mechanisms in bio-flight as well as to provide guidelines for bio-inspired micro air vehicles（MAVs） design,we propose a comprehensive computational framework,which integrates aerodynamics,flight dynamics,vehicle stability and maneuverability.This framework consists of（1） a Navier-Stokes unsteady aerodynamic model;（2） a linear finite element model for structural dynamics;（3） a fluidstructure interaction（FSI） model for coupled flexible wing aerodynamics aeroelasticity;（4） a free-flying rigid body dynamic（RBD） model utilizing the Newtonian-Euler equations of 6DoF motion;and（5） flight simulator accounting for realistic wing-body morphology,flapping-wing and body kinematics,and a coupling model accounting for the nonlinear 6DoF flight dynamics and stability of insect flapping flight.Results are presented based on hovering aerodynamics with rigid and flexible wings of hawkmoth and fruitfly.The present approach can support systematic analyses of bio- and bio-inspired flight.

Editorial: Computational mechanics of granular materials

Most of granular materials are highly heteroge- neous, composed of voids and particles with different sizes and shapes. Geological matter, soil and clay in nature, geo-structure, concrete, etc. are practical ex- amples among them. From the microscopic view, a lo- cal region in the medium is occupied by particles with small but finite sizes and granular material is naturally modeled as an assembly of discrete particles in contacts On the other hand, the local region is identified with a material point in the overall structure and this discon- tinuous medium can then be represented by an effective continuum on the macroscopic level

Toward a Unified Mathematical and Computational Framework for Control and Mechanics

This is a brief review on the recent book: Duality System in Applied Mechanics and Optimal Control, by Zhong Wan-Xie, published by Kluwer Academic Publishers, 2004. The book represents a significant effort to re-establish the historic and deep tie between control and mechanics by striving to connect and integrate concepts, methods, and algorithms in mechanics and control so that a unified framework can be established for both analytical and computational purposes. Clearly, it has demonstrated that the duality system method can be used as a mathematical and systematic foundation to deal with many important concepts and problems in both mechanics and control. This book is not only very useful for research and applications, but also extremely helpful for multidisciplinary curriculum development when students from one field are trying to learning and applying concepts and methods from the other field.

A Computational Fluid Dynamics (CFD) Analysis of an Undulatory Mechanical Fin Driven by Shape Memory Alloy

Many fishes use undulatory fin to propel themselves in the underwater environment. These locomotor mechanisms have a popular interest to many researchers. In the present study, we perform a three-dimensional unsteady computation of an undulatory mechanical fin that is driven by Shape Memory Alloy （SMA）. The objective of the computation is to investigate the fluid dynamics of force production associated with the undulatory mechanical fin. An unstructured, grid-based, unsteady Navier-Stokes solver with automatic adaptive remeshing is used to compute the unsteady flow around the fin through five complete cycles. The pressure distribution on fin surface is computed and integrated to provide fin forces which are decomposed into lift and thrust. The velocity field is also computed throughout the swimming cycle. Finally, a comparison is conducted to reveal the dynamics of force generation according to the kinematic parameters of the undulatory fin （amplitude, frequency and wavelength）.

An elastic mechanics model and computation method for geotechnical material

Internal friction characteristic is one of the basic properties of geotechnical materials and it exists in mechanical elements all the time. However,until now internal friction is only considered in limit analysis and plastic mechanics but not included in elastic theory for rocks and soils. We consider that internal friction exists in both elastic state and plastic state of geotechnical materials,so the mechanical unit of friction material is constituted. Based on study results of soil tests,the paper also proposes that cohesion takes effect first and internal friction works gradually with the increment of deformation. By assuming that the friction coefficient is proportional to the strain,the internal friction is computed. At last,by imitating the linear elastic mechanics,the nonlinear elastic mechanics model of friction material is established,where the shear modulus G is not a constant. The new model and the traditional elastic model are used simultaneously to analyze an elastic foundation. The results indicate that the displacements computed by the new model are less than those from the traditional method,which agrees with the fact and shows that the mechanical units of friction material are suitable for geotechnical material.

For Mega-Constellations: Edge Computing and Safety Management Based on Blockchain Technology

In mega-constellation Communication Systems, efficient routing algorithms and data transmission technologies are employed to ensure fast and reliable data transfer. However, the limited computational resources of satellites necessitate the use of edge computing to enhance secure communication.While edge computing reduces the burden on cloud computing, it introduces security and reliability challenges in open satellite communication channels. To address these challenges, we propose a blockchain architecture specifically designed for edge computing in mega-constellation communication systems. This architecture narrows down the consensus scope of the blockchain to meet the requirements of edge computing while ensuring comprehensive log storage across the network. Additionally, we introduce a reputation management mechanism for nodes within the blockchain, evaluating their trustworthiness, workload, and efficiency. Nodes with higher reputation scores are selected to participate in tasks and are appropriately incentivized. Simulation results demonstrate that our approach achieves a task result reliability of 95% while improving computational speed.

A review of data-driven whole-life state of health prediction for lithium-ion batteries:Data preprocessing,aging characteristics,algorithms,and future challenges

Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage rate.Throughout their whole life cycle,lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal materials.This degradation is reflected in the state of health(SOH)assessment.Therefore,this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years,highlighting common research focuses rooted in data-driven methods.It delves into various dimensions such as dataset integration and preprocessing,health feature parameter extraction,and the construction of SOH estimation models.These approaches unearth hidden insights within data,addressing the inherent tension between computational complexity and estimation accuracy.To enha nce support for in-vehicle implementation,cloud computing,and the echelon technologies of battery recycling,remanufacturing,and reuse,as well as to offer insights into these technologies,a segmented management approach will be introduced in the future.This will encompass source domain data processing,multi-feature factor reconfiguration,hybrid drive modeling,parameter correction mechanisms,and fulltime health management.Based on the best SOH estimation outcomes,health strategies tailored to different stages can be devised in the future,leading to the establishment of a comprehensive SOH assessment framework.This will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation protocols.This article reviews the current research landscape from four perspectives and discusses the challenges that lie ahead.Researchers and practitioners can gain a comprehensive understanding of battery SOH estimation methods,offering valuable insights for the development of advanced battery management systems and embedded application research.

Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources.However,the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes,thus making the application placement problem more complex than that in cloud computing.An approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing costs.This approach is particularly relevant in scenarios where latency,resource constraints,and cost considerations are crucial factors for the deployment of applications.In this study,we propose a hybrid approach that combines a genetic algorithm(GA)with the Flamingo Search Algorithm(FSA)to place application modules while minimizing cost.We consider four cost-types for application deployment:Computation,communication,energy consumption,and violations.The proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the system.An extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art approaches.The results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio(TGR)and total cost.

A hybrid physics-informed data-driven neural network for CO_(2) storage in depleted shale reservoirs

To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes.However,most current CO_(2) sequestration models do not adequately consider multiple transport mechanisms.Moreover,the evaluation of CO_(2) storage processes usually involves laborious and time-consuming numerical simulations unsuitable for practical prediction and decision-making.In this paper,an integrated model involving gas diffusion,adsorption,dissolution,slip flow,and Darcy flow is proposed to accurately characterize CO_(2) storage in depleted shale reservoirs,supporting the establishment of a training database.On this basis,a hybrid physics-informed data-driven neural network(HPDNN)is developed as a deep learning surrogate for prediction and inversion.By incorporating multiple sources of scientific knowledge,the HPDNN can be configured with limited simulation resources,significantly accelerating the forward and inversion processes.Furthermore,the HPDNN can more intelligently predict injection performance,precisely perform reservoir parameter inversion,and reasonably evaluate the CO_(2) storage capacity under complicated scenarios.The validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise sources.This study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO_(2) storage projects in depleted shale reservoirs.

Damage degradation mechanism and macro-meso structural response of mudstone after water wetting

The predominant presence of weak interlayers primarily composed of mudstone renders them highly susceptible to a reduction in bearing capacity due to the water-rock weakening effect,significantly impacting the safety of open-pit mining operations.This study focuses on the weak mudstone layers within open-pit mine slopes.The mineral composition of mudstone and the microstructure evolution characteristics before and after water wetting were analyzed by X-ray diffraction(XRD)and scanning electron microscope(SEM).The meso-structure and parameter variation characteristics of mudstone interior space after water-rock interaction were quantified by computed tomography scanning test,and the damage variable characterization method was proposed.Additionally,according to the uniaxial compression test,the degradation characteristics of the macroscopic mechanical behavior of mudstone under different water wetting time were explored,and the elastic modulus and strength attenuation model of mudstone based on mesoscopic damage were established.Finally,building upon the macro-meso structural response characteristics of mudstone,an exploration of the failure characteristics and deterioration mechanism under the influence of water-rock interactions was undertaken.The results show that the water-rock interaction makes the internal defects of mudstone gradually develop and form a fracture network structure,which eventually leads to the deterioration of its macroscopic mechanical properties.The porosity,fractal dimension and damage characteristics of mudstone show an exponential trend with the increase of water wetting time.Moreover,the deterioration mechanism of mudstone after water wetting are postulated to encompass factors such as the hydrophilicity of mineral molecular structures,hydration stress and expansion effects on clay particles,as well as the spatial distribution of microcracks and the phenomenon of fracture adsorption.The outcomes of this research endeavor aim to provide certain reference value for further understanding the water-rock interaction and stability control of mudstone slope.

Improved algorithmic mechanism based on game theory in computational grids

Computational grids （CGs） aim to offer pervasive access to a diverse collection of geographically distributed resources owned by different serf-interested agents or organizations. These agents may manipulate the resource allocation algorithm in their own benefit, and their selfish behavior may lead to severe performance degradation and poor efficiency. In this paper, game theory is introduced to solve the problem of barging for resource collection in heterogeneous distributed systems. By using the Cournot model that is an important model in static and complete information games, the algorithm is optimized in order to maximize the benefit. It can be seen that the approach is more suitable to the real situation and has practical use. Validity of the solutions is shown.

Overview of Computational Modeling in Nano/Micro Scaled Thin Films Mechanical Properties and Its Applications

This research reviews the application of computational mechanics on the properties of nano/micro scaled thin films,in which the application of different computational methods is included.The concept and fundamental theories of concerned applications,material behavior estimations,interfacial delamination behavior,strain engineering,and multilevel modeling are thoroughly discussed.Moreover,an example of an interfacial adhesion estimation is presented to systematically estimate the related mechanical reliability issue in the microelectronic industry.The presented results show that the peeled mode fracture is the dominant delamination behavior of layered material system,with high stiffness along the bonding interface.However,the shear mode fracture being dominated as the polymer cover plate with low moduli is considered.The occurrence of crack advance is also significantly influenced by the interfacial crack length and applied loading.Therefore,this paper could serve as a guideline of several engineering cases with the assistance of computational mechanics.

Alternating minimization for data-driven computational elasticity from experimental data: kernel method for learning constitutive manifold

Data-driven computing in elasticity attempts to directly use experimental data on material,without constructing an empirical model of the constitutive relation,to predict an equilibrium state of a structure subjected to a specified external load.Provided that a data set comprising stress-strain pairs of material is available,a data-driven method using the kernel method and the regularized least-squares was developed to extract a manifold on which the points in the data set approximately lie(Kanno 2021,Jpn.J.Ind.Appl.Math.).From the perspective of physical experiments,stress field cannot be directly measured,while displacement and force fields are measurable.In this study,we extend the previous kernel method to the situation that pairs of displacement and force,instead of pairs of stress and strain,are available as an input data set.A new regularized least-squares problem is formulated in this problem setting,and an alternating minimization algorithm is proposed to solve the problem.

AN ACCURATE PREDICTION MODEL FOR COMPUTATIONAL OVERHEADS OF SECURITY MECHANISMS IN WIRELESS SENSOR NETWORKS

In Wireless Sensor Networks (WSNs), it is necessary to predict computational overheads of security mechanisms without final implementations to provide guidelines for system design. This paper presents an accurate and flexible model to predict overheads of these mechanisms. This model is based on overheads of basic operations frequently used in cryptography algorithms, which are essential elements of security mechanisms. Several popular cryptography algorithms and security mechanisms are evaluated using this model. According to simulation results, relative prediction errors are less than 7% for most cryptography algorithms and security mechanisms.

Painlevé Analysis,Soliton Solutions and Bcklund Transformation for Extended (2 + 1)-Dimensional Konopelchenko-Dubrovsky Equations in Fluid Mechanics via Symbolic Computation

This paper is to investigate the extended(2+1)-dimensional Konopelchenko-Dubrovsky equations,which can be applied to describing certain phenomena in the stratified shear flow,the internal and shallow-water waves, plasmas and other fields.Painleve analysis is passed through via symbolic computation.Bilinear-form equations are constructed and soliton solutions are derived.Soliton solutions and interactions are illustrated.Bilinear-form Backlund transformation and a type of solutions are obtained.

Data-driven computing in elasticity via kernel regression

This paper presents a simple nonparametric regression approach to data-driven computing in elasticity. We apply the kernel regression to the material data set, and formulate a system of nonlinear equations solved to obtain a static equilibrium state of an elastic structure. Preliminary numerical experiments illustrate that, compared with existing methods, the proposed method finds a reasonable solution even if data points distribute coarsely in a given material data set.