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.

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.

A perspective on regression and Bayesian approaches for system identification of pattern formation dynamics

We present two approaches to system identification, i.e. the identification of partial differentialequations (PDEs) from measurement data. The first is a regression-based variational systemidentification procedure that is advantageous in not requiring repeated forward model solves andhas good scalability to large number of differential operators. However it has strict data typerequirements needing the ability to directly represent the operators through the available data.The second is a Bayesian inference framework highly valuable for providing uncertaintyquantification, and flexible for accommodating sparse and noisy data that may also be indirectquantities of interest. However, it also requires repeated forward solutions of the PDE modelswhich is expensive and hinders scalability. We provide illustrations of results on a model problemfor pattern formation dynamics, and discuss merits of the presented methods.

A Critical Review on Superchilling Preservation Technology in Aquatic Product

aquatic product, known as one of the good resources for white meat, has been widely accepted by the consumers due to its high protein, low fat, especially low cholesterol. With the fast development of living standards around the world, the consumer demands for high quality, nutrition, safety and freshness of ifshery food are increasing. Thus, high efifcient preservation technologies for aquatic products become particularly important. Superchilling is one of the controlled-temperature preservation technologies for seafood. Aquatic products can be kept in better quality under superchilling conditions. This review introduced the principle and development of superchilling process, mainly focusing on research progresses and technical dififculties of superchilling. The growth mechanism of ice crystals and the feasibility of application of computational lfuid dynamics in analyzing the temperatures variation and ice crystals during superchilling progress were also discussed, which will provide theoretical foundation for its improvement and application.

Scheme for Generating Cluster States with Charge Qubits in a Cavity

Based on superconducting charge qubits （SCCQs） coupled to a single-mode microwave cavity, we propose a scheme for generating charge cluster states. For all SCCQs, the controlled gate voltages are all in their degeneracy points, the quantum information is encoded in two logic states of charge basis. The generation of the multi-qubit cluster state can be achieved step by step on a pair of nearest-neighbor qubits. Considering effective long-rang coupling, we provide an efficient way to one-step generating of a highly entangled cluster state, in which the qubit-qubit coupling is mediated by the cavity mode. Our quantum operations are insensitive to the initial state of the cavity mode by removing the influence of the cavity mode via the periodical evolution of the system. Thus, our operation may be against the decoherence from the cavity.

Deformation and failure in nanomaterials via a data driven modelling approach

A data driven computational model that accounts for more than two material states has been presented in this work. Presented model can account for multiple state variables, such as stresses,strains, strain rates and failure stress, as compared to previously reported models with two states.Model is used to perform deformation and failure simulations of carbon nanotubes and carbon nanotube/epoxy nanocomposites. The model capability of capturing the strain rate dependent deformation and failure has been demonstrated through predictions against uniaxial test data taken from literature. The predicted results show a good agreement between data set taken from literature and simulations.

Physics-informed deep learning for digital materials

In this work,a physics-informed neural network(PINN)designed specifically for analyzing digital mate-rials is introduced.This proposed machine learning(ML)model can be trained free of ground truth data by adopting the minimum energy criteria as its loss function.Results show that our energy-based PINN reaches similar accuracy as supervised ML models.Adding a hinge loss on the Jacobian can constrain the model to avoid erroneous deformation gradient caused by the nonlinear logarithmic strain.Lastly,we discuss how the strain energy of each material element at each numerical integration point can be calculated parallelly on a GPU.The algorithm is tested on different mesh densities to evaluate its com-putational efficiency which scales linearly with respect to the number of nodes in the system.This work provides a foundation for encoding physical behaviors of digital materials directly into neural networks,enabling label-free learning for the design of next-generation composites.

MIXED COMPATIBLE ELEMENT AND MIXED HYBRID INCOMPATIBLE ELEMENT VARIATIONAL METHODS IN DYNAMICS OF VISCOUS BAROTROPIC FLUIDS

This paper presents and proves the mixed compatible finite element variationalprinciples in dynamics of viscous barotropic fluids. When the principles are proved, itis found that the compatibility conditions of stress can be naturally satisfied. The gene-rallzed variational principles with mixed hybrid incompatible finite elements are alsopresented and proved, and they can reduce the computation of incompatible elements indynamics of viscous barotropic flows.

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

Research on the Efficient Computation Mechanism-in the Case of N-vehicle Exploration Problem

The research of efficient computation focus on special structures of NP-hard problem instances and request providing reasonable computing cost of instances in polynomial time. Based on the theory of combinatorial optimization, by studying the clusters partition and the clusters complexity measurement in Nvehicle exploration problem, we build a frame of efficient computation and provide an application of tractability for NP-hard problem. Three N-vehicle examples show that when we use efficient computation mechanism on N-vehicle, through polynomial steps of tractability analysis, decision makers can get the computing cost of searching optimal solution before practical calculation.

Static analysis of elastic cable structures under mechanical load using discrete catenary theory

In this paper,the nonlinear mechanical response of elastic cable structures under mechanical load is studied based on the discrete catenary theory.A cable net is discretized into multiple nodes and edges in our numerical approach,which is followed by an analytical formulation of the elastic energy and the associated Hessian matrix to realize the dynamic simulation.A fully implicit framework is proposed based on the discrete differential geometry(DDG)theory.The equilibrium configuration of a target object is derived by adding damping force into the system,known as the dynamic relaxation method.The mechanical response of a single suspended cable is investigated and compared with the analytical solution for cross-validation.A more intricate scenario is further discussed in detail,where a structure consisting of multiple slender cables is connected through joints.Utilizing the robustness and efficiency of our discrete numerical framework,a systematic parameter sweep is performed to quantify the force displacement relationships of nets with the different number of cables and different directions of fibers.Finally,an empirical scaling law is provided to account for the rigidity of elastic cable net in terms of its geometric properties,material characteristics,component numbers,and cable orientations.Our results would provide new insight in revealing the connections between flexible structures and tensegrity structures,and could motivate innovative designs in both mechanical and civil engineered equipment.

A computational method for the load spectra of large-scale structures with a data-driven learning algorithm

For complex engineering systems, such as trains, planes, and offshore oil platforms, load spectra are cornerstone of their safety designs and fault diagnoses. We demonstrate in this study that well-orchestrated machine learning modeling, in combination with limited experimental data, can effectively reproduce the high-fidelity, history-dependent load spectra in critical sites of complex engineering systems, such as high-speed trains. To meet the need for in-service monitoring, we propose a segmentation and randomization strategy for long-duration historical data processing to improve the accuracy of our data-driven model for longterm load-time history prediction. Results showed the existence of an optimal length of subsequence, which is associated with the characteristic dissipation time of the dynamic system. Moreover, the data-driven model exhibits an excellent generalization capability to accurately predict the load spectra for different levels of passenger-dedicated lines. In brief, we pave the way, from data preprocessing, hyperparameter selection, to learning strategy, on how to capture the nonlinear responses of such a dynamic system, which may then provide a unifying framework that could enable the synergy of computation and in-field experiments to save orders of magnitude of expenses for the load spectrum monitoring of complex engineering structures in service and prevent catastrophic fatigue and fracture in those solids.

On-chip mechanical computing:status,challenges,and opportunities

With increasing challenges towards continued scaling and improve-ment in performance faced by electronic computing,mechanical com-puting has started to attract growing interests.Taking advantage of the mechanical degree of freedom in solid state devices,micro/nano-electromechanical systems(MEMS/NEMS)could provide alternative solutions for future computing and memory systems with ultralow power consumption,compatibility with harsh environments,and high reconfigurability.In this review,MEMS/NEMS-enabled memories and logic processors were surveyed,and the prospects and challenges for future on-chip mechanical computing were also analyzed.

Mechanics of tubular helical assemblies:ensemble response to axial compression and extension

Nature and technology often adopt structures that can be described as tubular helical assemblies.However,the role and mechanisms of these structures remain elusive.In this paper,we study the mechanical response under compression and extension of a tubular assembly composed of 8 helical Kirchholf rods,arranged in pairs with opposite chirality and connected by pin joints,both analytically and numerically.We first focus on compression and find that,whereas a single helical rod would buckle,the rods of the assembly deform coherently as stable helical shapes wound around a common axis.Moreover,we investigate the response of the assembly under different boundary conditions,highlighting the emergence of a central region where rods remain circular helices.Secondly,we study the effects of different hypotheses on the elastic properties of rods,i.e.,stress-free rods when straight versus when circular helices,Kirchhoff’s rod model versus Sadowsky’s ribbon model.Summing up,our findings highlight the key role of mutual interactions in generating a stable ensemble response that preserves the helical shape of the individual rods,as well as some interesting features,and they shed some light on the reasons why helical shapes in tubular assemblies are so common and persistent in nature and technology.

Particle swarm optimization model to predict scour depth around a bridge pier

Scour depth around bridge piers plays a vital role in the safety and stability of the bridges.The former approaches used in the prediction of scour depth are based on regression models or black box models in which the first one lacks enough accuracy while the later one does not provide a clear mathematical expression to easily employ it for other situations or cases.Therefore,this paper aims to develop new equations using particle swarm optimization as a metaheuristic approach to predict scour depth around bridge piers.To improve the efficiency of the proposed model,individual equations are derived for laboratory and field data.Moreover,sensitivity analysis is conducted to achieve the most effective parameters in the estimation of scour depth for both experimental and filed data sets.Comparing the results of the proposed model with those of existing regression-based equations reveal the superiority of the proposed method in terms of accuracy and uncertainty.Moreover,the ratio of pier width to flow depth and ratio of d50 (mean particle diameter)to flow depth for the laboratory and field data were recognized as the most effective parameters,respectively.The derived equations can be used as a suitable proxy to estimate scour depth in both experimental and prototype scales.

Movement law of the threshing material in threshing and cleaning machine for plot-bred wheat

In order to clarify and enhance the work performance of the threshing and cleaning machine for plot-bred wheat and further reduce the grain retention in all working areas in the machine,in this study,a discrete element model for the threshing material of plot-bred wheat and a gas-solid coupling simulation model for the machine were established by ensuring all the harvesting criteria for the machine.Then numerical simulation was completed on the movement process of the threshing material in the threshing and cleaning machine for plot-bred wheat,the movement law and motion trajectory of all components of the threshing material were explored,and the impact forms of unreasonable work parameters on the separating and cleaning process were analyzed.First,four working areas were divided in the threshing and cleaning machine for plot-bred wheat.Under gas-solid flow coupling effect,the number variation of threshing material in each working area was analyzed under the effect of gas-solid coupling,and the operation characteristics of“no retained seeds and convenient cleaning”of the threshing machine for plot-bred wheat were further improved.The verification test results showed that,when the feeding amount of wheat was 0.30 kg/s,the rotation speed of the shaft of the tooth-type threshing cylinder was set to 1350 r/min,the rotation speed of the winnower was set to 500 r/min,the rotation speed of the residue absorption fan was set to 1000 r/min,the average total loss rate in threshing of the sample machine was 0.56%,and average impurity rate of the threshing material was 5.26%,average damage rate in threshing was 0.68%.In the test,the status of material discharged from the residue absorption fan outlet and bottom of the cyclone separator was similar to that of the simulation results,showing that it was feasible to use the method of gas-solid coupling to simulate the movement law of threshing material in the threshing and cleaning machine for plot-bred wheat.

STUDY OF INTERACTION BETWEEN VORTICES AND A FREE SURFACE PART Ⅱ：INTERACTION OF DOUBLE VISCOUS VORTEX DIPOLES WITH A FREE SURFACE

In this paper the VOF (Volume of Fluid) method is used to numerically study the interaction of double viscous vortex dipoles with free surface in a two dimensional incompressible flow field. From the results of this research, it is found that the general consequence of the interaction is qualitatively equivalent to the problem of a single vortex dipole interacts with a free surface.

New approach for normalization and photon-number distributions of photon-added (-subtracted) squeezed thermal states

Using the thermal field dynamics theory to convert the thermal state into a ＂pure＂ state in doubled Fock space, we find that the average value of efa a under squeezed thermal state （STS） is just the generating function of Legendre polynomials. Based on this remarkable result, the normalization and photon-number distributions of m-photon added （or subtracted） STSs are conviently obtained as the Legendre polynomials. This new concise method can be expanded to the entangled case.