Effects of surface finish and treatment on the fatigue behaviour of vibrating cylinder block using frequency response approach

This paper presents the effects of surface finish and treatment on the high cycle fatigue behaviour of vibrating cylinder block of a new two-stroke free piston engine at complex variable amplitude loading conditions using frequency response approach. Finite element modelling and frequency response analysis was conducted using finite element analysis software Package MSC.PATRAN/MSC.NASTRAN and fatigue life prediction was carded out using MSC.FATIGUE software. Based on the finite element results, different frequency response approach was applied to predict the cylinder block fatigue life. Results for different load histories and material combinations are also discussed. Results indicated great effects for all surface finish and treatment. It is concluded that polished and cast surface finish conditions give the highest and lowest cylinder block lives, respectively; and that Nitrided treatment leads to longest cylinder block life. The results were used to draw contour plots of fatigue life and damage in the worst or most damaging case.

Applicability of discrete element method with spherical and clumped particles for constitutive study of granular materials

Discrete element method(DEM)has been intensively used to study the constitutive behaviour of granular materials.However,to what extent a real granular material can be reproduced by virtual DEM simulations remains unclear.This study attempts to answer this question by comparing DEM simulations with typical features of experimental granular materials.Three groups of models with spherical and clumped particles are investigated from four perspectives:(i)deviatoric stress and volumetric behaviour;(ii)critical state behaviour;(iii)stress-dilatancy relationship;and(iv)the evolution of principal stress ratio against axial strain.The results demonstrate that DEM with spherical or clumped particles is capable of qualitatively describing macroscopic deviatoric stress responses,volumetric behaviour,and critical state behaviour observed in experiments for granular materials.On the other hand,some qualitative deviations between experiments and the investigated DEM simulations are also observed,in terms of the stress-dilatancy behaviour and principal stress ratio against axial strain,which are proven to be critical for constitutive modelling.The results demonstrate that DEM with spherical or clumped particles may not necessarily fully capture experimental features of granular materials even from a qualitative perspective.It is thus encouraged to thoroughly validate DEM with experiments when developing constitutive models based on DEM observations.

Physics-informed deep learning for one-dimensional consolidation

Neural networks with physical governing equations as constraints have recently created a new trend in machine learning research.In this context,a review of related research is first presented and discussed.The potential offered by such physics-informed deep learning models for computations in geomechanics is demonstrated by application to one-dimensional(1D)consolidation.The governing equation for 1D problems is applied as a constraint in the deep learning model.The deep learning model relies on automatic differentiation for applying the governing equation as a constraint,based on the mathematical approximations established by the neural network.The total loss is measured as a combination of the training loss(based on analytical and model predicted solutions)and the constraint loss(a requirement to satisfy the governing equation).Two classes of problems are considered:forward and inverse problems.The forward problems demonstrate the performance of a physically constrained neural network model in predicting solutions for 1D consolidation problems.Inverse problems show prediction of the coefficient of consolidation.Terzaghi’s problem,with varying boundary conditions,is used as a numerical example and the deep learning model shows a remarkable performance in both the forward and inverse problems.While the application demonstrated here is a simple 1D consolidation problem,such a deep learning model integrated with a physical law has significant implications for use in,such as,faster realtime numerical prediction for digital twins,numerical model reproducibility and constitutive model parameter optimization.

Finite element simulation of stress intensity factors in elastic-plastic crack growth

A finite element program developed elastic-plastic crack propagation simulation using Fortran language. At each propagation step, the adaptive mesh is automatically refined based on a posteriori h-type refinement using norm stress error estimator. A rosette of quarter-point elements is then constructed around the crack tip to facilitate the prediction of crack growth based on the maximum normal stress criterion and to calculate stress intensity factors under plane stress and plane strain conditions. Crack was modelled to propagate through the inter-element in the mesh. Some examples are presented to show the results of the implementation.

Deep Learning Predicts Stress–Strain Relations of Granular Materials Based on Triaxial Testing Data

This study presents an AI-based constitutive modelling framework wherein the prediction model directly learns from triaxial testing data by combining discrete element modelling(DEM)and deep learning.A constitutive learning strategy is proposed based on the generally accepted frame-indifference assumption in constructing material constitutive models.The low-dimensional principal stress-strain sequence pairs,measured from discrete element modelling of triaxial testing,are used to train recurrent neural networks,and then the predicted principal stress sequence is augmented to other high-dimensional or general stress tensor via coordinate transformation.Through detailed hyperparameter investigations,it is found that long short-term memory(LSTM)and gated recurrent unit(GRU)networks have similar prediction performance in constitutive modelling problems,and both satisfactorily predict the stress responses of granular materials subjected to a given unseen strain path.Furthermore,the unique merits and ongoing challenges of data-driven constitutive models for granular materials are discussed.

Nonlinear beam formulation incorporating surface energy and size effect:application in nano-bridges

A nonlinear beam formulation is presented based on the Gurtin-Murdoch surface elasticity and the modified couple stress theory. The developed model theoretically takes into account coupled effects of the energy of surface layer and microstructures size- dependency. The mid-plane stretching of a beam is incorporated using von-Karman nonlinear strains. Hamilton＇s principle is used to determine the nonlinear governing equation of motion and the corresponding boundary conditions. As a case study, pull-in instability of an electromechanical nano-bridge structure is studied using the proposed formulation. The nonlinear governing equation is solved by the analytical reduced order method （ROM） as well as the numerical solution. Effects of various parameters including surface layer, size dependency, dispersion forces, and structural damping on the pull- in parameters of the nano-bridges are discussed. Comparison of the results with the literature reveals capability of the present model in demonstrating the impact of nano- scale phenomena on the pull-in threshold of the nano-bridges.

Integrated asset management:a case study of technical and economic optimization of surface and well facilities

Oil production could be increased by using optimization techniques in each stage of oil production system from field to production unit.However,the maximum profit will be obtained once integrated optimization of surface and subsurface components of the oil production system is implemented.In addition,when using high-tech equipment and facilities,an integrated study of well and surface facilities affects the economic benefits significantly.In this work,one of the Iranian brown fields(mature or declining production fields)was studied to find the best renovation plan with maximum profits.The base scenario was designed with four stages of separation,and the high-pressure gases from the first and the second separator were planned to be sold,while the low-pressure gases from subsequent stages were supposed to be flared.In this paper,two additional scenarios,namely separator optimization and full optimization,were proposed and 12 cases were defined.In the full optimization scenario,low-pressure gases were prevented from being flared.It was observed that by stopping the flaring systems,gas production rate increases as high as 20%and 150 to 200 MW power will be generated in all cases during the next 20 years.Finally,economic evaluation for all of the cases was done and different cases were compared in terms of incremental annual worth and payback period.

Prediction of Compressive Strength of Self-Compacting Concrete Using Intelligent Computational Modeling

In the present scenario,computational modeling has gained much importance for the prediction of the properties of concrete.This paper depicts that how computational intelligence can be applied for the prediction of compressive strength of Self Compacting Concrete(SCC).Three models,namely,Extreme Learning Machine(ELM),Adaptive Neuro Fuzzy Inference System(ANFIS)and Multi Adaptive Regression Spline(MARS)have been employed in the present study for the prediction of compressive strength of self compacting concrete.The contents of cement(c),sand(s),coarse aggregate(a),fly ash(f),water/powder(w/p)ratio and superplasticizer(sp)dosage have been taken as inputs and 28 days compressive strength(fck)as output for ELM,ANFIS and MARS models.A relatively large set of data including 80 normalized data available in the literature has been taken for the study.A comparison is made between the results obtained from all the above-mentioned models and the model which provides best fit is established.The experimental results demonstrate that proposed models are robust for determination of compressive strength of self-compacting concrete.

Effect of cavity flow control on high-speed train pantograph and roof aerodynamic noise

The pantograph and its recess on the train roof are major aerodynamic noise sources on high-speed trains.Reducing this noise is particularly important because conventional noise barriers usually do not shield the pantograph.However,less attention has been paid to the pantograph recess compared with the pantograph.In this paper,the flow features and noise contribution of two types of noise reduction treatments rounded and chamfered edges are studied for a simplified high-speed train pantograph recess,which is represented as a rectangular cavity and numerically investigated at 1/10 scale.Improved delayed detached-eddy simulations are performed for the near-field turbulent flow simulation,and the Ffowcs Williams and Hawkings aeroacoustic analogy is used for far-field noise prediction.The highly unsteady flow over the cavity is significantly reduced by the cavity edge modifications,and consequently,the noise radiated from the cavity is reduced.Furthermore,effects of the rounded cavity edges on the flow and noise of the pantographs(one raised and one folded)are investigated by comparing the flow features and noise contributions from the cases with and without rounding of the cavity edges.Different train running directions are also considered.Flow analysis shows that the highly unsteady flow within the cavity is reduced by rounding the cavity edges and a slightly lower flow speed occurs around the upper parts of the raised pantograph,whereas the flow velocity in the cavity is slightly increased by the rounding.Higher pressure fluctuations occur on the folded pantograph and the lower parts of the raised pantograph,whereas weaker fluctuations are found on the panhead of the raised pantograph.This study shows that by rounding the cavity edges,a reduction in radiated noise at the side and the top receiver positions can be achieved.Noise reductions in the other directions can also be found.

Effects of size-dependent elasticity on stability of nanotweezers

It is well-recognized that the electromechanical response of a nanostructure is affected by its element size. In the present article, the size dependent stability behavior and nanotweezers fabricated from nanowires are investigated by modified couple stress elasticity （MCSE）. The governing equation of the nanotweezers is obtained by taking into account the presence of Coulomb and intermolecular attractions. To solve the equation, four techniques, i.e., the modified variational iteration method （MVIM）, the monotonic iteration method （MIM）, the MAPLE numerical solver, and a lumped model, are used. The variations of the arm displacement of the tweezers versus direct current （DC） voltage are obtained. The instability parameters, i.e., pull-in voltage and deflection of the system, are computed. The results show that size-dependency will affect the stability of the nanotweezers significantly if the diameter of the nanowire is of the order of the length scale. The impact of intermolecular attraction on the size-dependent stability of the system is discussed.

Design of Finite Element Tools for Coupled Surface and Volume Meshes

Many problems with underlying variational structure involve a coupling of volume with surface effects.A straight-forward approach in a finite element discretiza- tion is to make use of the surface triangulation that is naturally induced by the volume triangulation.In an adaptive method one wants to facilitate'matching'local mesh modifications,i.e.,local refinement and/or coarsening,of volume and surface mesh with standard tools such that the surface grid is always induced by the volume grid. We describe the concepts behind this approach for bisectional refinement and describe new tools incorporated in the finite element toolbox ALBERTA.We also present several important applications of the mesh coupling.

Estimation and Comparison of Solar Radiation Intensity by Some Models in a Region of Iran

Due to the increasing explanation of new energy application on a world scale and because of the reduction of environmental pollution arise from usage of solar energy in high potential regions, the necessity of solar radiation measurement in different regions of Iran is obvious. Therefore, in this study, the amount of radiation is measured by Pyranometer from beginning of February to August 2010 in Semnan (The central part of Iran), and it is compared with daily sunny hours, different estimated models of daily radiation such as Angstrom-Pryskat (Montis, Climate classification based on the Terwartha scientific map, using the results of 40 worldwide station, using the results of 9 Synoptic stations in Iran), the Maximum likelihood (using the results of 100 stations in worldwide, using the results of 9 Synoptic station in Iran). Finally, Terwartha scientific map model is proposed because of matching more with measured data on location.

Technical-economical optimization of horizontal axis wind turbines by means of the genetic algorithm

Wind turbine design is a trade-off between its potentially generated energy and manufacturing cost represented by the area of turbine surface in this research, and both factors are highly influenced by a number of design parameters. In this research, first, a weighted sum of these factors, with a negative weight for power, is assumed as the performance function to be minimized. Then, blade element modeling was performed for class NACA turbines to estimate the generated power based on the effective wind velocity in the area. As a novelty, a new algorithm based on fuzzy logic was proposed to determine the effective wind velocity by using the history of wind velocity in the area. The wind velocity, therefore, the generated power by a wind turbine, is largely dependent on its operation area. In the end, the genetic algorithm with decimal numeric genes was employed to determine the optimal design parameters of the turbine based on the recorded data. This study resulted in a computer program which integrated calculations of fluid dynamics into the genetic algorithm to optimally determine an appropriate turbine (its geometric parameters). The implementation of the proposed method on two different regions ended up with the design of the blade NACA5413 for Manjil and the blade NACA4314 for Semnan, both in Iran.

Probabilistic quantum teleportation of shared quantum secret

Very recently,Lee et al.proposed a secure quantum teleportation protocol to transfer shared quantum secret between multiple parties in a network[Phys.Rev.Lett.124060501(2020)].This quantum network is encoded with a maximally entangled GHZ state.In this paper,we consider a partially entangled GHZ state as the entanglement channel,where it can achieve,probabilistically,unity fidelity transfer of the state.Two kinds of strategies are given.One arises when an auxiliary particle is introduced and a general evolution at any receiver's location is then adopted.The other one involves performing a single generalized Bell-state measurement at the location of any sender.This could allow the receivers to recover the transmitted state with a certain probability,in which only the local Pauli operators are performed,instead of introducing an auxiliary particle.In addition,the successful probability is provided,which is determined by the degree of entanglement of the partially multipartite entangled state.Moreover,the proposed protocol is robust against the bit and phase flip noise.

Towards More Snow Days in Summer since 2001 at the Great Wall Station,Antarctic Peninsula:The Role of the Amundsen Sea Low

The variation in the precipitation phase in polar regions represents an important indicator of climate change and variability.We studied the precipitation phase at the Great Wall Station and Antarctic Peninsula(AP)region,based on daily precipitation,synoptic records and ERA-Interim data during the austral summers of 1985?2014.Overall,there was no trend in the total precipitation amount or days,but the phase of summer precipitation(rainfall days versus snowfall days)showed opposite trends before and after 2001 at the AP.The total summer rain days/snow days increased/decreased during 1985?2001 and significantly decreased at a rate of?14.13 d(10 yr)?1/increased at a rate of 14.31 d(10 yr)?1 during 2001?2014,agreeing well with corresponding variations in the surface air temperature.Further,we found that the longitudinal location of the Amundsen Sea low(ASL)should account for the change in the precipitation phase since 2001,as it has shown a westward drift after 2001[?41.1°(10 yr)?1],leading to stronger cold southerly winds,colder water vapor flux,and more snow over the AP region during summertime.This study points out a supplementary factor for the climate variation on the AP.

Stress Analysis of Buried Pipeline Installed by Horizontal Directional Drilling Using ANSYS Finite Element Software

In designing a horizontal directional drilling (HDD) pipeline project, designers face the challenge of determining the regions of maximum and minimum stresses on pipelines, ensuring the stability of the bore-hole from collapse and minimizing the stresses induced on the pipeline due to the bore-profile. This study analyses the stress induced on an HDD pipeline system using the ANSYS Version 18, mechanical APDL finite element (FE) software. The pipeline used as the case study was a gas transmission pipeline installed in south-west Nigeria. A macro-file for ANSYS Version 18, mechanical APDL used to model the pipeline was developed. The results showed that the maximum and minimum stresses induced on the HDD pipeline were at the top and bottom of the pipe, respectively;while the stresses on the sides were uniform (≈888 kg/cm2) all through the pipeline, irrespective of element number. The maximum stress occurred at the curvature point with the highest entry angle (10°), resulting in a maximum deflection at this point. The model stress validation performed by comparing results with theoretical solutions, both with respect to radius of curvature and internal pressure, showed percentage difference (errors) less than 10%. The cross sectional area validation showed a percentage difference of 0.059%.

Chimeric antigen receptors:On the road to realising their full potential

Chimeric antigen receptors （CARs） are fusion molecules that may be genetically delivered ex-vivo to T-cells and other immune cell populations, thereby conferring specifcity for native target antigens found on the surface of tumour and other target cell types. Antigen recognition by CARs is neither restricted by nor dependent upon human leukocyte antigen antigen expression, favouring widespread use of this technology across transplantation barriers. Signalling is delivered by a designer endodomain that provides a tailored and target-dependent activation signal to polyclonal circulating T-cells. Recent clinical data emphasise the enormous promise of this emer-ging immunotherapeutic strategy for B-cell malignancy, notably acute lymphoblastic leukaemia. In that context, CARs are generally targeted against the ubiquitous B-cell antigen, CD19. However, CAR T-cell immunotherapy is limited by potential for severe ontarget toxicity, notably due to cytokine release syndrome. Furthermore, effcacy in the context of solid tumours remains unproven, owing in part to lack of availability of safe tumourspecific targets, inadequate CAR T-cell homing and hostility of the tumour microenvironment to immune effector deployment. Manufacture and commercial development encountered with more traditional drug products. Finally, there is increasing interest in the application of this technology to the treatment of non-malignant disease states, such as autoimmunity, chronic infection and in the suppression of allograft rejection. Here, we consider the background and direction of travel of this emerging and highly promising treatment for malignant and other disease types.

A Secure Multiparty Quantum Homomorphic Encryption Scheme

The significant advantage of the quantum homomorphic encryption scheme is to ensure the perfect security of quantum private data.In this paper,a novel secure multiparty quantum homomorphic encryption scheme is proposed,which can complete arbitrary quantum computation on the private data of multiple clients without decryption by an almost dishonest server.Firstly,each client obtains a secure encryption key through the measurement device independent quantum key distribution protocol and encrypts the private data by using the encryption operator and key.Secondly,with the help of the almost dishonest server,the non-maximally entangled states are preshared between the client and the server to correct errors in the homomorphic evaluation of T gates,so as to realize universal quantum circuit evaluation on encrypted data.Thirdly,from the perspective of the application scenario of secure multi-party computation,this work is based on the probabilistic quantum homomorphic encryption scheme,allowing multiple parties to delegate the server to perform the secure homomorphic evaluation.The operation and the permission to access the data performed by the client and the server are clearly pointed out.Finally,a concrete security analysis shows that the proposed multiparty quantum homomorphic encryption scheme can securely resist outside and inside attacks.

Intelligent structured nanocomposite adhesive for bioelectronics and soft robots

The remarkable functionality of biological systems in detecting and adapting to various environmental conditions has inspired the design of the latest electronics and robots with advanced features.This review focuses on intelligent bio-inspired strategies for developing soft bioelectronics and robotics that can accommodate nanocomposite adhesives and integrate them into biological surfaces.The underlying principles of the material and structural design of nanocomposite adhesives were investigated for practical applications with excellent functionalities,such as soft skin-attachable health care sensors,highly stretchable adhesive electrodes,switchable adhesion,and untethered soft robotics.In addition,we have discussed recent progress in the development of effective fabrication methods for micro/nanostructures for integration into devices,presenting the current challenges and prospects.

Design and parameters optimization of the curved sieve for an air suction jujube harvester

In order to improve the screening performance and cleaning effect of the jujube harvesting machinery cleaning device,a vibrating curved screen device was designed in this study.By analyzing the structure mechanism of the curved sieve body,it was obtained that the arc-shaped mesh hole spacing S was 15-25 mm and the curved mesh hole curvature U was 90°-150°.By exploring the movement state and stress of jujube and impurities on the curved sieve body,it was determined that the horizontal spacing L of the curved layer sieve was 30 mm and the vertical spacing H was 45-65 mm.Taking the vertical spacing H of the curved layer sieve,the curvature U of the curved mesh hole,and the spacing S of the curved mesh hole as the experimental factors,considering the screening efficiencyαand the impurity contentβof the jujube as the response values,the three-factor three-level quadratic regression orthogonal experiment was designed,establishing the regression mathematical model of each factor and response value,and the multiple target optimization algorithm of Design-expert software was used to optimize various factors.The results showed that the influence factors on the screening efficiency were in the descending order as:the arc screen spacing,the vertical spacing of the curved layer screen,and the curved screen hole curvature;The significant factors affecting the impurity content of jujube were in the descending order as:the arc screen spacing,the curved screen hole curvature,and the vertical spacing of the curved layer screen.The experimental results were verified by the optimized combination of parameters:when the vertical spacing H of the curved layer screen was 65 mm,the curved screen hole curvature U was 110°,and the arc screen spacing S was 23 mm,the average screening efficiencyαin the test was 91.09%.The relative error between the experimental verification value and the theoretical optimization value was 1.36%,which was less than 5%.The impurity content of jujubeβin the test was 1.02%.The relative error between the experimental verification value and the theoretical optimization value was 2.00%,which was also less than 5%.The test results can provide a reference for the research and optimization of the subsequent air-suction-type jujube harvester cleaning device.