Although there is currently no unified standard theoretical formula for calculating the contact stress of cylindrical gears with a circular arc tooth trace(referred to as CATT gear),a mathematical model for determining the contact stress of CATT gear is essential for studying how parameters affect its contact stress and building the contact stress limit state equation for contact stress reliability analysis.In this study,a mathematical relationship between design parameters and contact stress is formulated using the KrigingMetamodel.To enhance the model’s accuracy,we propose a new hybrid algorithm that merges the genetic algorithm with the Quantum Particle Swarm optimization algorithm,leveraging the strengths of each.Additionally,the“parental inheritance+self-learning”optimization model is used to fine-tune the KrigingMetamodel’s parameters.Following this,amathematicalmodel for calculating the contact stress of Variable Hyperbolic Circular-Arc-Tooth-Trace(VH-CATT)gears using the optimized Kriging model was developed.We then examined how different gear parameters affect the VH-CATT gears’contact stress.Our simulation results show:(1)Improvements in R2,RMSE,and RMAE.R2 rose from0.9852 to 0.9974(a 1.22%increase),nearing 1,suggesting the optimized Kriging Metamodel’s global error is minimized.Meanwhile,RMSE dropped from3.9210 to 1.6492,a decline of 57.94%.The global error of the GA-IQPSO-Kriging algorithm was also reduced,with RMAE decreasing by 58.69%from 0.1823 to 0.0753,showing the algorithm’s enhanced precision.In a comparison of ten experimental groups selected randomly,the GA-IQPSO-Kriging and FEM-based contact analysis methods were used to measure contact stress.Results revealed a maximum error of 12.11667 MPA,which represents 2.85%of the real value.(2)Several factors,including the pressure angle,tooth width,modulus,and tooth line radius,are inversely related to contact stress.The descending order of their impact on the contact stress is:tooth line radius>modulus>pressure angle>tooth width.(3)Com
Qi ZhangZhixin ChenYang WuGuoqi XiangGuang WenXuegang ZhangYongchun XieGuangchun Yang
Face stability is an essential issue in tunnel design and construction.Layered rock masses are typical and ubiquitous;uncertainties in rock properties always exist.In view of this,a comprehensive method,which combines the Upper bound Limit analysis of Tunnel face stability,the Polynomial Chaos Kriging,the Monte-Carlo Simulation and Analysis of Covariance method(ULT-PCK-MA),is proposed to investigate the seismic stability of tunnel faces.A two-dimensional analytical model of ULT is developed to evaluate the virtual support force based on the upper bound limit analysis.An efficient probabilistic analysis method PCK-MA based on the adaptive Polynomial Chaos Kriging metamodel is then implemented to investigate the parameter uncertainty effects.Ten input parameters,including geological strength indices,uniaxial compressive strengths and constants for three rock formations,and the horizontal seismic coefficients,are treated as random variables.The effects of these parameter uncertainties on the failure probability and sensitivity indices are discussed.In addition,the effects of weak layer position,the middle layer thickness and quality,the tunnel diameter,the parameters correlation,and the seismic loadings are investigated,respectively.The results show that the layer distributions significantly influence the tunnel face probabilistic stability,particularly when the weak rock is present in the bottom layer.The efficiency of the proposed ULT-PCK-MA is validated,which is expected to facilitate the engineering design and construction.
Jianhong ManTingting ZhangHongwei HuangDaniel Dias
Purpose:The purpose of this paper is to discuss the potential transfer of a metamodel for heritage-based urban development(HBUD)in a postcrisis urban recovery scenario.Design/methodology/approach:After an introduction to the feld of cultural heritage as a resource for urban development,the research question is elaborated,and the current understanding of urban heritage is explored.The use of the metamodel in a postcrisis urban recovery setting is described as a potential solution.The proposed metamodel is introduced along with the grounded theory and design research methodology through which it was developed.The specifc qualities of metamodels and how they can contribute to the proposed use are highlighted.The scenario is then developed further,and specifc ways in which the metamodel could contribute are elaborated.Finally,the metamodel is compared to other methods,such as the historic urban landscape(HUL)approach,and the limitations are discussed.Findings:The metamodel can potentially be used in a postcrisis urban recovery scenario.The metamodel cannot be used directly,owing to the nature of metamodels;however,it can be transferred to a specifc context and help to structure successful heritage-based urban recovery(HBUR)processes.Practical limitations/implications:One limitation is that it can be difcult to understand the diferences between models and metamodels.Only with a comprehensive understanding of the nature of metamodels can this metamodel be applied,for example,to select appropriate models for HBUR.The metamodel can help to ensure that all relevant‘elements’are part of the processes designed for HBUR and emphasise the need for thorough planning,or scoping,of such processes.Originality/value:Metamodelling has not previously been used for HBUD or HBUR.
Aiming to reduce the high expense of 3-Dimensional(3D)aerodynamics numerical sim-ulations and overcome the limitations of the traditional parametric learning methods,a point cloud deep learning non-parametric metamodel method is proposed in this paper.The 3D geometric data,corresponding to the object boundaries,are chosen as point clouds and a deep learning neural net-work metamodel fed by the point clouds is further established based on the PointNet architecture.This network can learn an end-to-end mapping between spatial positions of the object surface and CFD numerical quantities.With the proposed aerodynamic metamodel approach,the point clouds are constructed by collecting the coordinates of grid vertices on the object surface in a CFD domain,which can maintain the boundary smoothness and allow the network to detect small changes between geometries.Moreover,the point clouds are easily accessible from 3D sensors.The point cloud deep learning neural network,which employs re-sampling technique,the spatial transformer network and the fully connected layer,is developed to predict the aerodynamic char-acteristics of 3D geometry.The effectiveness of the proposed metamodel method is further verified by aerodynamic prediction and robust shape optimization of the ONERA M6 wing.The results show that the proposed method can achieve more satisfactory agreement with the experimental measurements compared to the parametric-learning-based deep neural network.
1Introduction 1.1 What is the book a bout?This article provides a review on the new publication of Matthias Ripp titled Meta-model for Heritage-based Urban Development:Enabling SustainableGrowth through Urban Cultural Heritage released by Springer in 2022.The subtitle is an inspirational,hope-giving,new perspective to heritage studies,if one puts aside the growthbased world economic systems towards development.