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  • Open Access


    Data-Driven Structural Design Optimization for Petal-Shaped Auxetics Using Isogeometric Analysis

    Yingjun Wang1, Zhongyuan Liao1, Shengyu Shi1, *, Zhenpei Wang2, *, Leong Hien Poh3

    CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 433-458, 2020, DOI:10.32604/cmes.2020.08680

    Abstract Focusing on the structural optimization of auxetic materials using data-driven methods, a back-propagation neural network (BPNN) based design framework is developed for petal-shaped auxetics using isogeometric analysis. Adopting a NURBS-based parametric modelling scheme with a small number of design variables, the highly nonlinear relation between the input geometry variables and the effective material properties is obtained using BPNN-based fitting method, and demonstrated in this work to give high accuracy and efficiency. Such BPNN-based fitting functions also enable an easy analytical sensitivity analysis, in contrast to the generally complex procedures of typical shape and size sensitivity More >

  • Open Access


    Data-Driven Prediction of Mechanical Properties in Support of Rapid Certification of Additively Manufactured Alloys

    Fuyao Yan1, #, Yu hin Chan2,#, Abhinav Saboo3 , Jiten Shah4, Gregory B. Olson1, 3, Wei Chen2, *

    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 343-366, 2018, DOI:10.31614/cmes.2018.04452

    Abstract Predicting the mechanical properties of additively manufactured parts is often a tedious process, requiring the integration of multiple stand-alone and expensive simulations. Furthermore, as properties are highly location-dependent due to repeated heating and cooling cycles, the properties prediction models must be run for multiple locations before the part-level performance can be analyzed for certification, compounding the computational expense. This work has proposed a rapid prediction framework that replaces the physics-based mechanistic models with Gaussian process metamodels, a type of machine learning model for statistical inference with limited data. The metamodels can predict the varying properties… More >

  • Open Access


    Data-Driven Upscaling of Orientation Kinematics in Suspensions of Rigid Fibres

    Adrien Scheuer1, 3, *, Amine Ammar2, Emmanuelle Abisset-Chavanne3, Elias Cueto4, Francisco Chinesta5, Roland Keunings1, Suresh G. Advani6

    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 367-386, 2018, DOI:10.31614/cmes.2018.04278

    Abstract Describing the orientation state of the particles is often critical in fibre suspen-sion applications. Macroscopic descriptors, the so-called second-order orientation tensor (or moment) leading the way, are often preferred due to their low computational cost. Clo-sure problems however arise when evolution equations for the moments are derived from the orientation distribution functions and the impact of the chosen closure is often unpre-dictable. In this work, our aim is to provide macroscopic simulations of orientation that are cheap, accurate and closure-free. To this end, we propose an innovative data-based approach to the upscaling of orientation kinematics… More >

  • Open Access


    Data-Driven Approach to Fluid Engineering

    Shigeru Obayashi*, Aiko Yakeno

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.21, No.4, pp. 71-71, 2019, DOI:10.32604/icces.2019.05229

    Abstract In our laboratory, we have been conducting research focusing on a data-driven approach to fluid engineering for design. Designing aerospace machines, these days requires more advanced factors. The recent development of supercomputer leads that we must treat more complex flow phenomena that brings uncertainty, with the more advanced design considered. Such as a reduced-order model and a data assimilation method, to estimate and reduce the uncertainty in numerical simulations, are potential ways to assist the advanced design. That is by use of experimental or observational data and numerical analysis of physical equations. Recently, we are… More >

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