Home / Journals / CMES / Vol.117, No.3, 2018
Table of Content
  • Open Access

    ARTICLE

    A Deep Learning-Based Computational Algorithm for Identifying Damage Load Condition: An Artificial Intelligence Inverse Problem Solution for Failure Analysis

    Shaofei Ren1,2, Guorong Chen2 , Tiange Li2 , Qijun Chen2, Shaofan Li2, *
    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 287-307, 2018, DOI:10.31614/cmes.2018.04697
    (This article belongs to this Special Issue: Data-driven Computational Modeling and Simulations)
    Abstract In this work, we have developed a novel machine (deep) learning computational framework to determine and identify damage loading parameters (conditions) for structures and materials based on the permanent or residual plastic deformation distribution or damage state of the structure. We have shown that the developed machine learning algorithm can accurately and (practically) uniquely identify both prior static as well as impact loading conditions in an inverse manner, based on the residual plastic strain and plastic deformation as forensic signatures. The paper presents the detailed machine learning algorithm, data acquisition and learning processes, and validation/verification examples. This development may have… More >

  • Open Access

    ARTICLE

    Machine Learning Models of Plastic Flow Based on Representation Theory

    R. E. Jones1,*, J. A. Templeton1, C. M. Sanders1, J. T. Ostien1
    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 309-342, 2018, DOI:10.31614/cmes.2018.04285
    (This article belongs to this Special Issue: Data-driven Computational Modeling and Simulations)
    Abstract We use machine learning (ML) to infer stress and plastic flow rules using data from representative polycrystalline simulations. In particular, we use so-called deep (multilayer) neural networks (NN) to represent the two response functions. The ML process does not choose appropriate inputs or outputs, rather it is trained on selected inputs and output. Likewise, its discrimination of features is crucially connected to the chosen inputoutput map. Hence, we draw upon classical constitutive modeling to select inputs and enforce well-accepted symmetries and other properties. In the context of the results of numerous simulations, we discuss the design, stability and accuracy of… More >

  • Open Access

    ARTICLE

    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
    (This article belongs to this Special Issue: Data-driven Computational Modeling and Simulations)
    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 within an entire part in… More >

  • Open Access

    ARTICLE

    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
    (This article belongs to this Special Issue: Data-driven Computational Modeling and Simulations)
    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 in the context of fibre… More >

  • Open Access

    ARTICLE

    Data Mining and Machine Learning Methods Applied to 3 A Numerical Clinching Model

    Marco Götz1,*, Ferenc Leichsenring1, Thomas Kropp2, Peter Müller2, Tobias Falk2, Wolfgang Graf1, Michael Kaliske1, Welf-Guntram Drossel2
    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 387-423, 2018, DOI:10.31614/cmes.2018.04112
    (This article belongs to this Special Issue: Data-driven Computational Modeling and Simulations)
    Abstract Numerical mechanical models used for design of structures and processes are very complex and high-dimensionally parametrised. The understanding of the model characteristics is of interest for engineering tasks and subsequently for an efficient design. Multiple analysis methods are known and available to gain insight into existing models. In this contribution, selected methods from various fields are applied to a real world mechanical engineering example of a currently developed clinching process. The selection of introduced methods comprises techniques of machine learning and data mining, in which the utilization is aiming at a decreased numerical effort. The methods of choice are basically… More >

  • Open Access

    ARTICLE

    A Survey of Image Information Hiding Algorithms Based on Deep Learning

    Ruohan Meng1,2,*, Qi Cui1,2, Chengsheng Yuan1,2,3
    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 425-454, 2018, DOI:10.31614/cmes.2018.04765
    Abstract With the development of data science and technology, information security has been further concerned. In order to solve privacy problems such as personal privacy being peeped and copyright being infringed, information hiding algorithms has been developed. Image information hiding is to make use of the redundancy of the cover image to hide secret information in it. Ensuring that the stego image cannot be distinguished from the cover image, and sending secret information to receiver through the transmission of the stego image. At present, the model based on deep learning is also widely applied to the field of information hiding. This… More >

  • Open Access

    REVIEW

    Structural Design Optimization Using Isogeometric Analysis: A Comprehensive Review

    Yingjun Wang1,*, Zhenpei Wang2,*, Zhaohui Xia3, Leong Hien Poh2
    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 455-507, 2018, DOI:10.31614/cmes.2018.04603
    Abstract Isogeometric analysis (IGA), an approach that integrates CAE into conventional CAD design tools, has been used in structural optimization for 10 years, with plenty of excellent research results. This paper provides a comprehensive review on isogeometric shape and topology optimization, with a brief coverage of size optimization. For isogeometric shape optimization, attention is focused on the parametrization methods, mesh updating schemes and shape sensitivity analyses. Some interesting observations, e.g. the popularity of using direct (differential) method for shape sensitivity analysis and the possibility of developing a large scale, seamlessly integrated analysis-design platform, are discussed in the framework of isogeometric shape… More >

  • Open Access

    ARTICLE

    A Method for Rapidly Determining the Optimal Distribution Locations of GNSS Stations for Orbit and ERP Measurement Based on Map Grid Zooming and Genetic Algorithm

    Qianxin Wang1,2,3, Chao Hu1,2,*, Ya Mao1,2
    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 509-525, 2018, DOI:10.31614/cmes.2018.04098
    Abstract Designing the optimal distribution of Global Navigation Satellite System (GNSS) ground stations is crucial for determining the satellite orbit, satellite clock and Earth Rotation Parameters (ERP) at a desired precision using a limited number of stations. In this work, a new criterion for the optimal GNSS station distribution for orbit and ERP determination is proposed, named the minimum Orbit and ERP Dilution of Precision Factor (OEDOP) criterion. To quickly identify the specific station locations for the optimal station distribution on a map, a method for the rapid determination of the selected station locations is developed, which is based on the… More >

  • Open Access

    ARTICLE

    Lattice Boltzmann Simulation of a Gas-to-Solid Reaction and Precipitation Process in a Circular Tube

    Matthew D. Lindemer1, Suresh G. Advani2,*, Ajay K. Prasad2
    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 527-553, 2018, DOI:10.31614/cmes.2018.00481
    Abstract The lattice Boltzmann method (LBM) is used to simulate the growth of a solid-deposit on the walls of a circular tube resulting from a gas-to-solid reaction and precipitation process. This process is of particular interest for the design of reactors for the production of hydrogen by the heterogeneous hydrolysis of steam with Zn vapor in the Zn/ZnO thermochemical cycle. The solid deposit of ZnO product on the tube wall evolves in time according to the temporally- and axially-varying convective-diffusive transport and reaction of Zn vapor with steam on the solid surface. The LBM is well-suited to solving problems with coupled… More >

  • Open Access

    ARTICLE

    An Image Classification Method Based on Deep Neural Network with Energy Model

    Yang Yang1,*, Jinbao Duan1, Haitao Yu1, Zhipeng Gao1, Xuesong Qiu1
    CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 555-575, 2018, DOI:10.31614/cmes.2018.04249
    Abstract The development of deep learning has revolutionized image recognition technology. How to design faster and more accurate image classification algorithms has become our research interests. In this paper, we propose a new algorithm called stochastic depth networks with deep energy model (SADIE), and the model improves stochastic depth neural network with deep energy model to provide attributes of images and analysis their characteristics. First, the Bernoulli distribution probability is used to select the current layer of the neural network to prevent gradient dispersion during training. Then in the backpropagation process, the energy function is designed to optimize the target loss… More >

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