Documents published in Scipedia

• Graph neural networks for accelerating the discrete element simulation of granular flow

  P. Zhi, Y. Wu

  WCCM2024.

  
  

  Abstract

  Granular flow is a phenomenon widely presented in both the natural and engineering fields. Here granular materials could be either solid particles, e.g. rocks, soil, and grains, or liquid particles, e.g. mud and fresh concrete mortar. Soil landslides, particle transport, and grain accumulation have been edge-cutting hot research topics. Discrete Element Method (DEM) has been regarded as one of the most important methods to simulate granular flows and to investigate discontinuous and large deformation problems. The basic principle of DEM was to view the simulated object as consisting of discrete particles, to define specific constitutive relationships for the particles, and to study the macroscopic properties of the simulated object from a microscopic perspective based on the interactions between particles. However, DEM simulations usually consume very high computational cost for particle contact searching and detection. To accelerate the computational process of discrete element simulation, the Graph Neural Network (GNN) based deep learning model was proposed in this paper. In GNNs, graph nodes and graph edges represent the particles and their interactions. The training and testing datasets were generated using an open-source software named YADE, while the neural network model was constructed using PyTorch and Deep Graph Library (DGL). Replacing the direct calculation of particle collisions in DEM with the trained neural network model, the state of the particles at the next moment could be predicted based on the current state of the particles. It significantly increased computational speed. The proposed technique was applied in various examples, such as drum rotation and hopper stacking, and its accuracy had been verified. This study established a solid foundation and provided robust support for further research and applications of granular flow simulation based on GNN

  Abstract
  Granular flow is a phenomenon widely presented in both the natural and engineering fields. Here granular materials could be either solid particles, e.g. rocks, soil, and grains, [...]

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• Prediction of plural crack propagation using discovered PDE

  G. Muraoka, Y. Wada

  WCCM2024.

  
  

  Abstract

  This study presents a prediction of plural crack propagation using the discovered partial differential equations. 80% of structures fracture due to fatigue failure. Therefore, the evaluation of fatigue cracks is essential. Numerical analysis is costly, and machine-learning surrogate models have been proposed. Hence, the crack propagation path and remaining life are predicted using machine learning. A dataset is obtained from the results of a crack propagation analysis using a s-version FEM combined with an automatic mesh generation technique. The input parameters are the coordinates of the four crack tips, and the output parameters are the crack propagation vector and the number of cycles of 0.25 mm. Also, physics-informed neural networks (PINNs) have been widely studied in recent years. Thus, we took inspiration from PINNs and added a regularization term of PDE discovered by AI Feynman to the loss. As a result, the loss of a validation dataset for training constrained by PDE was reduced by about 77% compared to the unconstrained loss. The error in crack length decreased from -0.50% to 0.17%.

  Abstract
  This study presents a prediction of plural crack propagation using the discovered partial differential equations. 80% of structures fracture due to fatigue failure. Therefore, [...]

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• Deep convolutional architectures for uncertainty quantification and forecast in inundation problems

  A. Soulaïmani, Y. Kumar, P. Bhatt, M. Moosa

  WCCM2024.

  
  

  Abstract

  This article provides a summary of our latest research, where we investigate the application of data-driven deep learning methods to simulate the dynamics of physical systems that are governed by partial differential equations (PDEs). The main challenge is the long-term temporal extrapolation for fluid dynamics problems that exhibit steep gradients and discontinuities. We make use of deep learning techniques, specifically designed for time-series predictions like LSTM, TCN, and Attention mechanism, as well as CNN. These methods are employed to model the dynamics of systems primarily influenced by advection. We propose a combination of a Convolutional Autoencoder (CAE) model for data compression and a novel CNN-based for forecasts. These models take a series of high-fidelity vector solutions and predict the solutions for the following time steps using auto-regression. To reduce complexity and computational demands during both online and offline stages, we implement deep auto-encoder networks. These techniques are used to compress the high-fidelity snapshots before feeding them into the forecasting models. Our models are evaluated on numerical benchmarks, such as the 1D Burgers’ equation and Stoker’s dam-break problem, to assess their long-term predictive accuracy, even in scenarios that extrapolate beyond the training domain. The model that demonstrates the highest accuracy is subsequently used to simulate a hypothetical dam break in a river with real 2D bathymetry. Due to space constraints, only a selection of results is showcased, with additional findings available in our work [1] and the newer ones will also be presented in the talk. Our findings indicate that the proposed CNN future-step predictor offers significantly accurate forecasts in the considered spatiotemporal problems.

  Abstract
  This article provides a summary of our latest research, where we investigate the application of data-driven deep learning methods to simulate the dynamics of physical systems [...]

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• CNN-based surrogate model and temperature prediction method using superposition principle

  M. Irikiin, Y. Iwata

  WCCM2024.

  
  

  Abstract

  A CNN-based surrogate model is being developed to accelerate CFD calculations. In order to use this surrogate model for design development, it is necessary to improve generalizability. One solution to this problem is to use the principle of superposition. For the multiple heating elements that make up the model, their temperatures are predicted by heating them individually. We devised a method to predict the temperature of the entire model by adding up these individually predicted temperature distributions. Radiation and convection phenomena, for which the superposition principle does not hold, were also considered.

  Abstract
  A CNN-based surrogate model is being developed to accelerate CFD calculations. In order to use this surrogate model for design development, it is necessary to improve generalizability. [...]

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• Investigation of CNN-based multigrid-bidirectional networks

  Y. Iwata, Y. Inagaki, M. Irikiin

  WCCM2024.

  
  

  Abstract

  We are developing a high-speed simulation technology for physics simulations using deep learning. This technology aims to accelerate simulation time by a factor of several hundred to a thousand, significantly enhancing product performance and quality by increasing development efficiency and optimization. Currently, we are focusing on a multi-grid convolutional neural network (CNN) based architecture designed for models incorporating a combination of coarse and dense grids, addressing the challenge of model scaling and high resolution. Our previous work, reported by the Society for Computational Engineering and Science in June 2023, demonstrated the propagation of physical information from a coarse grid to a dense grid. Building on this foundation, we have now developed a technique that facilitates the propagation of physical information among multiple grids with varying resolutions. We applied this novel method to a basic temperature distribution prediction model for circuit boards and verified its high accuracy in predicting temperature distribution.

  Abstract
  We are developing a high-speed simulation technology for physics simulations using deep learning. This technology aims to accelerate simulation time by a factor of several [...]

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• Investigation of geotribological behaviour in the soil-pile interface considering clay based on numerical and laboratory investigations

  S. Henke, P. Wiesenthal

  WCCM2024.

  
  

  Abstract

  The contact behavior between soil and structures is an important aspect in many geotechnical applications. One example is the contact between pile and soil during pile installation which especially for open-ended pipes can lead to soil plug formation. Within the present research, contact behavior between clayey soil and pile is investigated by means of numerical and laboratory experiments focusing on the contact behavior within tubular piles. First, the contact between kaolin clay and steel is experimentally investigated with respect to overburden pressure in the so-called Geo-Tribometer developed at HSU. The results of the experimental investigations show some unexpected results leading to the assumption that the contact failure surface inside the soil specimen changes with differing overburden pressure. Additional numerical simulations are carried out for better understanding of contact stress development. Second, further laboratory investigations using soil-filled tubular piles show that adhesion-like effects significantly influence the contact behavior between soil plug and internal surface of the tube. For estimation of the adhesion values, numerical simulations by means of finite element analyses are carried out showing that as expected with increasing soil’s overburden pressure adhesion effect increases. The results are finally discussed with respect to transferability from small scale in numerical and laboratory investigations toward prototype scale.

  Abstract
  The contact behavior between soil and structures is an important aspect in many geotechnical applications. One example is the contact between pile and soil during pile installation [...]

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• Space-time analysis for the container problem

  M. Göttlicher

  WCCM2024.

  
  

  Abstract

  The container problem describes the behaviour of elastic porous media in a rectangular container, which is completely saturated by an ideal incompressible liquid. By time the liquid extrudes on the surface of the container while the stress resulting from a given top load acts on the shrinking elastic solid due to its compression. The analysis bases on a space time potential that contains a linear elastic term, a darcy flow term, and a boundary load term. The variation of the potential results in a space-time principle. Its minimum preserves approximately equilibrium over space and time.

  Abstract
  The container problem describes the behaviour of elastic porous media in a rectangular container, which is completely saturated by an ideal incompressible liquid. By time [...]

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• Three-field model for wave propagation in porous media based on mixture theories

  B. Campos, R. Gracie

  WCCM2024.

  
  

  Abstract

  Simulations of wave propagation in porous media are important to the understanding of various phenomena, such as seismic effects and non-destructive testing. The derivation and implementation of finite element analysis for a fully dynamic three-field deformable porous media model based on the de la Cruz and Spanos (dCS) theory [1] is presented. The dCS theory accounts for the fluid viscous dissipation mechanism and nonreciprocal solid-fluid interactions, which are neglected in Biot theory [2]. While the Biot theory is based on experimental data, the dCS theory is derived from mixture theories associated with the volume fraction concept and representing the connection between micro and macro pore scales. dCS results presented build upon recent FE model for quasi-static analysis [3]. Here, for the fully dynamic case incorporating both fluid and solid inertia, the accuracy and robustness of the FEA model is verified by wave propagation examples in one and two dimensions. Time integration scheme utilized and the changes in convergence rates according to how strongly coupled is the system will be discussed. The required element approximation order for all variables to ensure numerical stability will be demonstrated. The presented model is compared with the results from Biot theory, allowing one to observe the differences between the two theories and their relevance. The solutions in the time and frequency domain are also discussed, where the analysis of the correspondent eigenproblem leads to important information regarding wave velocity and attenuation.

  Abstract
  Simulations of wave propagation in porous media are important to the understanding of various phenomena, such as seismic effects and non-destructive testing. The derivation [...]

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• Development of high-strength wooden pallets utilizing local timber from Ehime Prefecture

  X. Zhu, A. Ochi, T. Kaneko, D. Ito

  WCCM2024.

  
  

  Abstract

  Wooden pallets account for a large percentage of pallets indispensable for logistics. From the viewpoint of strength and rigidity, pallets made of foreign timber (e.g., American pine) are the mainstream, and pallets made of domestic timber, especially cypress and cedar, which are inexpensive in terms of log price, are rarely distributed. The objective of this research is to develop a domestic wood pallet with high strength and rigidity comparable to that of American pine. Structural analysis using the finite element method was conducted to calculate stresses and strains under bending and compressive loads. The analytical results were also verified by JIS flat pallet bending and compression tests.

  Abstract
  Wooden pallets account for a large percentage of pallets indispensable for logistics. From the viewpoint of strength and rigidity, pallets made of foreign timber (e.g., American [...]

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• Determination of orthotropic elastic modulus of wood by indentation with none-axisymmetric indenter and FEM simulation

  T. Tsuji, K. Saito

  WCCM2024.

  
  

  Abstract

  The purpose of this study is to estimate the material constants of woods as an anisotropic material, by indentation tests using an ellipsoid indenter. Firstly, uniaxial compression tests are performed to investigate anisotropic modulus. Secondary, indentation tests are conducted to get load-displacement curve by rotating the non-axisymmetric indenter along the indentation axis. Finally, the orthotropic elastic parameters are estimated by comparing the indentation test results and finite element analysis results. The modulus along longitudinal direction, which is given by the present indentation method, are in good agreement with the measured modulus by the compression test. Therefore, it is confirmed that the longitudinal modulus can be measured by using the present method with the non-axisymmetric indenter

  Abstract
  The purpose of this study is to estimate the material constants of woods as an anisotropic material, by indentation tests using an ellipsoid indenter. Firstly, uniaxial compression [...]

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