Documents published in Scipedia

• Numerical Simulation of the Influence of Different Opening Parameters on the Flexural Performance of Fixed Open-Web Steel Beams

  Q. Feng, M. Qian, C. Qu, N. Ye

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (3), 39

  
  

  Abstract

  Based on the ABAQUS finite element analysis software platform, this study investigated the effects of opening parameters, including end distance, opening rate, and opening distribution, on the flexural performance of fixed open-web steel beams. The results indicate that an open-web steel beam with fixed support exhibits a flexural shear failure mode, which initiates yielding at the hole corners in the flexural shear zone. As the load increases, the plastic zone progressively extends to the flange, ultimately forming a four-hinge plastic mechanism around the hole. Specifically, a mid-span hole reduces the flexural performance by approximately 10%, while an end hole reduces it by approximately 47%. It is recommended that mid-span openings with end distances no less than 1.75 times the beam height be prioritized in engineering design. Additionally, the flexural performance of fixed open-web steel beams decreases as the opening rate increases. Optimizing the opening geometry by reducing its length-toheight ratio under invariant opening ratio conditions provides a more than doubled flexural capacity gain, offering practical insights for beam design.OPEN ACCESS Received: 25/02/2025 Accepted: 24/03/2025 Published: 14/07/2025

  Abstract
  Based on the ABAQUS finite element analysis software platform, this study investigated the effects of opening parameters, including end distance, opening rate, and opening [...]

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• The Laplace Residual Power Series Method for Solving Fractional Fifth-Order Korteweg–de Vries Equations

  A. Hagag, Z. Alqahtani, A. Almuneef

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (3), 38

  
  

  Abstract

  This study explores a modern analytical approach for solving the fractional fifth-order Korteweg–de Vries (KdV) equations, which describe intricate wave phenomena influenced by nonlinearity, dispersion, and memory effects. Specifically, the Laplace residual power series method (LRPSM) is utilized to obtain accurate approximate analytical solutions for three fundamental fractional equations: the fractional Sawada–Kotera (SK) equation, the fractional Caudrey–Dodd–Gibbon (CDG) equation, and the fractional Kaup–Kuperschmidt (KK) equation. These equations represent special cases of the broader fractional fifth-order KdV equation. The novelty of this study lies in the application of LRPSM, which addresses the limitations of traditional methods by combining analytical precision with computational efficiency. The method successfully captures fractional dynamics, including soliton-like behaviors and memory effects, demonstrating its capability to model wave attenuation and smoothness influenced by fractional orders. The numerical results demonstrate that this method achieves minimal error margins, validating its robustness and precision in solving nonlinear fractional systems. Numerical examples validate the efficiency and robustness of this method, achieving high accuracy in solving nonlinear fractional systems. The results establish LRPSM as a versatile and reliable tool for solving fractional differential equations, paving the way for advancements in modern wave theory and applications across disciplines such as plasma physics, fluid mechanics, and nonlinear optics.OPEN ACCESS Received: 12/10/2024 Accepted: 21/01/2025 Published: 14/07/2025

  Abstract
  This study explores a modern analytical approach for solving the fractional fifth-order Korteweg–de Vries (KdV) equations, which describe intricate wave phenomena influenced [...]

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• Fast Solution Techniques for Polytopic hp-Discontinuous Galerkin Methods for Radiation Transport Problems

  P. Houston

  coupled25.

  
  

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• Accelerating Continental-Scale Groundwater Simulation with A Fusion of Machine Learning, Integrated Hydrologic Models and Community Platforms

  R. Maxwell

  coupled25.

  
  

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• Iterative and multilevel methods for PDE-constrained optimization under uncertainty

  F. Nobile

  coupled25.

  
  

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• Coupled Problems Arising in the Design of Drug Carriers

  A. Quaini

  coupled25.

  
  

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• Brain membranes and vasculature - a computational mathematics tale of dimensional gaps

  M. Rognes

  coupled25.

  
  

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• Coupled Problems: Marking 20 Years of Advancement and Continued Impact

  P. Wriggers

  coupled25.

  
  

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• Multiscale modeling of coupled thermo-hydro-mechanical problems in granular media

  J. Zhao

  coupled25.

  
  

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• Numerical Simulation and Optimization of Grey Relational Analysis Models for Panel Data

  X. Liu, F. Jian, X. Tang

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (2), 36

  
  

  Abstract

  The indicators-coupled grey relational analysis (ICGRA) models are important in clustering panel data with cross-sectional dependence. However, there is still little research on performance validation for the various ICGRA models. In this paper, we investigate the performance of the existing ICGRA models accounting for the reordering of indicators. Firstly, the robot execution failures (REF) dataset of the University of California Irvine (UCI) machine learning database is adopted to validate the robustness of four traditional ICGRA models. Then, we compared the grey relational orders for all arrangements of indicators in panel data. Simulation experiments showed that the four ICGRA models are not all robust against the grey relational order. To resolve this problem, we adopted the mean value theory and deep modeling to optimize the four models and compared them with the tetrahedral grey relational analysis (GRA) model that considers the coupling effect between indicators on the grey relational order, as well as with the k-nearest neighbor (KNN) algorithm. Results show that the classification accuracy of the averaged absolute GRA model was 97.73%, the other optimized ICGRA models and the k-nearest neighbor (KNN) method all achieved 100% accuracy, while the tetrahedral GRA model has an accuracy of 83.33%. Therefore, the average grey incidence degree for all arrangements of indicators and deep modeling significantly improves the stability of models and enhances the clustering accuracy in different cases.OPEN ACCESS Received: 09/12/2024 Accepted: 04/03/2025 Published: 30/06/2025

  Abstract
  The indicators-coupled grey relational analysis (ICGRA) models are important in clustering panel data with cross-sectional dependence. However, there is still little research [...]

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