Documents published in Scipedia

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

  P. Houston

  coupled2025.

  
  

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

  R. Maxwell

  coupled2025.

  
  

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

  F. Nobile

  coupled2025.

  
  

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• Coupled Problems Arising in the Design of Drug Carriers

  A. Quaini

  coupled2025.

  
  

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

  M. Rognes

  coupled2025.

  
  

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

  P. Wriggers

  coupled2025.

  
  

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

  J. Zhao

  coupled2025.

  
  

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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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• Optimization of Support for Deeply Buried Diversion Tunnels with Bad Geology Based on MOMVO Algorithm

  M. Huang, L. Zhao, F. Yang, Y. Jiang

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (2), 35

  
  

  Abstract

  During the construction of diversion tunnels, different support schemes exhibit varying stability conditions and entail distinct economic costs. To enhance the cost-effectiveness of tunnel support while ensuring its safety, a multi-objective optimization model has been developed for deeply buried diversion tunnels with bad geological conditions, enabling the execution of multi-objective optimization calculations regarding construction safety and economy. By considering the settlement of the vault and the cost of supporting materials as the objective function, a set of solution sets is computed using the multi-objective multi-verse algorithm (MOMVO), and subsequently, an optimal solution is obtained by integrating the entropy weight-technique for order preference by similarity to an ideal solution (TOPSIS) decision. The Luzhi River Tunnel of the Central Yunnan Water Diversion Project was selected as the subject of analysis to conduct support optimization calculations. The results demonstrate that, compared to the original design, there is an 8.16% reduction in the cost of support materials per linear meter. Although there is a 0.76% increase in settlement at the tunnel vault, it remains significantly below the safety critical value and aligns with project safety requirements. The proposed optimization method for tunnel support demonstrates remarkable efficacy, effectively reducing the cost of support materials while ensuring engineering safety. Moreover, it provides valuable technical support for optimizing the support system in water diversion tunnels.OPEN ACCESS Received: 07/11/2024 Accepted: 14/01/2025 Published: 30/06/2025

  Abstract
  During the construction of diversion tunnels, different support schemes exhibit varying stability conditions and entail distinct economic costs. To enhance the cost-effectiveness [...]

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• Fundamental Theorems and Wave Dynamics in Rotating Thermoelastic Diffusion with Multi-Phase Delays and Temperature-Dependent Properties

  S. Sharma, R. Kumar, H. Taha, A. El-Bary, K. Lofty

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (2), 34

  
  

  Abstract

  The current study uses a novel mathematical formulation of temperaturedependent thermoelastic diffusion with multi-phase delays in a rotating frame of reference (TDMR). Higher order time derivatives are included for the diffusing mass flux, the gradient of chemical potential, the gradient of temperature, and the TDMR model of heat flow vector in Fourier’s and Fick’s laws. The fundamental theorems (energy, uniqueness, reciprocity, and variational criterion) are examined using the preliminary equations for the simulated model TDMR. Reciprocity theorem applications are taken for particular scenarios including body forces, heat sources, and chemical potential sources. It has been noted that the variational criteria and these theorems are mostly influenced by the field variables’ susceptibility and the variations in the parameters of higher-order temporal derivatives. The two-dimensional example of the assumed model’s planar wave propagation is also provided. Four connected longitudinal waves have been identified: the primary (P) wave, secondary (S) wave, thermal (T) wave, and chemical potential (CP) wave. Wave properties such as phase velocity and attenuation coefficient are estimated numerically and displayed visually. A few special examples are also investigated and connected with the established outcomes. This work provides a foundation for further research into basic issues in thermoelastic continua under various physical field factors. Numerous applications in material science, geomechanics, soil dynamics, and the electronic sector can be made of the current findings.OPEN ACCESS Received: 22/02/2025 Accepted: 11/04/2025 Published: 30/06/2025

  Abstract
  The current study uses a novel mathematical formulation of temperaturedependent thermoelastic diffusion with multi-phase delays in a rotating frame of reference (TDMR). Higher [...]

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