Documents published in Scipedia

• Cost Optimization of Reinforced Concrete Frames Using Metaheuristic Algorithms

  Y. Duysak, S. Nigdeli, G. Bekda¸

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (3), 92

  
  

  Abstract

  In structural engineering, the primary objective of design engineers is to ensure structural safety under applied loads according to codes and standards while achieving the most economical design. To enhance cost efficiency in complex structures such as reinforced concrete (RC) frames, computational techniques are employed. The aim is to perform a more rapid and accurate optimum cost design of RC frame systems subjected to vertical loads. This study applies five metaheuristic approaches: three metaheuristic methods and two hybrid techniques developed from them. The matrix displacement method is used to determine displacements and sectional forces of RC frame systems, with design rules following ACI 318-19 (Building Code Requirements for Structural Concrete and Commentary). The study models five different RC structures with varying dimensions and member counts, including symmetrical and asymmetrical configurations. Internal forces and displacement values of structures analyzed using the matrix displacement method in MATLAB are verified with SAP2000 structural analysis software, confirming solution validity. Among the methods applied for the optimum design, the TeachingLearning-Based Optimization (TLBO) technique proves particularly suitable for RC frame systems. The proposed design method and TLBO algorithm offer civil engineers a rapid and efficient approach to achieving cost-optimized designs while ensuring structural safety. The developed method helps engineers efficiently solve complex design problems while achieving optimal structural solutions in terms of both cost and safety.OPEN ACCESS Received: 14/08/2025 Accepted: 24/10/2025 Published: 16/04/2026

  Abstract
  In structural engineering, the primary objective of design engineers is to ensure structural safety under applied loads according to codes and standards while achieving the [...]

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• Design and Performance of an Ultra Gain Boost Converter for SolarPowered Electric Vehicle Applications

  K. Neelima, B. Reddy, K. Sailaja, S. Reddy, P. Balachandran, T. Senjyu

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (3), 88

  
  

  Abstract

  This paper presents the design and performance analysis of an Ultra Gain Boost Converter (UGBC) intended for solar-powered electric vehicle (SPEV) applications. The proposed converter is derived from a cascaded boost–boost structure with a capacitor–diode voltage-lift cell, allowing high voltage gain without operating at extreme duty ratios. The proposed structure provides continuous input current, improved voltage boosting capability, and reduced voltage stress on semiconductor devices, making it suitable for photovoltaic based energy systems. Complete steady-state analysis, voltage-gain derivation, device stresses estimation, power-loss estimation, and small-signal dynamic modelling are carried out to evaluate the converter characteristics. The UGBC topology is validated with MATLAB/Simulink simulations for an operating condition of 24 to 72 V at a duty ratio of 0.33, indicating stable voltage regulation, low current ripple, and higher efficiencies. Additionally, system-level performance is given by interfacing the converter with a BLDC motor drive, where stable speed regulation and smooth torque response are observed. The results confirm that the proposed UGBC converter with a capacitor–diode cell is a feasible and efficient solution for high-gain Ultra Gain DC–DC conversion in solar-based electric mobility applications.OPEN ACCESS Received: 22/11/2025 Accepted: 29/01/2026 Published: 16/04/2026

  Abstract
  This paper presents the design and performance analysis of an Ultra Gain Boost Converter (UGBC) intended for solar-powered electric vehicle (SPEV) applications. The proposed [...]

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• LLM-Guided Multi-Agent Deep Reinforcement Learning for Distributed Energy Management in Renewable-Integrated Power Systems

  R. Shi, X. Zhang, Y. Chen, H. Ding

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (3), 87

  
  

  Abstract

  With the increasing penetration of renewable energy in power systems, distributed energy management faces numerous challenges including high-dimensional state spaces, multi-objective optimization, and realtime decision-making. This paper proposes a Large Language Model (LLM)-guided Multi-Agent Deep Reinforcement Learning (MADRL) framework for distributed energy management in renewable-integrated power systems. Building upon recent advances in LLM-guided reinforcement learning, we develop specialized mechanisms for power system control that leverage the semantic understanding and knowledge reasoning capabilities of LLMs to provide high-level strategic guidance and scenario-adaptive adjustments for MADRL agents. Specifically, we design a hierarchical architecture where the LLM layer is responsible for parsing grid operation states, generating optimization objective descriptions, and coordinating multi-agent behaviors, while the MADRL layer executes specific energy scheduling decisions. Experiments are conducted on real power grid datasets containing photovoltaic, wind power, energy storage systems, and flexible loads. Results demonstrate that the proposed method significantly outperforms traditional baseline methods in reducing operating costs, improving renewable energy utilization rates, and ensuring grid stability. Compared to standard MADRL, our method reduces system operating costs by 18.7%, decreases renewable energy curtailment by 23.4%, and improves convergence speed by 3.2 times. This study provides a novel approach for adaptive distributed energy management in smart grids.OPEN ACCESS Received: 15/11/2025 Accepted: 15/01/2026 Published: 16/04/2026

  Abstract
  With the increasing penetration of renewable energy in power systems, distributed energy management faces numerous challenges including high-dimensional state spaces, multi-objective [...]

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• Integrating Vector Computation and Numerical Methods for Complex Surface Design in Engineering via MATLAB

  J. Cuenca, V. García, J. Vásquez, M. Salazar

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (3), 83

  
  

  Abstract

  The teaching and practical use of vector calculus in engineering often face challenges rooted in mathematical abstraction and the limited availability of tools capable of supporting three-dimensional geometric analysis. These constraints hinder precision when designing complex structural surfaces. Addressing this gap, the present study proposes the development and implementation of an interactive computational tool—built in MATLAB App Designer that integrates vector-based formulations with numerical methods to parameterize, visualize, and compute the surface area of three-dimensional geometries, with a particular focus on sizing geomembranes for circular aquaculture ponds. The research methodology comprised theoretical, numerical, and experimental components. Exact vector parameterizations were formulated, symbolic integration and discretization algorithms were implemented, and the resulting computations were assessed through error estimation and convergence analysis. The findings demonstrate a close match between analytical and numerical solutions, with relative errors below 0.1%, stable computational behavior under moderate discretization settings, and distortion-free threedimensional visualizations. Overall, the study shows that combining exact vector modeling with adaptive numerical techniques and interactive visualization provides an efficient and low-cost framework for surface-area computation and structural design. This approach offers a practical alternative to conventional CAD platforms and delivers meaningful benefits for both engineering education and industrial applications within sustainable production systems.OPEN ACCESS Received: 10/11/2025 Accepted: 14/01/2026 Published: 16/04/2026

  Abstract
  The teaching and practical use of vector calculus in engineering often face challenges rooted in mathematical abstraction and the limited availability of tools capable of [...]

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• Numerical Simulation and Analysis of Annular Trapped Pressure during the Testing of Deepwater High-Temperature and High-Pressure Gas Wells

  Z. Rao, K. Liu, B. Chen, Y. You, Q. Zhang, Y. Jiao, B. Liu, M. He

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (3), 76

  
  

  Abstract

  During the testing of high-temperature and high-pressure (HTHP) offshore gas wells, heat transfer occurs between the high-temperature gas in the tubing and the low-temperature fluid in the annulus. This causes the annular fluid to expand, leading to an increase in annular trapped pressure, which poses a potential integrity risk. To accurately predict this pressure variation, a full-scale physical model of an offshore HTHP gas well was developed based on simplified assumptions of a constant geothermal gradient and homogeneous casing material. By integrating the Pressure–Volume–Temperature (PVT) equation of state with a transient heat transfer model of the wellbore, a section-by-section method for calculating annular temperature was proposed. A coupled prediction model for annular trapped pressure, incorporating both thermal effects and annular volume change, was then established. Using Well Y in the eastern South China Sea as a case study, numerical simulations of annular temperature and trapped pressure were performed. The results indicate that the model’s average relative error compared to experimental data is approximately 6.15%. When the production rate is 10× 104m3/d, the trapped pressures in annuli A, B, and C are 31, 25, and 21 MPa, respectively. Both temperature and pressure increase progressively from the wellhead to the bottom, with the most significant variations occurring in the shallow section. Sensitivity analysis shows that trapped pressure rises rapidly during the early stage of testing and gradually levels off over time. The annular trapped pressure is positively correlated with production rate, geothermal gradient, fluid expansion coefficient, and well depth, whereas increases in casing elastic modulus, Poisson’s ratio, and the thermal expansion coefficient of the fluid tend to reduce it. The study provides a theoretical foundation and practical support for evaluating wellbore integrity and controlling annular risks under complex conditions in deep-water HTHP oil and gas wells.OPEN ACCESS Received: 03/09/2025 Accepted: 11/11/2025 Published: 16/04/2026

  Abstract
  During the testing of high-temperature and high-pressure (HTHP) offshore gas wells, heat transfer occurs between the high-temperature gas in the tubing and the low-temperature [...]

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• Artificial Neural Network Analysis of Magnetohydrodynamic Flow and Thermal Radiation in Hybrid Nanofluid with Bio-Convection over a Stretching Sheet

  M. Akmal, A. Zafar, S. Abdal, N. Shah

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (3), 75

  
  

  Abstract

  This study investigates the flow and heat transfer of a hybrid nanofluid including alumina and titanium dioxide nanoparticles across a stretched sheet in the presence of a magnetic field, thermal radiation, and bioconvection. The problem is significant because hybrid nanofluids are increasingly employed in industrial cooling, biomedical transport, and energy systems that need accurate prediction of nonlinear thermal and solutal behavior. The governing nonlinear equations were transformed into ordinary differential form and computationally solved via a fourthorder Runge-Kutta method. In addition to this, an artificial neural network model was created to reproduce the numerical results and evaluate prediction abilities. The results show that stronger magnetic forces reduce velocity by about 18%, while higher radiation levels increase fluid temperature by nearly 22%. Increasing the Lewis number lowers nanoparticle concentration by roughly 15%, and higher bioconvection effects reduce microorganism density by about 12%. The neural network achieved excellent agreement with the numerical results, with regression values close to 1 and a mean squared error of order 10−10. The findings indicate that hybrid nano-fluids offer enhanced heat transfer and flow stability under MHD conditions, supporting potential improvements in fluid and thermal performance for applications requiring precise thermal management.OPEN ACCESS Received: 27/08/2025 Accepted: 05/11/2025 Published: 16/04/2026

  Abstract
  This study investigates the flow and heat transfer of a hybrid nanofluid including alumina and titanium dioxide nanoparticles across a stretched sheet in the presence of a [...]

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• Prioritizing Barriers to Total Quality Management Implementation for Sustainable Construction Using AHP–GDM

  D. Alqahtani, M. Awadh

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (3), 85

  
  

  Abstract

  The construction industry is increasingly challenged by complex decisionmaking environments involving economic, technical, and sustainability related factors. This study proposes a structured multi-criteria decisionmaking (MCDM) framework to systematically evaluate and prioritize critical factors influencing construction project performance in Saudi Arabia. The methodology integrates objective weight determination with rankingbased MCDM techniques to assess multiple, often conflicting criteria using expert-driven and data-informed analysis. The results identify the most influential factors affecting construction efficiency and sustainability, providing a transparent and reproducible decision-support model. The main contribution of this study lies in offering a context-specific, analytically robust framework that advances MCDM applications in construction management, particularly within emerging economies. From a practical perspective, the findings support policymakers, project managers, and industry stakeholders in improving strategic planning, resource allocation, and risk-informed decision-making aligned with national development goals. Overall, the proposed framework enhances evidence-based decision-making and contributes to improving the resilience and performance of the construction industry.OPEN ACCESS Received: 13/11/2025 Accepted: 19/01/2026

  Abstract
  The construction industry is increasingly challenged by complex decisionmaking environments involving economic, technical, and sustainability related factors. This study proposes [...]

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• Proposal and Energy Analysis of a New Design Integrating the SCPP into Electricity Pylon Using CFD Numerical Method: Towards Ensuring Electric Needs of Rural Dwellings

  A. Mebarki, M. Titi

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (3), 80

  
  

  Abstract

  Rural electrification is critical for increasing agricultural output, thereby reducing poverty and improving food security. Electricity pylons are steel structures that transport electricity at high voltages and are often the tallest objects in the countryside. The current study proposes a novel solar chimney power plant (SCPP) model in which the chimney is contained within the pylon’s vertical structure. The numerical methodology was carried out using Manzanares’ SCPP 3D axisymmetric CFD model, which was simulated with ANSYS Fluent software. The RNG k-ε turbulence model and a discrete ordinates non-grey radiation model were used. Following model validation, the results led to the identification of five turbine positions based on five collector radius intervals. To the best of our knowledge, this is the first time this has been observed in the SCPP system as a result of air velocity variations throughout the chimney. Equations based on collector radius and solar radiation are used to calculate the generated electric energy and the number of rural houses that could be electrified at each interval. Our findings confirmed the proposed model’s efficiency in generating electricity; for instance, one hour of 1000 W/m2 solar radiation using a collector radius of 200 m produces 14.82 kW that could supply 6.877 rural houses in India or 7.411 rural houses in China for 24 h. This study proposes a plan for electricity transmission companies to invest in infrastructure (pylons) to provide rural renewable electricity.OPEN ACCESS Received: 16/10/2025 Accepted: 27/11/2025

  Abstract
  Rural electrification is critical for increasing agricultural output, thereby reducing poverty and improving food security. Electricity pylons are steel structures that transport [...]

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• A Detail Enhancement Method for Weakly Illuminated Images Using Weighted Guided Filtering Technology

  Z. Qiu, X. Tang, Y. Zhou

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (3), 71

  
  

  Abstract

  In order to optimize the visual effect of weakly illuminated images, this study designed a detail enhancement method for weakly illuminated images based on weighted guided filtering technology. Low-light images undergo a series of transformations that include, among others, the grayscale transformation and the multi-scale weighted guided filtering. Filtration improves both the basic and detailed layers. The proposed method is validated through experimental results to be capable of effectively increasing the visibility and clarity of dimly lit images without losing the delicate texture and edge information. More precisely, the output images register an average gradient of 17.547, which shows that the edges are sharper; information entropy that is always above 4.0, which means that the detail content is richer; and a peak signal-to-noise ratio of 42.85 to 48.37, which asserts that the image quality is high. The whole enhancement process is very fast, and it takes only 1 min to complete, indicating that this method effectively achieves the design expectation.OPEN ACCESS Received: 08/08/2025 Accepted: 29/01/2026

  Abstract
  In order to optimize the visual effect of weakly illuminated images, this study designed a detail enhancement method for weakly illuminated images based on weighted guided [...]

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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), 101

  
  

  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.

  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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