Documents published in Scipedia

• Prediction of Entropy Production and Heat Transfer Characteristics of Al2O3-Cu/Water Hybrid Nanofluid in Convection-Radiation Interaction Flow in a Porous Cavity byMachine Learning Approach

  A. Çolak

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (2), 48

  
  

  Abstract

  Minimizing entropy production is critically important, particularly in nanofluid flows. Applying this principle to flows with porous cavity structures helps optimize heat transfer applications and enhance system efficiency. In this study, the entropy production and heat transfer characteristics of a hybrid nanofluid composed of Al2O3-Cu particles suspended in water were investigated using machine learning. The nanofluid was analyzed in the context of convection–radiation interaction flow within a porous cavity.An artificial neural networkmodel was developed to predict the average Nusselt number, Bejan number, and entropy production as functions of the Hartmann number and inclination parameters. The Bayesian Regularization algorithm was employed to train the multilayer perceptron network model. Prediction results obtained from the model with 10 neurons in the hidden layer were compared with the target values and showed excellent agreement.The developed artificial neural network model successfully predicted the Nusselt number, Bejan number, and entropy productionwith average deviation rates of−0.007%,−0.11%, and 0.0002%, respectively.

  Abstract
  Minimizing entropy production is critically important, particularly in nanofluid flows. Applying this principle to flows with porous cavity structures helps optimize heat [...]

  • 19
  •  read

• Truncated Modified Weighted Exponential Distribution with Different Estimation Methods and Applications

  A. Al-Omari, K. Aidi, G. Alomani

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (2), 47

  
  

  Abstract

  This study introduces the Truncated Modified Weighted Exponential (TrMWE) Distribution, developed by extending the traditional modified weighted exponential distribution with an additional parameter and truncating its support to a finite range, enhancing its adaptability for modeling lifetime and reliability data. The statistical properties of the TrMWE, including moments, the moment-generating function, the quantile function, and order statistics, are examined. Parameter estimation is performed via maximum likelihood estimation (MLE), least squares and weighted least squares methods, maximum product of spacing method, Cramer-Von-Mises method, Anderson-Darling method, and right and left tails Anderson-Darling methods, with analysis of the asymptotic behavior of the estimators. Rényi and Tsallis entropies are also derived to assess the distribution’s uncertainty. The practicality of the TrMWE is illustrated using three real datasets and compared with existing distributions based on goodness-of-fit criteria, such as the Akaike Information Criterion and Bayesian Information Criterion. The results highlight the distribution’s flexibility and superior performance in modeling complex datasets.OPEN ACCESS Received: 14/08/2025 Accepted: 05/11/2025 Published: 03/02/2026

  Abstract
  This study introduces the Truncated Modified Weighted Exponential (TrMWE) Distribution, developed by extending the traditional modified weighted exponential distribution with [...]

  • 16
  •  read

• Theoretical Analysis of Seepage Field Distribution Induced by Pre-Excavation Dewatering Considering the Thickness of Waterproof Curtain and Delayed Responses of Water Table

  D. Meng, J. Han, W. Lei, H. Zhang, C. Guan

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (2), 46

  
  

  Abstract

  Dewatering during foundation pit excavation generates substantial hydraulic gradients, potentially causing significant seepage forces at the excavation bottom, which threaten structural stability. To mitigate such risks, suspended waterproof curtains have been widely employed to elongate seepage paths and reduce groundwater flow velocities. However, accurately predicting seepage field, especially under transient groundwater conditions with phreatic surfaces and varying curtain geometries, remains challenging. This study develops a theoretical model addressing transient groundwater seepage in foundation pits, explicitly considering a moving phreatic surface, curtain penetration depth and thickness. The proposed analytical solution is validated against experimental results and numerical simulations performed using COMSOL Multiphysics. Parametric analyses reveal that decreasing the vertical distance between the retaining wall base and the impermeable layer from 30 to 10 m reduces external groundwater drawdown by approximately 52%. Additionally, thicker waterproof curtains markedly decrease internal drawdown magnitudes, redirect seepage pathways, and effectively lower external groundwater depletion. Analyses on specific yield reveal delayed water release significantly moderates drawdown rates, reducing ultimate drawdown magnitudes. Furthermore, elevated internal excavation water levels intensify hydraulic head differences, substantially extending seepage-affected zones and amplifying drawdown responses both inside and outside the foundation pit. Overall, these findings provide critical theoretical insights for optimizing foundation pit design and improving dewatering practices, ensuring excavation safety and mitigating environmental impacts.OPEN ACCESS Received: 11/08/2025 Accepted: 05/11/2025 Accepted: 03/02/2026

  Abstract
  Dewatering during foundation pit excavation generates substantial hydraulic gradients, potentially causing significant seepage forces at the excavation bottom, which threaten [...]

  • 3
  •  read

• NAS-Driven Quantitative Assessment of Overpressure Genesis in Sedimentary Basins

  H. Chen, C. Li, Y. Xu, S. Deng, H. Zhang, Z. Hu, H. Pan

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (2), 45

  
  

  Abstract

  The study of overpressure genesis mechanism is the foundation of hydrocarbon reservoir formation and pressure prediction research, and a thorough understanding of the formation and distribution patterns of hydrocarbon resources is essential for practical hydrocarbon exploration. However, the prediction of anomalous high-pressure genesis currently encounters numerous challenges, including the complexity of overpressure genesis, the superimposed effect of multiple mechanisms, and the dependence of traditional models on manual analysis, resulting in inefficiency and quantification challenges. To this end, this paper proposes a method for identifying and quantitatively evaluating stratigraphic overpressure mechanisms based on neural architecture search, to enable rapid and accurate quantitative evaluation of overpressure mechanisms. The results show that the main anomalous high-pressure genesis mechanisms in the target work area include undercompaction, fluid expansion and tectonic compression, with contribution rates of approximately 73% for undercompaction, and 9% and 18% for fluid expansion and tectonic compression, respectively. The model’s accuracy in the test set reaches 95.4%, significantly enhancing the identification accuracy of anomalous stratigraphic pressure genesis and its superposition relationships. The innovation of this paper lies in the combination of wave velocity-density rendezvous map with clustering algorithm and neural architecture search algorithm, offering an efficient approach to identify multiple overpressure genesis mechanisms and predict pore pressure through machine learning algorithms, which is of great theoretical significance and practical application value.OPEN ACCESS Received: 08/08/2025 Accepted: 14/10/2025 Published: 03/02/2026

  Abstract
  The study of overpressure genesis mechanism is the foundation of hydrocarbon reservoir formation and pressure prediction research, and a thorough understanding of the formation [...]

  • 4
  •  read

• Heat Transfer and Electroosmotic Flow over Stretching Sheet: A Sensitive Analysis through Response Surface Method

  J. Prakash, D. Tripathi, O. Bég, S. Kuharat, K. Mekheimer

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (2), 44

  
  

  Abstract

  Recent advancements in electro-osmotic surface coatings have led to significant theoretical and numerical exploration of how zeta potential influences the electroosmotic flow of viscous ionic fluids over a stretching sheet. The governing boundary layer equations are derived from the fundamental laws of mass, momentum, and energy conservation using appropriate similarity transformations and non-dimensionalization techniques. This system of equations is solved numerically using MATLAB’s bvp4c solver. The accuracy of the computational results is confirmed through comparison with previously published studies. To better understand the influence of various parameters on flow and thermal behavior, Response Surface Methodology and Factorial Plot analysis are applied. These statistical tools enable sensitivity analysis by systematically investigating the effects of zeta potential, electroosmosis parameter, electric field strength, and Prandtl number on key flow characteristics such as velocity, temperature distribution, skin friction coefficient, and Nusselt number. The results reveal that the electric field parameter plays a dominant role in enhancing axial velocity and increasing skin friction, making it a key factor in flow dynamics. The zeta potential significantly influences the boundary layer by modifying the electrical double layer and surface charge distribution, leading to noticeable deceleration. Meanwhile, the Prandtl number primarily governs thermal gradients and heat transfer rates, controlling the thermal behavior of the fluid. These physical insights, combined with the optimization capability of Response Surface Methodology, provide actionable guidelines for the design of electroosmotic coating processes and lab-on-chip biomedical devices.OPEN ACCESS Received: 07/08/2025 Accepted: 17/10/2025 Published: 03/02/2026

  Abstract
  Recent advancements in electro-osmotic surface coatings have led to significant theoretical and numerical exploration of how zeta potential influences the electroosmotic flow [...]

  • 26
  •  read

• Investigation on Dynamical Mechanics of Rock-Backfill Composite Samples under SHPB Test

  H. Ma, Y. Zhang, X. Feng

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (2), 43

  
  

  Abstract

  In blast-induced caving mining with backfilling, understanding the interaction mechanisms and deformation evolution between rock and cemented tailing backfill (CTB) under coupled conditions is essential for ensuring stability. This study conducted dynamic uniaxial impact tests using the Split Hopkinson Pressure Bar (SHPB) system on rock-CTB composite specimens to investigate their mechanical response at high strain rates. Stress–strain relationships were recorded across a range of strain rates, and corresponding failure mechanisms were analyzed. A coupled SHPB model was also developed using GDEM software to simulate internal stress wave propagation and crack evolution within the composite specimens. Experimental results revealed that the dynamic compressive strength initially increases, then decreases, and eventually stabilizes as the average strain rate (ASR) increases from 27.45 s−1to 68.73 s−1. At strain rates below 60 s−1, the stress–strain curves exhibit a “stress drop” pattern, whereas above 60 s−1, a “stress rebound” behavior is observed. Energy absorption increases with ASR up to 55 s−1, then decreases, followed by a secondary increase. Numerical simulations validated the experimental findings, revealing the formation of both transverse and longitudinal cracks within the CTB. Greater deformation was observed near the transmission bar interface compared to the rock interface. These results offer valuable insights into the dynamic failure behavior of backfilled systems and inform improved backfill design in blast-induced mining operations.OPEN ACCESS Received: 05/08/2025 Accepted: 05/09/2025 Published: 03/02/2026

  Abstract
  In blast-induced caving mining with backfilling, understanding the interaction mechanisms and deformation evolution between rock and cemented tailing backfill (CTB) under [...]

  • 4
  •  read

• AI-Driven Multimodal Analysis of User Experience in Immersive Environments: A Case Study of The Sphere

  Y. Seo, H. Ryoo

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (2), 42

  
  

  Abstract

  Artificial intelligence (AI) and sensing technologies are reshaping how people experience immersive environments. This study investigates how audiences perceive and emotionally respond to such environments through an AI-driven mixed-methods analysis. A dataset of 275 usergenerated YouTube videos documenting experiences with The Sphere, an AI-convergent immersive environment, totaling over 3000 min of content and 24 million cumulative views, was analyzed to extract experiential themes, dominant emotions, and their relationships with public engagement metrics. The analysis identified seven key experiential themes: Awe of the Display, Personalized Spatial Audio Experience, Full-Body Sensory Engagement, Dynamic Visual Spectacle, Joyful Human–AI Encounter, Futuristic Spatial Design Experience, and Transformative Event Environment. Sentiment analysis revealed that fear was the most dominant emotion in textual narratives (42.3%), followed by surprise, sadness, happiness, and anger, whereas video-based analysis highlighted happiness (25.8%) and sadness (24.5%) as the most salient visual emotions. This contrast suggests that linguistic expressions emphasized feelings of awe and overwhelm, while visual cues reflected affective immersion and emotional depth. Regression results showed that Awe of the Display had the strongest positive impact on engagement (views, likes, comments), while Personalized Spatial Audio Experience showed a negative effect. These findings deepen the understanding of user experience in immersive environments and demonstrate how AI-assisted multimodal analysis can reveal the dynamics between audience perception and engagement in next-generation immersive environments.OPEN ACCESS Received: 12/11/2025 Accepted: 15/12/2025 Published: 03/02/2026

  Abstract
  Artificial intelligence (AI) and sensing technologies are reshaping how people experience immersive environments. This study investigates how audiences perceive and emotionally [...]

  • 5
  •  read

• AI-Driven Predictive Analytics for Demand Forecasting in Transportation Logistics to Enhance Supply Chain Agility

  K. Abuhasel

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (2), 41

  
  

  Abstract

  The logistics and transportation sectors are struggling with major issues like demand variations, disruptions, and inefficiencies, which ultimately undermine the agility and efficiency of the entire supply chain. Most of the time, traditional forecasting models are not entirely accurate in response to life-changing factors like weather, traffic, and inventory levels. The present research intends to build an AI-powered predictive model that can seamlessly enhance not only demand forecasting and logistics but also by the integration of real-time data. The framework incorporates several Machine Learning (ML) models, which are Light GBM for demand forecasting, Random Forest for disruption prediction, Linear Regression for shipping cost estimation, and Support Vector Regression for delivery time deviation prediction. A thorough dataset containing historical demand, weather conditions, traffic, and stock levels was used for the model’s training and evaluation, and its performance was monitored using MAE, MSE, RMSE, and MAPE metrics. The findings indicate that the suggested framework is a lot better than the existing ones, with Light GBM getting the lowest MAE (0.056), MSE (0.005), RMSE (0.072), and MAPE (0.142). This means that the new system can predict much better than before, thus making it possible for the company to take the right decision at the right time and consequently improving the overall supply chain efficiency. The research paper reveals the future possibilities of AI-based solutions for optimising logistics operations and building supply chain resilience.OPEN ACCESS Received: 20/10/2025 Accepted: 25/12/2025 Published: 03/02/2026

  Abstract
  The logistics and transportation sectors are struggling with major issues like demand variations, disruptions, and inefficiencies, which ultimately undermine the agility and [...]

  • 12
  •  read

• Parametric Study of the Mechanical Behaviour of Membrane Tensegrity

  L. Ferragu, J. Rebeck, R. Oval

  membranes2025.

  
  

  • 22
  •  read

• Integration of parametric design and analysis tools

  P. Kast

  membranes2025.

  
  

  • 0
  •  read