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8 articles for “LS-DYNA”
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Numerical Analysis of Energy Absorption Ability of Polygonal Tubes under Crushing
Abstract: Honeycomb panels are used mostly in energy absorption application in engineering fields such as aerospace, transportation and packaging, etc. Honeycomb panels have good stiffness and are weight efficient structures. The honeycomb core consists of cellular cells. The core cell’s geometry of honeycomb affects the energy absorption ability. In this study, the different geometries of individual cell are analysed in tube form. Tubes of different geometries like square, pentagon, hexagon, octagon, …
Published in International Journal of Structural Mechanics and Finite Elements · Vol. 7, Issue 1, 2021 · pp. 22–30 Read article
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Sub-structure Geometry Effect on Energy Absorption Ability of Hierarchical Honeycomb under Crushing
Abstract: Honeycomb structures are stiff and light weight composite structures which show good energy absorption. Hierarchical honeycomb structures which are bio-inspired innovative form of honeycomb structure, are used for energy absorption applications. The crashworthiness of hierarchical honeycomb structures is function of sub-structure geometry. In the present investigation, crushing of vertex based hierarchical honeycomb with regular polygonal (n=3, 6, 9, 12, 15) and circular substructures is analysed using nonlinear explicit finite element …
Published in International Journal of Structural Mechanics and Finite Elements · Vol. 7, Issue 2, 2021 · pp. 1–17 Read article
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3D Stress State Dependent Failure Model for Fracture Simulations
Abstract: The state of stress is defined by triaxiality (hydrostatic pressure) and the lode angle parameter (deviatoric stress third invariant). The stress state not only varies with different kinds of loading but also with the location of the stress concentration, geometry of specimen and deformation. The LS-DYNA implementation of a strain-based, stress state dependent failure criterion for a ductile material is investigated through simulations of standard experiments in this paper. A …
Published in International Journal of Structural Mechanics and Finite Elements · Vol. 2, Issue 1, 2016 · pp. 44–65 Read article
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Evaluation of Various Numerical Methods in Large Scale FE Simulation Codes for 3D Crack Propagation
Abstract: In this paper, four different numerical methods implemented in the large scale simulation code LS-DYNA are evaluated to determine their abilities and limitations in fracture problems especially 3-d crack propagation problems. Similar formulations are implemented in the code ABAQUS as well. These methods are: Finite Element Method (FEM), Discrete Element Method (DEM), Element Free Galerkin (EFG) method and Extended Finite Element Method (XFEM). Their methodologies are briefly described and several …
Published in International Journal of Structural Mechanics and Finite Elements · Vol. 2, Issue 2, 2016 · pp. 44–66 Read article
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Deep Reinforcement Learning-Based MPPT for PV Systems Under Partial Shading: A Hybrid Metaheuristic-Optimized Control Framework.
Abstract: Partial shade and quickly changing environmental conditions make Maximum Power Point Tracking (MPPT) in photovoltaic (PV) arrays particularly difficult. In these situations, traditional algorithms frequently fail to find the global optimum or experience sluggish convergence and steady-state oscillations. To obtain reliable, real-time MPPT under non-uniform irradiance, this research suggests a hybrid control architecture that combines a deep recurrent reinforcement learning (DRL) agent with metaheuristic-based parameter optimization and a digital-twin training …
Published in International Journal of Electrical Power System and Technology · Vol. 12, Issue 1, 2026 · pp. 35–46 Read article
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Deep Reinforcement Learning-Based MPPT for PV Systems Under Partial Shading: A HybridMetaheuristic-Optimized Control Framework.
Abstract: Partial shade and quickly changing environmental conditions make Maximum Power Point Tracking (MPPT) in photovoltaic (PV) arrays particularly difficult. In these situations, traditional algorithms frequently fail to find the global optimum or experience sluggish convergence and steady-state oscillations. To obtain reliable, real-time MPPT under non-uniform irradiance, this research suggests a hybrid control architecture that combines a deep recurrent reinforcement learning (DRL) agent with metaheuristic-based parameter optimization and a digital-twin training …
Published in International Journal of Electrical Power System and Technology · Vol. 12, Issue 1, 2026 Read article
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Machine Learning-Based Channel Estimation for 5G NR Systems: A Comprehensive Review of TDL/CDL Channel Models and CNN Architectures
Abstract: Accurate channel estimation is a fundamental requirement for reliable communications in fifth-generation New Radio (5G NR) systems, where highly dynamic propagation environments impose severe challenges on conventional pilot-based methods. This study provides a thorough analysis of channel estimate methods for 5G NR, including everything from contemporary deep learning (DL) techniques based on Convolutional Neural Networks (CNNs) to traditional Least Squares (LS) and Minimum Mean Square Error (MMSE) estimators. Our analysis …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 12, Issue 2, 2026 Read article
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AI-Driven Predictive Maintenance in Smart Homes: Enhancing Efficiency and Reliability
Abstract: The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has transformed smart homes into intelligent, self-regulating environments. Despite these advancements, ensuring the long-term reliability and efficiency of interconnected devices remains a complex challenge due to device heterogeneity, dynamic usage patterns, and continuous data streams. This study investigates AI-driven predictive maintenance (PdM) as a proactive strategy for detecting early signs of equipment degradation in residential settings. The proposed …
Published in International Journal of Distributed Computing and Technology · Vol. 12, Issue 1, 2026 · pp. 1–6 Read article