This study presents a novel strain-based rectangular plate finite element, denoted as SBRPFE20, developed for the accurate analysis of thin plate bending problems. The formulation is established within the framework of Kirchhoff plate theory and incorporates a coupled strain-based membrane–bending approach to effectively capture the interaction between bending and in-plane responses, particularly in functionally graded materials. Unlike existing strain-based plate elements, the proposed formulation is specifically developed to accurately model functionally graded material (FGM) plates, accounting for the continuous variation of material properties through the thickness. The proposed element employs four corner nodes with essential kinematic degrees of freedom and is designed to achieve high accuracy while maintaining computational efficiency. The performance of the SBRPFE20 element is systematically assessed through a comprehensive set of numerical benchmarks, including static bending and free vibration analyses of isotropic and functionally graded plates under various loading conditions, boundary configurations, and geometrical complexities. The numerical results demonstrate that the proposed element exhibits rapid and smooth convergence, excellent numerical stability, and superior accuracy compared with well-established finite elements available in the literature. The formulation demonstrates a locking-free behavior, particularly in the thin plate limit, and does not exhibit spurious stiffness effects, as confirmed through convergence studies and mesh distortion analyses. Owing to its robustness, efficiency, and accuracy, the SBRPFE20 element provides a reliable and effective tool for the static and dynamic analysis of thin isotropic and functionally graded plates.
This study investigates the development of sustainable sandwich composites with bio-based cores and natural fiber face sheets, utilizing regionally sourced natural cellulose fibers to enhance sustainability and mechanical performance. Hybrid face sheets were fabricated from epoxy reinforced with unidirectional cellulose fibers from Typha latifolia stem and Agave americana leaf, extracted via chemical and mechanical methods, respectively. The cores were 3D-printed using neat polylactic acid (PLA) and a PLA/flax cellulose fiber biocomposite filament in three geometries (honeycomb, circular, tri-grid). Comprehensive characterization, including FTIR and TGA, confirmed the composition and thermal stability of the extracted cellulose fibers. Mechanical testing revealed that the tri-grid core geometry significantly outperformed other designs, with PLA/flax cores achieving a core shear strength of 5.27 MPa and a flexural modulus of 1.53 GPa. Notably, PLA/flax cellulose fiber cores exhibited a 131
This study is devoted to the preparation of a Ni–Al alloy (32 at.
Accurate fault diagnosis in photovoltaic (PV) systems is essential for ensuring reliable energy generation and system stability. This study presents a novel, lightweight fault diagnosis framework optimized for edge device deployment. The core innovation lies in a newly developed Adaptive Channel-wise (ACW) representation technique that automatically selects the most discriminative 2D transformation for each individual signal channel, addressing the limitations of fixed transformation methods such as Continuous Wavelet Transform (CWT), Gramian Angular Field (GAF), Markov Transition Field (MTF), and Hilbert-Huang Transform (HHT). Feature extraction is performed using pretrained backbones originally trained on large-scale image datasets. The ACW method is comprehensively benchmarked across multiple deep learning backbones including VGG16, InceptionV3, ResNet50, DenseNet121, NASNetMobile, and MobileNetV1. To the best of our knowledge, this is the first PV fault diagnosis framework to use automatic channel-wise selection of 2D transformations, bench-marked extensively on multiple deep learning backbones and validated for robust practical edge deployment. Experimental results on multi-class PV fault scenarios demonstrate high classification accuracy of 99.40% with extremely fast inference time (133 mu s). The proposed framework exhibits robustness under challenging real-world conditions such as noisy signals, sensor drop scenarios, and unbalanced fault distributions. These enhancements make the method particularly suitable for resource-constrained edge devices. While promising, some limitations remain, revealing directions for future research and practical adoption.
We investigated Bi-alloyed TiO2-based systems, Y1-xBixTiO(2) (Y = Ga, Al, In; x = 0-1), using HSE06 + SOC calculations to correlate bulk electronic tuning with surface H2O adsorption on anatase TiO2(101). Bi incorporation induces systematic lattice expansion and significant bandgap narrowing across all alloy families, suggesting a transition from wide-gap to visible-light-active compositions. Molecular H2O adsorption on anatase (101) becomes progressively stronger with increasing Bi content, with adsorption energies reaching - 0.938 eV at x = 1. Defect binding energies show a non-monotonic dependence on composition and are maximized near x approximate to 0.50, indicating enhanced stabilization of vacancy-related configurations at intermediate Bi contents. Surface descriptor analysis further indicates reduced work functions and increased charge transfer upon adsorption, while oxygen vacancies significantly amplify these effects and strengthen water-surface interaction. The combined trends suggest intermediate Bi concentrations as favorable for balancing defect stabilization and catalytic surface activation.
