The paper deals with an approximate method for calculating elastic-plastic stresses and strains on the surface of notched samples. The method is based on the Abdel-Karim-Ohno cyclic plasticity model. The plane stress condition is considered within the evaluation. The output of the approximation on several multiaxial axial-torsion load paths is compared to our own experimental results. Experiments were carried out on samples of two notch types manufactured from the 2124-T851 aluminum alloy. Strain distribution in the notch area was measured by digital image correlation. The comparison between computational solution and measured response shows that the new method allows for obtaining reasonably good approximation, even for relatively complicated multiaxial load cases.
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http://dx.doi.org/10.3390/ma15041432 | DOI Listing |
Sci Rep
January 2025
Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Diffusion MRI is a leading method to non-invasively characterise brain tissue microstructure across multiple domains and scales. Diffusion-weighted steady-state free precession (DW-SSFP) is an established imaging sequence for post-mortem MRI, addressing the challenging imaging environment of fixed tissue with short T and low diffusivities. However, a current limitation of DW-SSFP is signal interpretation: it is not clear what diffusion 'regime' the sequence probes and therefore its potential to characterise tissue microstructure.
View Article and Find Full Text PDFCommun Eng
January 2025
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
Large-scale optimal design problems involving nonlinear differential equations are widely applied in modeling such as craft manufacturing, chemical engineering and energy engineering. Herein we propose a fast and flexible holomorphic embedding-based method to solve nonlinear differential equations quickly, and further apply it to handle the industrial application of reverse osmosis desalination. Without solving nonlinear differential equations at each discrete point by a traditional small-step iteration approach, the proposed method determines the solution through an approximation function or approximant within segmented computational domain independently.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Industrial Engineering/Graduate School of Data Science/Research Center for Electrical and Information Science, Seoul National University of Science and Technology, Seoul, South Korea.
Electric load forecasting is crucial in the planning and operating electric power companies. It has evolved from statistical methods to artificial intelligence-based techniques that use machine learning models. In this study, we investigate short-term load forecasting (STLF) for large-scale electricity usage datasets.
View Article and Find Full Text PDFISA Trans
January 2025
School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China. Electronic address:
This study endeavors to develop a predefined-time adaptive neural network decentralized controller for large-scale interconnected nonlinear systems with input hysteresis. Within the framework of the backstepping technique, the proposed control scheme guarantees that the tracking error converges to a small bounded set within a predefined settling time. The upper limit of this convergence time is determined by a single adjustable control parameter.
View Article and Find Full Text PDFNeural Netw
January 2025
Department of Mathematics, Southern Methodist University, Dallas, 75275, TX, USA. Electronic address:
In this paper, we derive diffusion equation models in the spectral domain to study the evolution of the training error of two-layer multiscale deep neural networks (MscaleDNN) (Cai and Xu, 2019; Liu et al., 2020), which is designed to reduce the spectral bias of fully connected deep neural networks in approximating oscillatory functions. The diffusion models are obtained from the spectral form of the error equation of the MscaleDNN, derived with a neural tangent kernel approach and gradient descent training and a sine activation function, assuming a vanishing learning rate and infinite network width and domain size.
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