Results 101 to 110 of about 28,001 (222)

Complexity Analysis of Bubble Plumes in Power Law Fluids Based on Chaos Theory

Asia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT In order to reveal the complexity of the internal flow of bubble plume in power law fluid, the flow characteristics and chaotic characteristics of plume are studied by experiment and theory. The chaotic characteristic parameters (correlation dimension D, K entropy, and Lyapunov exponent λ) of gas velocity under different superficial gas ...
Xin Dong, Chong Dong, Can Xue, Meiheng Peng, Ying Feng, Jianwei Zhang, Yinuo Liu   +6 more
wiley   +1 more source

Enhanced Mass Transport and Elevated Active Site Density for Highly Efficient Overall Water Splitting Over Delignification‐Engineered Carbonized Wood

Carbon Energy, EarlyView.
Controlled delignification pretreatment tailors the hierarchical porous structure of wood to improve ion transport and create abundant anchoring sites for Ni/Ni(OH)2 catalysts. The optimized electrode shows enhanced mass transfer, increased active site density, and superior overall water‐splitting performance, demonstrating delignification as an ...
Ting He, Jiang Wei, Yakun Gao, Jianrong Zeng, Ziyun Xi, Hongling Qin, Qiuhong Lu, Jingjing Chen, Xin Feng, Xiaohua Lu, Libo Yao, Honglei Zhang, Lilong Zhang, Ge Meng, Lejin Xu, Jiahua Zhu   +15 more
wiley   +1 more source

Multiscale Empirical Interpolation for Solving Nonlinear PDEs using Generalized Multiscale Finite Element Methods

, 2014
In this paper, we propose a multiscale empirical interpolation method for solving nonlinear multiscale partial differential equations. The proposed method combines empirical interpolation techniques and local multiscale methods, such as the Generalized Multiscale Finite Element Method (GMsFEM).
Calo, Victor, Efendiev, Yalchin, Galvis, Juan, Ghommem, Mehdi   +3 more
openaire   +2 more sources

Machine Learning Paradigm for Advanced Battery Electrolyte Development

Carbon Energy, EarlyView.
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su, Mengfan Pei, Keyu Chen, Xigao Jian, Fangyuan Hu   +4 more
wiley   +1 more source

Invariant Measure and Universality of the 2D Yang–Mills Langevin Dynamic

Communications on Pure and Applied Mathematics, EarlyView.
ABSTRACT We prove that the Yang–Mills (YM) measure for the trivial principal bundle over the two‐dimensional torus, with any connected, compact structure group, is invariant for the associated renormalised Langevin dynamic. Our argument relies on a combination of regularity structures, lattice gauge‐fixing and Bourgain's method for invariant measures ...
Ilya Chevyrev, Hao Shen
wiley   +1 more source

Research progress and current status of dynamic wave propagation characteristics in rock mass: A review

Deep Underground Science and Engineering, EarlyView.
This review elucidates the velocity–dispersion–attenuation coupling mechanisms of wave propagation in rock masses, compares six representative models, and reveals how pressure, temperature, mineral composition, and anisotropy jointly control dynamic responses in complex geological media.
Jiajun Shu, Tao Li, Ruiqi Yuan, Yue Li, Bingni Wu, Bo Liu, Zhengding Deng, Galindo Rubén, Fausto Molina‐Gómez   +8 more
wiley   +1 more source

Computationally Driven Advances in Cu‐CNT On‐Chip Interconnect Materials: From First Principles to Machine Learning

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
A multiscale framework integrating electronic, mechanical, and thermal analysis with machine learning to optimize carbon nanotube interconnects. As the component dimensions in integrated circuits shrink to extreme scales, the complexity of interconnect systems is increasing significantly, necessitating an urgent and comprehensive upgrade of ...
Changhong Zhang, Jie Liang, Yi Li, Zhenyu Wang, Haoyuan Li, Zhen Yuan, Jiuren Zhou, Rongmei Chen, Wensheng Zhao, Lei Zhang, Hanshen Xin, Jianhua Zhang   +11 more
wiley   +1 more source

Prediction of Pipeline Defect Depth and Classification Based on CatBoost

Energy Science &Engineering, EarlyView.
Obtaining detection data using in‐line pipeline inspection, the synthetic minority oversampling technique (SMOTE) is applied to expand the sample set, thereby increasing the number of minority‐class samples. This approach effectively improves minority‐class detection and enhances pipeline safety assessment. ABSTRACT Magnetic flux leakage detection is a
Cong Chen, Rui Li, Guanwei Jia, Dongcun Qin   +3 more
wiley   +1 more source

Electricity Price Prediction Using Multikernel Gaussian Process Regression Combined With Kernel‐Based Support Vector Regression

Journal of Forecasting, EarlyView.
ABSTRACT This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on a combination of Gaussian process regression (GPR) and support vector regression (SVR). Although GPR is a competent model for learning stochastic patterns within data and for interpolation, its performance for out‐of‐sample data is not ...
Abhinav Das, Stephan Schlüter, Lorenz Schneider   +2 more
wiley   +1 more source

Benchmarking Hybrid CNN‐Transformer Versus Pure Transformer Architectures for Accelerated Hyperpolarized 129Xe MRI Reconstruction

Journal of Magnetic Resonance Imaging, EarlyView.
ABSTRACT Background Hyperpolarized 129Xe MRI faces technical challenges including low signal‐to‐noise ratio and breath‐hold constraints. Current literature focuses on proprietary deep learning methods or image‐domain enhancements. Purpose To present a comprehensive evaluation of transformer and hybrid CNN‐transformer architectures integrating dual ...
Ramtin Babaeipour, Matthew S. Fox, Grace Parraga, Alexei Ouriadov   +3 more
wiley   +1 more source