Results 211 to 220 of about 3,908,903 (353)

Detection of Gas Pipeline Leakage Using Distributed Optical Fiber Sensors: Multi-Physics Analysis of Leakage-Fiber Coupling Mechanism in Soil Environment. [PDF]

Sensors (Basel), 2023
Zhang S, Xie S, Li Y, Yuan M, Qian X.
europepmc   +1 more source

Atomistic Insights Into Lithium Alloying and Crystallization at Metal Interlayers in Zero‐Excess Lithium Batteries

Advanced Energy Materials, EarlyView.
Molecular dynamics simulations with machine learning potentials, combined with experiments, reveal how interlayer metals govern Li alloying and crystallization in zero‐excess lithium batteries. Mg and Zn promote solid‐solution alloy‐mediated pathways that influence Li diffusion and structural uniformity, while Bi forms ordered intermetallics with more ...
Neubi F. Xavier Jr., Nicolas Rospars, Mohammed Srout, Matthew K. Sharpe, Pablo Hernández‐Martín, Callum D. McAleese, Satheesh Krishnamurthy, Tao Chen, Celia Polop, Andrea Ingenito, Qiong Cai   +10 more
wiley   +1 more source

Effect of microstructure on hydrogen permeation and trapping in natural gas pipeline steels. [PDF]

Npj Mater Degrad
Islam A, Li Q, Storimans E, Ton K, Alam T, Farhat ZN.   +5 more
europepmc   +1 more source

A Semi Empirical Regression Model for Critical Dent Depth of Externally Corroded X65 Gas Pipeline. [PDF]

Materials (Basel), 2022
Yang Y, Liu X, Yang H, Fang W, Chen P, Li R, Gao H, Zhang H.   +7 more
europepmc   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Numerical Simulation of Hydrogen Mixing Process in T-Junction Natural Gas Pipeline. [PDF]

Materials (Basel)
Tian Y, Tian T, Ren G, Zhang J.
europepmc   +1 more source

Identification of the Fracture Process in Gas Pipeline Steel Based on the Analysis of AE Signals. [PDF]

Materials (Basel), 2022
Świt G, Dzioba I, Adamczak-Bugno A, Krampikowska A.   +3 more
europepmc   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

Advanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source

Data-Driven Flow Control for Natural Gas Pipeline Networks: Optimizing via Anomaly Detection and Residual Weight Coefficients. [PDF]

ACS Omega
Chen W, Bian R, Xu F, Yao B.
europepmc   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source