Results 161 to 170 of about 44,636 (300)

Toward Knowledge‐Based Workflows: A Semantic Approach to Atomistic Simulations for Mechanical and Thermodynamic Properties

Advanced Engineering Materials, EarlyView.
Knowledge‐based atomistic workflows are presented for mechanical and thermodynamic properties. By coupling modular simulations with ontology‐aligned metadata and provenance, Fe case studies on elastic behavior, defects, thermal properties, and Hall–Petch strengthening reveal how FAIR, queryable, and reusable simulation data can be generated. Mechanical
Abril Azócar Guzmán, Hoang‐Thien Luu, Sarath Menon, Tilmann Hickel, Nina Merkert, Stefan Sandfeld   +5 more
wiley   +1 more source

Nondestructive Testing of Welded Composite Metal Foams

Advanced Engineering Materials, EarlyView.
X‐ray computed tomography (CT) is used to evaluate welded steel–steel composite metal foam (CMF) joints of two density classes. It reports variation in postweld spatial void distribution and correlates it to weld‐induced changes, mechanical performance, and failure within welded CMF panels.
Chinmaya Prerana Inguva, Aman Kaushik, Afsaneh Rabiei   +2 more
wiley   +1 more source

Encyclopedia of 2D β′‐In2Se3 Growth Using Chemical Vapor Deposition: The Effects of Synthesis Parameters Onto Material Quality

Advanced Engineering Materials, EarlyView.
A distinct semi‐confined inner‐tube chemical vapor deposition geometry enables reproducible, large‐area growth of phase‐pure 2D β′‐In2Se3 from InI + Se precursors. Engineering local vapor transport and optimizing precursor delivery and temperature–time conditions yield uniform continuous films.
Dasun P. W. Guruge, P. Sanju S. Panawala, Joseph F. S. Fernando, Zhenhuan Chen, Chao Zhang, Dong‐Chen Qi, Liangzhi Kou, Konstantin L. Firestein, Dmitri V. Golberg   +8 more
wiley   +1 more source

Integrating Uncertainty Quantification into Computational Fluid Dynamics Models of Coronary Arteries Under Steady Flow. [PDF]

J Biomech Eng
Usman M, Castillo PN, Narayan A, Timmins LH.   +3 more
europepmc   +1 more source

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

Advanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran, Vincent Nebel, Dominik Perius, Kshitij Kumar, Yannis Heim, Laura Bies, Tobias Kraus   +6 more
wiley   +1 more source

A framework for quantifying and leveraging uncertainty in pre-trained CT denoising model. [PDF]

IEEE Trans Biomed Eng
Gong H, Huber NR, Kharat SA, Fletcher JG, Koo CW, Yu L, Leng S, Hsieh SS, McCollough CH.   +8 more
europepmc   +1 more source

Uncertainty Quantification and Reduction of Molecular Dynamics Models

, 2017
Xiaowang Zhou, Stephen M. Foiles
openaire   +2 more sources

Quantification and reduction of quantitative groundwater management models uncertainties

, 2017
The sustainable management of aquifers is a growing problem since the end of the 20th century. For groundwater withdrawals to be considered as sustainable, the capture of environmental flow should remain acceptable over a long-term period. Groundwater modeling is an essential tool to move from a reactive management to an anticipatory approach ...
openaire   +1 more source

Influence of Geometric Design on Mechanical Performance of Auxetic Metastructure

Advanced Engineering Materials, EarlyView.
Strategic geometric reinforcement transforms auxetic performance. This study evaluates 3D‐printed arrowhead metastructures, revealing that a modified design with local ring reinforcement suppresses premature failure to achieve superior energy absorption and structural efficiency.
Muhammad Gulzari, Supharoek Sornsomrit, Muhammad Ayaz Akbar, Stephan Rudykh   +3 more
wiley   +1 more source

Reliability-based mix design for concrete compressive strength using a physics-prior residual-learning surrogate with calibrated uncertainty. [PDF]

PLoS One
Qu P, Song L, Wang S.
europepmc   +1 more source