Results 171 to 180 of about 469,323 (348)

Design and Applications of Multi‐Frequency Programmable Metamaterials for Adaptive Stealth

Advanced Functional Materials, EarlyView.
This article provides a comprehensive overview of metamaterials, including their fundamental principles, properties, synthesis techniques, and applications in stealth, as well as their challenges and future prospects. It covers topics that are more advanced than those typically discussed in existing review articles, while still being closely connected ...
Jonathan Tersur Orasugh, Ankita Mohanty, Amit Malakar, Suryasarathi Bose, Suprakas Sinha Ray   +4 more
wiley   +1 more source

Computational Fluid Dynamics Simulations to Personalize Nasal Irrigations. [PDF]

J Pers Med
Radulesco T, Ebode D, Haddad R, Lechien JR, Meister L, Gargula S, Michel J.   +6 more
europepmc   +1 more source

Application of Computational Fluid Dynamics to Turbine Disc Cavities

, 1993
G. P. Virr, John W. Chew, Jeremy Coupland   +2 more
openalex   +1 more source

High-performance Java codes for computational fluid dynamics [PDF]

, 2001
Christopher J. Riley, Siddhartha Chatterjee, Rupak Biswas   +2 more
openalex   +1 more source

Microfluidic Synthesis of Channel‐Rich Pd‐Cu Alloy Nanodendrites for Efficient Electrocatalytic CO2 Reduction to Formate

Advanced Functional Materials, EarlyView.
A microfluidic system enables the rapid, room‐temperature fabrication of channel‐rich Pd‐Cu alloy nanodendrites with tunable composition, uniform morphology, and finely branched internal structures. The resulting catalysts exhibit over 90% formate selectivity across a broad potential window, along with excellent CO tolerance and enhanced long‐term ...
Xintong Huang, Xude Yu, Hengyuan Liu, Shunkai Xia, Yongqi Jia, Zhuo Chen, Jianhong Xu, Younan Xia   +7 more
wiley   +1 more source

Potential application of artificial concepts to aerodynamic simulation [PDF]

The concept of artificial intelligence as it applies to computational fluid dynamics simulation is investigated. How expert systems can be adapted to speed the numerical aerodynamic simulation process is also examined.
Andrews, A., Kutler, P., Mehta, U. B.
core   +1 more source

Optimization of ski jumping in-run posture using computational fluid dynamics. [PDF]

Sci Rep
Liu W, Lu F, Suo X, Tang W.
europepmc   +1 more source

A Computer Program for Three-Dimensional Time-Dependent Computational Fluid Dynamics

, 1993
L.L. Eyler, D.S. Trent, James A. Fort
openalex   +2 more sources

Reconstruction of carotid bifurcation hemodynamics and wall thickness using computational fluid dynamics and MRI [PDF]

, 2001
David A. Steinman, Jonathan B. Thomas, Hanif M. Ladak, Jaques S. Milner, Brian K. Rutt, J. David Spence   +5 more
openalex   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source