Results 161 to 170 of about 200,182 (303)

Dependency of the drag coefficient on boundary layer stability beneath drifting sea ice in the central Arctic Ocean. [PDF]

Sci Rep
Kawaguchi Y, Hoppmann M, Shirasawa K, Rabe B, Kuznetsov I.   +4 more
europepmc   +1 more source

A parabolic model of drag coefficient for storm surge simulation in the South China Sea. [PDF]

Sci Rep, 2015
Peng S, Li Y.
europepmc   +1 more source

ChatCFD: A Large Language Model‐Driven Agent for End‐to‐End Computational Fluid Dynamics Automation with Structured Knowledge and Reasoning

Advanced Intelligent Discovery, EarlyView.
Chat computational fluid dynamics (CFD) introduces an large language model (LLM)‐driven agent that automates OpenFOAM simulations end‐to‐end, attaining 82.1% execution success and 68.12% physical fidelity across 315 benchmarks—far surpassing prior systems.
E Fan, Kang Hu, Zhuowen Wu, Jiangyang Ge, Jiawei Miao, Yuzhi Zhang, He Sun, Weizong Wang, Tianhan Zhang   +8 more
wiley   +1 more source

Understanding the Importance of Drag Coefficient Assessment for a Deeper Insight into the Hydrodynamic Profile of Swimmers. [PDF]

J Hum Kinet
Morais JE, Marinho DA, Bartolomeu RF, Barbosa TM.   +3 more
europepmc   +1 more source

An experimental determination of the drag coefficient of a Mens 8+ racing shell. [PDF]

Springerplus, 2014
Buckmann JG, Harris SD.
europepmc   +1 more source

Evolution of Physical Intelligence Across Scales

Advanced Intelligent Discovery, EarlyView.
By following the evolution of physical intelligence across scales, this article shows how intelligence arises from materials, structures, physical interactions, and collectives. It establishes physical intelligence as the evolutionary foundation upon which embodied intelligence is built.
Ke Liu, Tianyun Huang, Aming Li, Pengyu Lv, Tingting Qin, Wenhui Chen, Hui Wei, Huiling Duan   +7 more
wiley   +1 more source

Determination of Density of Starch Hydrogel Microspheres from Sedimentation Experiments Using Non-Stokes Drag Coefficient. [PDF]

Gels
Cretella M, Fazilati M, Krcic N, Argatov I, Kocherbitov V.   +4 more
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Roughness length and drag coefficient at Anand

Journal of Agrometeorology, 2001
M. KUMAR, B.R.D. GUPTA, V. PANDEY, A.M. SHEKH, R.S. PARMAR   +4 more
doaj   +1 more source

Design, Control, and Clinical Applications of Magnetic Actuation Systems: Challenges and Opportunities

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This review aims to provide a broad understanding for interdisciplinary researchers in engineering and clinical applications. It addresses the development and control of magnetic actuation systems (MASs) in clinical surgeries and their revolutionary effects in multiple clinical applications.
Yingxin Huo, Lidong Yang, Tiantian Xu, Dong Sun   +3 more
wiley   +1 more source