Results 81 to 90 of about 11,374,889 (228)

Multi‐factor coupling effects in hydraulic fracturing of laminated shale: Experimental insights and physics‐informed neural network‐driven optimization

Deep Underground Science and Engineering, EarlyView.
This study establishes a multi‐factor coupling framework for predicting breakdown pressure in laminated shale by integrating experimental hydraulic fracturing tests, physics‐informed neural networks (PINNs), and Sobol sensitivity analysis. It reveals how differential stress, the bedding dip angle, and the injection rate interact to influence fracture ...
Tao Wang, Guoyao Xu, Liyuan Liu, Yao Zhang, Sitao Zhu, Hongyang Chu, Kaikai Zhao   +6 more
wiley   +1 more source

The matching of a “one-dimensional” numerical simulation and experiment results for low viscosity Newtonian and non-Newtonian fluids during fast filament stretching and subsequent break-up [PDF]

, 2012
This paper develops a model for fast filament stretching, thinning, and break-up for Newtonian and non-Newtonian fluids, and the results are compared against experimental data where fast filament relaxation occurs. A 1D approximation was coupled with the

core   +2 more sources

Charting Water‐in‐Natural‐Salt (WiNS) Electrolytes. Part I: Effect of Anion's Alkyl Tail Length

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
At 20 m, potassium carboxylates from formate to butyrate reveal anion tail‐controlled nanostructure. SAXS shows a pre‐peak shifting to lower q with chain length. Molecular dynamics reproduces S(q) and attributes it to concentration fluctuations forming bicontinuous segregation.
Alessandra Del Giudice, Laura Hildebrandt, Jin Han, Radhika Krishna Hema, Luciano Galantini, Stefano Passerini, Alberto Varzi, Alessandro Mariani   +7 more
wiley   +1 more source

AC Frequency Tuned Transverse Migration of Particles and Cells in a Rectangular Microchannel

ELECTROPHORESIS, EarlyView.
We observe, in the absence of hydrodynamic flow, an unprecedented transition of particle migration from center‐directed at low frequencies to wall‐directed at high frequencies in a straight rectangular microchannel subjected to AC electric fields. ABSTRACT Recent studies reported a wall‐repulsion of dielectric particles during alternating current (AC ...
Seyed Mojtaba Tabarhoseini, Mahmud Kamal Raihan, Walter Johnson, Megan Hardwick, Aidan Patterson, Tzuen‐Rong Tzeng, Caglar Elbuken, Hui Zhao, Xiangchun Xuan   +8 more
wiley   +1 more source

A Non-Newtonian Extension of Laplace–Sumudu–Elzaki Transforms

Mathematics
Classical Laplace-, Sumudu-, and Elzaki-type transforms are formulated within additive analytical frameworks and do not naturally accommodate multiplicative scaling structures arising in non-Newtonian calculus.
Numan Yalcin
doaj   +1 more source

Comparative Analysis of Al2O3/Water–TiO2/Chlorophyll Hybrid Nanofluids in a Photovoltaic–Thermal Solar Water Heating System

Energy Science &Engineering, EarlyView.
TiO2/chlorophyll bio‐hybrid nanofluid achieved the highest PVT performance, increasing thermal efficiency to 38.74% and overall efficiency to 51.35%, compared with 35.96% and 48.45% for Al2O3/water and 27.77% and 40.18% for water. The results demonstrate the potential of bio‐hybrid nanofluids for high‐efficiency solar thermal energy conversion ...
Oluwaseyi Omotayo Alabi, Oluwatoyin Joseph Gbadeyan, Oludolapo Akanni Olanrewaju   +2 more
wiley   +1 more source

Blending of Newtonian and Shear-Thinning Fluids in a Tank Stirred with a Helical Screw Agitator [PDF]

, 2000
Newtonian and non-Newtonian laminar fluid flow has been simulated using Computational Fluid Dynamics for a cylindrical vessel stirred by a helical screw agitator.
Aubin, Joelle, Xuereb, Catherine, Naude, Isabelle, Bertrand, Joël   +3 more
core   +1 more source

Machine Learning Modeling of Drill Cuttings Transport Based on Computational Fluid Dynamics Simulation Data

Energy Science &Engineering, EarlyView.
Machine learning surrogates accurately predict drill cuttings transport from computational fluid dynamics data, with Reynolds number dominating system behavior. While tree‐based models excel in accuracy, Gaussian process regression provides physically consistent, smooth predictions essential for reliable drilling simulations.
Kathrin Skinder, Mostafa M. Abdelhafiz, Khizar Shahid, Gunther Brenner   +3 more
wiley   +1 more source

Artificial Neural Networks as a Natural Tool in Solution of Variational Problems in Hydrodynamics

IEEE Access
Artificial neural networks are a powerful tool for spatial and temporal functions approximation. This study introduces a novel approach for modeling non-Newtonian fluid flows by minimizing a proposed power loss metric, which aligns with the variational ...
Ivan Stebakov, Alexei Kornaev, Elena Kornaeva, Nikita Litvinenko, Yuri Kazakov, Oleg Ivanov, Bulat Ibragimov   +6 more
doaj   +1 more source

Convergence and Residual Error Analysis of Unsteady MHD Stagnation Flow Over a Cylindrical Surface With Streamline and Isotherm Visualization

Energy Science &Engineering, EarlyView.
The novelty of the current study is that the unsteady MHD Cross nanofluid model is, for the first time, coupled with the melting heat transfer at the stagnation point over a horizontally extended cylinder in a Darcy–Forchheimer porous medium. This work connects complex rheological behaviors and phase‐change phenomena in a unique way, with the only ...
Fahad Maqbul Alamrani, Ebrahem A. Algehyne, Mounirah Areshi, Muhammad Bilal, Mustafa Bayram, Jihad Younis, Anwar Saeed   +6 more
wiley   +1 more source