Results 181 to 190 of about 149,345 (346)

Impact of Uncertain Parameters on Navier–Stokes Equations With Heat Transfer via Polynomial Chaos Expansion

open access: yesInternational Journal for Numerical Methods in Fluids, EarlyView.
This study investigates the impact of uncertain parameters on Navier–Stokes equations coupled with heat transfer using the Intrusive Polynomial Chaos Method (IPCM). Sensitivity equations are formulated for key input parameters, such as viscosity and thermal diffusivity, and solved numerically using the Finite Element‐Volume method.
N. Nouaime   +3 more
wiley   +1 more source

Downdraft Devices for Negative Emissions—Quantification Study and Environmental Implication

open access: yesGreenhouse Gases: Science and Technology, EarlyView.
ABSTRACT Methane (CH4) is a potent greenhouse gas with a global warming potential far exceeding that of CO2 over short time horizons. Its removal from the atmosphere remains challenging due to its low ambient concentration and chemical stability. This study explores downdraft energy towers (DETs) as an innovative CH4 mitigation technology that enhances
Xiaokun Yao   +4 more
wiley   +1 more source

Heat Transfer Performance of Biphilic Surfaces With Diatomite‐Based Composite Coatings in a Closed Thermosyphon System

open access: yesHeat Transfer, EarlyView.
ABSTRACT This study investigates the thermal performance of innovative biphilic boiling surfaces designed for CPU cooling applications using a two‐phase closed thermosyphon (TPCT) system. The surfaces were fabricated by applying composite coatings—comprising epoxy resin and diatomite particles functionalized with APTES and PFOTS silanes— onto Al1050 ...
José Pereira   +3 more
wiley   +1 more source

Direct Numerical Simulation of Magnetohydrodynamic Slip‐Flow Past a Stretching Surface Using Physics‐Informed Neural Network

open access: yesHeat Transfer, EarlyView.
ABSTRACT Traditional numerical methods, such as finite difference methods (FDM), finite element methods (FEM), and spectral methods, often face meshing challenges and high computational cost for solving nonlinear coupled differential equations. Machine learning techniques, specifically Physics‐informed machine learning, address these obstacles by ...
Ahmad, Feroz Soomro, Husna Zafar
wiley   +1 more source

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