Results 81 to 90 of about 4,323,986 (291)

Calculation of Heat Transfer Coefficient of Heavy Rail Steel in Quenching Process Based on Finite Element Method

Teshugang, 2016
A method to calculate the heat transfer coefficient is proposed by using finite element software combined with experimental calculation that is based on measured temperature curves, carrying out the nonlinear coupling on various factors which have ...
陈林, 王慧军, 陈昆宇, 刘志敏
doaj  

Effects of carbon nanotube concentration on heat transfer characteristics in turbulent mixtures

International Journal of Thermofluids
This study presents numerical simulations modeling convective heat transfer in turbulent flows of carbon nanotube (CNT) mixtures suspended in water. The RNG k-ε turbulence model is employed and simulations are performed using the FLUENT software.
Javad Zareei, John William Grimaldo Guerrero, Shoira Formanova, KDV Prasad, Sabir Tagelsir Hassan Widatalla   +4 more
doaj   +1 more source

Calculation of heat transfer coefficients

Metalurgija, 2019
In forced-convection furnaces for reheating Al-alloys, convective heat transfer mechanism dominates. Al-body temperature prediction model uses measured furnace temperature as boundary condition. To calibrate such model, a convective heat transfer coefficient h is to be determined.
F. Vode, F. Tehovnik, B. Arh, S. Malej, B. Podgornik   +4 more
openaire   +2 more sources

Modular Critical Element Recycling Platform Using a Nanoporous Additively Manufactured Gyroid

Advanced Engineering Materials, EarlyView.
A modular recycling platform integrates 3D‐printed nanoporous gyroid structures to enable efficient critical element recovery. This system utilizes a hierarchical architecture, combining macroscopic channels with polymerization‐induced nanoscale porosity. By systematically tuning structural wall thickness and resin formulation, the platform achieves an
Xiangyu Gao, Keldy Mason, Mert Gülçür, Rachel Paddock‐Lamb, Prince Alvin Fofanah, Zachariah A. Page, Michael Cullinan   +6 more
wiley   +1 more source

The calculus simulation to predict reliably heat transfer coefficient

Case Studies in Thermal Engineering
Strengthening heat transfer for energy conservation challenges current longer times and higher experimental costs on laboratory scale test, pilot scale test, industrialized production.
Kang Dai, Lu Qin, Yanliu Guo, Yunjing Liang, Yannan Yang, Dan Zhao, Xincai Xiao   +6 more
doaj   +1 more source

Workflow for Design of Experiments‐Based Modeling of Species Transport and Growth Kinetics in GaN Hydride Vapor Phase Epitaxy

Advanced Engineering Materials, EarlyView.
A novel workflow for investigating hydride vapor phase epitaxy for GaN bulk crystal growth is proposed. It combines Design of experiments (DoE) with physical simulations of mass transport and crystal growth kinetics, serving as an intermediate step between DoE and experiments.
J. Tomkovič, G. Lukin, H. Torkashvand, A. Kerschbaum, M. Müller, S. Besendörfer, J. Heitmann, J. Friedrich   +7 more
wiley   +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

Inverse Identification of Energy‐Dependent Laser Absorptivity in NiTi Laser Powder‐Bed Fusion via Calibrated Melt Pool Simulation

Advanced Engineering Materials, EarlyView.
A combined experimental–computational framework identifies energy‐dependent laser absorptivity for NiTi in laser powder‐bed fusion, applicable to conduction and transition modes. Single‐track experiments and thermofluid smoothed particle hydrodynamics simulations are coupled through inverse analysis of melt pool geometry.
Mohamadreza Afrasiabi, Hadi Moutablaleh, Ignatius A. Setiawan, Mehrshad Mehrpouya   +3 more
wiley   +1 more source

Using artificial intelligence algorithms to reconstruct the heat transfer coefficient during heat conduction modeling. [PDF]

Sci Rep, 2023
Gawronska E, Zych M, Dyja R, Domek G.
europepmc   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source