Results 51 to 60 of about 18,248 (264)

Modeling Dislocation Cutting of γ′ Precipitates in Ni‐Base Superalloys: Linking Atomistic and Dislocation Dynamics Simulations

Advanced Engineering Materials, EarlyView.
Dislocation cutting of γ′ precipitates in Ni‐based superalloys is investigated by linking atomistic simulations with discrete dislocation dynamics. The critical cutting stress is shown to be governed by the antiphase boundary energy, while line tension effects promote edge‐preferred cutting.
Frédéric Houllé, Siwen Gao, Marc Fivel, Anxin Ma, Daria Smirnova, Sriram Anand, Arun Prakash, Julien Guénolé, Alexander Hartmaier, Erik Bitzek   +9 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

Numerical Study of Non-Newtonian Flow Through Rectangular Microchannels [PDF]

Iranian Journal of Chemical Engineering, 2009
A numerical investigation was carried out to solve the flow dimensionless partial differential equations through rectangular microchannels. A purely viscous power law model was used to characterize the flow behavior of non-Newtonian fluids.
doaj  

Simplified Pressure Drop Model for Laminar Helical Flow of Power Law Fluid

Shiyou jixie, 2020
The theoretical models for predicting the pressure drop of the laminar helical flow of power-law fluids require tedious and complicated numerical calculations, which are difficult to directly apply to the field.
Zhai Kejun, Yu Yang, He Lang, Tang Ming
doaj  

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

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

Advanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle, Cesar Alvarez‐Llamas, Vincent Motto‐Ros, Christophe Dujardin, Jérémie Margueritat, David Rodney   +5 more
wiley   +1 more source

Model‐Guided Design of Paper Membrane Architectures for Controlled Flow and Enhanced Diagnostic Sensitivity

Advanced Engineering Materials, EarlyView.
Geometrical modifications on nitrocellulose membranes can result in observable differences in signal intensity relative to a reference membrane (top and bottom left). Using two reference experiments, we have developed a theoretical model that can reproduce the experimentally observed flow changes and provide information on membrane parameters, e.g ...
Maria Dimaki, Lea Holritz, Winnie E. Svendsen   +2 more
wiley   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

A parametric study to simulate the non‐Newtonian turbulent flow in spiral tubes

Energy Science & Engineering, 2020
Non‐Newtonian fluids are considered to those types of fluids that do not follow Newton's law of viscosity where viscosity would change in either more solid or liquid.
Kamran Valizadeh, Soroush Farahbakhsh, Amir Bateni, Amirhossein Zargarian, Afshin Davarpanah, Araz Alizadeh, Mojtaba Zarei   +6 more
doaj   +1 more source

Three‐Dimensional Anomalous Hall Sensing Enabled By Spin–Orbit Torque Driven Domain Reconfiguration In Chiral Multilayers

Advanced Functional Materials, EarlyView.
A three‐dimensional magnetic field sensor is realized in chiral W/CoFeB/MgO multilayers, where spin–orbit torques reversibly reconfigure homochiral stripe domains. The symmetry of the torque enables offset‐free in‐plane sensing, while the reproducible domain reconfiguration extends the linear out‐of‐plane range. An anomalous Hall readout delivers large‐
Sabri Koraltan, Rahul Gupta, Reshma Peremadathil Pradeep, Fabian Kammerbauer, Iryna Kononenko, Klemens Prügl, Michael Kirsch, Bernd Aichner, Santiago Helbig, Florian Bruckner, Claas Abert, Andrada Oana Mandru, Armin Satz, Gerhard Jakob, Hans Josef Hug, Mathias Kläui, Dieter Suess   +16 more
wiley   +1 more source