Results 41 to 50 of about 2,303 (197)

Benchmarking Coaxial and Angular Optical Emission Spectroscopy With Recommendations for Reliable Compositional In Situ Monitoring During Laser Powder Bed Fusion

Advanced Materials Technologies, EarlyView.
ABSTRACT Real‐time insight into local chemistry is critical for reliable part quality in additive manufacturing, especially laser powder bed fusion (PBF‑LB/M), where rapid thermal cycles and localized evaporation can undermine part performance. Optical emission spectroscopy (OES) offers non‑intrusive, in situ plume monitoring, but detection geometry ...
Philipp Gabriel, Bastian Jülich, Anna Rosa Ziefuss, Florian Eibl, Stephan Barcikowski   +4 more
wiley   +1 more source

Local Fuzzy Fractional Partial Differential Equations in the Realm of Fractal Calculus with Local Fractional Derivatives

Fractal and Fractional, 2023
In this study, local fuzzy fractional partial differential equations (LFFPDEs) are considered using a hybrid local fuzzy fractional approach. Fractal model behavior can be represented using fuzzy partial differential equations (PDEs) with local ...
Mawia Osman, Muhammad Marwan, Syed Omar Shah, Lamia Loudahi, Mahvish Samar, Ebrima Bittaye, Altyeb Mohammed Mustafa   +6 more
doaj   +1 more source

Dictionary‐based weak‐form training for noise‐robust series hybrid models with multiplicative unknowns

AIChE Journal, EarlyView.
ABSTRACT Hybrid modeling combines first‐principles equations with a data‐driven subcomponent. Training for the data‐driven part is sensitive to measurement noise when training targets are constructed using pointwise time derivatives. Beyond differentiation errors, hybrid models involve solving an inverse problem to estimate the data‐driven term, which ...
Hangjun Cho, Akshay Kudva, Parth Shah, Dongheon Lee, Joseph Sang‐Il Kwon   +4 more
wiley   +1 more source

Transfer learning for deep neural network-based partial differential equations solving

Advances in Aerodynamics, 2021
Deep neural networks (DNNs) have recently shown great potential in solving partial differential equations (PDEs). The success of neural network-based surrogate models is attributed to their ability to learn a rich set of solution-related features ...
Xinhai Chen, Chunye Gong, Qian Wan, Liang Deng, Yunbo Wan, Yang Liu, Bo Chen, Jie Liu   +7 more
doaj   +1 more source

On the summability of divergent power series solutions for certain first-order linear PDEs

Opuscula Mathematica, 2015
This article is concerned with the study of the Borel summability of divergent power series solutions for certain singular first-order linear partial differential equations of nilpotent type. Our main purpose is to obtain conditions which coefficients of equations should satisfy in order to ensure the Borel summability of divergent solutions.
openaire   +2 more sources

An Analytical Design Model of a Vacuum Pressure Swing Adsorption Process for Biogas Upgrading

Chemie Ingenieur Technik, EarlyView.
A steady‐state model was developed for a vacuum pressure swing adsorption (VPSA) process comprising adsorption, depressurization, and evacuation steps. The resulting analytical plant design model provides the optimal adsorber height required to achieve a target product purity with maximum bed utilization.
Thomas Welter, Jan Mackel, Niklas Döring, Hilario Vidal   +3 more
wiley   +1 more source

Solar and Wind Quantity 24 h—Series Prediction Using PDE-Modular Models Gradually Developed according to Spatial Pattern Similarity

Energies, 2023
The design and implementation of efficient photovoltaic (PV) plants and wind farms require a precise analysis and definition of specifics in the region of interest. Reliable Artificial Intelligence (AI) models can recognize long-term spatial and temporal
Ladislav Zjavka
doaj   +1 more source

Research progress and current status of dynamic wave propagation characteristics in rock mass: A review

Deep Underground Science and Engineering, EarlyView.
This review elucidates the velocity–dispersion–attenuation coupling mechanisms of wave propagation in rock masses, compares six representative models, and reveals how pressure, temperature, mineral composition, and anisotropy jointly control dynamic responses in complex geological media.
Jiajun Shu, Tao Li, Ruiqi Yuan, Yue Li, Bingni Wu, Bo Liu, Zhengding Deng, Galindo Rubén, Fausto Molina‐Gómez   +8 more
wiley   +1 more source

Numerical simulation by using the spectral collocation optimization method associated with Vieta-Lucas polynomials for a fractional model of non-Newtonian fluid

Results in Physics, 2022
The present study is made to develop the fractional model of non-Newtonian Casson and Williamson boundary layer flow in the fluid flow taking into account the heat flux and the slip velocity.
M. Adel, T.A. Assiri, M.M. Khader, M.S. Osman   +3 more
doaj   +1 more source

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