Numerical study of enclosure heat and gas species migration from cladding fires incorporating Artificial Neural Network

2021 ◽  
Author(s):  
Timothy Bo Yuan Chen ◽  
Anthony Chun Yin Yuen ◽  
Luzhe Liu ◽  
Guan Heng Yeoh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Numerical Study ◽  
Species Migration ◽  
Artificial Neural

An Artificial Neural Network (ANN) approach to extract micro-architectural properties of cortical bone using ultrasound attenuation: A numerical study

2019 ◽  
Vol 145 (3) ◽  
pp. 1860-1860
Author(s):  
Kaustav Mohanty ◽  
Omid Yousefian ◽  
Yasamin Karbalaeisadegh ◽  
Micah Ulrich ◽  
Marie M. Muller
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cortical Bone ◽  
Numerical Study ◽  
Ultrasound Attenuation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

scholarly journals Artificial neural network to estimate micro-architectural properties of cortical bone using ultrasonic attenuation: A 2-D numerical study

2019 ◽  
Vol 114 ◽  
pp. 103457 ◽  
Author(s):  
Kaustav Mohanty ◽  
Omid Yousefian ◽  
Yasamin Karbalaeisadegh ◽  
Micah Ulrich ◽  
Quentin Grimal ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cortical Bone ◽  
Ultrasonic Attenuation ◽  
Numerical Study ◽  
Artificial Neural

scholarly journals Analytically-Supported Hybrid Photonic-Plasmonic Crystal Design Using Artificial Neural Networks

2022 ◽  
Author(s):  
Jorge-Alberto Peralta-Ángeles ◽  
Jorge-Alejandro Reyes-Esq
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Numerical Study ◽  
Index Of Refraction ◽  
Square Lattice ◽  
High Symmetry ◽  
Plasmonic Crystals ◽  
Artificial Neural

Abstract An analytical and numerical study of hybrid photonic-plasmonic crystals is presented. The proposed theoretical model describes a system composed of a dielectric photonic crystal on a metallic thin film. To show the validity and usefulness of the model, four particular structures are analyzed, a one-dimensional crystal and three lattices of two-dimensional crystals. The model can calculate the photonic band structure of photonic-plasmonic crystals as a function of structural characteristics, showing two partial bandgaps for a square lattice, and complete bandgaps for triangular lattices. Furthermore, using a particular high-symmetry path, a full bandgap emerges in rectangular lattices, even with a small index of refraction contrast. Using the analytical model, a dataset is generated to train an artificial neural network to predict the center and width of the bandgap, that is, the forward design. In addition, an artificial neural network is trained to tune the optical response, that is, to perform the inverse design. The analytical results are consistent with the physics of the system studied and are supported by numerical simulations. Moreover, the prediction accuracy of the artificial neural networks is better than 95%. Overall, this paper reports a useful tool for tuning the optical properties of hybrid photonic-plasmonic crystals with potential applications in waveguides, nanocavities, mirrors, etc.

Study of mixed convection in a caterpillar wavy lid-driven triangular cavity filled with nanofluid using artificial neural network

2018 ◽  
Vol 96 (5) ◽  
pp. 476-493 ◽  
Author(s):  
Manoj Kr. Triveni ◽  
Rajsekhar Panua
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
Mixed Convection ◽  
Richardson Number ◽  
Volume Fraction ◽  
Numerical Study ◽  
Numerical Results ◽  
Grashof Number ◽  
Artificial Neural

The present numerical study is carried out for mixed convection in a nanofluid-filled lid-driven triangular cavity. The base wall of the cavity is in a caterpillar shape, which is assumed as a hot wall while the side and inclined walls are considered as cold walls. The finite volume method along with the SIMPLE algorithm is used to discretize the governing equations. The study is evaluated for constrained parameters, such as volume fraction of the nanoparticles, sliding direction of the side wall, Richardson number, and Grashof number. Fluid flow and heat transfer are presented in terms of streamlines and isotherms and rate of enhancement has been shown by local and average Nusselt number. It is observed from the study that the heat transfer rate is enhanced for each volume fraction of nanoparticles, for both directions of sliding wall, Richardson number, and Grashof number. The obtained numerical results are validated with the predicted results of artificial neural network (ANN). Good agreement is reported between the numerical results and the predicted results.

Numerical study on the blending of excellent anti-knock fuel using artificial neural network

Fuel ◽  
2022 ◽  
Vol 312 ◽  
pp. 122899
Author(s):  
Hongqing Feng ◽  
Zhisong Zhang ◽  
Ning Gao ◽  
Shuwen Xiao ◽  
Xuemeng Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Numerical Study ◽  
Artificial Neural

Estimation of wave reflection in aorta from radial pulse waveform by artificial neural network: a numerical study

2019 ◽  
Vol 182 ◽  
pp. 105064
Author(s):  
Hanguang Xiao ◽  
Lin Qi ◽  
Lisheng Xu ◽  
Decai Li ◽  
Bo Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wave Reflection ◽  
Numerical Study ◽  
Pulse Waveform ◽  
Radial Pulse ◽  
Artificial Neural

Analysis of the effect of roughness and concentration of Fe3O4/water nanofluid on the boiling heat transfer using the artificial neural network: An experimental and numerical study

2021 ◽  
Vol 163 ◽  
pp. 106863
Author(s):  
Yeping Peng ◽  
Milad Boroumand Ghahnaviyeh ◽  
Mohammad Nazir Ahmad ◽  
Ali Abdollahi ◽  
Seyed Amin Bagherzadeh ◽  
...  
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
Boiling Heat Transfer ◽  
Numerical Study ◽  
Artificial Neural
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