scholarly journals An extended one-dimensional arterial network model for the simulation of pressure and flow in upper and lower limb extremities

2018 ◽  
Vol 10 (1) ◽  
pp. 119
Author(s):  
H. Obeid ◽  
N. Stergiopulos ◽  
P. Boutouyrie ◽  
M. Hallab ◽  
P. Segers
Keyword(s):  
Network Model ◽  
Lower Limb ◽  
One Dimensional ◽  
Arterial Network

[PP.09.30] AN EXTENDED ONE-DIMENSIONAL ARTERIAL NETWORK MODEL FOR THE SIMULATION OF PRESSURE AND FLOW IN UPPER AND LOWER LIMB EXTREMITIES

2017 ◽  
Vol 35 ◽  
pp. e157
Author(s):  
H. Obeid ◽  
N. Stergiopulos ◽  
M. Hallab ◽  
P. Boutouyrie ◽  
S. Laurent ◽  
...  
Keyword(s):  
Network Model ◽  
Lower Limb ◽  
One Dimensional ◽  
Arterial Network

15.8 AN EXTENDED ONE-DIMENSIONAL ARTERIAL NETWORK MODEL FOR THE SIMULATION OF PRESSURE AND FLOW IN UPPER AND LOWER LIMB EXTREMITIES

2016 ◽  
Vol 16 (C) ◽  
pp. 87
Author(s):  
Hasan Obeid ◽  
Patrick Segers ◽  
Nikos Stergiopulos ◽  
Pierre Boutouyrie ◽  
Stephane Laurent ◽  
...  
Keyword(s):  
Network Model ◽  
Lower Limb ◽  
One Dimensional ◽  
Arterial Network

scholarly journals An Anatomically Detailed Arterial Network Model for One-Dimensional Computational Hemodynamics

2015 ◽  
Vol 62 (2) ◽  
pp. 736-753 ◽  
Author(s):  
Pablo J. Blanco ◽  
Sansuke M. Watanabe ◽  
Marco Aurelio R. F. Passos ◽  
Pedro A. Lemos ◽  
Raul A. Feijoo
Keyword(s):  
Network Model ◽  
Computational Hemodynamics ◽  
One Dimensional ◽  
Arterial Network

scholarly journals A one-dimensional arterial network model for bypass graft assessment

2017 ◽  
Vol 43 ◽  
pp. 39-47 ◽  
Author(s):  
A.R. Ghigo ◽  
S. Abou Taam ◽  
X. Wang ◽  
P-Y Lagrée ◽  
J-M Fullana
Keyword(s):  
Network Model ◽  
Bypass Graft ◽  
One Dimensional ◽  
Arterial Network

RADIAL BASIS PROBABILISTIC NEURAL NETWORKS: MODEL AND APPLICATION

1999 ◽  
Vol 13 (07) ◽  
pp. 1083-1101 ◽  
Author(s):  
DE-SHUANG HUANG
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Probabilistic Neural Network ◽  
Radial Basis Function Networks ◽  
Probabilistic Neural Networks ◽  
Huge Amount ◽  
Training Time ◽  
One Dimensional ◽  
Radial Basis

This paper investigates the capabilities of radial basis function networks (RBFN) and kernel neural networks (KNN), i.e. a specific probabilistic neural networks (PNN), and studies their similarities and differences. In order to avoid the huge amount of hidden units of the KNNs (or PNNs) and reduce the training time for the RBFNs, this paper proposes a new feedforward neural network model referred to as radial basis probabilistic neural network (RBPNN). This new network model inherits the merits of the two old odels to a great extent, and avoids their defects in some ways. Finally, we apply this new RBPNN to the recognition of one-dimensional cross-images of radar targets (five kinds of aircrafts), and the experimental results are given and discussed.

P140 ARTERIAL WAVE DYNAMICS IN THE HORSE: INSIGHTS OBTAINED FROM A 1D ARTERIAL NETWORK MODEL SIMULATION

2018 ◽  
Vol 24 (C) ◽  
pp. 120
Author(s):  
Daimé Campos Arias ◽  
Lisse Vera ◽  
Sofie Muylle ◽  
Nikos Stergiopulos ◽  
Gunther van Loon ◽  
...  
Keyword(s):  
Network Model ◽  
Model Simulation ◽  
Wave Dynamics ◽  
Arterial Network

Theoretical and Computational Analysis of an Auxiliary Power Unit (APU) Installation Cooling System

2012 ◽  
Author(s):  
Parthiv N. Shah ◽  
Tricia Waniewski Sur ◽  
R. Scott Miskovish ◽  
Albert Robinson
Keyword(s):  
Network Model ◽  
Cooling System ◽  
Dimensional Model ◽  
Ambient Conditions ◽  
Modeling Framework ◽  
Flow Network ◽  
One Dimensional ◽  
Dimensional Flow ◽  
System Pressure ◽  
The One

This paper presents a theoretical one-dimensional model and computational fluid dynamics (CFD) simulations of a tailcone-installed APU cooling system. The work is motivated by the need to deliver sufficient cooling airflow to critical components within an aircraft tailcone compartment. The cooling system considered herein utilizes (1) an eductor system at the APU exhaust and (2) a ram air scoop near an upstream inlet to the compartment to induce the necessary cooling flow during ground and in-flight APU operation. A one-dimensional flow network model provides a framework for the quantification and matching of eductor pumping and system pressure drop characteristics. Detailed CFD models that simulate internal tailcone compartment flows driven by ambient conditions external to the aircraft in ground or flight operation support the one-dimensional model and are used to characterize component performance and assess different scoop and eductor designs. The one-dimensional flow network model is calibrated to the CFD results to predict system cooling performance under known APU loads at points on the ground and in the flight envelope. The agreement between the models is encouraging and suggests the modeling framework and CFD techniques discussed will be applicable to future designs and improvements of eductor-driven aircraft compartment cooling systems.

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