loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Osama Sabir 1 and T. M. Y. S. Tuan Ya 2

Affiliations: 1 Universiti Teknologi Petronas, Malaysia ; 2 Department of Mechanical Engineering, Malaysia

ISBN: 978-989-758-054-3

Keyword(s): Artificial Neural Networks (ANNS), Flow Visualization, Flow Velocity, Uniform Flow, Natural Convection, Geometrical Boundaries Profile, Real Time Response Simulation.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computational Neuroscience ; Computer-Supported Education ; Domain Applications and Case Studies ; Enterprise Information Systems ; Fuzzy Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial, Financial and Medical Applications ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: In this research we describe our attempt to get instantaneous numerical simulation for fluid flow by using Artificial Neural networks (ANN). Such simulation should provide a reliable perception about the fluid behaviour with respect to both momentum and energy equations. In addition to the preceding recorded data, the proposed method consider the geometrical boundaries profile as a major contributions for ANN training phase. Our study is driven by the need of rapid response especially in medical cases, surgeon diagnosis, engineering emergency situations, and when novel circumstances occurs. Furthermore, the existing computational fluid dynamics tools require long time to response and the present of professional expert to set the parameters for the different cases. In fact, ANN can deal with the lack of proper physical models or the present of uncertainty about some conditions that usually affect the outcomes form the other approaches. We manage to get acceptable result for 1D-flow eq uations with respect to both energy and momentum equations. Our ANN approach is able to handle fluid flow prediction with known boundaries velocity. This approach can be the first step for neural network computational program that can tackle variance type of problems. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.91.106.44

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Sabir, O. and M. Y. S. Tuan Ya, T. (2014). A New Artificial Neural Network Approach for Fluid Flow Simulations.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 334-338. DOI: 10.5220/0005157503340338

@conference{ncta14,
author={Osama Sabir. and T. M. Y. S. Tuan Ya.},
title={A New Artificial Neural Network Approach for Fluid Flow Simulations},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={334-338},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005157503340338},
isbn={978-989-758-054-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - A New Artificial Neural Network Approach for Fluid Flow Simulations
SN - 978-989-758-054-3
AU - Sabir, O.
AU - M. Y. S. Tuan Ya, T.
PY - 2014
SP - 334
EP - 338
DO - 10.5220/0005157503340338

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.