Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Pendulum Modeling of Sloshing Motion Using Particle Swarm Optimization

Pendulum Modeling of Sloshing Motion Using Particle Swarm Optimization In this paper, we propose a technique for modeling the sloshing phenomenon as a pendulum. Sloshing model is generated under gravity conditions for nine inner mass ratios to the storage tank volume and 12 input types. The mass ratio ranges from 10 to 90%, in units of 10%. The input is divided into three types and four levels of magnitude. The particle swarm optimization algorithm, which is a metaheuristic method, is applied to perform the pendulum modeling. The pendulum consists of three variables: the mass ratio of the pendulum to the inner fluid mass, length, and damping coefficient. Using the optimized pendulum modeling variables for 108 cases, we analyze the change in pendulum model parameters due to the variation in the inner fluid mass and input. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Aeronautical & Space Sciences Springer Journals

Pendulum Modeling of Sloshing Motion Using Particle Swarm Optimization

Download PDF
11 pages

Loading next page...
 
/lp/springer-journals/pendulum-modeling-of-sloshing-motion-using-particle-swarm-optimization-4KwnuVCVJk

References (28)

Publisher
Springer Journals
Copyright
Copyright © 2018 by The Korean Society for Aeronautical & Space Sciences and Springer Nature Singapore Pte Ltd.
Subject
Engineering; Aerospace Technology and Astronautics; Fluid- and Aerodynamics
ISSN
2093-274X
eISSN
2093-2480
DOI
10.1007/s42405-018-0089-0
Publisher site
See Article on Publisher Site

Abstract

In this paper, we propose a technique for modeling the sloshing phenomenon as a pendulum. Sloshing model is generated under gravity conditions for nine inner mass ratios to the storage tank volume and 12 input types. The mass ratio ranges from 10 to 90%, in units of 10%. The input is divided into three types and four levels of magnitude. The particle swarm optimization algorithm, which is a metaheuristic method, is applied to perform the pendulum modeling. The pendulum consists of three variables: the mass ratio of the pendulum to the inner fluid mass, length, and damping coefficient. Using the optimized pendulum modeling variables for 108 cases, we analyze the change in pendulum model parameters due to the variation in the inner fluid mass and input.

Journal

International Journal of Aeronautical & Space Sciences – Springer Journals

Published: Oct 13, 2018

There are no references for this article.