Design-point performance adaptation of small gas turbine using particle swarm optimization

Affiani Machmudah, Tamiru Alemu Lemma

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Modeling of design-point performances is an important step for designing the gas turbine engine. It is also a necessary step in an off-design performance analysis where during the modeling of design-point performance, some engine parameters are typically not known. These unknown parameters should be estimated and adapted to obtain design-point target performances. This paper employs meta-heuristic optimizations, namely genetic algorithm (GA), particle swarm optimization (PSO), gray wolf optimizer (GWO), and whale optimizer algorithm (WOA) to solve the design-point adaptation of a small gas turbine designed for marine applications. Target parameters are shaft power, fuel flow, turbine exit temperature, turbine exit pressure, and thermal efficiency with seven adapted parameters as the optimization parameters. Due to multiple solutions and constraints of the searching area, the meta-heuristic optimization can encounter the computational difficulty. The PSO shows an outstanding performance among other meta-heuristic optimizations where it has the smallest fitness value, i.e., 0.02233. The GWO has the minimum fitness value as compared with the GA and WOA, but the value of turbine inlet temperature (TIT) is approaching its upper bound, in which this condition is not expected. The GA has a problem of escaping from the initial value for the TIT parameter. The WOA has the largest fitness value, i.e., 0.0971, although it does not have a problem of escaping the initial value and approaching the upper/lower bound.

Original languageEnglish
Title of host publicationAdvances in Manufacturing Engineering - Selected Articles from ICMMPE 2019
EditorsSeyed Sattar Emamian, Farazila Yusof, Mokhtar Awang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages411-423
Number of pages13
ISBN (Print)9789811557521
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event5th International Conference on Mechanical, Manufacturing and Plant Engineering, ICMMPE 2019 - Kuala Lumpur, Malaysia
Duration: 19 Nov 201921 Nov 2019

Publication series

NameLecture Notes in Mechanical Engineering
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364

Conference

Conference5th International Conference on Mechanical, Manufacturing and Plant Engineering, ICMMPE 2019
Country/TerritoryMalaysia
CityKuala Lumpur
Period19/11/1921/11/19

Fingerprint

Dive into the research topics of 'Design-point performance adaptation of small gas turbine using particle swarm optimization'. Together they form a unique fingerprint.

Cite this