CFD Based Stochastic Optimization of Pelton Turbine Bucket in Stationery Condition

Suyesh Bhattarai, Keshav Dahal, Parag Vichare, Bhupesh Mishra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Computational fluid dynamics (CFD) and stochastic optimization are both highly computationally expensive processes. These processes may not produce the same unique result every time and demand large computing resources. The outcomes are determined as the final if the results repeat themselves for some predefined number of iterations causing convergence. Due to this expensive and non-deterministic nature, research on CFD optimization using stochastic optimization method such as Genetic Algorithm has been limited. This paper presents a noble method in which the CFD codes can be used together with genetic algorithm to optimize the shape of a responsive surface such as a Pelton turbine bucket. An existing Pelton bucket's model has been acquired and a set of random surfaces have been created as the initial population to optimize the shape of the bucket in stationery condition. The results show that an increase in efficiency by 13.21% to the normalized efficiency of existing design can be obtained by incorporating the changes suggested.

Original languageEnglish
Title of host publicationProceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018
PublisherIEEE
Pages53-57
Number of pages5
ISBN (Electronic)9781538672297
ISBN (Print)978-1-5386-7230-3
DOIs
Publication statusPublished - 20 Sep 2018
Event9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018 - Budapest, Hungary
Duration: 10 Jul 201813 Jul 2018

Conference

Conference9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018
CountryHungary
CityBudapest
Period10/07/1813/07/18

Fingerprint

Stochastic Optimization
Turbine
Computational Fluid Dynamics
Computational fluid dynamics
Turbines
Dynamic Optimization
Genetic algorithms
Optimise
Genetic Algorithm
Random Surfaces
Stochastic Methods
Optimization Methods
Iteration
Resources
Computing
Model

Keywords

  • bucket design
  • Computational fluid dynamics
  • design optimization
  • genetic algorithms
  • Pelton turbine
  • turbomachinery

Cite this

Bhattarai, S., Dahal, K., Vichare, P., & Mishra, B. (2018). CFD Based Stochastic Optimization of Pelton Turbine Bucket in Stationery Condition. In Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018 (pp. 53-57). [8467607] IEEE. https://doi.org/10.1109/ICMAE.2018.8467607
Bhattarai, Suyesh ; Dahal, Keshav ; Vichare, Parag ; Mishra, Bhupesh. / CFD Based Stochastic Optimization of Pelton Turbine Bucket in Stationery Condition. Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018. IEEE, 2018. pp. 53-57
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title = "CFD Based Stochastic Optimization of Pelton Turbine Bucket in Stationery Condition",
abstract = "Computational fluid dynamics (CFD) and stochastic optimization are both highly computationally expensive processes. These processes may not produce the same unique result every time and demand large computing resources. The outcomes are determined as the final if the results repeat themselves for some predefined number of iterations causing convergence. Due to this expensive and non-deterministic nature, research on CFD optimization using stochastic optimization method such as Genetic Algorithm has been limited. This paper presents a noble method in which the CFD codes can be used together with genetic algorithm to optimize the shape of a responsive surface such as a Pelton turbine bucket. An existing Pelton bucket's model has been acquired and a set of random surfaces have been created as the initial population to optimize the shape of the bucket in stationery condition. The results show that an increase in efficiency by 13.21{\%} to the normalized efficiency of existing design can be obtained by incorporating the changes suggested.",
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Bhattarai, S, Dahal, K, Vichare, P & Mishra, B 2018, CFD Based Stochastic Optimization of Pelton Turbine Bucket in Stationery Condition. in Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018., 8467607, IEEE, pp. 53-57, 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018, Budapest, Hungary, 10/07/18. https://doi.org/10.1109/ICMAE.2018.8467607

CFD Based Stochastic Optimization of Pelton Turbine Bucket in Stationery Condition. / Bhattarai, Suyesh; Dahal, Keshav; Vichare, Parag; Mishra, Bhupesh.

Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018. IEEE, 2018. p. 53-57 8467607.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Computational fluid dynamics (CFD) and stochastic optimization are both highly computationally expensive processes. These processes may not produce the same unique result every time and demand large computing resources. The outcomes are determined as the final if the results repeat themselves for some predefined number of iterations causing convergence. Due to this expensive and non-deterministic nature, research on CFD optimization using stochastic optimization method such as Genetic Algorithm has been limited. This paper presents a noble method in which the CFD codes can be used together with genetic algorithm to optimize the shape of a responsive surface such as a Pelton turbine bucket. An existing Pelton bucket's model has been acquired and a set of random surfaces have been created as the initial population to optimize the shape of the bucket in stationery condition. The results show that an increase in efficiency by 13.21% to the normalized efficiency of existing design can be obtained by incorporating the changes suggested.

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Bhattarai S, Dahal K, Vichare P, Mishra B. CFD Based Stochastic Optimization of Pelton Turbine Bucket in Stationery Condition. In Proceedings of 2018 9th International Conference on Mechanical and Aerospace Engineering, ICMAE 2018. IEEE. 2018. p. 53-57. 8467607 https://doi.org/10.1109/ICMAE.2018.8467607