CFD based design optimization of a cabinet nitrogen generator

Bárbara Arizmendi Gutiérrez, Edmondo Minisci, Greig Chisholm

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The design of mechanical enclosures is evolving to be more compact and quieter and this compromises the cooling of the internal components. Computational Fluid Dynamics (CFD) based optimization could significantly improve the cooling efficiency of the critical parts of the components to ensure their performance and reliability. This work presents the CFD surrogate based optimization of the forced cooling of two reciprocating compressors located in an enclosure from a gas generator. Due to the challenging project time constraints, the accuracy of the results was compromised to make optimization feasible. The parameters to be optimized were related to the position of the compressors and the cooling fans. The boundary conditions associated to the cooling of the critical parts were derived by experimental data. Artificial Neural Networks (ANNs) were used to construct a surrogate model of the computational model to reduce the time and resources required. The combination of the ANN model with a multi start-gradient based algorithm optimized the position of compressors and cooling fans to minimize the average temperature on the critical parts. A set of new enclosure designs were found with outstanding CFD based performance compared with the design elaborated by engineering intuition.

Original languageEnglish
Title of host publicationEvolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems
EditorsE. Andrés-Pérez, L. González, J. Periaux , N. Gauger , D. Quagliarella , K. Giannakoglou
Place of PublicationCham, Switzerland
PublisherSpringer
Pages247-260
Number of pages14
ISBN (Print)978-3-319-89889-6
DOIs
Publication statusPublished - 1 Jan 2019

Publication series

NameComputational Methods in Applied Sciences
Volume49
ISSN (Print)1871-3033

Fingerprint

Computational Fluid Dynamics
Nitrogen
Cooling
Computational fluid dynamics
Generator
Compressor
Enclosure
Enclosures
Fans
Artificial Neural Network
Compressors
Optimization
Neural networks
Reciprocating compressors
Multistart
Surrogate Model
Gas generators
Neural Network Model
Computational Model
Design optimization

Keywords

  • artificial neural networks
  • enclosure cooling
  • experimental
  • forced convection
  • multi-start gradient algorithms

Cite this

Gutiérrez, B. A., Minisci, E., & Chisholm, G. (2019). CFD based design optimization of a cabinet nitrogen generator. In E. Andrés-Pérez, L. González, J. Periaux , N. Gauger , D. Quagliarella , & K. Giannakoglou (Eds.), Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems (pp. 247-260). (Computational Methods in Applied Sciences; Vol. 49). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-89890-2_16
Gutiérrez, Bárbara Arizmendi ; Minisci, Edmondo ; Chisholm, Greig. / CFD based design optimization of a cabinet nitrogen generator. Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems. editor / E. Andrés-Pérez ; L. González ; J. Periaux ; N. Gauger ; D. Quagliarella ; K. Giannakoglou . Cham, Switzerland : Springer, 2019. pp. 247-260 (Computational Methods in Applied Sciences).
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Gutiérrez, BA, Minisci, E & Chisholm, G 2019, CFD based design optimization of a cabinet nitrogen generator. in E Andrés-Pérez, L González, J Periaux , N Gauger , D Quagliarella & K Giannakoglou (eds), Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems. Computational Methods in Applied Sciences, vol. 49, Springer, Cham, Switzerland, pp. 247-260. https://doi.org/10.1007/978-3-319-89890-2_16

CFD based design optimization of a cabinet nitrogen generator. / Gutiérrez, Bárbara Arizmendi; Minisci, Edmondo; Chisholm, Greig.

Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems. ed. / E. Andrés-Pérez; L. González; J. Periaux ; N. Gauger ; D. Quagliarella ; K. Giannakoglou . Cham, Switzerland : Springer, 2019. p. 247-260 (Computational Methods in Applied Sciences; Vol. 49).

Research output: Chapter in Book/Report/Conference proceedingChapter

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AU - Minisci, Edmondo

AU - Chisholm, Greig

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N2 - The design of mechanical enclosures is evolving to be more compact and quieter and this compromises the cooling of the internal components. Computational Fluid Dynamics (CFD) based optimization could significantly improve the cooling efficiency of the critical parts of the components to ensure their performance and reliability. This work presents the CFD surrogate based optimization of the forced cooling of two reciprocating compressors located in an enclosure from a gas generator. Due to the challenging project time constraints, the accuracy of the results was compromised to make optimization feasible. The parameters to be optimized were related to the position of the compressors and the cooling fans. The boundary conditions associated to the cooling of the critical parts were derived by experimental data. Artificial Neural Networks (ANNs) were used to construct a surrogate model of the computational model to reduce the time and resources required. The combination of the ANN model with a multi start-gradient based algorithm optimized the position of compressors and cooling fans to minimize the average temperature on the critical parts. A set of new enclosure designs were found with outstanding CFD based performance compared with the design elaborated by engineering intuition.

AB - The design of mechanical enclosures is evolving to be more compact and quieter and this compromises the cooling of the internal components. Computational Fluid Dynamics (CFD) based optimization could significantly improve the cooling efficiency of the critical parts of the components to ensure their performance and reliability. This work presents the CFD surrogate based optimization of the forced cooling of two reciprocating compressors located in an enclosure from a gas generator. Due to the challenging project time constraints, the accuracy of the results was compromised to make optimization feasible. The parameters to be optimized were related to the position of the compressors and the cooling fans. The boundary conditions associated to the cooling of the critical parts were derived by experimental data. Artificial Neural Networks (ANNs) were used to construct a surrogate model of the computational model to reduce the time and resources required. The combination of the ANN model with a multi start-gradient based algorithm optimized the position of compressors and cooling fans to minimize the average temperature on the critical parts. A set of new enclosure designs were found with outstanding CFD based performance compared with the design elaborated by engineering intuition.

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KW - experimental

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KW - multi-start gradient algorithms

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M3 - Chapter

SN - 978-3-319-89889-6

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BT - Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems

A2 - Andrés-Pérez, E.

A2 - González, L.

A2 - Periaux , J.

A2 - Gauger , N.

A2 - Quagliarella , D.

A2 - Giannakoglou , K.

PB - Springer

CY - Cham, Switzerland

ER -

Gutiérrez BA, Minisci E, Chisholm G. CFD based design optimization of a cabinet nitrogen generator. In Andrés-Pérez E, González L, Periaux J, Gauger N, Quagliarella D, Giannakoglou K, editors, Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems. Cham, Switzerland: Springer. 2019. p. 247-260. (Computational Methods in Applied Sciences). https://doi.org/10.1007/978-3-319-89890-2_16