The increasing demand for robust and high-performance centrifugal compressor stages has led to the development of several optimization and uncertainty quantification approaches. However, in the industrial scenario, geometric variations of such pre-engineered stages can occur during customer orders or non-conformity evaluations. In this regard, a rapid low-effort quantification of the impact of these changes has become critical for manufacturers. Against this backdrop, the present study provides an approach based on the joint use of computational fluid dynamics (CFDs) and artificial neural networks to instantly assess the impact of geometric variations on aerodynamic performance and operating range of centrifugal compressor stages. As a theoretical contribution, the research investigates the capacity of a CFD-based surrogate approach for evaluating variations of stage efficiency and work coefficient. On a practical level, a business-friendly tool for stage performance assessment is provided. As an example case study, the approach is applied on a group of stages for medium-high Mach number applications. Results show how the multi-point surrogate approach enables a rapid quantification of stage performance changes without requiring additional CFD analyses. The research lays the foundation for future studies aiming to reduce efforts when assessing geometric variation impacts on centrifugal compressor stages.

Multi-Point Surrogate-Based Approach for Assessing Impacts of Geometric Variations on Centrifugal Compressor Performance / Bicchi Marco, Marconcini Michele, Bellobuono Ernani Fulvio, Belardini Elisabetta, Toni Lorenzo, Arnone Andrea. - In: ENERGIES. - ISSN 1996-1073. - ELETTRONICO. - 16:(2023), pp. 1584.0-1584.0. [10.3390/en16041584]

Multi-Point Surrogate-Based Approach for Assessing Impacts of Geometric Variations on Centrifugal Compressor Performance

Bicchi Marco;Marconcini Michele
;
Arnone Andrea
2023

Abstract

The increasing demand for robust and high-performance centrifugal compressor stages has led to the development of several optimization and uncertainty quantification approaches. However, in the industrial scenario, geometric variations of such pre-engineered stages can occur during customer orders or non-conformity evaluations. In this regard, a rapid low-effort quantification of the impact of these changes has become critical for manufacturers. Against this backdrop, the present study provides an approach based on the joint use of computational fluid dynamics (CFDs) and artificial neural networks to instantly assess the impact of geometric variations on aerodynamic performance and operating range of centrifugal compressor stages. As a theoretical contribution, the research investigates the capacity of a CFD-based surrogate approach for evaluating variations of stage efficiency and work coefficient. On a practical level, a business-friendly tool for stage performance assessment is provided. As an example case study, the approach is applied on a group of stages for medium-high Mach number applications. Results show how the multi-point surrogate approach enables a rapid quantification of stage performance changes without requiring additional CFD analyses. The research lays the foundation for future studies aiming to reduce efforts when assessing geometric variation impacts on centrifugal compressor stages.
2023
16
0
0
Goal 7: Affordable and clean energy
Bicchi Marco, Marconcini Michele, Bellobuono Ernani Fulvio, Belardini Elisabetta, Toni Lorenzo, Arnone Andrea
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1296299
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 1
social impact