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Venturelli, Giovanni (2015) Development of numerical procedures for turbomachinery optimizaion. [Ph.D. thesis]

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Abstract (english)

This Doctoral Thesis deals with high speed turbomachinery optimization and all those tools employed in the optimization process, mainly the optimization algorithm, the parameterization framework and the automatic CFD-based optimization loop. Optimization itself is not just a mean to improve the performance of a generic system, but can be a powerful instigator that helps gaining insight on the physic phenomena behind the observed improvements.
As for the optimization engine, a novel surrogate-assisted (SA) genetic algorithm for multi-objective optimization problems, namely GeDEA-II-K, was developed. GeDEA-II-K is grounded on the cooperation between a genetic algorithm, namely GeDEA-II, and the Kriging methodology, with the aim at speeding up the optimization process by taking advantage of the surrogate model. The comparison over two- and three-objective test functions revealed the effectiveness of GeDEA-II-K approach.
In order to carry out high speed turbomachinery optimizations, an automatic CFD-based optimization loop built around GeDEA-II-K was constructed. The loop was realized for a UNIX/Linux cluster environment in order to exploit the computational resources of parallel computing. Among the tools, a dedicated parameterization framework for 2D airfoils and 3D blades has been designed based on the displacement filed approach.
The effectiveness of both the CFD-based automatic loop and the parameterization was verified on two real-life multi-objective optimization problems: the 2D shape optimization of a supersonic compressor cascade and the 3D shape optimization of the NASA Rotor 67. To better understand the outcomes of the optimization process, a wide section has been dedicated to supersonic flows and their behavior when forced to work throughout compressor cascades.
The results obtained surely have demonstrated the effectiveness of the optimization approach, and even more have given deep insight on the physic of supersonic flows in the high speed turbomachinery applications that were studied.

Abstract (italian)

“Sviluppo di procedure numeriche per l’ottimizzazione di turbomacchine” raccoglie la ricerca svolta dall’autore nel periodo di Dottorato che va dal 2010 al 2013. Il lavoro è nato con una duplice finalità: da una parte sviluppare un algoritmo per l’ottimizzazione multi obiettivo; dall’altra, accoppiare il motore di ottimizzazione con strumenti di analisi basati sulla fluidodinamica computazionale (CFD) per studiare casi di interesse nell’ambito del “high speed turbomachinery”.
Gli algoritmi evolutivi hanno dimostrato alta affidabilità e robustezza nel raggiungimento del “Fronte di Pareto” (i.e., è la soluzione di un problema multi obiettivo), richiedendo però un numero di valutazioni delle funzioni obiettivo molto elevato, talvolta impraticabile dal punto di vista industriale. Infatti, quando la CFD è impiegata per valutare le funzioni obiettivo del sistema in esame, il costo computazionale può diventare il vero collo di bottiglia dell’intero processo. Una possibile soluzione viene fornita dai modelli surrogati, o metamodelli, cioè tecniche matematiche il cui scopo è quello di approssimare le funzioni obiettivo permettendo, di fatto, di diminuire le chiamate dirette alla CFD e di conseguenza anche il tempo totale del processo di ottimizzazione. Il vero dilemma è come affiancare gli algoritmi evoluti a uno o a più modelli surrogati, al fine di migliorare le prestazioni del motore di ottimizzazione. A oggi il problema non ha una soluzione univoca.
La tesi è costituita da cinque capitoli. Il primo capitolo vuol essere di introduzione sia ai modelli surrogati visti nell'ottica dell’ottimizzazione, sia alle strategie di ottimizzazione che sono state applicate per migliorare i compressori transonici e le schiere supersoniche di compressori, che rappresentano i casi di interesse studiati in questa Tesi. Il secondo capitolo è dedicato al motore di ottimizzazione sviluppato dall’autore, denominato GeDEA-II-K. Il GeDEA-II-K nasce dall’unione del preesistente algoritmo genetico GeDEA-II e di un modello surrogato basato sul Kriging. Le prestazioni del nuovo algoritmo sono state testate su problemi matematici a due e a tre obiettivi ben noti in letteratura. Nel terzo capitolo è stato approfondito in grande dettaglio la fisica alla base delle schiere supersoniche, cercando di comprendere il legame profondo tra la geometria della schiera e il campo di moto che si viene a creare. Nel quarto e nel quinto capitolo è stato testato il loop automatico di ottimizzazione sviluppato dall’autore che comprende il motore di ottimizzazione, il tool di parametrizzazione della geometria, i modelli CFD, e tutti quegli elementi indispensabili per garantire robustezza ad una procedura automatica. Nello specifico è stata condotta l’ottimizzazione di una schiera supersonica e del compressore transonico NASA Rotor 67.


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EPrint type:Ph.D. thesis
Tutor:Benini, Ernesto
Ph.D. course:Ciclo 26 > Scuole 26 > INGEGNERIA INDUSTRIALE > INGEGNERIA DELL' ENERGIA
Data di deposito della tesi:02 February 2015
Anno di Pubblicazione:02 February 2015
Key Words:Supersonic compressor cascades, optimization, transonic compressor
Settori scientifico-disciplinari MIUR:Area 09 - Ingegneria industriale e dell'informazione > ING-IND/08 Macchine a fluido
Struttura di riferimento:Dipartimenti > Dipartimento di Ingegneria Industriale
Codice ID:7922
Depositato il:10 Nov 2015 12:12
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