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Author:Maakala, Viljami
Title:Multi-objective optimization of recovery boiler dimensions using computational fluid dynamics
Soodakattilan dimensioiden monitavoiteoptimointi laskennallisen virtausmekaniikan avulla
Publication type:Master's thesis
Publication year:2013
Pages:xiv + 92 s. + liitt. 6      Language:   eng
Department/School:Sovelletun mekaniikan laitos
Main subject:Lentotekniikka   (Kul-34)
Supervisor:Siikonen, Timo
Instructor:Miikkulainen, Pasi
Electronic version URL: http://urn.fi/URN:NBN:fi:aalto-201306146492
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
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Location:P1 Ark Aalto  4025   | Archive
Keywords:computational fluid dynamics
error estimation
genetic algorithm
optimization
radial basis function
recovery boiler
geneettinen algoritmi
laskennallinen virtausmekaniikka
optimointi
radiaalikantafunktio
soodakattila
virhetarkastelu
Abstract (eng): The purpose of this work was to develop a multi-objective optimization program based on computational fluid dynamics (CFD).
The program combines a CFD model with a genetic algorithm optimizer and a radial basis function network learner.

The tool was applied to optimizing a furnace geometry of a recovery boiler using two approaches: an uncoupled method and a coupled method.
Before solving the optimization task, a study was done on the CFD model errors and the CFD-optimization program was verified.

Error analyses revealed that the simulations have substantial iteration errors.
Discretization errors were studied using grid convergence index values, but iteration errors made their interpretation difficult.
The program was verified in test problems and the proposed methodology was concluded to work as intended.
Both optimization approaches found several geometries that deliver better performance than the original boiler design.
The coupled method is more reliable, because it performs numerous CFD evaluations near the final solutions.

Future research should be done to reduce iteration errors when modeling recovery boilers.
It would also be useful to develop the CFD-optimization methodology further and to use it in different applications.
Areas for development include improved integration of preferences into the optimization and usage of hybrid optimization methods.
Abstract (fin): Työn tarkoituksena oli kehittää laskennallista virtausmekaniikkaa (CFD) käyttävä monitavoiteoptimointiohjelma.
Ohjelma yhdistää CFD-mallin geneettisen algorimiin pohjautuvaan optimoijaan sekä radiaalikantafunktioverkkoa käyttävään oppijaan.

Työkalua käytettiin soodakattilan tulipesägeometrian optimointiin kahdella lähestymistavalla: kytkemättömällä sekä kytketyllä menetelmällä.
Ennen tehtävän ratkaisemista CFD-mallille suoritettiin virhetarkastelu ja CFD-optimointiohjelma verifioitiin.

Virhetarkastelussa havaittiin simuloinneissa merkittäviä iteraatiovirheitä.
Diskretointivirheitä tutkittiin hilakonvergenssi-indeksin avulla, mutta iteraatiovirheet hankaloittivat tulosten tulkintaa.
Ohjelma verifioitiin testiongelmissa, ja menetelmän havaittiin toimivan halutusti.
Molemmat optimointilähestymistavat löysivät useita geometrioita, joilla kattilan suorituskyky paranee alkuperäiseen geometriaan verrattuna.
Kytketty menetelmä on luotettavampi, koska se tekee useita CFD-laskentoja lopullisten ratkaisujen läheisyydessä.

Tutkimusta tulisi tehdä iteraatiovirheiden pienentämiseksi soodakattiloiden mallinnuksessa.
Lisäksi olisi hyödyllistä kehittää CFD-optimointia eteenpäin ja käyttää sitä muissakin sovelluksissa.
Kehittämisalueita ovat esimerkiksi preferenssien parempi integrointi optimointiin sekä hybridimenetelmien käyttö.
ED:2013-05-16
INSSI record number: 46146
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