Robust Optimisation of Ultrasonic Flow Meters by Computational Fluid Dynamics and Enhanced Turbulence Modelling

Mario Javier Rincón

Robust Optimisation of Ultrasonic Flow Meters by Computational Fluid Dynamics and Enhanced Turbulence Modelling

Bidragets oversatte titel: Robust Optimering af Ultralydsflowmålere ved brug af Beregningsmæssig Strømningsdynamik og Forbedret Turbulensmodellering

Mario Javier Rincón

Institut for Mekanik og Produktion - Fluider og Energi

Publikation: Bog/antologi/afhandling/rapport › Ph.d.-afhandling

10 Downloads (Pure)

Abstract

En nøjagtig måling af fluidstrømninger er essentiel indenfor en bred vifte af industrielle applikationer. I dette henseende præsenterer ultralyd-flowmålere en nøjagtig og omkostningseffektiv løsning; disse systemer introducerer dog udfordringer grundet deres komplekse geometri og interaktion med fluidstrømningerne. Derfor er forståelsen af deres dynamiske rolle afgørende for forbedring af deres design og drift. Samtidig har datadrevne metoder til turbulensmodellering vist sig som et lovende værktøj til at forbedre præcisionen i simuleringer af fluidstrømninger. Denne ph.d.-afhandling fokuserer på at bygge bro mellem disse to områder ved at forudsige komplekse fluidstrømninger i ultralyd-flowmålere, udvikle en robust metode til deres geometriske optimering, og forbedre eksisterende Reynolds-averaged Navier-Stokes (RANS) turbulensmodeller med progressive og generaliserbare datadrevne metoder.

Der er udviklet en metode baseret på computational fluid dynamics (CFD) til at forudsige fluidstrømningerne i ultralyd-flowmålere ved hjælp af RANS k-ω SST. Metoden er eksperimentelt valideret med laser Doppler velocimetri og tryktabseksperimenter for hele driftsområdet af disse systemer. Derefter styres en række simuleringer af surrogate og Bayesianske multi-objektive optimeringsmetoder for at levere forbedrede geometriske designs. Disse forbedrede geometrier optimeres yderligere ved hjælp af en adjoint-baseret formoptimeringsmetode, der muliggør tredimensionel formændring af geometrien, hvilket muliggør større forbedringer, der ikke kan opnås ved traditionelle ingeniørmetoder.

Inden for datadreven turbulensmodellering findes to centrale kritiske aspekter: generalisering og konsistensen af a posteriori resultater. Denne afhandling adresserer disse udfordringer gennem en systematisk tilgang, der kombinerer de CFD-drevne optimeringsteknikker, der er udviklet til forbedring af flowmåleren, med en progressiv strategi for augmentering af k-ω SST turbulensmodellen. Målet er at forbedre forudsigelsen af turbulensbaserede sekundære strømninger og flowseparation (to almindelige udfordringer i fluidstrømningssimuleringer) uden at ændre den oprindelige models vellykkede forudsigelse af den logaritmiske lov. To korrektionstermer er omhyggeligt defineret og indført i henholdsvis bevægelsesligningerne og transportligningerne for den specifikke dissipationshastighed (ω). Efter omfattende numerisk verifikation, viser de augmenterede modeller betydelige forbedringer i forudsigelsen af sekundære strømninger, løsrivelse og gentilknytning i grænselag, samt friktionskoefficienter i både trænings- og testsenarier, hvilket bekræfter deres a posteriori pålidelighed og generalisering.

Afslutningsvis præsenterer denne ph.d.-afhandling en robust og omkostningseffektiv metode til systematiske forbedringer af præstationen for komplekse ingeniørsystemer baseret på CFD-simuleringer. Derudover er der udviklet to generaliserbare metoder til at forbedre turbulensmodellering baseret på datadrevne progressive tilgange. Ved at adressere udfordringer i forbindelse med flowseparation og forudsigelse af sekundære strømninger i komplekse geometrier, sammen med design- og formoptimeringsteknikker, bidrager denne forskning til mere nøjagtige forudsigelser af fluidstrømninger og simuleringsbaseret optimering, hvilket gavner industrier som er afhængige af præcise flowmålinger og simuleringer. Samspillet mellem datadreven modellering og kompleks systemoptimering demonstrerer potentiale for innovative løsninger inden for både turbulensmodellering og flowmåleteknologier med brede implikationer inden for akademiske og industrielle ingeniørsektorer.

Bidragets oversatte titel	Robust Optimering af Ultralydsflowmålere ved brug af Beregningsmæssig Strømningsdynamik og Forbedret Turbulensmodellering
Originalsprog	Engelsk

Forlag	Aarhus University
Antal sider	171
Status	Udgivet - jan. 2024

2 Tidsskriftartikel

Validating the design optimisation of ultrasonic flow meters using computational fluid dynamics and surrogate modelling
Rincón, M. J., Reclari, M., I. A. Yang, X. & Abkar, M., apr. 2023, I: International Journal of Heat and Fluid Flow. 100, 15 s., 109112.
Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

Åben adgang
Fil
8 Citationer (Scopus)

79 Downloads (Pure)
Turbulent flow in small-diameter ultrasonic flow meters: A numerical and experimental study
Rincón, M. J., Reclari, M. & Abkar, M., okt. 2022, I: Flow Measurement and Instrumentation. 87, 9 s., 102227.
Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

Åben adgang
Fil
7 Citationer (Scopus)

101 Downloads (Pure)

Citationsformater

@phdthesis{0813f501bd47492da3acd137a366bcbd,

title = "Robust Optimisation of Ultrasonic Flow Meters by Computational Fluid Dynamics and Enhanced Turbulence Modelling",

abstract = "The accurate measurement of fluid flow is essential in a wide range of industrial applications. In this regard, ultrasonic flow meters present an accurate and cost-effective solution; however, these systems introduce challenges due to their complex geometry and interaction with fluid flow. Hence, understanding their flow dynamics is paramount in the improvement of their design and operation. Simultaneously, data-driven turbulence modelling techniques have emerged as promising tools to improve the precision of fluid flow simulations. This doctoral thesis focuses on bridging these two domains by predicting complex flows in ultrasonic flow meters, developing a robust methodology for their geometrical optimisation, and enhancing existing Reynolds-averaged Navier-Stokes (RANS) turbulence models with progressive and generalisable data-driven methods.A methodology based on computational fluid dynamics (CFD) is developed to predict the flow dynamics throughout ultrasonic flow meters by the use of RANS k-ω SST. The method is experimentally validated with laser Doppler velocimetry and pressure drop experiments for the whole dynamic range of operation of these systems. Subsequently, a series of flow simulations are driven by surrogate and Bayesian multi-objective optimisation methods to provide improved geometrical designs. These improved geometries are further optimised by an adjoint-based shape optimisation method that enables the 3-dimensional morphing of the geometry, allowing higher improvement gains which cannot be achieved by traditional engineering ingenuity.In the realm of data-driven turbulence modelling, two critical aspects take centre stage: generalisability and the consistency of a posteriori results. This thesis addresses these challenges through a systematic approach that combines the CFD-driven optimisation techniques developed for flow meter improvement with a progressive augmentation strategy in the k-ω SST turbulence model. The goal is to improve the prediction of turbulence anisotropy-based secondary flows, and flow separation - two common stumbling blocks in fluid flow simulations - without altering the successful law of the wall prediction by the original model. Two correction terms are carefully defined and introduced in the momentum and specific dissipation rate (ω) transport equations. After extensive numerical verification, the enhanced models exhibit significant improvements in the prediction of secondary flows, boundary-layer detachment and reattachment, and friction coefficients in both training and testing cases, reaffirming their a posteriori reliability and generalisability.In conclusion, this doctoral thesis presents a robust and cost-efficient methodology to systematically improve the performance of complex engineering systems based on CFD simulations. Additionally, two generalisable methods are developed to enhance turbulence modelling based on data-driven progressive approaches. By addressing challenges related to flow separation and secondary flow predictions in complex geometries together with design and shape optimisation techniques, this research contributes to more accurate fluid flow predictions and simulation-based optimisation; benefiting industries dependent on precise flow measurements and simulations. The synergy between data-driven modelling and complex system optimisation demonstrates the potential for innovative solutions in both turbulence modelling and flow measurement technologies, with broad implications across academic and industrial engineering sectors.",

keywords = "Fluid mechanics, Optimisation, CFD, Turbulence modelling",

author = "Rinc{\'o}n, {Mario Javier}",

note = "Published October 2023",

year = "2024",

month = jan,

language = "English",

publisher = "Aarhus University",

}

TY - BOOK

T1 - Robust Optimisation of Ultrasonic Flow Meters by Computational Fluid Dynamics and Enhanced Turbulence Modelling

AU - Rincón, Mario Javier

N1 - Published October 2023

PY - 2024/1

Y1 - 2024/1

N2 - The accurate measurement of fluid flow is essential in a wide range of industrial applications. In this regard, ultrasonic flow meters present an accurate and cost-effective solution; however, these systems introduce challenges due to their complex geometry and interaction with fluid flow. Hence, understanding their flow dynamics is paramount in the improvement of their design and operation. Simultaneously, data-driven turbulence modelling techniques have emerged as promising tools to improve the precision of fluid flow simulations. This doctoral thesis focuses on bridging these two domains by predicting complex flows in ultrasonic flow meters, developing a robust methodology for their geometrical optimisation, and enhancing existing Reynolds-averaged Navier-Stokes (RANS) turbulence models with progressive and generalisable data-driven methods.A methodology based on computational fluid dynamics (CFD) is developed to predict the flow dynamics throughout ultrasonic flow meters by the use of RANS k-ω SST. The method is experimentally validated with laser Doppler velocimetry and pressure drop experiments for the whole dynamic range of operation of these systems. Subsequently, a series of flow simulations are driven by surrogate and Bayesian multi-objective optimisation methods to provide improved geometrical designs. These improved geometries are further optimised by an adjoint-based shape optimisation method that enables the 3-dimensional morphing of the geometry, allowing higher improvement gains which cannot be achieved by traditional engineering ingenuity.In the realm of data-driven turbulence modelling, two critical aspects take centre stage: generalisability and the consistency of a posteriori results. This thesis addresses these challenges through a systematic approach that combines the CFD-driven optimisation techniques developed for flow meter improvement with a progressive augmentation strategy in the k-ω SST turbulence model. The goal is to improve the prediction of turbulence anisotropy-based secondary flows, and flow separation - two common stumbling blocks in fluid flow simulations - without altering the successful law of the wall prediction by the original model. Two correction terms are carefully defined and introduced in the momentum and specific dissipation rate (ω) transport equations. After extensive numerical verification, the enhanced models exhibit significant improvements in the prediction of secondary flows, boundary-layer detachment and reattachment, and friction coefficients in both training and testing cases, reaffirming their a posteriori reliability and generalisability.In conclusion, this doctoral thesis presents a robust and cost-efficient methodology to systematically improve the performance of complex engineering systems based on CFD simulations. Additionally, two generalisable methods are developed to enhance turbulence modelling based on data-driven progressive approaches. By addressing challenges related to flow separation and secondary flow predictions in complex geometries together with design and shape optimisation techniques, this research contributes to more accurate fluid flow predictions and simulation-based optimisation; benefiting industries dependent on precise flow measurements and simulations. The synergy between data-driven modelling and complex system optimisation demonstrates the potential for innovative solutions in both turbulence modelling and flow measurement technologies, with broad implications across academic and industrial engineering sectors.

AB - The accurate measurement of fluid flow is essential in a wide range of industrial applications. In this regard, ultrasonic flow meters present an accurate and cost-effective solution; however, these systems introduce challenges due to their complex geometry and interaction with fluid flow. Hence, understanding their flow dynamics is paramount in the improvement of their design and operation. Simultaneously, data-driven turbulence modelling techniques have emerged as promising tools to improve the precision of fluid flow simulations. This doctoral thesis focuses on bridging these two domains by predicting complex flows in ultrasonic flow meters, developing a robust methodology for their geometrical optimisation, and enhancing existing Reynolds-averaged Navier-Stokes (RANS) turbulence models with progressive and generalisable data-driven methods.A methodology based on computational fluid dynamics (CFD) is developed to predict the flow dynamics throughout ultrasonic flow meters by the use of RANS k-ω SST. The method is experimentally validated with laser Doppler velocimetry and pressure drop experiments for the whole dynamic range of operation of these systems. Subsequently, a series of flow simulations are driven by surrogate and Bayesian multi-objective optimisation methods to provide improved geometrical designs. These improved geometries are further optimised by an adjoint-based shape optimisation method that enables the 3-dimensional morphing of the geometry, allowing higher improvement gains which cannot be achieved by traditional engineering ingenuity.In the realm of data-driven turbulence modelling, two critical aspects take centre stage: generalisability and the consistency of a posteriori results. This thesis addresses these challenges through a systematic approach that combines the CFD-driven optimisation techniques developed for flow meter improvement with a progressive augmentation strategy in the k-ω SST turbulence model. The goal is to improve the prediction of turbulence anisotropy-based secondary flows, and flow separation - two common stumbling blocks in fluid flow simulations - without altering the successful law of the wall prediction by the original model. Two correction terms are carefully defined and introduced in the momentum and specific dissipation rate (ω) transport equations. After extensive numerical verification, the enhanced models exhibit significant improvements in the prediction of secondary flows, boundary-layer detachment and reattachment, and friction coefficients in both training and testing cases, reaffirming their a posteriori reliability and generalisability.In conclusion, this doctoral thesis presents a robust and cost-efficient methodology to systematically improve the performance of complex engineering systems based on CFD simulations. Additionally, two generalisable methods are developed to enhance turbulence modelling based on data-driven progressive approaches. By addressing challenges related to flow separation and secondary flow predictions in complex geometries together with design and shape optimisation techniques, this research contributes to more accurate fluid flow predictions and simulation-based optimisation; benefiting industries dependent on precise flow measurements and simulations. The synergy between data-driven modelling and complex system optimisation demonstrates the potential for innovative solutions in both turbulence modelling and flow measurement technologies, with broad implications across academic and industrial engineering sectors.

KW - Fluid mechanics

KW - Optimisation

KW - CFD

KW - Turbulence modelling

M3 - Ph.D. thesis

BT - Robust Optimisation of Ultrasonic Flow Meters by Computational Fluid Dynamics and Enhanced Turbulence Modelling

PB - Aarhus University

ER -

Robust Optimisation of Ultrasonic Flow Meters by Computational Fluid Dynamics and Enhanced Turbulence Modelling

Abstract

Fingeraftryk

Publikation

Validating the design optimisation of ultrasonic flow meters using computational fluid dynamics and surrogate modelling

Turbulent flow in small-diameter ultrasonic flow meters: A numerical and experimental study

Citationsformater