sperimentazione e ottimizzazione avanzata applicate a … padova.pdf · dipartimento di ingegneria...

Dipartimento di Ingegneria Industriale

Sperimentazione e Ottimizzazione Avanzata

Applicate a Progettazione e Sviluppo di

Turbomacchine

Ardizzon G., Benini E., Cavazzini G., Pavesi G., Stoppato A.

University of Padova – Dep. Industrial Engineering

Giornata di studio sulle TurbomacchineBergamo, 15 Luglio 2016

- 01 -

DIPARTIMENTO DI INGEGNERIA INDUSTRIALE

Test Rigs and Resources for NumericalAnalyses

Close Rig for Centrifugal Pumps

Open Rig for Axial Pump

Open Rig for Centrifugal Pump

Open Rig for Francis and Kaplan Turbine

Open Rig for Pelton Turbine

Micro Pump Test Lab

Low Velocity Wind Tunnel

3 Test Rigs for Fan

Cluster of about 120 CPU with an average clock of 2.67GHz

- 02 -Prof. G. Pavesi | Sperimentazione e Ottimizzazione Avanzata Applicate a Progettazione e Sviluppo di

TurbomacchineGiornata di studio sulle Turbomacchine - Bergamo, 15 Luglio 2016


Research Activities on Turbomachineries

• Pump and Hydro Turbines

• Micro Pumps

• Fan

• Wind Turbine

• Gas Turbine

• Transonic Compressors

• Turboshaft

- 03 -Prof. G. Pavesi | Sperimentazione e Ottimizzazione Avanzata Applicate a Progettazione e Sviluppo di

TurbomacchineGiornata di studio sulle Turbomacchine - Bergamo, 15 Luglio 2016


Multi-objective Optimization Agorithms

- 04 -Prof. G. Pavesi | Sperimentazione e Ottimizzazione Avanzata Applicate a

Progettazione e Sviluppo di TurbomacchineGiornata di studio sulle Turbomacchine - Bergamo, 15 Luglio 2016

Response surface typesavailable:

- Parametric models;- Nonparametric models;- Artificial Neural

Networks;- Multivariate splines;- Radial basis functions;- Support vector

regression;- Regression Kriging;- Moving Least Squares;- …

Decision VariablesX=(x1, x2 ,.., xn)

(design parameters)

Mathematical Model(Objective function(s))

F(X)=(f1,f2 ,.., fm)

RegionAdaptation

Pareto Optimal SolutionsX*=(x*1, x*2 ,.., x*n)

OptimizationAlgorithm

Algoritms typesavailable:

Non Linear; Geometric;Dynaimc;GA;PSO;Hybrid;Neural-Network-Based;Stochastic;…

Turbomachinery and Energy System

Group

Principal Investigators: Ardizzon G. Benini E.


- 05 -

Radial Size

Axial Size

Overall size

Head

Efficiency

Prof. G. Pavesi | Sperimentazione e Ottimizzazione Avanzata Applicate a Progettazione e Sviluppo di Turbomacchine

Giornata di studio sulle Turbomacchine - Bergamo, 15 Luglio 2016


Group

PumpsSize Reduction

Cavitation Optimization

Principal Investigators: Ardizzon G. Pavesi G.


PumpsSize Reduction

Cavitation Optimization

- 06 -

Optimization of the cavitating behaviour and of the performance

and size reduction in cellular/multistage pump design




Group

Principal Investigator: Ardizzon G.


Micro PumpsPerformance Optimization




Group

Applications: BiomedicalMicro cooling

Principal Investigator: Pavesi G.


Micro PumpsPerformance Optimization/ Blood

Volume Damaged Reduction




Group

Blo

od

Vo

lum

e D

am

ag

ed %



Hydro TurbineOptimization of the Pelton

Bucket Geometry

- 09 -

Optimization of the Pelton bucket

geometry by hybrid Eulerian-Lagrangian

numerical approach

Variable-speed Pelton turbine




Group



Hydro TurbineVariable-speed




Group

• Commissioning testshave been carried outin a 12 months period

• Results showedcorrespondence withthe data obtained byexperimental trials

The use of a variable-speed system led to an increase of the plant’s averageefficiency of about 11% that can be translated into an extra 300MWhproduction each year.



Hydro TurbineCavitation




Group

1. Describing the formation of clouds

2. Understanding the dynamic behavior of clouds and its consequences

collapse, impact

3. Predicting the surface stress by collapsing clouds and bubblesmaterial damage



Hydro TurbineMini Hydro Turbine




Group

• Influence Scale/rpm Effects• New Standardized Design Criteria• Strict Control of Cavitation

Max guarantee

Min guarantee


Test in Field


Hydro Pump/Turbine StoragePressure Pulsation Reduction




Group

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.00 0.20 0.40 0.60 0.80 1.00

Pres

ure

Coef

fici

ent

t fblade

Experimental Data Incompressible Compressible



-0.06

-0.02

0.02

0.06

0.4580.4680.4780.488

Cp

n ED

D1

Hydro Pump/Turbine StorageLoad Reduction Scenario




Group

Turbine

Pump

- - - -

Principal Investigators: Pavesi G. Cavazzini G.


FanEnergy Consumption

Noise Reduction




Group

5500

6000

6500

7000

7500

8000

8500

0.4 0.5 0.6 0.7 0.8 0.9 1.0

rpm

Flow Rate/Flow Rate Max

Reference

Optimized

0.70

0.75

0.80

0.85

0.90

0.95

1.00

1.05

0.4 0.5 0.6 0.7 0.8 0.9 1.0

Pow

er /

Pow

er M

ax

Flow Rate/Flow Rate Max

Reference

Optimized



Horizontal Axis Wind TurbineCoupled Aerodynamic-Structural Wind

Turbine Blade Optimization




Group

Principal Investigator: Ernesto BeniniGroup: Andrea Dal Monte, Stefano De Betta, Gabriele Bedon, Hagar Elarga

Objectives: Maximize Annual Energy Production (AEP), Maximize rotor stiffness, Maximum aeroelastic response under lateral flow and wind gusts.

Methodology: in-house Multi-objective surrogate-assisted optimization algorithm + validated aero-codes (BEM and/or CFD) + FEM structural codes

Achievements: Composite layup optimized along with chord/twist distribution (Pareto solutions); Optimal rotor configuration for unsteady gusts


Vertical Axis Wind TurbineAerodynamic-Structural VAWT Turbine Blade

Optimization




Group

Principal Investigator: Ernesto BeniniGroup: Andrea Dal Monte, Stefano De Betta, Gabriele Bedon, Hagar Elarga

Objectives: Maximize rotor total torqueand maximize rotor upwind torque

Methodology: 2D URANS + FEM

Achievements: New profile named “WUP 1615” characterized by a peak power coefficient value 8% higher than the baseline configuration (patent pending).


Vertical Axis Wind TurbineSelf Starting Turbine




Group

Urban and Sub-Urban Area:

Low Visual Impact

Low Noise < 30 dB

Power < 3 kW

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0

Cp

/ C

p

ma

x

l[-]

Reference Test_120_05 Ibrid Darrieus Savonius



Gas Turbine (LPT)Multi-criteria turbine cascade

optimization




Group

Principal Investigator: Ernesto Benini (EU Clean Sky “iTURB”, Topic Leader: GE-Avio)Group: Lorenzo Dalmas, Francesco Pellegrino, G. A. Misté

Objectives: Assess best strategies for LPT optimization including efficiency, weight, acoustics, structural objectives

Methodology: Gradient-based vs. population-based vs. DOE+surrogates algorithms and quasi 3-D code

Achievements: Best strategy for preliminary to detailed multi-criteria optimization


Transonic CompressorTransonic compressors optimization




Group

Principal Investigator: Ernesto BeniniGroup: Francesco De Vanna, Roberto Biollo, Giovanni Venturelli

Objectives: Maximize rotor efficiency, pressure ratio and stall margin

Methodology: in-house Multi-objective surrogate-assisted optimization algorithm + validated aero-codes

Achievements: -1.5% reduction in total pressure losses, +8% in ressure ratio, +4% in stall margin


TurboshaftMulti-objective optimization of

turboshaft air intakes and exhausts




Group

Principal Investigator: Ernesto BeniniGroup: Andrea Garavello, Rita Ponza (EU Clean Sky“TilTOp”, Topic Leader: AgustaWestland)

Objectives: Maximize Intake/exhaust pressure ratio, minimize flow distortions

Methodology: in-house Multi-objective surrogate-assisted optimization algorithm + validated aero-codes

Achievements: -2.1% reduction in fuel consumption in cruise, -1.5% in hover (experimental). Tests conducted at RUAG wind Tunnel (CH).


INDUSTRIAL FANS DESIGN:

i) High-efficiency tube-axial fansii) Cross-flow fans

Massimo MASI 1 et al. 2

University of Padova

1 Department of Management and Engineering2 Department of Industrial Engineering

Coordinamento Nazionale dei Professori di

Macchine a Fluido e Sistemi per l’Energia e l’Ambiente

Bergamo 15 luglio 2016 - Giornata di studio sulle Turbomacchine:

«I gruppi di ricerca delle Università Italiane incontrano le imprese”

Modelling

Analysis and

Research in

Turbomachinery and

Energy

Systems

Modelling

Analysis and

Research in

Turbomachinery

and

Energy

Systems

MASI et al.i) High-efficiency tube-axial fans

• From experimental testing & validated CFD modelsOperation of actual non free-vortex bladings [1]

Basic theory for swept fan rotors [3]

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

-0.5 0 0.5 1 1.5downstream axial velocity ratio

non

dim

ensi

onal

radi

al c

oord

inat

e x

Σa M2

Σa M3

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 0.5 1 1.5upstream axial velocity ratio

non

dim

ensi

onal

radi

al c

oord

inat

e x

Σa,up M2

Σa,up M3

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0 0.2 0.4 0.6 0.8 1.0downstream tangential velocity ratio

non

dim

ensi

onal

radi

al c

oord

inat

e x

εs M2

εs M3

Coordinamento Nazionale dei Professori di Macchine a Fluido e Sistemi per l’Energia e l’Ambiente

Giornata di studio sulle Turbomacchine: «I gruppi di ricerca delle Università

Italiane incontrano le imprese” – Bergamo 15 luglio 2016

Modelling

Analysis and

Research in

Turbomachinery

and

Energy

Systems

• From basic theory for swept fan rotors: Increase of performance [2] & efficiency [4] of existing fans

Preliminary design of non Free-Vortex fans (speed/size constraints) [5]

MASI et al.

i) High-efficiency tube-axial fans




Modelling

Analysis and

Research in

Turbomachinery

and

Energy

Systems

MASI et al.

ii) Cross-flow fans




• From experimental analysis of cross-flow fans:

Optimal design under casing constraints [6]

[1] Masi, M., Lazzaretto, A., 2012, “CFD models for the analysis of rotor-only industrial axial-flow fans”, FAN 2012.

[2] Masi, M., Piva, M., Lazzaretto, A., 2014, “Design guidelines to increase the performance of a rotor-only axial fan with constant-swirl blading”, ASME paperGT2014-27176.

[3] Masi, M., Lazzaretto, A., 2015, “A simplified Theory to justify forward sweep in low hub-to-tip ratio axial fan”, ASME paper GT2015-43029.

[4] Masi, M, Castegnaro, S., Lazzaretto, A., 2016, “Forward sweep to improve the efficiency of rotor-only tube-axial fans with controlled vortex design blades”,Proc. IMechE, Part A: J. Power and Energy, doi:10.1177/0957650915625520.

[5] Masi, M, Castegnaro, S., Lazzaretto, A., 2016, “A criterion for the preliminary design of high-efficiency tube-axial fans”, ASME paper GT2016-56690.

[6] Spinola, M., Gobbato, P., Lazzaretto, A., Masi. M.,”Effect of reduced suction side volume on cross-flow fan performance, FAN 2015.

Modelling

Analysis and

Research in

Turbomachinery

and

Energy

Systems

Modelling

Analysis and

Research in

Turbomachinery

and

Energy

Systems



Department of Industrial Engineering

Research on Turbomachinery

Andrea Lazzaretto Giovanni Manente Luca Da Lio


Department of Industrial Engineering

Via Venezia, 1 35131 Padova

26

Bergamo 15 luglio 2016 - Giornata di studio sulle Turbomacchine:

«I gruppi di ricerca delle Università Italiane incontrano le imprese”

Lazzaretto et al.

Design of turbines for ORC applications

27

Geometry

Efficiency maps

Aim: predicting the design and efficiency of axial/radial turbinesoperating with any kind of organic fluid

Lazzaretto et al.

Inclusion of turbine efficiency maps in a design optimization tool of ORC systems

28

Max ORC system efficiency (black point) is generally obtained at non-max efficiencyof the turbine

References: 1. Manente G., Da Lio L., Lazzaretto A., 2016, Influence of axial turbine efficiency maps on the performance of subcritical and supercritical Organic RankineCycle systems, Energy, Volume 107, 15 July 2016, Pages 761–772.2. Da Lio L., Manente G., Lazzaretto A., 2016, Predicting the optimum design of single stage axial expanders in ORC systems: Is there a single efficiencymap for different working fluids? Applied Energy, Volume 167, 1 April, Pages 44-58.3. Da Lio L., Manente G., Lazzaretto A., 2015, On the Optimum axial flow turbine design in organic Rankine cycles, Poster in the 3rd International Seminar on ORC Power Systems, ASME, Brussels, Belgium, October 12-14.4. Da Lio L., Manente G., Lazzaretto A., 2014, New efficiency charts for the optimum design of axial flow turbines for organic Rankine cycles, Energy,Volume 77, 1 December, Pages 447-459.5. Lazzaretto A., Manente G., 2014, A New Criterion to Optimize ORC Design Performance using Efficiency Correlations for Axial and Radial Turbines, International Journal of Thermodynamics, (IJoT) Vol. 17 (No. 3), September, pp. 173-181.

sperimentazione e ottimizzazione avanzata applicate a … padova.pdf · dipartimento di ingegneria...

Documents