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Prediction of the Open-Water Performance of Ducted Propellers With a Panel Method

This document discusses the prediction of open-water performance for ducted propellers using a panel method. Panel methods provide a computationally efficient tool for analysis and design of marine propulsors. Application to ducted propellers involves additional modeling issues such as the complex interaction between propeller blades and duct surfaces. The document reviews previous work developing the panel method for ducted propellers, including models for the gap flow, wake alignment, duct boundary layers, and new Kutta conditions. It presents comparisons of panel method predictions to experimental and RANS results. The motivation is to improve performance predictions near bollard pull conditions through refinements to the numerical methods.

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Prediction of the Open-Water Performance of Ducted Propellers With a Panel Method J. Baltazar1, D. Rijpkema2, J.A.C. Falc˜ao de Campos1, J. Bosschers2 1Instituto Superior T´ecnico, Universidade de Lisboa, Portugal 2Maritime Research Institute Netherlands, Wageningen, the Netherlands smp’17 Espoo, Finland June 12-15 1
Introduction smp’17 Espoo, Finland June 12-15 2
Introduction Panel Methods (or BEM) still provide a most useful computational tool for analysis and design of marine propulsors smp’17 Espoo, Finland June 12-15 2
Introduction Panel Methods (or BEM) still provide a most useful computational tool for analysis and design of marine propulsors Main reasons for using Panel Methods: smp’17 Espoo, Finland June 12-15 2
Introduction Panel Methods (or BEM) still provide a most useful computational tool for analysis and design of marine propulsors Main reasons for using Panel Methods: Simplicity smp’17 Espoo, Finland June 12-15 2
Introduction Panel Methods (or BEM) still provide a most useful computational tool for analysis and design of marine propulsors Main reasons for using Panel Methods: Simplicity Computational efficiency smp’17 Espoo, Finland June 12-15 2
Introduction Panel Methods (or BEM) still provide a most useful computational tool for analysis and design of marine propulsors Main reasons for using Panel Methods: Simplicity Computational efficiency Direct relation to simpler design tools (lifting line and lifting surface) smp’17 Espoo, Finland June 12-15 2
Introduction Panel Methods (or BEM) still provide a most useful computational tool for analysis and design of marine propulsors Main reasons for using Panel Methods: Simplicity Computational efficiency Direct relation to simpler design tools (lifting line and lifting surface) Application to ducted propellers involves additional modelling issues: smp’17 Espoo, Finland June 12-15 2
Introduction Panel Methods (or BEM) still provide a most useful computational tool for analysis and design of marine propulsors Main reasons for using Panel Methods: Simplicity Computational efficiency Direct relation to simpler design tools (lifting line and lifting surface) Application to ducted propellers involves additional modelling issues: Complex interaction of propeller blades and duct surface – Gap flow – smp’17 Espoo, Finland June 12-15 2
Introduction Panel Methods (or BEM) still provide a most useful computational tool for analysis and design of marine propulsors Main reasons for using Panel Methods: Simplicity Computational efficiency Direct relation to simpler design tools (lifting line and lifting surface) Application to ducted propellers involves additional modelling issues: Complex interaction of propeller blades and duct surface – Gap flow – Thick duct trailing edges in practical applications – Kutta condition for round trailing edges – smp’17 Espoo, Finland June 12-15 2
Introduction Panel Methods (or BEM) still provide a most useful computational tool for analysis and design of marine propulsors Main reasons for using Panel Methods: Simplicity Computational efficiency Direct relation to simpler design tools (lifting line and lifting surface) Application to ducted propellers involves additional modelling issues: Complex interaction of propeller blades and duct surface – Gap flow – Thick duct trailing edges in practical applications – Kutta condition for round trailing edges – smp’17 Espoo, Finland June 12-15 2
Previous Work smp’17 Espoo, Finland June 12-15 3
Previous Work Rigid wake model, open and closed gap models (smp’09) smp’17 Espoo, Finland June 12-15 3
Previous Work Rigid wake model, open and closed gap models (smp’09) Wake alignment model for the blade wake, closed and transpiration velocity gap models, duct boundary layer correction (smp’11) smp’17 Espoo, Finland June 12-15 3
Previous Work Rigid wake model, open and closed gap models (smp’09) Wake alignment model for the blade wake, closed and transpiration velocity gap models, duct boundary layer correction (smp’11) Panel method results compared with RANS calculations and experimental open-water measurements (smp’13) smp’17 Espoo, Finland June 12-15 3
Previous Work Rigid wake model, open and closed gap models (smp’09) Wake alignment model for the blade wake, closed and transpiration velocity gap models, duct boundary layer correction (smp’11) Panel method results compared with RANS calculations and experimental open-water measurements (smp’13) New Kutta condition for thick duct trailing edges (smp’15) smp’17 Espoo, Finland June 12-15 3
Previous Work Rigid wake model, open and closed gap models (smp’09) Wake alignment model for the blade wake, closed and transpiration velocity gap models, duct boundary layer correction (smp’11) Panel method results compared with RANS calculations and experimental open-water measurements (smp’13) New Kutta condition for thick duct trailing edges (smp’15) RANS-BEM coupling for thick duct trailing edges (smp’15) smp’17 Espoo, Finland June 12-15 3
Duct Geometries smp’17 Espoo, Finland June 12-15 4
Open-Water Prediction Ka4-70 (P/D=1.0) inside Duct 19Am (shart duct t.e.) J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Experiments: Re=ΩR/ν=1.23×10 6 KT 10KQ KTD P smp’17 Espoo, Finland June 12-15 5
Open-Water Prediction Ka4-70 (P/D=1.0) inside Duct 19Am (shart duct t.e.) J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Experiments: Re=ΩR/ν=1.23×10 6 KT 10KQ KTD P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Experiments: Re=ΩR/ν=1.23×10 6 Rigid Wake Model KT 10KQ KTD P smp’17 Espoo, Finland June 12-15 5
Open-Water Prediction Ka4-70 (P/D=1.0) inside Duct 19Am (shart duct t.e.) J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Experiments: Re=ΩR/ν=1.23×10 6 KT 10KQ KTD P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Experiments: Re=ΩR/ν=1.23×10 6 Rigid Wake Model KT 10KQ KTD P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Experiments: Re=ΩR/ν=1.23×10 6 Rigid Wake Model Wake Alignment Model (WAM) KT 10KQ KTD P smp’17 Espoo, Finland June 12-15 5
Open-Water Prediction Ka4-70 (P/D=1.0) inside Duct 19Am (shart duct t.e.) J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Experiments: Re=ΩR/ν=1.23×10 6 KT 10KQ KTD P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Experiments: Re=ΩR/ν=1.23×10 6 Rigid Wake Model KT 10KQ KTD P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Experiments: Re=ΩR/ν=1.23×10 6 Rigid Wake Model Wake Alignment Model (WAM) KT 10KQ KTD P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Experiments: Re=ΩR/ν=1.23×10 6 Rigid Wake Model Wake Alignment Model (WAM) WAM with Duct Boundary Layer Correction KT 10KQ KTD P smp’17 Espoo, Finland June 12-15 5
New Wake Model Loading is critically dependent on the blade wake pitch, especially at tip RANS Panel Method smp’17 Espoo, Finland June 12-15 6
New Kutta Condition Location of the pressure-equality points as percentage of the duct length 0.40 0.45 0.50 0.55 0.60 Duct 19A 100.0% 99.9% 99.8% 99.5% 99.0% 97.0% 96.5% 98.0% smp’17 Espoo, Finland June 12-15 7
Open-Water Prediction Ka4-70 (P/D=1.0) inside Duct 19A J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments WAM, Closed Gap, Location at 97%, δ/R=4% WAM, Closed Gap, Location at 98%, δ/R=4% WAM, Closed Gap, Location at 99%, δ/R=4% KT 10KQ KT D P smp’17 Espoo, Finland June 12-15 8
Motivation smp’17 Espoo, Finland June 12-15 9
Motivation Panel Method: smp’17 Espoo, Finland June 12-15 9
Motivation Panel Method: Gap model (closed) smp’17 Espoo, Finland June 12-15 9
Motivation Panel Method: Gap model (closed) Wake alignment model smp’17 Espoo, Finland June 12-15 9
Motivation Panel Method: Gap model (closed) Wake alignment model Correction due to duct boundary layer smp’17 Espoo, Finland June 12-15 9
Motivation Panel Method: Gap model (closed) Wake alignment model Correction due to duct boundary layer New Kutta condition for thick duct t.e. smp’17 Espoo, Finland June 12-15 9
Motivation Panel Method: Gap model (closed) Wake alignment model Correction due to duct boundary layer New Kutta condition for thick duct t.e. Significant differences in the performance prediction close to bollard pull condition! smp’17 Espoo, Finland June 12-15 9
Motivation Panel Method: Gap model (closed) Wake alignment model Correction due to duct boundary layer New Kutta condition for thick duct t.e. Significant differences in the performance prediction close to bollard pull condition! Comparison between Panel Method and RANS calculations smp’17 Espoo, Finland June 12-15 9
Motivation Panel Method: Gap model (closed) Wake alignment model Correction due to duct boundary layer New Kutta condition for thick duct t.e. Significant differences in the performance prediction close to bollard pull condition! Comparison between Panel Method and RANS calculations smp’17 Espoo, Finland June 12-15 9
Numerical Methods smp’17 Espoo, Finland June 12-15 10
Numerical Methods Panel Code PROPAN smp’17 Espoo, Finland June 12-15 10
Numerical Methods Panel Code PROPAN IST in-house low-order potential-based panel method smp’17 Espoo, Finland June 12-15 10
Numerical Methods Panel Code PROPAN IST in-house low-order potential-based panel method Fredholm integral equation solved by the collocation method smp’17 Espoo, Finland June 12-15 10
Numerical Methods Panel Code PROPAN IST in-house low-order potential-based panel method Fredholm integral equation solved by the collocation method Structured surface grids smp’17 Espoo, Finland June 12-15 10
Numerical Methods Panel Code PROPAN IST in-house low-order potential-based panel method Fredholm integral equation solved by the collocation method Structured surface grids RANS Code ReFRESCO smp’17 Espoo, Finland June 12-15 10
Numerical Methods Panel Code PROPAN IST in-house low-order potential-based panel method Fredholm integral equation solved by the collocation method Structured surface grids RANS Code ReFRESCO Viscous flow CFD code (development led by MARIN) smp’17 Espoo, Finland June 12-15 10
Numerical Methods Panel Code PROPAN IST in-house low-order potential-based panel method Fredholm integral equation solved by the collocation method Structured surface grids RANS Code ReFRESCO Viscous flow CFD code (development led by MARIN) Finite volume discretisation smp’17 Espoo, Finland June 12-15 10
Numerical Methods Panel Code PROPAN IST in-house low-order potential-based panel method Fredholm integral equation solved by the collocation method Structured surface grids RANS Code ReFRESCO Viscous flow CFD code (development led by MARIN) Finite volume discretisation Turbulence model: k − ω SST 2-equation model by Menter (1994) smp’17 Espoo, Finland June 12-15 10
Numerical Methods Panel Code PROPAN IST in-house low-order potential-based panel method Fredholm integral equation solved by the collocation method Structured surface grids RANS Code ReFRESCO Viscous flow CFD code (development led by MARIN) Finite volume discretisation Turbulence model: k − ω SST 2-equation model by Menter (1994) No wal functions are used (y+ ∼ 1) smp’17 Espoo, Finland June 12-15 10
Grids PROPAN (left) and ReFRESCO (right) Blade: 50×26 27M Cells Duct: 190×160 smp’17 Espoo, Finland June 12-15 11
Blade Pressure Distribution at J = 0.5 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.5 0.0 0.5 1.0 RWM r/R=0.95 Cp=-1.0 smp’17 Espoo, Finland June 12-15 12
Blade Pressure Distribution at J = 0.5 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.5 0.0 0.5 1.0 RWM r/R=0.95 Cp=-1.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.5 0.0 0.5 1.0 RWM WAM r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -0.5 0.0 0.5 1.0 smp’17 Espoo, Finland June 12-15 12
Blade Pressure Distribution at J = 0.5 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.5 0.0 0.5 1.0 RWM r/R=0.95 Cp=-1.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.5 0.0 0.5 1.0 RWM WAM r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -0.5 0.0 0.5 1.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.5 0.0 0.5 1.0 ReFRESCO RWM WAM r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -0.5 0.0 0.5 1.0 smp’17 Espoo, Finland June 12-15 12
Duct Pressure Distribution at J = 0.5 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.2 -0.1 0.0 0.1 RWM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 smp’17 Espoo, Finland June 12-15 13
Duct Pressure Distribution at J = 0.5 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.2 -0.1 0.0 0.1 RWM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.2 -0.1 0.0 0.1 RWM WAM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 smp’17 Espoo, Finland June 12-15 13
Duct Pressure Distribution at J = 0.5 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.2 -0.1 0.0 0.1 RWM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.2 -0.1 0.0 0.1 RWM WAM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.2 -0.1 0.0 0.1 ReFRESCO RWM WAM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 smp’17 Espoo, Finland June 12-15 13
Blade Pressure Distribution at J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM r/R=0.95 Cp =-4.0 smp’17 Espoo, Finland June 12-15 14
Blade Pressure Distribution at J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM r/R=0.95 Cp =-4.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM WAM r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 smp’17 Espoo, Finland June 12-15 14
Blade Pressure Distribution at J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM r/R=0.95 Cp =-4.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM WAM r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 ReFRESCO RWM WAM r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 smp’17 Espoo, Finland June 12-15 14
Duct Pressure Distribution at J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM θ=0º smp’17 Espoo, Finland June 12-15 15
Duct Pressure Distribution at J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM θ=0º s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM WAM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 smp’17 Espoo, Finland June 12-15 15
Duct Pressure Distribution at J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM θ=0º s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM WAM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 ReFRESCO RWM WAM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 smp’17 Espoo, Finland June 12-15 15
Reduced Gap Pitch (P/D=0.9) X Y Z Blade Blade Wake Gap Strip smp’17 Espoo, Finland June 12-15 16
Reduced Gap Pitch (P/D=0.9) X Y Z Blade Blade Wake Gap Strip X Y Z Blade Blade Wake Gap Strip smp’17 Espoo, Finland June 12-15 16
Tip Leakage Flow smp’17 Espoo, Finland June 12-15 17
Pressure Distribution at J = 0.5 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.5 0.0 0.5 1.0 ReFRESCO RWM WAM WAM with Reduced Gap Pitch r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -0.5 0.0 0.5 1.0 smp’17 Espoo, Finland June 12-15 18
Pressure Distribution at J = 0.5 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.2 -0.1 0.0 0.1 ReFRESCO RWM WAM WAM with Reduced Gap Pitch θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 smp’17 Espoo, Finland June 12-15 19
Blade Pressure Distribution at J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM r/R=0.95 Cp =-4.0 smp’17 Espoo, Finland June 12-15 20
Blade Pressure Distribution at J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM r/R=0.95 Cp =-4.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM WAM r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 smp’17 Espoo, Finland June 12-15 20
Blade Pressure Distribution at J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM r/R=0.95 Cp =-4.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM WAM r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM WAM WAM with Reduced Gap Pitch r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 smp’17 Espoo, Finland June 12-15 20
Blade Pressure Distribution at J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM r/R=0.95 Cp =-4.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM WAM r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM WAM WAM with Reduced Gap Pitch r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 ReFRESCO RWM WAM WAM with Reduced Gap Pitch r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 smp’17 Espoo, Finland June 12-15 20
Duct Pressure Distribution at J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM θ=0º smp’17 Espoo, Finland June 12-15 21
Duct Pressure Distribution at J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM θ=0º s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM WAM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 smp’17 Espoo, Finland June 12-15 21
Duct Pressure Distribution at J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM θ=0º s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM WAM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM WAM WAM with Reduced Gap Pitch θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 smp’17 Espoo, Finland June 12-15 21
Duct Pressure Distribution at J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM θ=0º s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM WAM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM WAM WAM with Reduced Gap Pitch θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 ReFRESCO RWM WAM WAM with Reduced Gap Pitch θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 smp’17 Espoo, Finland June 12-15 21
Open-Water Prediction Ka4-70 (P/D=1.0) inside Duct 19A J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments KT 10KQ KT D P smp’17 Espoo, Finland June 12-15 22
Open-Water Prediction Ka4-70 (P/D=1.0) inside Duct 19A J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments KT 10KQ KT D P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments ReFRESCO KT 10KQ KT D P smp’17 Espoo, Finland June 12-15 22
Open-Water Prediction Ka4-70 (P/D=1.0) inside Duct 19A J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments KT 10KQ KT D P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments ReFRESCO KT 10KQ KT D P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments ReFRESCO WAM KT 10KQ KT D P smp’17 Espoo, Finland June 12-15 22
Open-Water Prediction Ka4-70 (P/D=1.0) inside Duct 19A J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments KT 10KQ KT D P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments ReFRESCO KT 10KQ KT D P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments ReFRESCO WAM KT 10KQ KT D P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments ReFRESCO WAM WAM with Reduced Gap Pitch KT 10KQ KT D P smp’17 Espoo, Finland June 12-15 22
Conclusions smp’17 Espoo, Finland June 12-15 23
Conclusions Prediction of the ducted propeller loading is critically dependent on the blade wake pitch, especially at tip! smp’17 Espoo, Finland June 12-15 23
Conclusions Prediction of the ducted propeller loading is critically dependent on the blade wake pitch, especially at tip! The flow in the tip region is particularly important near bollard pull smp’17 Espoo, Finland June 12-15 23
Conclusions Prediction of the ducted propeller loading is critically dependent on the blade wake pitch, especially at tip! The flow in the tip region is particularly important near bollard pull Future work: smp’17 Espoo, Finland June 12-15 23
Conclusions Prediction of the ducted propeller loading is critically dependent on the blade wake pitch, especially at tip! The flow in the tip region is particularly important near bollard pull Future work: Include reduction of the gap strip in wake alignment model smp’17 Espoo, Finland June 12-15 23
Conclusions Prediction of the ducted propeller loading is critically dependent on the blade wake pitch, especially at tip! The flow in the tip region is particularly important near bollard pull Future work: Include reduction of the gap strip in wake alignment model Robustness issues are expected smp’17 Espoo, Finland June 12-15 23
Pressure Distribution at J = 0.2 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 ReFRESCO RWM WAM WAM with Reduced Gap Pitch r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 ReFRESCO RWM WAM WAM with Reduced Gap Pitch θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 smp’17 Espoo, Finland June 12-15 24
Wake Geometry at J = 0.2 x/R = 0.2 x/R = 0.4 smp’17 Espoo, Finland June 12-15 25
Wake Geometry at J = 0.2 smp’17 Espoo, Finland June 12-15 26
Wake Geometry at J = 0.1 x/R = 0.2 x/R = 0.4 smp’17 Espoo, Finland June 12-15 27
Wake Geometry at J = 0.1 smp’17 Espoo, Finland June 12-15 28
Wake Geometry at J = 0.5 x/R = 0.2 x/R = 0.4 smp’17 Espoo, Finland June 12-15 29
Influence of the Wake Model Model KTP KTD 10KQ J = 0.1 RWM 0.412 0.206 0.5882 WAM 0.313 0.231 0.4664 WAM with Reduced Gap Pitch 0.284 0.226 0.4228 Experiments 0.254 0.214 0.4387 J = 0.2 RWM 0.383 0.160 0.5538 WAM 0.297 0.176 0.4456 WAM with Reduced Gap Pitch 0.273 0.171 0.4083 Experiments 0.248 0.166 0.4279 J = 0.5 RWM 0.266 0.054 0.4041 WAM 0.208 0.057 0.3246 WAM with Reduced Gap Pitch 0.193 0.055 0.3010 Experiments 0.196 0.053 0.3506 smp’17 Espoo, Finland June 12-15 30
Wake Model Rigid wake X Y Z smp’17 Espoo, Finland June 12-15 31
Wake Model Aligned wake with b.l. correction X Y Z smp’17 Espoo, Finland June 12-15 32

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Prediction of the Open-Water Performance of Ducted Propellers With a Panel Method

  • 1.
    Prediction of theOpen-Water Performance of Ducted Propellers With a Panel Method J. Baltazar1, D. Rijpkema2, J.A.C. Falc˜ao de Campos1, J. Bosschers2 1Instituto Superior T´ecnico, Universidade de Lisboa, Portugal 2Maritime Research Institute Netherlands, Wageningen, the Netherlands smp’17 Espoo, Finland June 12-15 1
  • 2.
  • 3.
    Introduction Panel Methods (orBEM) still provide a most useful computational tool for analysis and design of marine propulsors smp’17 Espoo, Finland June 12-15 2
  • 4.
    Introduction Panel Methods (orBEM) still provide a most useful computational tool for analysis and design of marine propulsors Main reasons for using Panel Methods: smp’17 Espoo, Finland June 12-15 2
  • 5.
    Introduction Panel Methods (orBEM) still provide a most useful computational tool for analysis and design of marine propulsors Main reasons for using Panel Methods: Simplicity smp’17 Espoo, Finland June 12-15 2
  • 6.
    Introduction Panel Methods (orBEM) still provide a most useful computational tool for analysis and design of marine propulsors Main reasons for using Panel Methods: Simplicity Computational efficiency smp’17 Espoo, Finland June 12-15 2
  • 7.
    Introduction Panel Methods (orBEM) still provide a most useful computational tool for analysis and design of marine propulsors Main reasons for using Panel Methods: Simplicity Computational efficiency Direct relation to simpler design tools (lifting line and lifting surface) smp’17 Espoo, Finland June 12-15 2
  • 8.
    Introduction Panel Methods (orBEM) still provide a most useful computational tool for analysis and design of marine propulsors Main reasons for using Panel Methods: Simplicity Computational efficiency Direct relation to simpler design tools (lifting line and lifting surface) Application to ducted propellers involves additional modelling issues: smp’17 Espoo, Finland June 12-15 2
  • 9.
    Introduction Panel Methods (orBEM) still provide a most useful computational tool for analysis and design of marine propulsors Main reasons for using Panel Methods: Simplicity Computational efficiency Direct relation to simpler design tools (lifting line and lifting surface) Application to ducted propellers involves additional modelling issues: Complex interaction of propeller blades and duct surface – Gap flow – smp’17 Espoo, Finland June 12-15 2
  • 10.
    Introduction Panel Methods (orBEM) still provide a most useful computational tool for analysis and design of marine propulsors Main reasons for using Panel Methods: Simplicity Computational efficiency Direct relation to simpler design tools (lifting line and lifting surface) Application to ducted propellers involves additional modelling issues: Complex interaction of propeller blades and duct surface – Gap flow – Thick duct trailing edges in practical applications – Kutta condition for round trailing edges – smp’17 Espoo, Finland June 12-15 2
  • 11.
    Introduction Panel Methods (orBEM) still provide a most useful computational tool for analysis and design of marine propulsors Main reasons for using Panel Methods: Simplicity Computational efficiency Direct relation to simpler design tools (lifting line and lifting surface) Application to ducted propellers involves additional modelling issues: Complex interaction of propeller blades and duct surface – Gap flow – Thick duct trailing edges in practical applications – Kutta condition for round trailing edges – smp’17 Espoo, Finland June 12-15 2
  • 12.
    Previous Work smp’17 Espoo,Finland June 12-15 3
  • 13.
    Previous Work Rigid wakemodel, open and closed gap models (smp’09) smp’17 Espoo, Finland June 12-15 3
  • 14.
    Previous Work Rigid wakemodel, open and closed gap models (smp’09) Wake alignment model for the blade wake, closed and transpiration velocity gap models, duct boundary layer correction (smp’11) smp’17 Espoo, Finland June 12-15 3
  • 15.
    Previous Work Rigid wakemodel, open and closed gap models (smp’09) Wake alignment model for the blade wake, closed and transpiration velocity gap models, duct boundary layer correction (smp’11) Panel method results compared with RANS calculations and experimental open-water measurements (smp’13) smp’17 Espoo, Finland June 12-15 3
  • 16.
    Previous Work Rigid wakemodel, open and closed gap models (smp’09) Wake alignment model for the blade wake, closed and transpiration velocity gap models, duct boundary layer correction (smp’11) Panel method results compared with RANS calculations and experimental open-water measurements (smp’13) New Kutta condition for thick duct trailing edges (smp’15) smp’17 Espoo, Finland June 12-15 3
  • 17.
    Previous Work Rigid wakemodel, open and closed gap models (smp’09) Wake alignment model for the blade wake, closed and transpiration velocity gap models, duct boundary layer correction (smp’11) Panel method results compared with RANS calculations and experimental open-water measurements (smp’13) New Kutta condition for thick duct trailing edges (smp’15) RANS-BEM coupling for thick duct trailing edges (smp’15) smp’17 Espoo, Finland June 12-15 3
  • 18.
    Duct Geometries smp’17 Espoo,Finland June 12-15 4
  • 19.
    Open-Water Prediction Ka4-70 (P/D=1.0)inside Duct 19Am (shart duct t.e.) J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Experiments: Re=ΩR/ν=1.23×10 6 KT 10KQ KTD P smp’17 Espoo, Finland June 12-15 5
  • 20.
    Open-Water Prediction Ka4-70 (P/D=1.0)inside Duct 19Am (shart duct t.e.) J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Experiments: Re=ΩR/ν=1.23×10 6 KT 10KQ KTD P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Experiments: Re=ΩR/ν=1.23×10 6 Rigid Wake Model KT 10KQ KTD P smp’17 Espoo, Finland June 12-15 5
  • 21.
    Open-Water Prediction Ka4-70 (P/D=1.0)inside Duct 19Am (shart duct t.e.) J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Experiments: Re=ΩR/ν=1.23×10 6 KT 10KQ KTD P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Experiments: Re=ΩR/ν=1.23×10 6 Rigid Wake Model KT 10KQ KTD P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Experiments: Re=ΩR/ν=1.23×10 6 Rigid Wake Model Wake Alignment Model (WAM) KT 10KQ KTD P smp’17 Espoo, Finland June 12-15 5
  • 22.
    Open-Water Prediction Ka4-70 (P/D=1.0)inside Duct 19Am (shart duct t.e.) J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Experiments: Re=ΩR/ν=1.23×10 6 KT 10KQ KTD P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Experiments: Re=ΩR/ν=1.23×10 6 Rigid Wake Model KT 10KQ KTD P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Experiments: Re=ΩR/ν=1.23×10 6 Rigid Wake Model Wake Alignment Model (WAM) KT 10KQ KTD P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Experiments: Re=ΩR/ν=1.23×10 6 Rigid Wake Model Wake Alignment Model (WAM) WAM with Duct Boundary Layer Correction KT 10KQ KTD P smp’17 Espoo, Finland June 12-15 5
  • 23.
    New Wake Model Loadingis critically dependent on the blade wake pitch, especially at tip RANS Panel Method smp’17 Espoo, Finland June 12-15 6
  • 24.
    New Kutta Condition Locationof the pressure-equality points as percentage of the duct length 0.40 0.45 0.50 0.55 0.60 Duct 19A 100.0% 99.9% 99.8% 99.5% 99.0% 97.0% 96.5% 98.0% smp’17 Espoo, Finland June 12-15 7
  • 25.
    Open-Water Prediction Ka4-70 (P/D=1.0)inside Duct 19A J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments WAM, Closed Gap, Location at 97%, δ/R=4% WAM, Closed Gap, Location at 98%, δ/R=4% WAM, Closed Gap, Location at 99%, δ/R=4% KT 10KQ KT D P smp’17 Espoo, Finland June 12-15 8
  • 26.
  • 27.
  • 28.
    Motivation Panel Method: Gap model(closed) smp’17 Espoo, Finland June 12-15 9
  • 29.
    Motivation Panel Method: Gap model(closed) Wake alignment model smp’17 Espoo, Finland June 12-15 9
  • 30.
    Motivation Panel Method: Gap model(closed) Wake alignment model Correction due to duct boundary layer smp’17 Espoo, Finland June 12-15 9
  • 31.
    Motivation Panel Method: Gap model(closed) Wake alignment model Correction due to duct boundary layer New Kutta condition for thick duct t.e. smp’17 Espoo, Finland June 12-15 9
  • 32.
    Motivation Panel Method: Gap model(closed) Wake alignment model Correction due to duct boundary layer New Kutta condition for thick duct t.e. Significant differences in the performance prediction close to bollard pull condition! smp’17 Espoo, Finland June 12-15 9
  • 33.
    Motivation Panel Method: Gap model(closed) Wake alignment model Correction due to duct boundary layer New Kutta condition for thick duct t.e. Significant differences in the performance prediction close to bollard pull condition! Comparison between Panel Method and RANS calculations smp’17 Espoo, Finland June 12-15 9
  • 34.
    Motivation Panel Method: Gap model(closed) Wake alignment model Correction due to duct boundary layer New Kutta condition for thick duct t.e. Significant differences in the performance prediction close to bollard pull condition! Comparison between Panel Method and RANS calculations smp’17 Espoo, Finland June 12-15 9
  • 35.
    Numerical Methods smp’17 Espoo,Finland June 12-15 10
  • 36.
    Numerical Methods Panel CodePROPAN smp’17 Espoo, Finland June 12-15 10
  • 37.
    Numerical Methods Panel CodePROPAN IST in-house low-order potential-based panel method smp’17 Espoo, Finland June 12-15 10
  • 38.
    Numerical Methods Panel CodePROPAN IST in-house low-order potential-based panel method Fredholm integral equation solved by the collocation method smp’17 Espoo, Finland June 12-15 10
  • 39.
    Numerical Methods Panel CodePROPAN IST in-house low-order potential-based panel method Fredholm integral equation solved by the collocation method Structured surface grids smp’17 Espoo, Finland June 12-15 10
  • 40.
    Numerical Methods Panel CodePROPAN IST in-house low-order potential-based panel method Fredholm integral equation solved by the collocation method Structured surface grids RANS Code ReFRESCO smp’17 Espoo, Finland June 12-15 10
  • 41.
    Numerical Methods Panel CodePROPAN IST in-house low-order potential-based panel method Fredholm integral equation solved by the collocation method Structured surface grids RANS Code ReFRESCO Viscous flow CFD code (development led by MARIN) smp’17 Espoo, Finland June 12-15 10
  • 42.
    Numerical Methods Panel CodePROPAN IST in-house low-order potential-based panel method Fredholm integral equation solved by the collocation method Structured surface grids RANS Code ReFRESCO Viscous flow CFD code (development led by MARIN) Finite volume discretisation smp’17 Espoo, Finland June 12-15 10
  • 43.
    Numerical Methods Panel CodePROPAN IST in-house low-order potential-based panel method Fredholm integral equation solved by the collocation method Structured surface grids RANS Code ReFRESCO Viscous flow CFD code (development led by MARIN) Finite volume discretisation Turbulence model: k − ω SST 2-equation model by Menter (1994) smp’17 Espoo, Finland June 12-15 10
  • 44.
    Numerical Methods Panel CodePROPAN IST in-house low-order potential-based panel method Fredholm integral equation solved by the collocation method Structured surface grids RANS Code ReFRESCO Viscous flow CFD code (development led by MARIN) Finite volume discretisation Turbulence model: k − ω SST 2-equation model by Menter (1994) No wal functions are used (y+ ∼ 1) smp’17 Espoo, Finland June 12-15 10
  • 45.
    Grids PROPAN (left) andReFRESCO (right) Blade: 50×26 27M Cells Duct: 190×160 smp’17 Espoo, Finland June 12-15 11
  • 46.
    Blade Pressure Distributionat J = 0.5 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.5 0.0 0.5 1.0 RWM r/R=0.95 Cp=-1.0 smp’17 Espoo, Finland June 12-15 12
  • 47.
    Blade Pressure Distributionat J = 0.5 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.5 0.0 0.5 1.0 RWM r/R=0.95 Cp=-1.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.5 0.0 0.5 1.0 RWM WAM r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -0.5 0.0 0.5 1.0 smp’17 Espoo, Finland June 12-15 12
  • 48.
    Blade Pressure Distributionat J = 0.5 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.5 0.0 0.5 1.0 RWM r/R=0.95 Cp=-1.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.5 0.0 0.5 1.0 RWM WAM r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -0.5 0.0 0.5 1.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.5 0.0 0.5 1.0 ReFRESCO RWM WAM r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -0.5 0.0 0.5 1.0 smp’17 Espoo, Finland June 12-15 12
  • 49.
    Duct Pressure Distributionat J = 0.5 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.2 -0.1 0.0 0.1 RWM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 smp’17 Espoo, Finland June 12-15 13
  • 50.
    Duct Pressure Distributionat J = 0.5 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.2 -0.1 0.0 0.1 RWM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.2 -0.1 0.0 0.1 RWM WAM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 smp’17 Espoo, Finland June 12-15 13
  • 51.
    Duct Pressure Distributionat J = 0.5 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.2 -0.1 0.0 0.1 RWM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.2 -0.1 0.0 0.1 RWM WAM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.2 -0.1 0.0 0.1 ReFRESCO RWM WAM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 smp’17 Espoo, Finland June 12-15 13
  • 52.
    Blade Pressure Distributionat J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM r/R=0.95 Cp =-4.0 smp’17 Espoo, Finland June 12-15 14
  • 53.
    Blade Pressure Distributionat J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM r/R=0.95 Cp =-4.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM WAM r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 smp’17 Espoo, Finland June 12-15 14
  • 54.
    Blade Pressure Distributionat J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM r/R=0.95 Cp =-4.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM WAM r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 ReFRESCO RWM WAM r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 smp’17 Espoo, Finland June 12-15 14
  • 55.
    Duct Pressure Distributionat J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM θ=0º smp’17 Espoo, Finland June 12-15 15
  • 56.
    Duct Pressure Distributionat J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM θ=0º s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM WAM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 smp’17 Espoo, Finland June 12-15 15
  • 57.
    Duct Pressure Distributionat J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM θ=0º s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM WAM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 ReFRESCO RWM WAM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 smp’17 Espoo, Finland June 12-15 15
  • 58.
    Reduced Gap Pitch(P/D=0.9) X Y Z Blade Blade Wake Gap Strip smp’17 Espoo, Finland June 12-15 16
  • 59.
    Reduced Gap Pitch(P/D=0.9) X Y Z Blade Blade Wake Gap Strip X Y Z Blade Blade Wake Gap Strip smp’17 Espoo, Finland June 12-15 16
  • 60.
    Tip Leakage Flow smp’17Espoo, Finland June 12-15 17
  • 61.
    Pressure Distribution atJ = 0.5 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.5 0.0 0.5 1.0 ReFRESCO RWM WAM WAM with Reduced Gap Pitch r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -0.5 0.0 0.5 1.0 smp’17 Espoo, Finland June 12-15 18
  • 62.
    Pressure Distribution atJ = 0.5 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.2 -0.1 0.0 0.1 ReFRESCO RWM WAM WAM with Reduced Gap Pitch θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 smp’17 Espoo, Finland June 12-15 19
  • 63.
    Blade Pressure Distributionat J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM r/R=0.95 Cp =-4.0 smp’17 Espoo, Finland June 12-15 20
  • 64.
    Blade Pressure Distributionat J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM r/R=0.95 Cp =-4.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM WAM r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 smp’17 Espoo, Finland June 12-15 20
  • 65.
    Blade Pressure Distributionat J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM r/R=0.95 Cp =-4.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM WAM r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM WAM WAM with Reduced Gap Pitch r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 smp’17 Espoo, Finland June 12-15 20
  • 66.
    Blade Pressure Distributionat J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM r/R=0.95 Cp =-4.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM WAM r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 RWM WAM WAM with Reduced Gap Pitch r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 ReFRESCO RWM WAM WAM with Reduced Gap Pitch r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 smp’17 Espoo, Finland June 12-15 20
  • 67.
    Duct Pressure Distributionat J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM θ=0º smp’17 Espoo, Finland June 12-15 21
  • 68.
    Duct Pressure Distributionat J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM θ=0º s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM WAM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 smp’17 Espoo, Finland June 12-15 21
  • 69.
    Duct Pressure Distributionat J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM θ=0º s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM WAM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM WAM WAM with Reduced Gap Pitch θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 smp’17 Espoo, Finland June 12-15 21
  • 70.
    Duct Pressure Distributionat J = 0.1 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM θ=0º s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM WAM θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 RWM WAM WAM with Reduced Gap Pitch θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 ReFRESCO RWM WAM WAM with Reduced Gap Pitch θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 smp’17 Espoo, Finland June 12-15 21
  • 71.
    Open-Water Prediction Ka4-70 (P/D=1.0)inside Duct 19A J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments KT 10KQ KT D P smp’17 Espoo, Finland June 12-15 22
  • 72.
    Open-Water Prediction Ka4-70 (P/D=1.0)inside Duct 19A J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments KT 10KQ KT D P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments ReFRESCO KT 10KQ KT D P smp’17 Espoo, Finland June 12-15 22
  • 73.
    Open-Water Prediction Ka4-70 (P/D=1.0)inside Duct 19A J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments KT 10KQ KT D P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments ReFRESCO KT 10KQ KT D P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments ReFRESCO WAM KT 10KQ KT D P smp’17 Espoo, Finland June 12-15 22
  • 74.
    Open-Water Prediction Ka4-70 (P/D=1.0)inside Duct 19A J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments KT 10KQ KT D P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments ReFRESCO KT 10KQ KT D P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments ReFRESCO WAM KT 10KQ KT D P J 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Experiments ReFRESCO WAM WAM with Reduced Gap Pitch KT 10KQ KT D P smp’17 Espoo, Finland June 12-15 22
  • 75.
  • 76.
    Conclusions Prediction of theducted propeller loading is critically dependent on the blade wake pitch, especially at tip! smp’17 Espoo, Finland June 12-15 23
  • 77.
    Conclusions Prediction of theducted propeller loading is critically dependent on the blade wake pitch, especially at tip! The flow in the tip region is particularly important near bollard pull smp’17 Espoo, Finland June 12-15 23
  • 78.
    Conclusions Prediction of theducted propeller loading is critically dependent on the blade wake pitch, especially at tip! The flow in the tip region is particularly important near bollard pull Future work: smp’17 Espoo, Finland June 12-15 23
  • 79.
    Conclusions Prediction of theducted propeller loading is critically dependent on the blade wake pitch, especially at tip! The flow in the tip region is particularly important near bollard pull Future work: Include reduction of the gap strip in wake alignment model smp’17 Espoo, Finland June 12-15 23
  • 80.
    Conclusions Prediction of theducted propeller loading is critically dependent on the blade wake pitch, especially at tip! The flow in the tip region is particularly important near bollard pull Future work: Include reduction of the gap strip in wake alignment model Robustness issues are expected smp’17 Espoo, Finland June 12-15 23
  • 81.
    Pressure Distribution atJ = 0.2 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 -0.5 0.0 0.5 ReFRESCO RWM WAM WAM with Reduced Gap Pitch r/R=0.95 s/c Cp 0.00 0.01 0.02 0.03 0.04 0.05 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 s/c Cp 0.0 0.2 0.4 0.6 0.8 1.0 -0.3 -0.2 -0.1 0.0 0.1 ReFRESCO RWM WAM WAM with Reduced Gap Pitch θ=0º s/c Cp 0.80 0.85 0.90 0.95 1.00 -0.03 -0.02 -0.01 0.00 0.01 0.02 smp’17 Espoo, Finland June 12-15 24
  • 82.
    Wake Geometry atJ = 0.2 x/R = 0.2 x/R = 0.4 smp’17 Espoo, Finland June 12-15 25
  • 83.
    Wake Geometry atJ = 0.2 smp’17 Espoo, Finland June 12-15 26
  • 84.
    Wake Geometry atJ = 0.1 x/R = 0.2 x/R = 0.4 smp’17 Espoo, Finland June 12-15 27
  • 85.
    Wake Geometry atJ = 0.1 smp’17 Espoo, Finland June 12-15 28
  • 86.
    Wake Geometry atJ = 0.5 x/R = 0.2 x/R = 0.4 smp’17 Espoo, Finland June 12-15 29
  • 87.
    Influence of theWake Model Model KTP KTD 10KQ J = 0.1 RWM 0.412 0.206 0.5882 WAM 0.313 0.231 0.4664 WAM with Reduced Gap Pitch 0.284 0.226 0.4228 Experiments 0.254 0.214 0.4387 J = 0.2 RWM 0.383 0.160 0.5538 WAM 0.297 0.176 0.4456 WAM with Reduced Gap Pitch 0.273 0.171 0.4083 Experiments 0.248 0.166 0.4279 J = 0.5 RWM 0.266 0.054 0.4041 WAM 0.208 0.057 0.3246 WAM with Reduced Gap Pitch 0.193 0.055 0.3010 Experiments 0.196 0.053 0.3506 smp’17 Espoo, Finland June 12-15 30
  • 88.
    Wake Model Rigid wake X Y Z smp’17Espoo, Finland June 12-15 31
  • 89.
    Wake Model Aligned wakewith b.l. correction X Y Z smp’17 Espoo, Finland June 12-15 32