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Sailing site investigation through CFD modelling ofmicrometeorology

Malo Le Guellec, Yann Amice

To cite this version:Malo Le Guellec, Yann Amice. Sailing site investigation through CFD modelling of micrometeorology.The Third International Conference on Innovation in High Performance Sailing Yachts, INNOVSAIL,Lorient, France, Jun 2013, Lorient, France. pp.223-228. �hal-02110243�

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The Third International Conference on Innovation in High Performance Sailing Yachts, Lorient, France

26TH – 28TH June, 2013

SAILING SITE INVESTIGATION THROUGH CFD MODELLING OF MICROMETEOROLOGY

M. Le Guellec,Fluidyn FRANCE, France, malo.leguellec@fluidyn.com Y.Amice,DépartementMétéorologie - Institut de Recherche de l’ÉcoleNavale, France, yann.amice@gmail.com;

To have a prior accurate knowledge of the local wind currents on a water body is of crucial importance for the performance of the sailing team. In the recent years, Computational Fluid Dynamics (CFD) has proven itself a powerful tool in atmospheric modelling. By solving the Navier-Stokes equations and with correct description of the atmospheric boundary layer and turbulence at the domain boundaries, the local influences of the shore topography and the obstacles on the wind flows can be investigated in detail. Two examples of the use of CFD (Fluidyn PANWIND software) are presented here. The first one shows the coastal wind analysis of 2012 Olympic sailing site of Weymouth, UK. The local wind effects due to the harbour and hill have been determined and compared to observations of wind velocity and direction for several wind conditions.The second example required to model the wind over the training base of the French Sailing Teamin Brest, France. This landlocked bay, surrounded by two steep hills and linked to the Atlantic Ocean by a strait, emphasizes the need for a CFD simulation of the wind which provided the patterns of wind around the racing areacompared with empirical observations.

NOMENCLATURE

C1 k-εturbulence model constant C2 k-εturbulence model constant CS dimensionless turbulence production factor CE dimensionless turbulence viscosity constant

for the k-ε model Cp specific heat of air (J g-1 K-1) Fg/p force due to: (g) gravitational acceleration, (p)

interaction with droplets/particles (N m-2) g gravitational acceleration (9.8 m s-2) G turbulence production rate by shear = σ∇u (m2

s-3) hm specific enthalpy of species m (J kg-1) I specific internal energy (J kg-1) J heat flux vector (W m-2) k turbulent kinetic energy per unit mass (m2 s-2) kc thermal conductivity (W m-1 K-1) L Monin-Obukhov turbulent length scale (m) Qh

rate of specific internal energy gain due to (h) surface energy budget (J kg-1 s-1)

Ri Richardson number, dimensionless t time since the start of the release (s) T temperature (K) u fluid velocity (m s-1) u* surface friction velocity (m s-1) v wind speed (m s-1) Wp Turbulence production due to interaction with

particles (m2 s-3) z height (m) z0 ground roughness length(m)

Greek letters ρ density of air µ primary (shear) viscosity of fluid (kg m−1 s−1) λ secondary (bulk) viscosity of fluid (kg m−1 s−1) σ Newtonian viscous stress tensor (N m-2) ε dissipation of turbulent kinetic energy (m2 s-3)

ζ Monin-Obukhov similarity variable = z/L, dimensionless

κ Von Karman constant = 0.41, dimensionless θ potential temperature (K) θ* temperature scale σh turbulent Prandtl number, dimensionless σk dimensionless turbulence model constant for the

k equation σε dimensionless turbulence model constant for the

ε equation Ψ1/2(ς) similarity profiles νt turbulent viscosity (m−1 s−1)

TThe Third I

1 INTRO

Accurate wdirection ansailing comsailing is measuremecharacteriseshort, andFurthermormass consifrom the pdonot proviof interest.

The atmosparticularlyterrain. Theland and roughness smodify largbe evaluateThe compleneed for a The wind fspeed and w

In many wibreeze effelocal weathfocuses onpredefined and consideTwo differeframe of thi

Weymouth,2012 OlymcompetitionWeymouth south of tharea, there itopographyflow can be

The seconknownroadof France (characteristassess ttopographytwo very sAtlantic Ocorientation.The wind mthe QuelernArmorique

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DUCTION

wind field dnd turbulence

mpetitions. Thbetween 2.5

ent data obtaie fully the flo

d only definre the interpolistent techniqupoint measureide the require

spheric circuly dependent oe topography

sea (estuarsurface (urbange scale flowd in details. ex topographycomplete 3D

flow modellingwind direction

ind studies, thect based on her station men the local wind bounda

ering topograpent interestingis project.

, a coastal cimpic Sailing En spots are shois a city surr

he city, i.e., inis a hilly islan

y is complex ae very difficul

nd sailing arstead (bay) of(see Figure 2tic of this wathe impacty.Indeed, it is asteep hills (50cean by a strai. modelling focn Peninsula (2(3rd sub-doma

al Conferenc

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ind speed, wmportant roled speed range

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8TH June, 20

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The Third International Conference on Innovation in High Performance Sailing Yachts, Lorient, France

26TH – 28TH June, 2013

∂ρ∂

∇• ρ σ ∇ where σ is Newtonian viscous stress tensor (σ= µ[∇u+(∇u)T]+λ(∇•u)i,µ, λ = first and second coefficients of viscosity, λ = -2/3µ; T = matrix transpose; i = unit dyadic - product of vectors). The energy conservation equation is:

∂ρ∂

∇• ρ ∇• ρε

Where is the specific internal energy, is theheat flux = kc∇.T + ρ∑[hm∇(ρm/ρ)], is the rate of specific internal energy gain due surface energy budget.

2.2 GEOMETRY AND MESH

(a)

(b)

Figure 3: Weymouth unstructured mesh at ground level ((a) full domain (b) nested domain)

For Weymouth case, the topography of the site was collected from Landform Profile Plus data on a global domain of 24 km * 25 km. This data has a 15–25 centimetre root mean square error (RMSE) accuracy and a grid resolution of 2 metres to 10 meters - sufficiently detailed to represent key terrain features. The harbour and jetties were modelled in finer details in an embedded domain of dimensions 5 km * 10 km. The finest cell size of the unstructured mesh in the harbour is 12 m and the averaged size dimension in

the sailing area is 25 m (see figure 3) resulting in a total of 2 million cells. Vertical mesh gets a 2m resolution from ground level to 12m of altitude. The vertical mesh is then coarser till until 200 m high.

A large main domain of 43km by 32km was used for Brest’s Roadstead case. A first nested domain of 30 km*28 km has been defined. Two smaller embedded domains with an area around 80 km² were used in order to evaluate accurately the wind flows as shown in figure 2.The topography was extracted from The NASAShuttle Radar Topographic Mission (SRTM) who has provided digital elevation data (DEMs) for over 80% of the globe. The SRTM data is available as 3 arc second (approx. 90m resolution) Digital Elevation Model. The finest cell size of the unstructured mesh in the two nested domains is 30m and the averaged size dimension in the sailing area is 50m resulting in a 2.3 million cells. Vertical mesh gets a 3m resolution from ground level to 15m of altitude. The vertical mesh is then coarser until 200 m high.

2.3 TURBULENCE MODEL, BOUNDARY AND INITIAL CONDITIONS

The standard k-ε model has been used throughout the simulations. The k-ε model is a two-equation linear eddy viscosity model. The PANACHE implementation of this model is derived from the standard high-Re form with corrections for buoyancy and compressibility. It solves the transport equations for turbulent kinetic energy, k, and its dissipation rate, ε. The incompressible versions of the equations are:

( ) νν εσ

∂+ ∇ ⋅ = ∇ ⋅ + ∇ + + − ∂ t

tl k b

k

k k k P PU

( ) ( )ε εε

νε εε ν ε εσ

∂ + ∇ ⋅ = ∇ ⋅ + ∇ + + − ∂

1 2tt

l k b bC P C P Ck

U

where, Pk=ν γ ∇& :t U , the mechanical production rateof k

Pb=ν βσ∇g

th

T, the buoyancy production rate

of k σk= Prandtl number for turbulent diffusion of k σε= Prandtl number for turbulent diffusion of ε µt= turbulent eddy diffusivity

Cs1,Cs2=k-ε turbulence model dimensionless constants

The eddy diffusivity is computed using:

ν CE ε

The Third International Conference on Innovation in High Performance Sailing Yachts, Lorient, France

26TH – 28TH June, 2013

Ambient mean wind speed and air temperature profiles are specified within the model domain and are represented by logarithmic functions, such that:

κ Ψ ς

θ σ θκ

Ψ ς

Whereθ* = temperature scale; Ψ ς and Ψ ς = similarity profile. The surface friction velocity, , the temperature scale θ*, and the Monin-Obukhov length, L are related by: L = u*2T / (g κθ*)and θ* = Qh / (ρCp u*).The micrometeorological parameters , θ*, and L are evaluated for different atmospheric stability classes. They have been evaluated for neutral conditions.

In this study, the roughness lengthhas been chosen equal to 0.001m (typical of water body). The roughness length for the land has been chosen equal to 0.4m.

2.4 SIMULATION AND SOLVER PARAMETERS

In the frame of this study, the solver is a pressure-based fully implicit segregated method on unstructured meshes. It is well suited for flows that are steady or quasi-unsteady (slowly changing). It solves all governing equations separately. It uses an iterative procedure for both steady state and transient cases. SIMPLE schemeis used for pressure computation. It uses a formulation valid for flows at all speeds and for any thermodynamic model.

3 RESULTS

3.1 BREST ROADSTEAD CASE ANALYSIS

3.1.1. Climatology

The dominant flux in all seasons comes from west to west-southwest even if a few nuances exist depending on the season. The most significant factor is the south pathwayof the low pressure zone. During winter, stronger west or southwest winds are usually observed and frequent disturbances which impact the Atlantic coast. During the summer, this scheme remains relevant but strengthening anti-cyclonic depression requires a more northern flow, which allows the Atlantic coast to sample light winds and a more conventional summer time. The North Atlantic Oscillation (NAO) is a climatic phenomenon in the North Atlantic Ocean of fluctuations in the difference of atmospheric pressure at sea level between the Icelandic low and the Azores high (see Figure 4). Through east-west oscillation motions of the Icelandic low and the Azores high, it controls the strength and direction of westerly winds across the North Atlantic.

Figure 4: The North Atlantic Oscillation (NAO) is a climatic phenomenon in the North Atlantic Ocean - L : Low pressure area in Iceland - H: High pressure area in Azores and Northern Africa

3.1.2. Local effect of terrain on the wind flows

The results of the modelling focus on the wind direction modification due to the topography around the sailing area. The wind directions areSW (225°) and E(90°) and the simulations were done for a wind speed of 10 m/s at a height of 10m for both directions. In Figure 5, the white colour arrows and the pink colour contours represent a deviation greater than +15° from the mean direction and the black colour arrowsand the blue colour contours indicate a deviation greater than -15°. All the views are voluntarily schematic for an easy understanding of the wind fields in the area by non-specialist people.

Figure 5: Wind deviation in case of SW conditions

The wind flow is channelled through the axis of the strait, exceptwhere a little deviation is observednear the tip of Spanish peninsula Quernel. The flow is divided in a West-Southwest and a South-Southwest part when reaching the peninsula ofPlougastel (see figure 6).

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BIOGRAPHY

lec holds theLUIDYN FR

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d modelling atpedestrian comment in hilly r

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ng Yachts, L

013

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