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Adil RASHEED Research Scientist Applied Mathematics Strindveien 4, Trondheim, NORWAY www.adilrasheed.com Adil.rasheed@sintef.no

Application of a Multiscale Turbulence Prediction System for Aviation Safety and Wind Turbine Siting

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• Background • Aviation • Wind Energy

• Flow in complex terrain • Forecast • Computational efficiency and robustness • Validation strategy • Application to Wind Energy • Conclusion

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OVERVIEW

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AMSTERDAM GENEVA

PARIS FRANKFURT

NON-NORWEGIAN AIRPORTS (Terrain)

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NORWEGIAN AIRPORTS (Terrain)

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Background: Aviation

Wideroe DH8A on May 1st 2005 The Aviation Herald

Hammerfest Airport Just before landing the wind speed veered and increased, creating a tail wind. The increase in the descent rate was compensated, but was insufficient, and the plane had a touch-down on the right main landing gear, with the leg failing and the aircraft sliding on its belly. The aircraft was written off and Widerøe was criticized for permitting landings under too high winds and gusts Norwegian Civil Aviation Authority imposed stricter wind regulations upon the airport.

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Motivation: Wind Energy in complex terrain

Wind potential might change significantly vary from one site to another Experimental and measurement techniques Are not good enough Farm Operators need wind forecast

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Wind shear in mountainous terrain

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HORIZONTAL SHEAR

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Mountain waves: Qualilative Characteristics

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Mountain waves: Characteristics

• Maximum amplitude on the leeward-side of the hill • Successive hills might enhance or diminish the strength of the waves • The waves are more pronounced when the buoyant and inertial forces are comparable. The ratio is defined by Froude no.

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Can the flow characteristics be modelled ?

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Governing Equations

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Mountain Waves

Fr=1, stable stratification Fr=U/(NL) N2=(g/T)(dT/dz)

Maximum amplitude on the leeward side of the hill

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Stokka SANDNESSJØEN AIRPORT: STOKKA

Tail wind on both directions of the runway

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Fr=0.2, Lateral movement of air more pronounced

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Fr=1, Ideal condition for the propagation of waves Waves are diminished by destructive interference

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Confirm the Pilots experidence "Tail Wind from both sides of the runway"

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Can we forecast flight conditions ?

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The simulations seem to confirm pilot's reports BUT…..

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Global scales: seasonal changes, Sea currents etc. Meso scales: effects of large

mountains, sea, forest, precipitation

Micro scales: terrain effects, mountain waves

Each model is capable of resolving only a particular range of spatio-temporal scales The problem can be handled through nesting of different models

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NESTING

UM4

UM1

UM1

UM1

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SANDNESSJØEN AIRPORT

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Hammerfest

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Is the model Computationally efficient and robust?

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NJORD: Hardware Configurations

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• Technically 192 nodes partitioned into 186 nodes, 4 input/ output nodes. 186 nodes are shared memory nodes with 8 dual core power 5+ 1.9GHz processors each 180 of the computational noes have 32 GB memory each The code is parallelized using MPI

• Mythologically NJORD is the God of the wind and fertility as well as the sea and merchants at sea and therefore was invoked before setting out to sea on hunting and fishing expeditions. He is also known to have the ability to calm the waters as well as fire.

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Robustness ?

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www.ippc.no

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Validation strategy ?

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ALTA Normal Flight path

PILOTS REPORT:

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Realistic Boundary condition to run offline simulations

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Free stream speed = 20m/s

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VÆRNES AIRPORT

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15 March 2012: Five people died when a Hercules aircraft crashed in the peak of Kebnekaise in northern Sweden

REASON: INCONCLUSIVE

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Airport Siting: Farøe Island

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Application to Wind Energy: Bessaker Wind Farm

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Wind rose

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Forecasting domain

HARMONIE

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Conclusion

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• A fully functional Multiscale Model for terrain induced turbulence prediction is in place • The prediction system confirms the experiences recorded in the pilots reports and gives

possible explanations • The code has been validated extensively against wind tunnel data for cubes, hills, cylinders • There is a scarcity of data for the validation of numerical codes but flight data, wind farm

data, weather station data can be used together to get better insight into the flow at microscales.

• The data from the different sources can be used for fine tuning the models

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NORWAY IS BEAUTIFUL

Adil.rasheed@sintef.no