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High Resolution Local Wind Gust Modelling Richard Turner, Amir Pirooz Sep 28, 2021
High Resolution Local Wind Gust Modelling
Richard Turner, Amir Pirooz
Sep 28, 2021
Outline
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• Motivation
• Homogenisation process
• NZ Convective Scale Model (NZCSM)
• CFD/downscaled simulation cases:
- WAsP – Samoa
- GERRIS – Belmont
- ANSYS – Auckland Harbour Bridge
- PALM – Mt. Taranaki
Motivation
• Improve the resilience of New Zealand’s infrastructureagainst the effects of extreme winds.
• Analysis of historical wind speed records across New Zealand.
• Simulate airflow over complex terrain and urban areas.
Why?
4
𝑉𝑠𝑖𝑡,𝛽 = 𝑉𝑅𝑀𝑑 𝑀𝑧,𝑐𝑎𝑡𝑀𝑠𝑀𝑡
Pedestrian Safety:
Structural Damage:
• Methodology and Approach
5
Station Analysis
Homogenisation and Quality Control
• Change in instrumentation and signal processing
• Anemometer height
• Surrounding roughness
• Site relocation
• Local topographic effect
6
1
Auckland Wellington
Homogenisation Algorithm
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– Correcting the effective roughness using the gradientwind speeds, and converting to terrain Category 2.
– Calculate Mean-Speed Correction Factors
– Accounting for the response characteristics ofanemometers and the 3-s gust using Wind-Tunnelresults, and later to convert to 0.2-s.
– Calculate Gust-Speed Correction Factors
– Topography effects? If yes, CFD simulation
– Statistical tests for detecting and eliminatingundocumented shifts
Richard Turner, Amir A.S. Pirooz, Richard GJ Flay, Stuart Moore, Mike Revell (2019),“Using High-Resolution Numerical Models and Statistical Approaches to Understand NewZealand Historical Wind Speed and Gust Climatology”, Journal of Applied Meteorologyand Climatology, 58: 1195-1218, https://doi.org/10.1175/JAMC-D-18-0347.1
Sample results: Wellington Airport
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Gerris Model
Homogenised Data
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▪ Maximum Annual Gusts : Corrected VS Raw data (Wellington Airport)
NZCSM Peak Daily Max Gust 95th Percentile Daily Max Gust
2014 to 2018
Lee Zones
▪ NZCSM: Determine the high-wind locations.
▪ Station Data: Compute lee-zonemultiplier values
NZ Design Wind Speeds forAS/NZS1170.2: 2021
NZ Design Wind Speeds forAS/NZS1170.2: 2021
WAsPcalculates speed-ups based on slope and potential theory.
• WAsP – Samoa
CFD: GERRIS – Belmont Hill
(a) View of Belmont Hill towards the northwest; (b) Locations of the instrumented masts.
(Note the very complex terrain – typical of NZ)
(a) (b)
■ Wind Tunnel Test
WT gust speed-up multipliers for 340°. (The wind is blowing from the left.)
Methodology
GERRIS – Belmont Hill
■ CFD Simulation
Methodology
GERRIS – Belmont Hill
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
y = 0.9627x
5
10
15
20
5 10 15 20
Gerris speed, m/s
Observed speed from 5 m mast, m/s
Mast no. in ( )
Results
GERRIS – Belmont Hill
0.0
0.5
1.0
1.5
2.0
0 1 2 3 4 5 6 7 8 9 10
Gu
st s
pe
ed
up
Met mast number
Belmont gust speedups determined by various methods
Full-scale Obs CFD Gerris Wind Tunnel
CFD WASP 1170 NIWA 1170 sea flat (UoA)
1170 valley flat (UoA)
(a) Gust speedups from different methods; (b) Belmont Hill topographic factors from several Standards
(a) (b)
On 18th September 2020, gustsassociated with a short-lived(10 minute) period of strongwesterly winds in Aucklandcaused two trucks to roll overwhile heading south on theAuckland Harbour Bridge,resulting in:
• damage to the bridge's superstructure (repair cost>NZ$1M);
• severe disruption to trafficflows.
CFD downscaling of / NWP Auckland Harbour Bridge case
Objectives
Following this event, we were motivated to investigate two research questions:
1. How accurately did NIWA’s very high-resolution NWP model(s) (the Auckland Model) predict this event?
2. What are the effects, i.e. wind speed-up, of the Auckland Harbour Bridge structure itself on the local airflow and is it feasible to couple detailed CFD models with the Auckland Model to produce bridge-specific gust forecasts?
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To address these questions, in this study, we use high-resolution NWP forecasts of wind speed to force a CFD simulation of airflow around the Auckland Harbour Bridge to estimate the highly localised wind speed up effects that may have contributed to the vehicle overturning.
NWP and Downscaling
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Note: non-time varying boundary conditions are used for the CFD simulation.
NWP Wind Speed Timeseries on 18 Sep 2020
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The mean wind speed forecasts of theevent at different vertical levels shown in(a) are used as input to CFD simulation. Mean wind speeds: 1 m to 104 m
Gust wind speed at 10 m
Results: NWP Validation
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Comparison of gust and mean wind speedsduring 17 – 18 September 2020 18:00 UTC fromthe 333-m NWP model and observations, at: (a)Auckland Aero; (b) Whenuapai; (c) MOTAT
Auckland Aero
Whenuapai
MOTAT
Results: NWP Validation
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Comparison of (a) gust and (b) mean windspeeds during 17 – 18 September 2020 18:00UTC from the 333-m NWP model and the twoanemometers mounted on the Sky Tower.
NW SE
Max Gust Wind Speed Mean Wind SpeedStation name Correlation Mean Bias RMSE Correlation Mean Bias RMSE
Sky Tower (SE anemometer) 0.47 −3.73 5.44 0.15 2.57 5.14Sky Tower (NW anemometer) 0.65 −4.67 5.42 0.49 1.03 2.44
Gust Speed
Mean Speed
SE (obs)
NW (obs)NWP
SE (obs)
NW (obs)NWP
CFD Setup
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(a) 𝑈 𝑧 =𝑢∗
𝜅𝑙𝑛
𝑧
𝑧0+ 𝐶𝑈1
𝑧
𝐻+ 𝐶𝑈2
𝑧
𝐻
2+ 𝐶𝑈3
𝑧
𝐻
3+ 𝐶𝑈4
𝑧
𝐻
4,
(b) 𝑘 𝑧 = 𝑢∗2 𝐶𝑘1 + 𝐶𝑘2 1 −
𝑧
𝐻
2+ 𝐶𝑘3 1 −
𝑧
𝐻
4+ 𝐶𝑘4 1 −
𝑧
𝐻
6,
(c) 𝜔 𝑧 =𝑘 𝑧
𝜅𝑢∗𝑧1 + 1 + 𝐶𝑈1
𝑧
𝐻+ 1 + 𝐶𝑈1 + 2𝐶𝑈2
𝑧
𝐻
2+ 1 + 𝐶𝑈1 + 2𝐶𝑈2 + 3𝐶𝑈3
𝑧
𝐻
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Pressure-driven ABL simulate3D, steady RANS using the k-w Shear StressTransport (SST) turbulence model.
Grid:32.7 million cells4 m resolution0.02 m resolution aroundthe bridge small details.
BCs:• Lateral and top BC:
Symmetry• Outlet BC: zero-gauge
pressure• Ground BC: automatic
near-wall treatment(sea surface roughness)
Inlet profiles:
TKEMean Speed Eddy Freq.
Results: CFD
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Figure (a), depicts wind speed-ups on the edge of the road as well as between the struts of the bridge. Figure (b)shows the mean speed on a plane at the centre of the bridge along the road. The locations of high (red) and low(blue) wind speeds can be seen. Sheltering effects of the bridge components results in lower wind speeds
Westerly wind Westerly wind
Contour of mean speeds Velocity Streamlines
Results
Results: CFD
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The mean speed can be increased by up to 1.15 – 1.20 in themiddle of the bridge where vehicles travel.
Mean speed-upMean wind speed
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Results: CFD
NWP Gust wind speed at the top of the bridge
(a)
(b)
Figure (a) (CFD), the gust windspeeds reach around 22 – 24 m/sclose to the road surface. Flowseparation from the top arch of thebridge with high gust wind speeds ofabout 25 – 30 m/s.
The maximum gust wind speedduring this event estimated by NWPat 64-m is 25 m/s (Figure b).
An anemometer at the top of thearch that recorded a maximum gustof 127 km/h = 35.3 m/s during theevent on 18 September 2020.
Estimated gust wind speeds
at 64 m
at 10 m
25 m/s
CFD with PALM (LES)
Mt Taranaki
Objectives:
▪ Design wind speed assessment.
▪ NWP Bias correction.
Methodology:
▪ LES CFD simulation.
CFD Mt Taranaki
CFD Domain:
24-m Resolution 8-m Resolution
Wind Speed Around the Mountain
NS
Wake Region Behind the Mountain
Gust Speed-up
8-m Resolution: Max Wind Speed
Conclusion and Ongoing work
• Importance of homogenising historical wind records.
• Estimation of design wind speeds using local gust records, and utilising NWP models to determine downslope winds.
• Coupling CFD/NWP to simulate localised wind speed-ups that are not captured in NWP.
• Potential of using CFD simulations for NWP gust wind speed bias corrections.
• More work is underway to develop time-varying NWP/CFD coupling.
Selected References:
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1. A Pirooz, S Moore, R Turner, RGJ Flay (2021), “Coupling High-Resolution Numerical Weather Prediction and ComputationalFluid Dynamics: Auckland Harbour Case Study”. Applied Sciences. 11, 3982. https://doi.org/10.3390/app11093982
2. A Pirooz, RGJ Flay, R Turner (2020), “New Zealand Design Wind Speeds, Directional and Lee-Zone Multipliers Proposed forAS/NZS 1170.2:2021”, Journal of Wind Engineering and Industrial Aerodynamics. https://doi.org/10.1016/j.jweia.2020.104412
3. A Pirooz, RGJ Flay, L Minola, C Azorin-Molina, D Chen (2020) “Effects of Sensor Response and Moving Average Filter Durationon Maximum Wind Gust Measurements”, Journal of Wind Engineering and Industrial Aerodynamics, 206: 104354.https://doi.org/10.1016/j.jweia.2020.104354
4. L Minola, F Zhang, C Azorin-Molina, A Pirooz, RGJ Flay, H Hersbach, D Chen (2020) "Near-Surface Wind and Gust Speeds inERA5 across Sweden: Towards an Improved Gust Parametrization Model", Journal of Climate Dynamics, 55(3): 887-907
5. A Pirooz, RGJ Flay, R Turner, C Azorin-Molina (2019) “Effects of climate change on New Zealand design wind speeds”, NationalEmergency Response 32:14-20
6. R Turner, A Pirooz, RGJ Flay, S Moore, M. Revell (2019), “Using High-Resolution Numerical Models and Statistical Approachesto Understand New Zealand Historical Wind Speed and Gust Climatology”, Journal of Applied Meteorology and Climatology,58: 1195-1218, https://doi.org/10.1175/JAMC-D-18-0347.1
7. A Pirooz, RGJ Flay (2018) “Comparison of Speed-up Over Hills Derived from Wind-Tunnel Experiments, Wind-LoadingStandards, and Numerical Modelling”, Journal of Boundary-Layer Meteorology, 168:213-246, https://doi.org/10.1007/s10546-018-0350-x
8. A Pirooz, RGJ Flay (2018) “Response Characteristics of Anemometers Used in New Zealand”. In: 19th Australasian WindEngineering Society (AWES) workshop, Torquay, Victoria, April 4-6, 2018
