# Convectively unstable ABL for wind turbine simulations This benchmark problem is a slightly convective unstable atmospheric boundary layer that is used as an inflow for the [actuator line NREL5MW](https://github.com/Exawind/exawind-benchmarks/tree/main/amr-wind/actuator_line/NREL5MW_ALM_BD) and [geometry resolved NREL5MW](https://github.com/Exawind/exawind-benchmarks/tree/main/exawind/NREL_5MW_Turbine) benchmark cases. The conditions and details of the case are summarized below: - Hub-height wind speed: 11.4 m/s - Hub-height wind direction: 240 degrees SW - Surface roughness: 0.01 m - Surface temperature flux: 0.005 K-m/s - Domain size: 5120m x 5120m x 1920m - Mesh size: 512 x 512 x 192 - Total mesh size: 50331648 cells **Contents** - [Simulation setup](#simulation-setup) - [Performance](#performance) - [Results](#results) ## Simulation Setup Full details of the simulation setup are provided in [**setup documentation**](setup/README.md). The case was set up using the [AMR-Wind frontend](https://github.com/Exawind/amr-wind-frontend) with the notebook [convectiveABL_setup.ipynb](setup/convectiveABL_setup.ipynb). This allows the locations of the refinement regions and sampling planes to be setup properly relative to the location of the NREL5MW turbine. Input files are in the [input_files](https://github.com/Exawind/exawind-benchmarks/tree/main/amr-wind/atmospheric_boundary_layer/convective_abl_nrel5mw/input_files) directory. There are two stages to this run: 1. Spin-up of the precursor: Use the [convective_abl.inp](https://github.com/Exawind/exawind-benchmarks/tree/main/amr-wind/atmospheric_boundary_layer/convective_abl_nrel5mw/input_files/convective_abl.inp) input file to run the case from t=0 to 15,000 seconds. 2. Capture the boundary plane and sampling plane data: Use [convective_abl_bndry.inp](https://github.com/Exawind/exawind-benchmarks/tree/main/amr-wind/atmospheric_boundary_layer/convective_abl_nrel5mw/input_files/convective_abl_bndry.inp) to run the case from t=15,000 to 20,000 seconds to output the data needed to run the turbine cases. Current case was run with AMR-Wind version [f67a52dd6aa1882595d16700527470bc8097cb13](https://github.com/Exawind/amr-wind/commit/f67a52dd6aa1882595d16700527470bc8097cb13) ## Performance Full details provided in [**performance documentation**](performance/README.md). The simulation was run on the Sandia Flight HPC cluster using the following resources: | Parameter | Value | |--- |--- | | Number of nodes | 8 | | Number of CPUs | 896 | | Wall-time | 5.4 hours| | CPU-hours | 4821.7 | ## Results Statistics from the ABL are calculated over the time interval $t \in [15000 , 20000]s$ using scripts and notebooks from the [postprocessing](https://github.com/Exawind/exawind-benchmarks/tree/main/amr-wind/atmospheric_boundary_layer/convective_abl_nrel5mw/postprocessing) directory. The results are saved to the [results](https://github.com/Exawind/exawind-benchmarks/tree/main/amr-wind/atmospheric_boundary_layer/convective_abl_nrel5mw/results/) directory. Horizontally averaged statistics are computed in the [AVG_horiz_profiles.ipynb](postprocessing/AVG_horiz_profiles.ipynb) notebook and are summarized in the following table at the bottom-tip, hub-height, and top-tip location of the NREL5MW: | z | Horizontal Velocity | Wind Direction | TI (TKE) | Shear Exponent | Veer| |--|--|--|--|--|--| |27 m | 9.89 m/s | 239.74 deg | 8.29 % | 0.1300 | 0.0087 deg/m |90 m | 11.40 m/s | 240.00 deg | 6.17 % | 0.0815 | 0.0006 deg/m |153 m | 11.90 m/s | 240.02 deg | 5.38 % | 0.0693 | 0.0001 deg/m The veer and shear exponent over the rotor disk are 0.0015 deg/m and 0.0944, respectively. The corresponding friction velocity, $u^*$, inversion height, $z_i$, and Obukhov length-scale for this simulation are: - $u^*$ = 0.468 m/s - $z_i$ = 803.133 m - Obukhov Length = -2447.446 m (computed at z=27 m) ### Flow Visualizations An instantaneous hub-height visualization of the horizontal velocity at $t=15,000s$ is included below.  ### Horizontal Profiles Horizontally averaged profiles are computed in the [AVG_horiz_profiles.ipynb](postprocessing/AVG_horiz_profiles.ipynb) Jupyter notebook are shown below. **Note**: The python routines for computing horizontal profiles from the statistics file(s) generated by AMR-Wind rely on importing the [postproamrwindabl](https://github.com/Exawind/amr-wind-frontend/blob/main/postproamrwindabl.py) module from the [AMR-Wind front end](https://github.com/Exawind/amr-wind-frontend) library. It is not necessary to import the entire AMR-Wind front end library to compute the horizontal profiles. If necessary, download the module files and edit the lines in the python code which define `postproamrwinddir` to include the location of that module: ```python # Location of postproamrwindabl module inside the amr-wind-frontend postproamrwinddir = '~/src/amr-wind-frontend/' import sys, os, shutil, io if postproamrwinddir not in sys.path: sys.path.append(postproamrwinddir) ``` #### Horizontal velocity:  #### Temperature:  #### Wind Direction:  #### Turbulence Intensity (TKE):  #### Resolved Reynolds stresses:  #### Resolved Temperature Fluxes:  ### Metmast Profiles Averaged wind profiles from a virtual metmast at the turbine location are computed in the [AVG_metmast.ipynb](postprocessing/AVG_metmast.ipynb) Jupyter notebook and are shown below.  **Note**: The path to the [AMR-Wind front end](https://github.com/Exawind/amr-wind-frontend) library must be provided in the [AVG_metmast.ipynb](postprocessing/AVG_metmast.ipynb) Jupyter notebook. If necessary, download the library and edit the lines which define `amrwindfedirs` to include any locations of that library, e.g., ```python # Add any possible locations of amr-wind-frontend here amrwindfedirs = ['/projects/wind_uq/lcheung/amrwind-frontend/', '/ccs/proj/cfd162/lcheung/amrwind-frontend/'] import sys, os, shutil, io for x in amrwindfedirs: sys.path.insert(1, x) ``` ### Wavenumber Spectra Two-dimensional (2D) wavenumber spectra are computed from the XY planes sampled from AMR-Wind using the [post-processing engine](https://github.com/Exawind/amr-wind-frontend/tree/main/postproengine) in the the [AMR-Wind front end](https://github.com/Exawind/amr-wind-frontend) library. The [yaml file](https://github.com/Exawind/exawind-benchmarks/blob/main/amr-wind/atmospheric_boundary_layer/convective_abl_nrel5mw/postprocessing/postpro_windspectra.yaml) for computing 2D wavenumber spectra of vertical planes sampled in this benchmark case can be called using the [ppengine.py](https://github.com/Exawind/amr-wind-frontend/blob/main/utilities/ppengine.py) utility as ``` python ppengine.py postpro_windspectra.yaml ``` Details of the 2D wavenumber computation can be found in the [documentation](https://github.com/Exawind/amr-wind-frontend/tree/main/postproengine/doc) for the post-processing engine. The [ABL_wavenumber_spectra.ipynb](postprocessing/ABL_wavenumber_spectra.ipynb) Jupyter notebook plots the energy, horizontal, and vertical 2D wavenumber spectra, which are reported below at the bottom-tip and hub-height locations of the NREL5MW:  ### Temporal Spectra Temporal spectra are computed from the XY planes sampled from AMR-Wind using the [post-processing engine](https://github.com/Exawind/amr-wind-frontend/tree/main/postproengine) in the [AMR-Wind front end](https://github.com/Exawind/amr-wind-frontend) library. The [ABL_temporal_spectra.ipynb](postprocessing/ABL_temporal_spectra.ipynb) Jupyter notebook can be used to compute the temporal spectra and to plot the results against a Kaimal spectra. Details of the temporal spectra computation can be found in the [documentation](https://github.com/Exawind/amr-wind-frontend/tree/main/postproengine/doc) for the post-processing engine. **Note**: The path to the [AMR-Wind front end](https://github.com/Exawind/amr-wind-frontend) library must be provided in the [ABL_temporal_spectra.ipynb](postprocessing/ABL_temporal_spectra.ipynb) Jupyter notebook. If necessary, download the library and edit the lines which define `amrwindfedirs` to include any locations of that library, e.g., ```python # Add any possible locations of amr-wind-frontend here amrwindfedirs = ['/projects/wind_uq/lcheung/amrwind-frontend/', '/ccs/proj/cfd162/lcheung/amrwind-frontend/'] import sys, os, shutil, io for x in amrwindfedirs: sys.path.insert(1, x) ``` The streamwise, lateral, and vertical temporal spectra sampled from XY planes at the bottom-tip and hub-height locations of the NREL5MW are reported below: #### z = 27m  #### z = 90m  ### Integral Length scale The longitudinal and latitudinal integral length scales are computed in the [ABL_integral_lengthscale.ipynb](postprocessing/ABL_integral_lengthscale.ipynb) notebook, resulting in - Longitudinal length scale = 539.61 m - Latitudinal length scale = 97.13 m The computation of the integral length scale relies on the [AMR-Wind front end](https://github.com/Exawind/amr-wind-frontend) library to compute the two-point correlation. **Note**: The path to the [AMR-Wind front end](https://github.com/Exawind/amr-wind-frontend) library and the AMR-Wind front end [utilities](https://github.com/Exawind/amr-wind-frontend/tree/main/utilities) must be provided in the [ABL_integral_lengthscale.ipynb](postprocessing/ABL_integral_lengthscale.ipynb) notebook. If necessary, download the library and edit the lines which define `amrwindfedirs` to include any locations of that library, e.g., ```python # Add any possible locations of amr-wind-frontend here amrwindfedirs = ['/projects/wind_uq/lcheung/amrwind-frontend/', '/ccs/proj/cfd162/lcheung/amrwind-frontend/'] import sys, os, shutil for x in amrwindfedirs: sys.path.insert(1, x) for x in amrwindfedirs: sys.path.insert(1, x+'/utilities') ``` The two-point correlation, $R_{ij}$, as a function of longitudinal and latitudinal separation distance $\boldsymbol{\xi}$ are shown below at multiple locations, $\boldsymbol{x}$, on the XY-plane at z=90m: