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aero_geometry_coarse_discretisation.yaml
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171 lines (170 loc) · 5.74 KB
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wing_sections:
# ---------------------------------------------------------------
# headers:
# - airfoil_id: integer, unique identifier for the airfoil (matches wing_airfoils)
# - LE_x: x-coordinate of leading edge
# - LE_y: y-coordinate of leading edge
# - LE_z: z-coordinate of leading edge
# - TE_x: x-coordinate of trailing edge
# - TE_y: y-coordinate of trailing edge
# - TE_z: z-coordinate of trailing edge
# ---------------------------------------------------------------
# NOTE: Reduced to 10 sections to match 20 structural WING points (10 LE/TE pairs)
# Constraint: n_structural_points (20) == 2 * n_vsm_sections (10)
# Selected sections match structural point spanwise positions
headers: [airfoil_id, LE_x, LE_y, LE_z, TE_x, TE_y, TE_z]
data:
# Sections match structural points exactly from struc_geometry.yaml
# Section 1: points 2 (LE) and 3 (TE)
- [
18,
-0.16686417,
4.07190711,
8.25938552,
0.8950856,
4.23504033,
8.28564737,
]
# Section 2: points 4 (LE) and 5 (TE)
- [
15,
-0.66385373,
3.97158462,
9.10345727,
0.84466799,
4.04893109,
9.16869541,
]
# Section 3: points 6 (LE) and 7 (TE)
- [
11,
-1.00179344,
3.13938093,
10.08117033,
0.95411064,
3.19044693,
10.20618443,
]
# Section 4: points 8 (LE) and 9 (TE)
- [
7,
-1.11933902,
1.96778538,
10.69146438,
1.03150699,
1.98490264,
10.81013669,
]
# Section 5: points 10 (LE) and 11 (TE)
- [
3,
-1.16537227,
0.67379745,
10.96630823,
1.09790203,
0.6605361,
11.06585663,
]
# Section 6: points 12 (LE) and 13 (TE) - mirror of section 5
- [
3,
-1.16537227,
-0.67379745,
10.96630823,
1.09790203,
-0.6605361,
11.06585663,
]
# Section 7: points 14 (LE) and 15 (TE) - mirror of section 4
- [
7,
-1.11933902,
-1.96778538,
10.69146438,
1.03150699,
-1.98490264,
10.81013669,
]
# Section 8: points 16 (LE) and 17 (TE) - mirror of section 3
- [
11,
-1.00179344,
-3.13938093,
10.08117033,
0.95411064,
-3.19044693,
10.20618443,
]
# Section 9: points 18 (LE) and 19 (TE) - mirror of section 2
- [
15,
-0.66385373,
-3.97158462,
9.10345727,
0.84466799,
-4.04893109,
9.16869541,
]
# Section 10: points 20 (LE) and 21 (TE) - mirror of section 1
- [
18,
-0.16686417,
-4.07190711,
8.25938552,
0.8950856,
-4.23504033,
8.28564737,
]
wing_airfoils:
# ---------------------------------------------------------------
# headers:
# - airfoil_id: integer, unique identifier for the airfoil
# - type: one of [neuralfoil, breukels_regression, masure_regression, polars]
# - info_dict: dictionary with parameters depending on 'type'
#
# info_dict fields by type:
# - breukels_regression:
# t: Tube diameter non-dimensionalized by chord (required)
# kappa: Maximum camber height/magnitude, non-dimensionalized by chord (required)
# - neuralfoil:
# dat_file_path: Path to airfoil .dat file (x, y columns)
# model_size: NeuralFoil model size (e.g., "xxxlarge")
# xtr_lower: Lower transition location (0=forced, 1=free)
# xtr_upper: Upper transition location
# n_crit: Critical amplification factor (see guidelines below)
# n_crit guidelines:
# Sailplane: 12–14
# Motorglider: 11–13
# Clean wind tunnel: 10–12
# Average wind tunnel: 9 (standard "e^9 method")
# Dirty wind tunnel: 4–8
# - polars:
# csv_file_path: Path to polar CSV file (columns: alpha [rad], cl, cd, cm)
# - masure_regression:
# t, eta, kappa, delta, lamba, phi: Regression parameters
# - inviscid:
# no further data is required
# ---------------------------------------------------------------
alpha_range: [-15, 50, 0.5] # [deg], in this range the polars are calculated
reynolds: !!float 1e6 # Reynolds number
headers: [airfoil_id, type, info_dict]
data:
- [1, polars, { csv_file_path: '2D_polars_drag_adjusted/1.csv' }]
- [2, polars, { csv_file_path: '2D_polars_drag_adjusted/2.csv' }]
- [3, polars, { csv_file_path: '2D_polars_drag_adjusted/3.csv' }]
- [4, polars, { csv_file_path: '2D_polars_drag_adjusted/4.csv' }]
- [5, polars, { csv_file_path: '2D_polars_drag_adjusted/5.csv' }]
- [6, polars, { csv_file_path: '2D_polars_drag_adjusted/6.csv' }]
- [7, polars, { csv_file_path: '2D_polars_drag_adjusted/7.csv' }]
- [8, polars, { csv_file_path: '2D_polars_drag_adjusted/8.csv' }]
- [9, polars, { csv_file_path: '2D_polars_drag_adjusted/9.csv' }]
- [10, polars, { csv_file_path: '2D_polars_drag_adjusted/10.csv' }]
- [11, polars, { csv_file_path: '2D_polars_drag_adjusted/11.csv' }]
- [12, polars, { csv_file_path: '2D_polars_drag_adjusted/12.csv' }]
- [13, polars, { csv_file_path: '2D_polars_drag_adjusted/13.csv' }]
- [14, polars, { csv_file_path: '2D_polars_drag_adjusted/14.csv' }]
- [15, polars, { csv_file_path: '2D_polars_drag_adjusted/15.csv' }]
- [16, polars, { csv_file_path: '2D_polars_drag_adjusted/16.csv' }]
- [17, polars, { csv_file_path: '2D_polars_drag_adjusted/17.csv' }]
- [18, polars, { csv_file_path: '2D_polars_drag_adjusted/18.csv' }]
- [19, polars, { csv_file_path: '2D_polars_drag_adjusted/19.csv' }]