A library of aircraft conceptual design and performance analysis tools, including virtual (design) atmospheres, constraint analysis methods, propulsion system performance models, conversion functions and much else.
For a detailed description of the library, please consult the Documentation. To get started, follow the instructions below.
author: Andras Sobester
ADRpy is written in Python 3 and tested in Python 3.4, 3.5, 3.5-dev, 3.6, 3.6-dev and 3.7-dev. It is not available for Python 2.
On most systems you should be able to simply open an operating system terminal and at the command prompt type
$ pip install ADRpy
or
$ python -m pip install ADRpy
NOTE: pip
is a Python package; if it is not available on your system, download
get-pip.py and run it in Python by entering
$ python get-pip.py
at the operating system prompt.
An alternative approach to installing ADRpy is to clone the GitHub repository, by typing
$ git clone https://github.com/sobester/ADRpy.git
at the command prompt and then executing the setup file in the same directory by entering:
$ python setup.py install
There are several options for running the examples shown here: you could copy and paste them
into a .py
file, save it and run it in Python, or you could enter the lines, in sequence,
at the prompt of a Python terminal. You could also copy and paste them into a Jupyter notebook
(.ipynb
file) cell and execute the cell.
from ADRpy import atmospheres as at
from ADRpy import unitconversions as co
# Instantiate an atmosphere object: an ISA with a +10C offset
isa = at.Atmosphere(offset_deg=10)
# Query the ambient density in this model at 41,000 feet
print("ISA+10C density at 41,000 feet (geopotential):",
isa.airdens_kgpm3(co.feet2m(41000)), "kg/m^3")
You should see the following output:
ISA+10C density at 41,000 feet (geopotential): 0.274725888531 kg/m^3
# Compute the thrust to weight ratio required for take-off, given
# a basic design brief, a basic design definition and a set of
# atmospheric conditions
from ADRpy import atmospheres as at
from ADRpy import constraintanalysis as ca
from ADRpy import unitconversions as co
# The environment: 'unusually high temperature at 5km' atmosphere
# from MIL-HDBK-310.
# Extract the relevant atmospheric profiles...
profile_ht5_1percentile, _ = at.mil_hdbk_310('high', 'temp', 5)
# ...then use them to create an atmosphere object
m310_ht5 = at.Atmosphere(profile=profile_ht5_1percentile)
#====================================================================
# The take-off aspects of the design brief:
designbrief = {'rwyelevation_m':1000, 'groundrun_m':1200}
# Basic features of the concept:
# aspect ratio, engine bypass ratio, throttle ratio
designdefinition = {'aspectratio':7.3, 'bpr':3.9, 'tr':1.05}
# Initial estimates of aerodynamic performance:
designperf = {'CDTO':0.04, 'CLTO':0.9, 'CLmaxTO':1.6,
'mu_R':0.02} # ...and wheel rolling resistance coeff.
# An aircraft concept object can now be instantiated
concept = ca.AircraftConcept(designbrief, designdefinition,
designperf, m310_ht5)
#====================================================================
# Compute the required standard day sea level thrust/MTOW ratio reqd.
# for the target take-off performance at a range of wing loadings:
wingloadinglist_pa = [2000, 3000, 4000, 5000]
tw_sl, liftoffspeed_mpstas, _ = concept.twrequired_to(wingloadinglist_pa)
# The take-off constraint calculation also supplies an estimate of
# the lift-off speed; this is TAS (assuming zero wind) - we convert
# it to equivalent airspeed (EAS), in m/s:
liftoffspeed_mpseas = \
m310_ht5.tas2eas(liftoffspeed_mpstas, designbrief['rwyelevation_m'])
print("Required T/W and V_liftoff under MIL-HDBK-310 conditions:")
print("\nT/W (std. day, SL, static thrust):", tw_sl)
print("\nLiftoff speed (KEAS):", co.mps2kts(liftoffspeed_mpseas))
You should see the following output:
Required T/W and V_liftoff under MIL-HDBK-310 conditions:
T/W (std. day, SL, static thrust): [ 0.19618164 0.2710746 0.34472518 0.41715311]
Liftoff speed (KEAS): [ 96.99203483 118.79049722 137.1674511 153.35787248]
Click on to open a library of examples recorded in Jupyter notebooks. You can play
with these 'live' in Binder, or you can click File / Download as / ... to create your own local
copy in any number of formats. [Note: if you don't want to wait for Binder to generate the library,
you can still access the 'static' versions of the notebooks through nbviewer - click on the required
notebook in the lower half of the holding page. ]