def calcAeroCoeff(inputFile, dataSet):
'''
DESCRIPTION: This function calculates the lift coefficient for the static measurements. (CL \approx CN)
========
INPUT:\n
... inputFile [String]: Name of excel file; choose between 'reference' or 'actual'\n
... dataSet [String]: Name of data set; choose between 'static1', 'static2a' or 'static2b'\n
... SI [Condition]: By default set to SI=True\n
OUTPUT:\n
... aeroCoeff [Dataframe]: Pandas dataframe containing Cl, Cd, e, Cla, Cd0, aoa0
'''
# Import data
param = imPar.parametersStatic()
static = imStat.staticMeas(inputFile, dataSet)
staticNotSI = imStat.staticMeas(inputFile, dataSet, SI=False)
staticFlightCond = imStat.staticFlightCondition(inputFile, dataSet)
staticTp = imStat.staticThrust(inputFile, dataSet)
staticWeight = imWeight.calcWeightCG(inputFile, dataSet)
# Obtain vales from data
S = param.S
A = param.A
aoa_rad = static['aoa'].to_numpy()
aoa_deg = staticNotSI['aoa'].to_numpy()
rho = staticFlightCond['rho'].to_numpy()
Vt = staticFlightCond['Vt'].to_numpy()
W = staticWeight['Weight'].to_numpy()
Tp = staticTp['Tp'].to_numpy()
# Calculations
Cl = W / (0.5 * rho * Vt**2 * S)
Cd = Tp / (0.5 * rho * Vt**2 * S)
aeroCoeff = {}
if dataSet == 'static1':
Cl_aoa = stats.linregress(aoa_deg,Cl)
Cd_Cl2 = stats.linregress(Cl**2,Cd)
Cla = Cl_aoa.slope
aoa0 = -Cl_aoa.intercept / Cla
e = 1/(np.pi*A*Cd_Cl2.slope)
Cd0 = Cd_Cl2.intercept
dataNames = ['Cl','Cd','e','Cla','Cd0','aoa0']
for name in dataNames:
aeroCoeff[name] = locals()[name]
else:
dataNames = ['Cl','Cd']
for name in dataNames:
aeroCoeff[name] = locals()[name]
aeroCoeff = pd.DataFrame(data=aeroCoeff)
return aeroCoeff
def calcElevContrForce(inputFile):
'''
DESCRIPTION: This function calculates the reduced elevator control force for each meassurement taken during the second stationary measurement series.
========
INPUT:\n
... inputFile [String]: Name of excel file; choose between 'reference' or 'actual'\n
... SI [Condition]: By default set to SI=True\n
OUTPUT:\n
... FeRed [Arary]: Numpy array containing the reduced elevator control force
'''
# Import data
param = imPar.parametersStatic()
static2a = imStat.staticMeas(inputFile, 'static2a', SI=True)
static2aWeight = imWeight.calcWeightCG(inputFile, 'static2a')
# Obtain values from data
Ws = param.Ws
Fe = static2a['Fe'].to_numpy()
W = static2aWeight['Weight'].to_numpy()
# Calculation
FeRed = Fe * Ws / W
return FeRed
def calcElevDeflection(inputFile):
'''
DESCRIPTION: This function calculates the reduced elevator deflection for each meassurement taken during the second stationary meassurement series.
========
INPUT:\n
... inputFile [String]: Name of excel file; choose between 'reference' or 'actual'\n
... SI [Condition]: By default set to SI=True\n
OUTPUT:\n
... deltaRed [Array]: Numpy array containing the reduced elevator deflections
... Cma [float]: Longitudinal stability parameter
'''
# Import data
param = imPar.parametersStatic()
static2a = imStat.staticMeas(inputFile, 'static2a', SI=True)
staticThrust2aRed = imStat.staticThrust(inputFile, 'static2a', standard=True)
staticThrust2a = imStat.staticThrust(inputFile, 'static2a', standard=False)
# Obtain values from data
CmTc = param.CmTc
delta = static2a['delta'].to_numpy()
aoa = static2a['aoa'].to_numpy()
Tcs = staticThrust2aRed['Tcs'].to_numpy()
Tc = staticThrust2a['Tc'].to_numpy()
Cmdelta = calcElevEffectiveness(inputFile)
# Calculations
deltaRed = delta - CmTc * (Tcs - Tc) / Cmdelta
linregress = stats.linregress(aoa, delta)
Cma = -1* linregress.slope * Cmdelta
return deltaRed, Cma
def plotElevTrimCurve(inputFile):
'''
DESCRIPTION: Function description
========
INPUT:\n
... inputFile [String]: Name of excel file; choose between 'reference' or 'actual'\n
OUTPUT:\n
... None, but running this function creates a figure which can be displayed by calling plt.plot()
'''
# Import data
static2a_nonSI = imStat.staticMeas(inputFile,'static2a',SI=False)
Weight2a = imWeight.calcWeightCG(inputFile, 'static2a')
# Obtain values from data
aoa_deg = static2a_nonSI['aoa'].to_numpy()
delta_deg = static2a_nonSI['delta'].to_numpy()
Xcg = np.average(Weight2a['Xcg'].to_numpy())
Weight = np.average(Weight2a['Weight'].to_numpy())
# Calculations
linregress = stats.linregress(aoa_deg, delta_deg)
# Start plotting
plt.figure('Elevator trim curve',[10,7])
plt.title("Elevator Trim Curve",fontsize=22)
plt.scatter(aoa_deg,delta_deg)
plt.plot(np.sort(aoa_deg),np.sort(aoa_deg)*linregress.slope + linregress.intercept, 'k--')
plt.ylim(1.2*max(delta_deg),1.2*min(delta_deg))
plt.grid()
plt.xlim(0.9*np.sort(aoa_deg)[0],1.1*np.sort(aoa_deg)[-1])
plt.xlabel(r'$\alpha$ [$\degree$]',fontsize=16)
plt.ylabel(r'$\delta_{e}$ [$\degree$]',fontsize=16)
plt.axhline(0,color='k')
props = dict(boxstyle='round', facecolor='white', alpha=0.5)
# plt.text(1.03*np.sort(aoa_deg)[1],1.05*delta_deg[1],'$x_{cg}$ = 7.15 m\nW = 59875 kg',bbox=props,fontsize=16)
plt.text(1.03*np.sort(aoa_deg)[1],1.05*delta_deg[1],'$x_{cg}$ = '+str(round(Xcg,2))+' m\nW = '+str(int(round(Weight,0)))+' kg',bbox=props,fontsize=16)
props = dict(boxstyle='round', facecolor='white', alpha=0.5)
plt.show()
return