def GenerateLiftingSurface(self, ChordFactor, ScaleFactor, OptimizeChordScale=0):
# This is the main method of this class. It builds a lifting surface
# (wing, tailplane, etc.) with the given ChordFactor and ScaleFactor or
# an optimized ChordFactor and ScaleFactor, with the local search started
# from the two given values.
x0 = [ChordFactor, ScaleFactor]
if OptimizeChordScale:
self._CheckOptParCorrectlySpec()
self._NormaliseWeightings()
self._PrintTargetsAndWeights()
print("Optimizing scale factors...")
# An iterative local hillclimber type optimizer is needed here. One
# option might be SciPy's fmin as below:
# x0, fopt, iter, funcalls, warnflag, allvecs = scipy.optimize.fmin(self._LSObjective, x0, retall=True, xtol=0.025, full_output=True)
# However, SciPy is not supported on 64-bit Rhino installations, so
# so here we use an alternative: a simple evoltionary optimizer
# included with AirCONICS_tools.
MaxIter = 50
xtol = 0.025
deltax = [x0[0]*0.25,x0[1]*0.25]
x0, fopt = act.boxevopmin2d(self._LSObjective, x0, deltax, xtol, MaxIter)
x0[0] = abs(x0[0])
x0[1] = abs(x0[1])
print("Optimum chord factor %5.3f, optimum scale factor %5.3f" % (x0[0], x0[1]))
LS, ActualSemiSpan, LSP_area, RootChord, AR, WingTip = self._BuildLS(x0[0], x0[1])
self._ClearConstructionGeometry()
# Moving the wing into position
AP = rs.AddPoint(self.ApexPoint)
MoveVec = rs.VectorCreate(AP, (0,0,0))
rs.MoveObject(LS, MoveVec)
if WingTip:
rs.MoveObject(WingTip, MoveVec)
rs.DeleteObject(AP)
# FINAL REPORT ON THE COMPLETED SURFACE
SA = rs.SurfaceArea(LS)
print("Wing complete. Key features (all related to both wings):")
print("Proj.area: %5.4f Wet.area: %5.4f Span:%5.4f Aspect ratio: %5.4f Root chord: %5.4f" % (2*LSP_area, 2.0*SA[0], 2.0*ActualSemiSpan, AR, RootChord))
return LS, ActualSemiSpan, LSP_area, RootChord, AR, WingTip
def GenerateLiftingSurface(self, ChordFactor, ScaleFactor, OptimizeChordScale=0):
# This is the main method of this class. It builds a lifting surface
# (wing, tailplane, etc.) with the given ChordFactor and ScaleFactor or
# an optimized ChordFactor and ScaleFactor, with the local search started
# from the two given values.
x0 = [ChordFactor, ScaleFactor]
if OptimizeChordScale:
self._CheckOptParCorrectlySpec()
self._NormaliseWeightings()
self._PrintTargetsAndWeights()
print("Optimizing scale factors...")
# An iterative local hillclimber type optimizer is needed here. One
# option might be SciPy's fmin as below:
# x0, fopt, iter, funcalls, warnflag, allvecs = scipy.optimize.fmin(self._LSObjective, x0, retall=True, xtol=0.025, full_output=True)
# However, SciPy is not supported on 64-bit Rhino installations, so
# so here we use an alternative: a simple evoltionary optimizer
# included with AirCONICS_tools.
MaxIter = 50
xtol = 0.025
deltax = [x0[0]*0.25,x0[1]*0.25]
x0, fopt = act.boxevopmin2d(self._LSObjective, x0, deltax, xtol, MaxIter)
x0[0] = abs(x0[0])
x0[1] = abs(x0[1])
print("Optimum chord factor %5.3f, optimum scale factor %5.3f" % (x0[0], x0[1]))
LS, ActualSemiSpan, LSP_area, RootChord, AR, WingTip = self._BuildLS(x0[0], x0[1])
self._ClearConstructionGeometry()
# Moving the wing into position
AP = rs.AddPoint(self.ApexPoint)
MoveVec = rs.VectorCreate(AP, (0,0,0))
rs.MoveObject(LS, MoveVec)
if WingTip:
rs.MoveObject(WingTip, MoveVec)
rs.DeleteObject(AP)
# FINAL REPORT ON THE COMPLETED SURFACE
SA = rs.SurfaceArea(LS)
print("Wing complete. Key features (all related to both wings):")
print("Proj.area: %5.4f Wet.area: %5.4f Span:%5.4f Aspect ratio: %5.4f Root chord: %5.4f" % (2*LSP_area, 2.0*SA[0], 2.0*ActualSemiSpan, AR, RootChord))
return LS, ActualSemiSpan, LSP_area, RootChord, AR, WingTip
