openmdao：约束不存在，代码中的值错误

时间：2017-09-26 22:47:42

标签： python openmdao

我一直在创建一个项目来优化飞机形状以获得最低阻力，并且遇到了两个问题，一个是在应用了约束的情况下发生并且收到了以下错误

 File "/home/name/Desktop/x1ac3opt.py", line 202, in <module>
    top.setup()

  File "/usr/local/lib/python2.7/dist-packages/openmdao/core/problem.py", line 498, in setup
    connections = self._setup_connections(params_dict, unknowns_dict)

  File "/usr/local/lib/python2.7/dist-packages/openmdao/core/problem.py", line 197, in _setup_connections
    connections = self.root._get_explicit_connections()

  File "/usr/local/lib/python2.7/dist-packages/openmdao/core/group.py", line 685, in _get_explicit_connections
    (src, tgt, tgt))

NameError: Source 'p.Sp1' cannot be connected to target 'con.Sp1': 'con.Sp1' does not exist.

这会在引用的约束条件下发生 `

File "/home/name/Desktop/x1ac3opt.py", line 63, in solve_nonlinear
    unknowns['Cdi'] = (324/((7750)*(m.pi)*(((Sp1+Sp2+Sp3+Sp4+Sp5)**2)/(Sp1*(Rc+Tc1)/2+Sp2*(Tc1+Tc2)/2+Sp3*(Tc2+Tc3)/2+Sp4*(Tc3+Tc4)/2+Sp5*(Tc4+Tc5)/2))))

  File "/usr/local/lib/python2.7/dist-packages/openmdao/core/vec_wrapper.py", line 435, in __setitem__
    self._dat[name].set(value)

  File "/usr/local/lib/python2.7/dist-packages/openmdao/core/vec_wrapper.py", line 313, in _set_scalar
    self.val[0] = value

ValueError: setting an array element with a sequence.

`

关于为什么会发生这种情况的任何想法？

编辑： 这是代码的完整性

`＃对于打印，如果您运行的是Python 2.x，请使用此导入 来自 future import print_function

将数学导入为m

来自openmdao.api导入IndepVarComp，组件，问题，组，ExecComp，ScipyOptimizer，SqliteRecorder

类Outershell（组件）：     msgstr“”“包含Outershell的组件。”“”

def __init__(self):
    super(Outershell, self).__init__()
    self.add_param('Sp1', val=23)      #Sec1Span
    self.add_param('Sp2', val=13)      #Sec2Span
    self.add_param('Sp3', val=20)      #Sec3Span
    self.add_param('Sp4', val=35)      #Sec4Span
    self.add_param('Sp5', val=35)      #Sec5Span

    self.add_param('Sw1', val=60)      #Sec1Sweep
    self.add_param('Sw2', val=60)      #Sec2sweep
    self.add_param('Sw3', val=50)      #Sec3sweep
    self.add_param('Sw4', val=37)      #Sec4sweep
    self.add_param('Sw5', val=35)      #Sec5sweep

    self.add_param('Rc', val=130)      #Sec1RC
    self.add_param('Tc1', val=90)      #Sec1TC
    self.add_param('Tc2', val=66)      #Sec2TC
    self.add_param('Tc3', val=42)     #Sec3TC
    self.add_param('Tc4', val=24)      #Sec4TC
    self.add_param('Tc5', val=10)      #Sec5TC

    self.add_output('Cdi', shape=1)     #Objective output as low as possible


def solve_nonlinear(self, params, unknowns, resids):

    #0.0324 and 0.775 are the squared Cl and the oswald efficiency number in the case that I can find a way to add in those values to the optimization problem

    Sp1 = params['Sp1']
    Sp2 = params['Sp2']
    Sp3 = params['Sp3']
    Sp4 = params['Sp4']
    Sp5 = params['Sp5']

    Sw1 = params['Sw1']
    Sw2 = params['Sw2']
    Sw3 = params['Sw3']
    Sw4 = params['Sw4']
    Sw5 = params['Sw5']

    Rc = params['Rc']
    Tc1 = params['Tc1']
    Tc2 = params['Tc2']
    Tc3 = params['Tc3']
    Tc4 = params['Tc4']
    Tc5 = params['Tc5']


    unknowns['Cdi'] = (324/((7750)*(m.pi)*(((Sp1+Sp2+Sp3+Sp4+Sp5)**2)/(Sp1*(Rc+Tc1)/2+Sp2*(Tc1+Tc2)/2+Sp3*(Tc2+Tc3)/2+Sp4*(Tc3+Tc4)/2+Sp5*(Tc4+Tc5)/2))))


def linearize(self, params, unknowns, resids):


    Sp1 = params['Sp1']
    Sp2 = params['Sp2']
    Sp3 = params['Sp3']
    Sp4 = params['Sp4']
    Sp5 = params['Sp5']

    sw1 = params['Sw1']
    sw2 = params['Sw2']
    sw3 = params['Sw3']
    sw4 = params['Sw4']
    sw5 = params['Sw5']

    Rc = params['Rc']
    Tc1 = params['Tc1']
    Tc2 = params['Tc2']
    Tc3 = params['Tc3']
    Tc4 = params['Tc4']
    Tc5 = params['Tc5']

    J ={}
    J['Cdi', 'Sp1']=unknowns['Cdi']/Sp1
    J['Cdi', 'Sp2']=unknowns['Cdi']/Sp2
    J['Cdi', 'Sp3']=unknowns['Cdi']/Sp3
    J['Cdi', 'Sp4']=unknowns['Cdi']/Sp4
    J['Cdi', 'Sp5']=unknowns['Cdi']/Sp5
    J['Cdi', 'Sw1']=unknowns['Cdi']/sw1
    J['Cdi', 'Sw2']=unknowns['Cdi']/sw2
    J['Cdi', 'Sw3']=unknowns['Cdi']/sw3
    J['Cdi', 'Sw4']=unknowns['Cdi']/sw4
    J['Cdi', 'Sw5']=unknowns['Cdi']/sw5
    J['Cdi', 'Tc1']=unknowns['Cdi']/Tc1
    J['Cdi', 'Tc2']=unknowns['Cdi']/Tc2
    J['Cdi', 'Tc3']=unknowns['Cdi']/Tc3
    J['Cdi', 'Tc4']=unknowns['Cdi']/Tc4
    J['Cdi', 'Tc5']=unknowns['Cdi']/Tc5
    J['Cdi', 'Rc']=unknowns['Cdi']/Rc

if __name__ == "__main__": 

top = Problem()

root = top.root = Group()

root.add('p1', IndepVarComp('Sp1', 23))
root.add('p2', IndepVarComp('Sp2', 13))
root.add('p3', IndepVarComp('Sp3', 20))
root.add('p4', IndepVarComp('Sp4', 35))
root.add('p5', IndepVarComp('Sp5', 35))
root.add('p6', IndepVarComp('Sw1', 60))
root.add('p7', IndepVarComp('Sw2', 60))
root.add('p8', IndepVarComp('Sw3', 50))
root.add('p9', IndepVarComp('Sw4', 37))
root.add('p10', IndepVarComp('Sw5', 35))
root.add('p11', IndepVarComp('Tc1', 90))
root.add('p12', IndepVarComp('Tc2', 66))
root.add('p13', IndepVarComp('Tc3', 42))
root.add('p14', IndepVarComp('Tc4', 24))
root.add('p15', IndepVarComp('Tc5', 10))
root.add('p16', IndepVarComp('Rc', 130))
root.add('p', Outershell())


root.add('con', ExecComp('L = (15067/100000000)/(Sp1(Rc+Tc1)/2+Sp2(Tc1+Tc2)/2+Sp3(Tc2+Tc3)/2+Sp4(Tc3+Tc4)/2+Sp5(Tc4+Tc5)/2)'))
 #Cl=0.18 rho = 0.000737 v**2 = 810471.67 Area = ... 597.31762079


root.connect('p1.Sp1', 'p.Sp1')
root.connect('p2.Sp2', 'p.Sp2')
root.connect('p3.Sp3', 'p.Sp3')
root.connect('p4.Sp4', 'p.Sp4')
root.connect('p5.Sp5', 'p.Sp5')
root.connect('p6.Sw1', 'p.Sw1')
root.connect('p7.Sw2', 'p.Sw2')
root.connect('p8.Sw3', 'p.Sw3')
root.connect('p9.Sw4', 'p.Sw4')
root.connect('p10.Sw5', 'p.Sw5')
root.connect('p11.Tc1', 'p.Tc1')
root.connect('p12.Tc2', 'p.Tc2')
root.connect('p13.Tc3', 'p.Tc3')
root.connect('p14.Tc4', 'p.Tc4')
root.connect('p15.Tc5', 'p.Tc5')
root.connect('p16.Rc', 'p.Rc')

root.connect('p.Sp1', 'con.Sp1')
root.connect('p.Sp2', 'con.Sp2')
root.connect('p.Sp3', 'con.Sp3')
root.connect('p.Sp4', 'con.Sp4')
root.connect('p.Sp5', 'con.Sp5')
root.connect('p.Sw1', 'con.Sw1')
root.connect('p.Sw2', 'con.Sw2')
root.connect('p.Sw3', 'con.Sw3')
root.connect('p.Sw4', 'con.Sw4')
root.connect('p.Sw5', 'con.Sw5')
root.connect('p.Tc1', 'con.Tc1')
root.connect('p.Tc2', 'con.Tc2')
root.connect('p.Tc3', 'con.Tc3')
root.connect('p.Tc4', 'con.Tc4')
root.connect('p.Tc5', 'con.Tc5')
root.connect('p.Rc', 'con.Rc')



top.driver = ScipyOptimizer()
top.driver.options['optimizer'] = 'COBYLA'


top.driver.add_desvar('p1.Sp1', lower=13, upper=33)
top.driver.add_desvar('p2.Sp2', lower=3, upper=23)
top.driver.add_desvar('p3.Sp3', lower=10, upper=30)
top.driver.add_desvar('p4.Sp4', lower=25, upper=45)
top.driver.add_desvar('p5.Sp5', lower=25, upper=45)
top.driver.add_desvar('p6.Sw1', lower=55, upper=65)
top.driver.add_desvar('p7.Sw2', lower=55, upper=65)
top.driver.add_desvar('p8.Sw3', lower=45, upper=55)
top.driver.add_desvar('p9.Sw4', lower=32, upper=42)
top.driver.add_desvar('p10.Sw5', lower=30, upper=40)
top.driver.add_desvar('p11.Tc1', lower=80, upper=100)
top.driver.add_desvar('p12.Tc2', lower=56, upper=76)
top.driver.add_desvar('p13.Tc3', lower=37, upper=45)
top.driver.add_desvar('p14.Tc4', lower=19, upper=29)
top.driver.add_desvar('p15.Tc5', lower=5, upper=15)
top.driver.add_objective('p.Cdi')
top.driver.add_constraint('con.L', lower=220000, upper=240000)


recorder = SqliteRecorder('Outershell')
recorder.options['record_params'] = True
recorder.options['record_metadata'] = True
top.driver.add_recorder(recorder)

top.setup()
top.run()
top.cleanup()  # this closes all recorders


print('\n')
print('Minimum of %f found at: ' % (top['p.Cdi']))
print('\n')
print('Lift produced is: %f ' % (top['con.L']))
print('SP1 = %f' % (top['p.Sp1']))
print('\n')
print('SP2 = %f' % (top['p.Sp2']))
print('\n')
print('SP3 = %f' % (top['p.Sp3']))
print('\n')
print('SP4 = %f' % (top['p.Sp4']))
print('\n')
print('SP5 = %f' % (top['p.Sp5']))
print('\n')
print('SW1 = %f' % (top['p.Sw1']))
print('\n')
print('SW2 = %f' % (top['p.Sw2']))
print('\n')
print('SW3 = %f' % (top['p.Sw3']))
print('\n')
print('SW4 = %f' % (top['p.Sw4']))
print('\n')
print('SW5 = %f' % (top['p.Sw5']))
print('\n')
print('Rc = %f' % (top['p.Rc']))
print('\n')
print('TC1 = %f' % (top['p.Tc1']))
print('\n')
print('TC2 = %f' % (top['p.Tc2']))
print('\n')
print('TC3 = %f' % (top['p.Tc3']))
print('\n')
print('TC4 = %f' % (top['p.Tc4']))
print('\n')
print('TC5 = %f' % (top['p.Tc5']))
print('\n')

`

2 个答案:

答案 0 :(得分：1)

第一个错误：

L = (15067/100000000)/(Sp1(Rc+Tc1)/2+Sp2(Tc1+Tc2)/2+Sp3(Tc2+Tc3)/2+Sp4(Tc3+Tc4)/2+Sp5(Tc4+Tc5)/2)')

我认为你需要明确地进行乘法，所以

L = (15067/100000000)/(Sp1*(Rc+Tc1)/2+Sp2*(Tc1+Tc2)/2+Sp3*(Tc2+Tc3)/2+Sp4*(Tc3+Tc4)/2+Sp5*(Tc4+Tc5)/2)')

答案 1 :(得分：0)

第一个错误：

没有看到你的实际模型，我无法诊断为什么会发生这种情况。但是你试图将IndepVarComp的输出连接到其他组件（可能是exec comp）的输入（我猜测是什么）。您可能已经提升了源，目标或两者。因此，您没有使用正确考虑促销的名称来引用它们。

如果您已经提升了两个变量，因为它们都是名称Sp1，然后会自动连接。如果您只提升其中一个，那么您需要在connect语句中考虑到这一点。例如：

self.connect('Sp1', 'con.Sp1')

第二个错误： Cdi与您必须计算该值的等式之间存在某种大小不匹配。看起来这个等式可能是标量，但我无法确定，因为我不知道任何中间变量是什么。无论如何，该任务的一方或另一方的大小不正确。你可以通过添加一些print语句来调试它，看看OpenMDAO的大小是什么。 

print(unknowns['Cdi'])
print((324/((7750)*(m.pi)*(((Sp1+Sp2+Sp3+Sp4+Sp5)**2)/(Sp1*(Rc+Tc1)/2+Sp2*(Tc1+Tc2)/2+Sp3*(Tc2+Tc3)/2+Sp4*(Tc3+Tc4)/2+Sp5*(Tc4+Tc5)/2))))))
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