openmdao:约束不存在，cod中存在值错误

Question

我一直在创建一个项目，以优化最低阻力的飞机形状，并已运行到两个问题，一个与约束发生应用和收到以下错误

 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.

这是在引用约束条件时发生的 `你知道吗

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.

`

你知道为什么会这样吗？你知道吗

编辑： 下面是代码的完整性

`#对于打印，如果您运行的是Python2.x，请使用此导入 从未来导入打印功能

将数学作为m导入

从openmdao.api导入IndepVarComp、Component、Problem、Group、ExecComp、ScipyOptimizer、SqliteRecorder

类Outershell（组件）： “”“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')

`

Answer 1

对于第一个错误：

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

我想你需要把乘法明确地放进去，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)')

Answer 2

对于第一个错误：

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。但您正试图将IndepVarComp的输出（我猜是）连接到其他组件（可能是exec comp）的输入。您可能已经提升了源、目标或两者。所以你没有提到他们的名字是正确的考虑晋升。你知道吗

如果您已经升级了这两个变量，因为它们都是名称Sp1，那么它们会自动连接起来。如果您只提升了其中一个，那么您需要在connect语句中说明这一点。例如：

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

对于第二个错误： 在Cdi和必须计算值的等式之间存在某种大小不匹配。看起来这个方程可能是标量的，但我不能确定，因为我不知道中间变量是什么。无论如何，该赋值的一边或另一边的大小都不正确。您可以通过添加一些print语句来调试它，以查看OpenMDAO的大小。你知道吗

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))))))