openmdao：约束不存在且代码中存在值错误
openmdao: constraint does not exist and value error in code
我一直在创建一个项目来优化飞机形状以实现最低阻力，并且 运行 遇到了两个问题，一个是在应用约束的情况下发生的，并收到以下错误
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.
这发生在引用约束的情况下
`
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有什么想法吗？
编辑：
这是完整的代码
`# 对于打印，如果您是 运行 Python 2.x，请使用此导入
从未来导入print_function
将数学导入为 m
从 openmdao.api 导入 IndepVarComp、组件、问题、组、ExecComp、ScipyOptimizer、SqliteRecorder
class 外壳（组件）：
"""Component containing Outershell."""
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')
`
对于第一个错误：
在没有看到您的实际模型的情况下，我无法诊断为什么会发生这种情况。但是您正在尝试将 IndepVarComp 的输出连接（我猜是）到其他组件（可能是 exec comp）的输入。不过，您可能已经升级了源、目标或两者。因此，考虑到促销活动，您没有使用正确的名称来引用它们。
如果您已经提升了两个变量，因为它们都是名称 Sp1 那么它们会自动连接。如果您只提升了其中一个，则需要在连接语句中说明这一点。例如：
self.connect('Sp1', 'con.Sp1')
对于你的第二个错误：
Cdi 与您必须计算该值的方程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，因为我不知道任何中间变量是什么。无论如何，该分配的一侧或另一侧大小不正确。您可以通过添加一些打印语句来调试它，以查看 OpenMDAO 的大小。
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))))))
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)')
我认为你需要明确地输入乘法，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)')
我一直在创建一个项目来优化飞机形状以实现最低阻力，并且 运行 遇到了两个问题，一个是在应用约束的情况下发生的，并收到以下错误
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.
这发生在引用约束的情况下 `
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有什么想法吗？
编辑： 这是完整的代码
`# 对于打印，如果您是 运行 Python 2.x，请使用此导入 从未来导入print_function
将数学导入为 m
从 openmdao.api 导入 IndepVarComp、组件、问题、组、ExecComp、ScipyOptimizer、SqliteRecorder
class 外壳（组件）： """Component containing Outershell."""
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')
`
对于第一个错误：
在没有看到您的实际模型的情况下，我无法诊断为什么会发生这种情况。但是您正在尝试将 IndepVarComp 的输出连接（我猜是）到其他组件（可能是 exec comp）的输入。不过，您可能已经升级了源、目标或两者。因此，考虑到促销活动，您没有使用正确的名称来引用它们。
如果您已经提升了两个变量，因为它们都是名称 Sp1 那么它们会自动连接。如果您只提升了其中一个，则需要在连接语句中说明这一点。例如：
self.connect('Sp1', 'con.Sp1')
对于你的第二个错误：
Cdi 与您必须计算该值的方程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，因为我不知道任何中间变量是什么。无论如何，该分配的一侧或另一侧大小不正确。您可以通过添加一些打印语句来调试它，以查看 OpenMDAO 的大小。
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))))))
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)')
我认为你需要明确地输入乘法，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)')