InputResidsComp#
InputResidsComp
is a specialized implementation of ImplicitComponent that is intended to allow the user to simply add residuals to a system by treating any inputs to the components as the value of the associated residual.
Unlike BalanceComp
, implicit outputs do not map one-to-one with the inputs. That is, the number of output variables for InputResidsComp
does not have to match the number of input variables, but the total size of the inputs and outputs must be the same.
InputResidsComp
can make it easier to convert an MDO problem from a “SAND” (simultaneous analysis and design) formumlation to an “MDF” (multiple design feasible) formulation by adding zero-valued equality constraints to it as inputs, and the associated design variables to it as implicit outputs.
InputResidsComp Constructor#
The call signature for the InputResidsComp
constructor is:
- InputResidsComp.__init__()[source]
Initialize the InputResidsComp.
- Parameters:
- **kwargsdict
Keyword arguments passed to the __init__ method of ImplicitComponent
Example: Single state vector, multiple equations of constraint#
The following example uses a InputResidsComp to implicitly solve the equations:
import openmdao.api as om
prob = om.Problem()
bal = om.BalanceComp()
bal.add_balance('x', use_mult=True)
exec_comp = om.ExecComp(['y[0]=x[0] + x[1] - 5',
'y[1]=x[2] + x[3] - 10',
'y[2]=x[0] - x[3]',
'z=dot(x, x)**0.5 - 9'],
x={'shape': (4,)},
y={'val': [1., 1., 1.]},
z={'val': 2.})
prob.model.add_subsystem(name='exec', subsys=exec_comp)
resids = prob.model.add_subsystem(name='resids', subsys=om.InputResidsComp())
resids.add_output('x', shape_by_conn=True)
resids.add_input('res_0', shape_by_conn=True)
resids.add_input('res_1', shape_by_conn=True)
prob.model.connect('resids.x', 'exec.x')
prob.model.connect('exec.y', 'resids.res_0')
prob.model.connect('exec.z', 'resids.res_1')
prob.model.linear_solver = om.DirectSolver(assemble_jac=True)
prob.model.nonlinear_solver = om.NewtonSolver(solve_subsystems=False, maxiter=100, iprint=0)
prob.set_solver_print(2)
prob.setup()
prob.set_val('resids.x', [1., 1., 10, 5])
prob.final_setup()
prob.run_model()
prob.model.list_vars(print_arrays=True);
NL: Newton 0 ; 8.00453567 1
NL: Newton 1 ; 1.27590224 0.159397408
NL: Newton 2 ; 0.167205529 0.020888848
NL: Newton 3 ; 0.00698219699 0.000872280077
NL: Newton 4 ; 1.504573e-05 1.87965056e-06
NL: Newton 5 ; 7.05764336e-11 8.81705529e-12
NL: Newton Converged
6 Variables(s) in 'model'
varname val io prom_name
------- ------------------- ------ ------------
exec
x |9.0| input exec.x
val:
array([2., 3., 8., 2.])
y |0.0| output exec.y
val:
array([0., 0., 0.])
z [0.] output exec.z
resids
res_0 |0.0| input resids.res_0
val:
array([0., 0., 0.])
res_1 [0.] input resids.res_1
x |9.0| output resids.x
val:
array([2., 3., 8., 2.])
/usr/share/miniconda/envs/test/lib/python3.11/site-packages/openmdao/utils/coloring.py:395: DerivativesWarning:'exec' <class ExecComp>: Coloring was deactivated. Improvement of 0.0% was less than min allowed (5.0%).