FMUFunction basics

FMUFunction enables to use OpenTURNS’ high level algorithms by wrapping the FMU into an openturns.Function object.

---

First, retrieve the path to the FMU deviation.fmu. Recall the deviation model is static, i.e. its output does not evolve over time.

import openturns as ot
import otfmi.example.utility
path_fmu = otfmi.example.utility.get_path_fmu("deviation")

Wrap the FMU into an OpenTURNS function:

function = otfmi.FMUFunction(
    path_fmu, inputs_fmu=["E", "F", "L", "I"], outputs_fmu=["y"]
)
print(type(function))

<class 'openturns.func.Function'>

Simulate the FMU on a point:

inputPoint = ot.Point([3.0e7, 30000, 200, 400])
outputPoint = function(inputPoint)
print("y = {}".format(outputPoint))

y = [6.66667]

Simulate the FMU on a sample:

inputSample = ot.Sample(
    [[3.0e7, 30000, 200, 400], [3.0e7, 30000, 250, 400], [3.0e7, 30000, 300, 400]]
)
inputSample.setDescription(["E", "F", "L", "I"])
outputSample = function(inputSample)
print(outputSample)

    [ y        ]
0 : [  6.66667 ]
1 : [ 13.0208  ]
2 : [ 22.5     ]