Add Response Models and Datum Models

Adding New Response Models

Use the Fit models common task to set up response models.

To add new response models to an existing test plan, in the Test Plan tab view, double-click the Responses output port of the block diagram on the test plan tab, or select File > New Response Model.

The Response Model Setup dialog box has a list box containing all the variables in the selected data set except the inputs to the local and global models; you cannot use an input also as a response.

You can also change the local and global models also by clicking the Set Up buttons to open the Local Model and Global Model Setup dialog boxes (see Explore Local Model Types and Explore Global Model Types). You can add Datum Models (maximum or minimum) if the local model supports this.

You can return to the local or global setup options individually at any time by double-clicking the block in the test plan diagram.

In the local model view, in the Common Tasks pane, you can click Edit Model. In the Local Model Setup dialog box, select the Response Features tab, and the Name list shows available response features.

Datum Models

A datum model tracks the maximum or minimum of the local models. This is equivalent to adding the maximum or minimum as a response feature, which can be useful for analysis if those points are interesting from an engineering point of view.

If you are modeling spark sweeps with a datum model, use the workflow in Fit a Two-Stage Model. In the Fit Models dialog box, do not define responses at the project level. Instead click OK to finish. To set up your datum model and local model type, use the Fit Models common task at the test plan node. In the Fit Models Wizard, on the Response Models screen, set up your local model and add a datum. Datum models are only available for some local models — polynomial splines and polynomials (but see Linked datum models following). Other local models cannot have a datum model, because they do not necessarily have a unique turning point.

You can also choose a datum model when setting up a new response model.

The Datum options are

  • None

  • Maximum — This can be useful in cases using polyspline modeling of torque against spark. The maximum is often a point of engineering interest.

  • Minimum —- This can be useful for cases where the object is to minimize factors such as fuel consumption or emissions.

  • Linked datum model — This is only available to subsequent two-stage models within a test plan in which the first two-stage model has a datum model defined. In this case, you can use that datum model. The linked datum option carries the name of the response of the first two-stage model, where it originated.

If the maximum and minimum are at points of engineering interest, like MBT or minimum fuel consumption, you can add other response features later using the datum model (for example, MBT plus or minus 10 degrees of spark angle) and track these across local models too. It can be useful to know the value of MBT when modeling exhaust temperature, so you can use a linked datum model from a previous torque/spark model. Having responses relative to datum means that the response features are more likely to relate to a feature within the range of the data points.

You can also export the datum model along with local, global, and response models.

Fitting Process for Polynomial Splines with a Datum Model

The fitting process for a polynomial spline with a maximum datum is:

  1. The toolbox fits a quadratic polynomial to the data.

  2. The toolbox finds the x-location of the maximum of this polynomial (if it does not have a maximum, then the model will not be fitted).

  3. The toolbox uses this x-value as a starting point in an optimization to find the best knot position for the polynomial spline. Note this optimization does not have any constraint that Bhigh2 stays negative.

  4. The toolbox checks the result to see if the new knot position is still at the maximum of the curve. If so, then finish.

    If not, then the algorithm returns to the quadratic polynomial fitted in step 1, which has the required maximum.