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Setting up the optimization
In optimization, the goal is to find values for the design variables that minimize the difference between the reference data and the correlated solution results.
Understanding the optimization convergence criterion
The optimization process in TMG Correlation is iterative, so one of the most important considerations when setting up the optimization is determining when the process should stop.

User help
- Introduction to TMG Correlation
- TMG Correlation workflow
- TMG Correlation interface
  The TMG Correlation user interface is integrated in Simcenter 3D using NX Open and contains unique features to conduct a thermal correlation analysis, in addition to standard tools and commands described in the Simcenter 3D Online help and the NX Online help.
- Thermal correlation analysis preparation
  Before conducting a thermal correlation analysis, it is important to properly prepare your simulation model and the reference data.
- Understanding thermal correlation analysis
  The thermal correlation is a calibration process that helps you find the best thermal solution that minimizes the error between the computed and target temperatures at different locations and times.
- Managing the reference data
  In TMG Correlation, reference data refers to the temperature and time data that come from experimental tests or from prior and validated simulation models. They are named sensors and are used as targets for the optimization of the correlated solution.
- Understanding design variables
  A design variable is a simulation entity of your model that the TMG Correlation solver varies within a specific range during the optimization to reduce the gap between the reference data and the simulation results.
- Setting up the optimization
  In optimization, the goal is to find values for the design variables that minimize the difference between the reference data and the correlated solution results.
  - Specify the weight and status of the targets
    The weight of a target defines its priority in the optimization process. By default, TMG Correlation assigns equal weights for all targets. The following example shows you how to specify the weight and the status of targets of two sensors for the optimization stage of the thermal correlation analysis.
  - Targets page
    The Targets page lets you manage targets created from the analysis sensors when you select the Targets node in the main panel.
  - Specify the attributes of multiple design variables
    The following example shows how to specify the initial, minimum, and maximum values of two design variables. To obtain realistic results for the thermal correlation analysis, it is highly recommended to specify the minimum and maximum values, or bounds, for design variables.
  - Deactivate an optimization design variable
    This procedure shows how to deactivate an optimization design variable.
  - Optimization Design Variables page
    The Optimization Design Variables page lets you manage the selected the design variables for the thermal correlation analysis when you select the Optimization Design Variables node in the main panel.
  - Set up the solver settings
    This example shows how to set up the solver settings for a transient solution before an optimization using time varying convergence criterion method.
  - Solver Settings page
    The Solver Settings page lets you control the thermal correlation analysis and set up the solver options to run a thermal correlation analysis when you select the Solver Settings node in the main panel.
  - Launch the optimization
    The following procedure shows you how to launch the optimization of the active thermal correlation analysis.
  - Understanding the optimization convergence criterion
    The optimization process in TMG Correlation is iterative, so one of the most important considerations when setting up the optimization is determining when the process should stop.
  - Solve page
    The Solve page provides the options to run the optimization for a thermal correlation analysis when you select the Solve node in the main panel.
  - Optimization page
    The Optimization page summarizes the optimization setup for the thermal correlation analysis when you select the Optimization node in the main panel. This page automatically updates when you make changes in the Targets, Optimization Design Variable, or Solver Settings pages.
- Monitoring the optimization
  Use the Solution Monitor and Solution Control Monitor to track the analysis during the solve. The monitors provide solver-specific information for the TMG thermal solver and the thermal correlation analysis.
- Analyzing the optimization results
  After you solve a thermal correlation analysis, you can analyze the results of the optimization process in the TMG Correlation dialog box.
- Updating the simulation model
  After you verified the optimization results, you must update the simulation model to reflect any changes that were made during the thermal correlation analysis so that you can continue your modeling in Simcenter 3D.
- Theory
- Legal notice
- License agreement

Understanding the optimization convergence criterion

The optimization process in TMG Correlation is iterative, so one of the most important considerations when setting up the optimization is determining when the process should stop.

The convergence criterion you specify in TMG Correlation is compared to the value of the optimization objective function at the end of each design cycle. When the objective function is smaller than the convergence criterion you specify, the optimization process stops.

Defining the objective function

The objective function is based on the root mean square error between the reference data and the correlated solution results for all the sensors and all the correlation times, and it has temperature unit.

The mathematical expression of the objective function is:

where:

is the computed temperature at the location of the ith sensor.
is the target temperature at the location of the ith sensor, which comes from the reference data.
is the number of correlation times on which the optimizer minimizes the objective function.
the weight factor specified for the ith target temperature. For more information, see Targets page.

Specifying the convergence criterion

The convergence criterion defines the value at which the optimization stops because the computed objective function is smaller. The objective function can be interpreted as the absolute error calculated between the reference data and the simulation model results and averaged over all the correlation times and all targets. Thus, the convergence criterion represents the maximum absolute averaged error you would like to obtain between your reference data and the optimized solution.

For example, if you set a convergence criterion equal to one, the optimization will stop when the absolute averaged error between the reference data and your simulation model is about one degree.

The default value for the Convergence Criterion is 1.000.

If the convergence is not achieved, the design process continues until the maximum number of design cycles is reached. The default maximum number of iterations is 2000, which you can modify on the Solver Settings page. For more information, see the Solver Settings page.