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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.
Design variable gradient graph
The design variable gradient graph represents the evolution of the optimization design variable gradients from one design cycle to the next. This graph is useful when you want to know which design variables impact the optimization.

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.
- 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.
  - Objective function graph
    The objective function graph represents the change of the objective function with respect to the design cycle for each convergence criterion.
  - Design variable graph
    The design variable graph displays the evolution of all the optimization design variables from one design cycle to the next. This graph is useful when you want to know which design variables converged to a constant value.
  - Design variable gradient graph
    The design variable gradient graph represents the evolution of the optimization design variable gradients from one design cycle to the next. This graph is useful when you want to know which design variables impact the optimization.
- 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

Design variable gradient graph

The design variable gradient graph represents the evolution of the optimization design variable gradients from one design cycle to the next. This graph is useful when you want to know which design variables impact the optimization.

The following image shows the gradient histories of 23 different design variables.

When the design variable gradient value tends to zero, it means the design variable is not evolving anymore. The optimizer has found an optimal value for the design variable.

When the design variable gradient value tends to zero from beginning of the optimization, it means that the design variable has no effect on the optimization targets. The contribution of this design variable is negligible for minimizing the objective function.

When the design variable gradient value fluctuates, this design variable contributes to the minimization of the objective function. At the end of the optimization, if the design variable gradient does not tend to zero, the objective function did not reach its minimum.