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Performing a thermal correlation analysis on a steady state problem
In this tutorial, you will learn how to set up and solve a thermal correlation analysis, analyze the results, and update the model with the optimized results for a steady-state problem.
9. Set up the solver settings.
You will define the TMG Correlation solver parameters for the optimization.

Tutorials
- TMG Correlation tutorials
  These examples are designed to help you become familiar with some of the major features available in TMG Correlation.
- Performing a thermal correlation analysis on a steady state problem
  In this tutorial, you will learn how to set up and solve a thermal correlation analysis, analyze the results, and update the model with the optimized results for a steady-state problem.
  - 1. Open the Simulation file.
    In addition to opening the file used for this tutorial, you will reset the dialog boxes to ensure that they are in the expected initial state.
  - 2. Examine the model.
    You will explore the boundary conditions in the model to learn more about how the model is constructed.
  - 3. Create a thermal correlation analysis.
    You will create a thermal correlation analysis using TMG Correlation.
  - 4. Define the sensors.
    You will create four sensors by importing reference data from a CSV file.
  - 5. Pair the sensors with the simulation model.
    You will pair the sensors with the elements of the simulation model using the proximity method.
  - 6. Create the design variables.
    You will use the multiplier and substitution methods to create design variables from the boundary condition parameters that are present in the correlated solution.
  - 7. Set up the targets.
    You will set the weight factor of the bus targets for the optimization process.
  - 8. Define the optimization design variables.
    You will specify the minimum, and maximum values of the four design variables you previously created to limit their variation during the optimization process.
  - 9. Set up the solver settings.
    You will define the TMG Correlation solver parameters for the optimization.
  - 10. Solve the thermal correlation analysis.
    You will launch the optimization of the active thermal correlation analysis.
  - 11. Plot the thermal correlation graph during the optimization process.
    The Solution Monitor dialog box is still open from the previous step. During the solve, you will plot the evolution of the objective function and the evolution of the design variables gradient.
  - 12. Analyze the optimized solution results.
    You will load and analyze the optimized solution results after solving a thermal correlation analysis. You will compare the reference and optimized temperature values of the targets after solving the thermal correlation analysis.
  - 13. Update the simulation model.
    You will update and save the new simulation model that contains the optimized design variable values applied to the related parameters.
- Performing a thermal correlation analysis on a transient problem
  In this tutorial, you will learn how to set up and solve a thermal correlation analysis, analyze the results, and update the model with the optimized results for a transient problem.
- Correlation analysis of a turbomachinery compressor model
  This is an instruction-based tutorial. Instead of following a step-by-step procedure, you are given a scenario and problem statement to solve. Complete the activities below to achieve the objective.
- Best practices for TMG Correlation analyses
  To determine the best settings for your thermal correlation analysis, it is recommended that you start with exploratory analyses before you run your final optimization analysis.

9. Set up the solver settings.

You will define the TMG Correlation solver parameters for the optimization.

In the TMG Correlation dialog box, select the Optimization > Solver Settings node.
In the Convergence Criterion box, type 0.08 to set up the value at which the objective function of the optimization must converge.
In the Maximum Design Cycles box, type 150 to set the maximum number of optimization iterations.
From the Optimization Algorithm list, select Sequential Least Squares Programming.