Analyzing results from a thermal correlation analysis

When you analyze results from your thermal correlation analysis, especially the exploratory runs, you verify first the convergence results, then the design variable results, and finally the sensor results to update the settings for your next run.

Use the results from the exploratory runs to set up, optimize, and update your original thermal solution.

Convergence results
Has the objective function decreased?
  • No—It means that your correlation setup is not well defined. TMG Correlation cannot improve the solution whatever the optimization design variable values are.
    • Verify if the quantities you defined as design variables have an impact on the temperature computation at the sensor locations. If your design variables are located too far from the sensor locations in your model, they may not have any effect on the temperature computed at these locations.
    • Verify your thermal model definition. Do you correctly model all heat transfer aspects as they exist in your reference data set? TMG Correlation uses the numerical representation (discretization) of your thermal problem through the energy equation to compute the adjoint equation, and then the gradient (sensitivities) of the design variables. If your thermal model does not represent with enough accuracy the reference data set, for example the test data, TMG Correlation is not able to converge toward a solution that matches the reference data. Your model may be missing a boundary condition (BC), such as a thermal coupling or convective BC, to model accurately the phenomenon.

  • Yes—It means that the correlation tool could reduce the gap with the test data. Check the profile of the objective function history plot to evaluate if it was converging toward a smaller value or not.

Design variables results
From the design variable values, evaluate the percentage each design variable varied after the exploratory run. This allows you to identify the most and least influential design variables.

Check whether some design variables reached their minimum or maximum bounds. TMG Correlation solves a mathematical problem: minimizing a temperature difference between two data sets. If the best way to solve this problem is to maximize or minimize a specific design variable, high chance to reach the min/max bound, TMG Correlation will do it. If a design variable reaches the min or max bound:

  • Does it make sense? For example, a heat transfer coefficient for natural convection should not reach very high value (>20), if a design variable defined on a heat transfer coefficient reaches a bound of 15 or 20, maybe there is something wrong in the model.

  • Do you have a sensor located close to the design variable?
    • Yes—This sensor may have a large impact on the objective function. Use weights on other sensors to minimize the effect of this sensor, if it is relevant.
    • No—Verify your thermal model definition around that part of your model. Maybe, there is a heat sink or heat load that controls the temperature difference at sensor locations.
  • Do you have enough sensors versus design variables?
    • If you have more sensors than design variables, the problem is over constrained, which can lead to unfeasible or non converged solutions that minimize the objective function.
    • If you have fewer sensors than design variables, the problem is under constrained, which can lead to unrealistic solutions that minimize the objective function.
    • A reasonable ratio for sensors to design variables is generally within this range: 0.5 – 2, which means 1 for 2 or 2 for 1. Outside this range, depending on the locations of sensors versus design variables, you may face difficulties converging toward a solution.
Sensors results
Compare the sensor results before and after the correlation run and assess whether your correlation setup helped reduce the errors at sensor locations.