How do you handle nonlinear and transient thermal problems with CAE?

1 What are nonlinear and transient thermal problems?

Nonlinear and transient thermal problems are those that involve time-dependent and/or nonlinear behavior of the thermal system. Time-dependence means that the temperature and heat flux vary with time, and depend on the previous history of the system. Nonlinearity means that the thermal properties of the materials, such as thermal conductivity, specific heat, and density, change with temperature, or that the heat transfer mechanisms, such as convection and radiation, depend on temperature or heat flux. Nonlinear and transient thermal problems are usually more difficult to solve than linear and steady-state problems, because they require more computational resources and more careful numerical settings.

2 Select method and software

First, define the geometry, materials, heat sources, boundary conditions, and initial conditions of the thermal system. Choose the appropriate numerical method and software for solving the thermal equations. Depending on the complexity and scale of the problem, you may use different types of CAE methods, such as finite element analysis (FEA), finite difference method (FDM), or computational fluid dynamics (CFD). You may also use different types of software, such as ANSYS, COMSOL, Fluent, or MATLAB. The key is to select the method and software that can capture the relevant physics and features of the problem, while ensuring accuracy, efficiency, and robustness.

3 Verify and validate

Verification is the process of ensuring that your model is free of errors and that your numerical solution is consistent with the mathematical formulation of the problem. Validation is the process of ensuring that your numerical solution agrees with the experimental or analytical data, or with the physical reality of the problem. To verify and validate your model and results, you may use different techniques such as mesh refinement, convergence analysis, sensitivity analysis, uncertainty quantification, or comparison with benchmarks or measurements.

4 Define and optimize

Next, define the objective function and the design variables of the problem. The objective function is the measure of performance or quality that you want to maximize or minimize such as temperature, heat flux, thermal stress, or efficiency. The design variables are the parameters that you can control or change, such as geometry, materials, heat sources, or boundary conditions. To optimize your problem, you may use different methods, such as gradient-based methods, evolutionary algorithms, or surrogate models. You may also use different software, such as MATLAB, Python, or ANSYS Workbench. The goal is to find the optimal combination of design variables that satisfies the objective function and the constraints of the problem.