CoTherm 2025.1

CoTherm 2025.1, TAI’s co-simulation and process automation software, empowers users with new and improved capabilities for parallelization of process tasks, categorical DOE variables, and coupling data exchange.

• Faster Parallel Execution with Iteration Sub-Process: For CAE processes involving repetitive tasks like DOE studies or generating large datasets of IR renderings, CoTherm 2025.1 introduces the Iteration Sub-Process. This new compound task container enables the parallel execution of any user-defined set of calculations based on a template and a list of inputs from a CoTherm Table. This process workflow allows effective parallelization of serial calculations or parallelized simulations which do not scale efficiently to many CPUs. The overall set of iterations can be completed in much less time, allowing faster turnaround or more data to be generated for sensitivity analysis, training artificial intelligence/machine learning models, or other applications.  

Examples demonstrating parallel BRDF rendering (using MuSES) and parallel DOE analysis (using TAITherm):

Parallel BRDF rendering (generation of massive IR rendering datasets using MuSES for AI/ML or signature analysis) is demonstrated using the new parallel-brdf-rendering example.

• The default example generates a small set of renderings for two fast-running ground vehicle models.
• The process can be extended to any user-provided MuSES models - An Iteration Sub-Process is used to define the template work for one rendering (calling MuSES and converting the resulting data file to a PNG image file).
• The process uses a CoTherm DOE Table to generate a table of rendering parameters varying inputs as defined by the user (e.g. azimuth, elevation, range, time of day, and waveband).

DOE analysis, as demonstrated in the existing thermal-doe-study-sensitivity-surrogate example, has been updated to use an Iteration Sub-Process to execute DOE iterations of a TAITherm thermal simulation

• The Iteration Sub-Process can be configured to run one iteration at a time and use all CPUs for parallelization of the model, or multiple iterations concurrently with the CPUs distributed among the concurrent iterations.
• As before, the DOE iterations can be used to perform statistical analyses / sensitivity studies, train a fast-running surrogate model, or simply generate large sets of output data for any user-provided steady-state or transient model.

Easy Definition of Discrete (Categorical) Variables for DOE Tables: CoTherm DOE Tables now support DOE Defined Choice Columns, allowing users to easily define discrete (categorical) variables alongside continuous ones. This enables more comprehensive DOE studies incorporating various choices or categories. CoTherm's built-in DOE sampling methods (Full Factorial and Optimal Latin Hypercube) have been updated to handle discrete variables, and DOE plots now support their visualization with appropriate labels.

• In the case of Full Factorial, the number of choices will be the number of samples for discrete variables.
• If Optimal Latin Hypercube is used, the total number of samples will be evenly distributed among the choices.

The DOE Defined Choice Column can be used in the process by referencing its choice string, choice index, or normalized value depending on what is convenient to convey the value to models or calculations.

DOE Defined Choice Columns are demonstrated in the existing thermal-doe-study-sensitivity-surrogate example included with CoTherm, which now uses a discrete variable to represent a choice of surface conditions used for the thermal model study.

Intuitive Coupling and Optimization with Electrical and Human Thermal Variables: CoTherm's process variables and Variable Assignment tasks make data exchange intuitive. In 2025.1

• TAITherm Result Variables now support human thermal whole-body output parameters (Berkeley Overall Sensation, Overall Comfort, Fiala DTS, Equivalent Temperature, and PMV). This allows CoTherm processes to easily read these metrics for comfort-based control or optimization.
• TAITherm Input Variables now support TAITherm Input Parameters based on electrical quantities (current or voltage). This enables CoTherm processes to specify dynamic electrical inputs to battery or other thermo-electrical models, facilitating two-way coupling with third-party control schemes or drive cycles and streamlining electrical input-based optimization.