Renaming simulation or analysis outputs refers to changing the identifier (e.g., filename, dataset name) assigned to computational results after they are generated. This differs from initial naming conventions set before execution. It's done to improve clarity, organization, or reflect updated understanding of the results, such as appending a version number, experiment identifier, or descriptive tag like "_optimized_results.csv".

For example, a mechanical engineer might rename a large displacement simulation result file from "strut_analysis.dat" to "strut_analysis_DesignRevisionB_vibration_critical.dat" after identifying a key finding. In data science, renaming the output file of an image classification model from "predictions.npy" to "modelA_98percentAcc_predictions.npy" provides immediate context. Tools like Python (using os.rename), MATLAB file browser functionalities, and dedicated analysis platforms like COMSOL or Ansys include renaming capabilities.

Renaming outputs offers significant advantages in traceability and project management, preventing confusion among result versions and streamlining collaboration. However, inconsistent or disorganized renaming can negate these benefits and create new confusion; it's crucial to establish a clear team protocol. Linking renamed outputs directly to detailed run logs within version control systems (like Git LFS) is a growing best practice enhancing reproducibility. Adherence to consistent naming minimizes rework risk.