Comments
Comment #216 Bernd Flemisch @ 2025-08-28 15:55
As the responsible topical editor, I would like to thank the two reviewers for their detailed and constructive feedback. After consideration of the comments, I advise the authors to revise the paper according to the suggestions provided in the reviews. After completion, the new version of the paper should be uploaded again and will be given again to the reviewers. Thank you.
Invited Review Comment #215 Hamza Oukili @ 2025-08-27 18:45
Overall Assessment
SOFIRpy is a Python tool for co-simulation of Functional Mock-up Units (FMUs) with integrated research data management (RDM). The software descriptor is generally well-written, clearly structured, and follows a standard format for a software paper. However, several revisions are recommended to improve clarity, accuracy, and completeness before publication.
1. Target Audience
The paper currently lists researchers, planners, and operators as potential users, but these groups have very different needs. Focus primarily on researchers, it is more realistic. The software doesn’t seem to focus on UI/UX.
2. Statement of Need
The current statement is generic and could apply to many tools. It should highlight the unique gaps SOFIRpy fills, mainly for Modelica.
Additionally, ensure consistency between Sections 1.1 and 1.2 regarding supported workflow steps, avoid implying that all five steps are fully covered Line 36 when only three are implemented.
3. Proprietary Dependencies
The paper claims SOFIRpy “does not rely on proprietary dependencies,” yet mentions tools like Dymola (proprietary) for exporters. This ensures transparency and avoids misleading claims.
4. Example Code Completeness
The PID controller example (lines 294–349) is incomplete, leaving essential methods unimplemented. Provide working implementations. These are short methods and completing them ensures users can understand the example.
5. Installation Instructions
The installation section in the repository currently states only:
pip install sofirpyAdd in the repository Python version requirements, list dependencies, and note platform-specific considerations. A virtual environment setup guide would also improve onboarding.
6. Metadata Standardization
Lines 213–218 note that metadata is not standardized. Provide at least one default schema in an example or template following established standards (e.g., FAIR principles) while allowing customization. SOFIRpy claims RDM is its main strength providing one example schema is important.
7. Documentation and Validation
Add some README files for the examples, they are using specific versions of proprietary software.
Show whether SOFIRpy has been tested by colleagues or applied in case studies. The code has only one developer showing quantitative evidence builds credibility.
8. Comparison with Other Tools
The paper states SOFIRpy’s advantage is Modelica focus and integrated RDM, while broader frameworks like Mosaik offer more features. Highlight SOFIRpy’s lightweight nature and ease of modification, which is valuable for researchers needing flexibility over features.
9. Other Points
Avoid hard-coded paths for the exporters
Address long-term maintainability, mention any commitment of use within your research group or institute.
Final Remark
With these revisions the paper will provide a clear, credible, and user-friendly description of SOFIRpy suitable for publication.
Invited Review Comment #213 Anonymous @ 2025-07-18 15:08
The Paper presents SOFIRpy, a tool for simulation of fluid systems along with there control algorithms, with the goal of optimizing this controller. Along with capabilities supporting this goal, SOFIRpy also includes some RDM reproducibility features.
The tool helps users to:
- export there model created using dedicated modelling software such as Modelica into to independent FMU format
- Co-Simulate the Modell with a Controller (which is the software to be optimized)
- Storing of the Model and simulation results as well as metadata in HDF5 formate
The paper is well written, and the topic fits the scope of the Journal.
However I have a few suggestions:
The FMI Standard, and what part of it are used within SOFIRpy should be discussed in more detail. I Example: Is it possible to couple system models with this approach, or only Controllers with Systems? How is time controlled within this coupling? (i. e. by using fixed integration step sizes)
Is there a way to connect the stored results in HDF5 to specific version of the controllers source code? One suggestion would be to store it together with the code, i.e. using MLFlow (originating from machine learning applications)
Meta data: Even given the goal of flexibility, a fixed minimum set of required Methadata would help the interoperability of this approach. (like date, subject, model version, controller version…)
For clarity 168 should include some hint to the goal of exporting form script (this is clear later, but points to the wrong direction)
448 reads Government of the “Länder”, I am not sure if this is translated correctly