Add a new regression test: random SN in a box

[chongchonghe]

Description

Refactored RandomBlast Problem: The testRandomBlast.cpp problem was significantly refactored to utilize the new particle system for SN feedback, removing the previous stochastic sampling method and integrating particle initialization from a file.

Particle Generation Script: Added a new Python script (create_particles.py) to generate initial particle distributions for various types (CIC, Sink, Rad, StochasticStellarPop) with options for spatial sampling (uniform, Gaussian, Plummer) and physical parameters.

Dependent on #1546 : Supernova (SN) Explosion Tracking: Modified the C++ particle physics core to accurately track and return the number of supernova explosions occurring within a simulation timestep, enhancing diagnostics and logging

Checklist

Before this pull request can be reviewed, all of these tasks should be completed. Denote completed tasks with an x inside the square brackets [ ] in the Markdown source below:

• I have added a description (see above).
• I have added a link to any related issues (if applicable; see above).
• I have read the Contributing Guide.
• I have added tests for any new physics that this PR adds to the code.
• (For quokka-astro org members) I have manually triggered the GPU tests with the magic comment /azp run.

[gemini-code-assist]

Summary of Changes

Hello @chongchonghe, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request integrates a new regression test, 'random SN in a box,' into the Quokka astrophysical simulation framework. It introduces a dedicated Python utility for generating diverse particle initial conditions and significantly enhances the C++ backend to track and process supernova feedback from these particles. The changes streamline the setup of particle-based simulations and improve the robustness of particle data handling during checkpoint and restart operations, particularly across varying MPI configurations.

Highlights

• New Regression Test: Introduced a new regression test named 'random SN in a box' to simulate supernova feedback using stochastic stellar population particles.
• Particle Generation Script: Added a new Python script (create_particles.py) to generate initial particle distributions for various types (CIC, Sink, Rad, StochasticStellarPop) with options for spatial sampling (uniform, Gaussian, Plummer) and physical parameters.
• Supernova (SN) Explosion Tracking: Modified the C++ particle physics core to accurately track and return the number of supernova explosions occurring within a simulation timestep, enhancing diagnostics and logging.
• Refactored RandomBlast Problem: The testRandomBlast.cpp problem was significantly refactored to utilize the new particle system for SN feedback, removing the previous stochastic sampling method and integrating particle initialization from a file.
• Checkpoint/Restart Robustness: Improved the particle checkpoint/restart mechanism to handle scenarios where the number of MPI processes changes between runs, ensuring data integrity and proper redistribution.

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[gemini-code-assist]

Code Review

This pull request adds a new regression test for random supernovae in a box, driven by particles. This involved a significant and valuable refactoring of the RandomBlast problem to use the StochasticStellarPop particle framework. A new Python script is also added to generate particle data. The supernova deposition logic is updated to count SN explosions for diagnostics, and the robustness of checkpoint/restarts is improved. The changes are generally excellent. I've found a few issues, including a bug in the new particle generation script, the use of non-portable system calls, and a bug in an existing test that would cause it to always report success.

[chatgpt-codex-connector]

💡 Codex Review

quokka/src/particles/particle_deposition.hpp

Lines 341 to 344 in 537a36b

	// update the particle's mass: subtract ejecta mass
	const amrex::Real mass_dead_star = p.rdata(mass_index) - m_ej;
	// AMREX_ASSERT_WITH_MESSAGE(mass_dead_star > 0.0, "SN progenitor mass should be greater than ejecta mass (10 M_sun)");
	p.rdata(mass_index) = std::max(m_dead_min, mass_dead_star);

Cap SN ejecta by available stellar mass

The SN deposition path always removes a fixed m_ej = 10 M_sun and then clamps the remaining particle mass to m_dead_min (lines 299–344). With the new RandomBlast setup, many particles in particles_stochastic_n100.txt are well below 10 M⊙, yet when their death_time is reached this code still ejects 10 M⊙ and leaves a 1.4 M⊙ remnant, creating several solar masses out of nothing. Each low‑mass star that explodes will inflate the total mass and momentum budget, breaking conservation and the physical model. Consider skipping SNs for sub‑10 M⊙ stars or limiting ejecta to the particle’s current mass minus the remnant.

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[chongchonghe]

/azp run

[azure-pipelines]

Azure Pipelines successfully started running 2 pipeline(s).

[chongchonghe]

@BenWibking @markkrumholz This is ready for your review.

I have created regression test benchmark files for this new test problem. Once merged to the main branch, it will demonstrate that hydro+AMR+gravity+particles+cooling is fully reproducible.
The regression test will fail now because the main branch is missing a particle data file required for the new test problem.

Dependent on #1546

[chatgpt-codex-connector]

💡 Codex Review

Here are some automated review suggestions for this pull request.

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[chongchonghe]

@BenWibking @markkrumholz Can you review this PR? I need this new test problem to test radiation module and tall box benchmark.

[BenWibking]

Can you fix the merge conflict? Then I can review it.

[chongchonghe]

@BenWibking Ready

[sonarqubecloud]

Quality Gate passed

Issues
12 New issues
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code

See analysis details on SonarQube Cloud

[BenWibking]

LGTM assuming GPU tests pass

[BenWibking]

/azp run

[azure-pipelines]

Azure Pipelines successfully started running 2 pipeline(s).