majiayu000 Time Stepping
Goal
Provide a reliable workflow for choosing, ramping, and monitoring time steps plus output/checkpoint cadence.
Requirements
- Python 3.8+
- No external dependencies (uses stdlib)
Inputs to Gather
| Input | Description | Example |
|---|---|---|
| Stability limits | CFL/Fourier/reaction limits | dt_max = 1e-4 |
| Target dt | Desired time step | 1e-5 |
| Total run time | Simulation duration | 10 s |
| Output interval | Time between outputs | 0.1 s |
| Checkpoint cost | Time to write checkpoint | 120 s |
Decision Guidance
Time Step Selection
Is stability limit known?
├── YES → Use min(dt_target, dt_limit × safety)
└── NO → Start conservative, increase adaptively
Need ramping for startup?
├── YES → Start at dt_init, ramp to dt_target over N steps
└── NO → Use dt_target from start
Ramping Strategy
| Problem Type | Ramp Steps | Initial dt |
|---|---|---|
| Smooth IC | None needed | Full dt |
| Sharp gradients | 5-10 | 0.1 × dt |
| Phase change | 10-20 | 0.01 × dt |
| Cold start | 10-50 | 0.001 × dt |
Script Outputs (JSON Fields)
| Script | Key Outputs |
|---|---|
scripts/timestep_planner.py |
dt_limit, dt_recommended, ramp_schedule |
scripts/output_schedule.py |
output_times, interval, count |
scripts/checkpoint_planner.py |
checkpoint_interval, checkpoints, overhead_fraction |
Workflow
- Get stability limits - Use numerical-stability skill
- Plan time stepping - Run
scripts/timestep_planner.py - Schedule outputs - Run
scripts/output_schedule.py - Plan checkpoints - Run
scripts/checkpoint_planner.py - Monitor during run - Adjust dt if limits change
Conversational Workflow Example
User: I'm running a 10-hour phase-field simulation. How often should I checkpoint?
Agent workflow:
- Plan checkpoints based on acceptable lost work:
python3 scripts/checkpoint_planner.py --run-time 36000 --checkpoint-cost 120 --max-lost-time 1800 --json - Interpret: Checkpoint every 30 minutes, overhead ~0.7%, max 30 min lost work on crash.
Pre-Run Checklist
- Confirm dt limits from stability analysis
- Define ramping strategy for transient startup
- Choose output interval consistent with physics time scales
- Plan checkpoints based on restart risk
- Re-evaluate dt after parameter changes
CLI Examples
# Plan time stepping with ramping
python3 scripts/timestep_planner.py --dt-target 1e-4 --dt-limit 2e-4 --safety 0.8 --ramp-steps 10 --json
# Schedule output times
python3 scripts/output_schedule.py --t-start 0 --t-end 10 --interval 0.1 --json
# Plan checkpoints for long run
python3 scripts/checkpoint_planner.py --run-time 36000 --checkpoint-cost 120 --max-lost-time 1800 --json
Error Handling
| Error | Cause | Resolution |
|---|---|---|
dt-target must be positive |
Invalid time step | Use positive value |
t-end must be > t-start |
Invalid time range | Check time bounds |
checkpoint-cost must be < run-time |
Checkpoint too expensive | Reduce checkpoint size |
Interpretation Guidance
dt Behavior
| Observation | Meaning | Action |
|---|---|---|
| dt stable at target | Good | Continue |
| dt shrinking | Stability issue | Check CFL, reduce target |
| dt oscillating | Borderline stability | Add safety factor |
Checkpoint Overhead
| Overhead | Acceptability |
|---|---|
| < 1% | Excellent |
| 1-5% | Good |
| 5-10% | Acceptable |
| > 10% | Too frequent, increase interval |
Limitations
- Not adaptive control: Plans static schedules, not runtime adaptation
- Assumes constant physics: If parameters change, re-plan
References
references/cfl_coupling.md- Combining multiple stability limitsreferences/ramping_strategies.md- Startup policiesreferences/output_checkpoint_guidelines.md- Cadence rules
Version History
- v1.1.0 (2024-12-24): Enhanced documentation, decision guidance, examples
- v1.0.0: Initial release with 3 planning scripts