Workflow Recipes and Config Cookbook

Table of Contents

• 1. Core Workflow Idea
• 2. Common Mix-and-Match Patterns
• 3. Command Recipes
• 4. Example Config Combinations
• 5. Drift Verification Workflow
• 6. Configuration Patterns Worth Reusing
• 7. Which Example To Start From
• 7. Scatter Verification Recipe
• 8. Related Pages
  • CFD Reader Guidance and Practical Use
    • What To Extract Before Changing A Case
    • Practical CFD Troubleshooting Pattern

This page is the practical companion to the reference docs. Use it when you already understand the config roles and want proven patterns for combining them cleanly.

1. Core Workflow Idea

PICurv is designed around modular config roles:

• case.yml: physics, grid, BCs, run duration
• solver.yml: numerical strategy
• monitor.yml: logging, output cadence, directories
• post.yml: analysis recipe
• optional cluster.yml: scheduler policy
• optional study.yml: parameter sweep definition

The intended UX is:

• keep each file focused on one concern,
• reuse stable profiles across many runs,
• only clone and specialize the roles that actually changed.

2. Common Mix-and-Match Patterns

Pattern A: same case, different monitoring

• keep one trusted case.yml
• pair it with a lightweight monitor.yml for fast iteration
• switch to a richer monitor.yml for production output

Pattern B: same case, different analysis

• run once with a standard monitor profile
• post-process the same run directory multiple times with different post.yml recipes

Pattern C: same solver profile reused across many cases

• keep one validated solver.yml for a known momentum strategy
• pair it with multiple physical cases as long as the strategy is appropriate

Pattern D: same cluster profile across many jobs

• reuse one cluster.yml for site policy
• vary only case/solver/monitor/post inputs

Pattern E: same case, different local launch environment

• keep case/solver/monitor/post unchanged
• use default mpiexec on ordinary workstations
• add a repo-local or case-local .picurv-execution.yml when a cluster login node or cluster batch job needs site-specific launcher flags

3. Command Recipes

First-time local workflow:

./bin/picurv init flat_channel --dest my_case
./bin/picurv validate --case my_case/flat_channel.yml --solver my_case/Imp-MG-Standard.yml --monitor my_case/Standard_Output.yml --post my_case/standard_analysis.yml
./bin/picurv run --solve --post-process -n 4 --case my_case/flat_channel.yml --solver my_case/Imp-MG-Standard.yml --monitor my_case/Standard_Output.yml --post my_case/standard_analysis.yml

Solver-only run:

./bin/picurv run --solve -n 8 --case case.yml --solver solver.yml --monitor monitor.yml

Solver-only run on a cluster login node with a persistent site launcher config:

# Preferred: create one repo-root `.picurv-execution.yml` in your cluster clone.
# `picurv init` seeds new cases from it automatically.
# For an existing case, copy config/runtime/execution.example.yml once and rename it.
./bin/picurv run --solve -n 8 --case case.yml --solver solver.yml --monitor monitor.yml

Cluster run that reuses the same site launcher defaults unless cluster.yml overrides them:

# Preferred: create one repo-root `.picurv-execution.yml` in your cluster clone.
# `picurv init` seeds new cases from it automatically.
# For an existing case, copy config/runtime/execution.example.yml once and rename it.
./bin/picurv run --solve --post-process --case case.yml --solver solver.yml --monitor monitor.yml --post post.yml --cluster cluster.yml

Post-process an existing run with a different recipe:

./bin/picurv run --post-process --run-dir runs/<run_id> --post alt_analysis.yml

Catch up the same recipe in batches while the solver is still producing new source steps:

./bin/picurv run --post-process --continue --run-dir runs/<run_id> --post post.yml

Operational notes:

• keep the full logical analysis window in post.yml; --continue moves the effective launch start internally for the same recipe.
• if the requested window is already complete for that recipe, PICurv skips the launch and reports that the run is already caught up.
• if the solver has produced source data only through the current frontier, PICurv launches only the fully available contiguous prefix instead of failing.
• if another post job already holds the same run directory, PICurv refuses the new writer immediately.

Dry-run planning:

./bin/picurv run --solve --post-process --case case.yml --solver solver.yml --monitor monitor.yml --post post.yml --dry-run --format json

Cluster generation without submit:

./bin/picurv run --solve --post-process --case case.yml --solver solver.yml --monitor monitor.yml --post post.yml --cluster cluster.yml --no-submit

Delayed submit from existing staged artifacts:

./bin/picurv submit --run-dir runs/<run_id>

Cancel a submitted run by directory:

./bin/picurv cancel --run-dir runs/<run_id> --stage solve

Generated Slurm solver jobs already enable the runtime walltime guard by default. Override it only when needed:

execution:
  walltime_guard:
    enabled: true
    warmup_steps: 10
    multiplier: 2.0
    min_seconds: 60
    estimator_alpha: 0.35

Keep an early signal as fallback protection for preemption/manual termination:

execution:
  extra_sbatch:
    signal: "USR1@300"

For direct mpirun batch launches, use signal: "B:USR1@300" and prefer exec mpirun ....

Run naming note:

• cluster.yml does not provide a run-name field.
• picurv creates runs/<case_basename>_<timestamp>/ automatically.
• generated Slurm job names are derived from that run ID.

4. Example Config Combinations

Programmatic baseline:

• case: examples/flat_channel/flat_channel.yml
• solver: examples/flat_channel/Imp-MG-Standard.yml
• monitor: examples/flat_channel/Standard_Output.yml
• post: examples/flat_channel/standard_analysis.yml

File-grid baseline:

• case: examples/bent_channel/bent_channel.yml
• solver: examples/bent_channel/Imp-MG-Standard.yml
• monitor: examples/bent_channel/Standard_Output.yml
• post: examples/bent_channel/standard_analysis.yml

Analytical zero-flow Brownian verification:

• case: examples/brownian_motion/brownian_motion.yml
• solver: examples/brownian_motion/Analytical-Zero.yml
• monitor: examples/brownian_motion/Standard_Output.yml
• post: examples/brownian_motion/brownian_analysis.yml

Analytical uniform-flow deterministic drift verification:

• case: examples/drift_uniform_flow/drift_uniform_flow.yml
• solver: examples/drift_uniform_flow/Analytical-UniformFlow.yml
• monitor: examples/drift_uniform_flow/Standard_Output.yml
• post: examples/drift_uniform_flow/uniform_flow_analysis.yml

Analytical zero-flow plus verification-source diffusivity drift verification:

• case: examples/drift_diffusivity_gradient/drift_diffusivity_gradient.yml
• solver: examples/drift_diffusivity_gradient/Analytical-Zero-Verification-LinearDiffusivity.yml
• monitor: examples/drift_diffusivity_gradient/Standard_Output.yml
• post: examples/drift_diffusivity_gradient/drift_diffusivity_gradient_analysis.yml

These are examples, not fixed bundles. You can intentionally swap roles when the contract makes sense.

5. Drift Verification Workflow

Use the drift examples as separate, end-to-end checks for the two deterministic particle-position terms:

• examples/drift_uniform_flow/ isolates the carrier-velocity term in X_new = X_old + (vel + diffusivitygradient) * dt + Brownian.
• examples/drift_diffusivity_gradient/ isolates the diffusivity-gradient term while keeping the carrier flow at zero.

The intended policy is:

• prefer ordinary production pathways first,
• use analytical solver modes when the solver already supports them,
• only use verification.* overrides when there is no cleaner end-to-end way to prescribe the field needed for verification.

For the current diffusivity-drift case, the curated source override lives behind:

verification:
  sources:
    diffusivity:
      mode: "analytical"
      profile: "LINEAR_X"
      gamma0: 1.0e-3
      slope_x: 2.0e-4

Contributor note:

• new verification source injections must be implemented in include/verification_sources.h and src/verification_sources.c
• production call sites should stay thin and delegate into that module
• do not introduce one-off verification flags directly into unrelated solver files unless there is no alternative and the delegation point remains explicit

6. Configuration Patterns Worth Reusing

Minimal zero-flow startup:

properties:
  initial_conditions:
    mode: "Zero"

Poiseuille with a clear scalar input:

properties:
  initial_conditions:
    mode: "Poiseuille"
    peak_velocity_physical: 1.25

Generated grid workflow:

grid:
  mode: grid_gen
  generator:
    config_file: config/grids/coarse_square_tube_curved.cfg
    grid_type: cpipe
    cli_args: ["--ncells-i", "96", "--ncells-j", "96"]

Restart with explicit particle re-initialization:

run_control:
  start_step: 200
 
models:
  physics:
    particles:
      count: 10000
      restart_mode: "init"

Continue a run that ended at step 500 for 1000 more steps:

run_control:
  start_step: 500
  total_steps: 1000

operation_mode:
  eulerian_field_source: "load"

Optional particle choice:

models:
  physics:
    particles:
      restart_mode: "load"   # or "init"

./bin/picurv run --solve --post-process \
  --case restart_case/case.yml \
  --solver restart_case/solver.yml \
  --monitor restart_case/monitor.yml \
  --post restart_case/post.yml

Expected step range:

• saved state loaded from step 500
• first new step is 501
• final step after this run is 1500

7. Which Example To Start From

Choose flat_channel when:

• you want the simplest first runnable case,
• you are learning programmatic_c,
• you want a clean baseline for solver/monitor/post swaps.

Choose bent_channel when:

• you need file-based curvilinear geometry,
• you want to understand grid.mode: file,
• you plan to compare against grid_gen later.

Choose brownian_motion when:

• you want an analytical zero-flow validation case,
• you want a particle-focused test with minimal flow complexity,
• you want a reference for ZERO_FLOW analytical mode.

7. Scatter Verification Recipe

Use solver.yml -> verification.sources.scalar when you need a known particle scalar truth field and there is no ordinary production workflow that can create that truth end-to-end.

Recommended pattern:

• keep operation_mode.eulerian_field_source: analytical
• use analytical_type: ZERO_FLOW for pure deposition checks
• choose a scalar profile under verification.sources.scalar
• reuse the production scatter operator rather than adding a second deposition path
• read the runtime metric from logs/scatter_metrics.csv

Separation rules:

• when verification.sources.scalar is absent, ordinary production runs are unchanged
• only the particle Psi update is bypassed in verification mode
• carrier-flow selection remains independent, so the same scalar truth can later be paired with UNIFORM_FLOW or TGV3D

This pathway is useful for more than one study. It enables:

• static deposition verification
• moving-cloud scatter checks
• conservation diagnostics on deposited scalar fields
• grid-sensitivity studies using scatter_metrics.csv
• future scalar-transport verification reuse

A complete runnable example is provided in examples/scatter_verification/.

8. Related Pages

• The Conductor Script: picurv
• Configuration Reference: Case YAML
• Configuration Contract (YAML -> Generated Artifacts -> Runtime)
• Run Lifecycle Guide
• Grid Generator Guide: scripts/grid.gen
• Particle Initialization and Restart Guide

CFD Reader Guidance and Practical Use

This page describes Workflow Recipes and Config Cookbook within the PICurv workflow. For CFD users, the most reliable reading strategy is to map the page content to a concrete run decision: what is configured, what runtime stage it influences, and which diagnostics should confirm expected behavior.

Treat this page as both a conceptual reference and a runbook. If you are debugging, pair the method/procedure described here with monitor output, generated runtime artifacts under runs/<run_id>/config, and the associated solver/post logs so numerical intent and implementation behavior stay aligned.

What To Extract Before Changing A Case

• Identify which YAML role or runtime stage this page governs.
• List the primary control knobs (tolerances, cadence, paths, selectors, or mode flags).
• Record expected success indicators (convergence trend, artifact presence, or stable derived metrics).
• Record failure signals that require rollback or parameter isolation.

Practical CFD Troubleshooting Pattern

1. Reproduce the issue on a tiny case or narrow timestep window.
2. Change one control at a time and keep all other roles/configs fixed.
3. Validate generated artifacts and logs after each change before scaling up.
4. If behavior remains inconsistent, compare against a known-good baseline example and re-check grid/BC consistency.