# Study 01 — TPE v1: Isogrid Mass Minimization (Campaign 01)

**Parent project:** [isogrid-dev-plate](../../README.md)
**Context:** [../../CONTEXT.md](../../CONTEXT.md)
**Status:** Ready to run — not yet started
**Created:** 2026-02-18

---

## 1. Overview

This is the first optimization campaign for the ACS Stack Main Plate isogrid lightweighting project.
It uses Optuna TPE (Tree-structured Parzen Estimator) with a budget of 200 trials.

**Goal:** Find the set of 8 density-field parameters that minimizes total plate mass subject to a
stress constraint (σ_max ≤ 100.6 MPa, AL7075-T6, SF=5).

Each trial: Python Brain generates a triangular isogrid rib pattern → NX imports it into the sketch
→ NX Nastran solves SOL 101 → extractors pull mass from the idealized part and max stress from the OP2.

---

## 2. Engineering Problem

**Client:** ACS — Attitude Control System, spacecraft structural assembly
**Part:** Main structural plate (AL7075-T6)
**Challenge:** Remove as much material as possible from the plate interior via an isogrid rib pattern,
while keeping peak stress within the allowable under the primary axial load case.

**Load case:** FZ = 1,372.9 N, linear static (SOL 101), Subcase 1
**Material:** AL7075-T6 — σ_yield = 503 MPa, ρ = 2810 kg/m³, SF = 5 → σ_allow = 100.6 MPa

The rib pattern is generated in 2 sandbox regions. Ribs automatically cluster around bolt holes
and the plate perimeter based on physics-inspired density field parameters.

---

## 3. Mathematical Formulation

### Objective
```
minimize    mass_kg(η₀, α, β, γ_stress, R₀, R_edge, s_min, s_max)
```

### Constraint
```
subject to  σ_max ≤ 100.6 MPa   (stress only — no displacement constraint)
```

### Penalty
```
objective_value = mass_kg + penalty

penalty = 1e4 × ((σ_max / σ_allow) − 1)²   if σ_max > σ_allow
        = 0                                  otherwise
```

### Design Variables

| Variable | Low | High | Units | Description |
|----------|-----|------|-------|-------------|
| `eta_0` | 0.0 | 0.4 | — | Baseline density offset |
| `alpha` | 0.3 | 2.0 | — | Hole influence scale |
| `beta` | 0.0 | 1.0 | — | Edge influence scale |
| `gamma_stress` | 0.0 | 1.5 | — | FEA stress feedback gain |
| `R_0` | 10 | 100 | mm | Base hole influence radius |
| `R_edge` | 5 | 40 | mm | Edge influence radius |
| `s_min` | 15 | 35 | mm | Min cell size (densest) — manufacturing floor: 15 mm |
| `s_max` | 40 | 60 | mm | Max cell size (sparsest) — lower bound 40 guarantees s_min < s_max |

Fixed parameters (manufacturing constraints + math constants): see `optimization_engine/isogrid/study.py`.

---

## 4. Optimization Algorithm

| Property | Value |
|----------|-------|
| Algorithm | Optuna TPE (Tree-structured Parzen Estimator) |
| Sampler seed | 42 |
| Direction | Minimize |
| N startup trials | 10 (random exploration before TPE kicks in) |
| Trial budget | 200 |
| Storage | SQLite — `3_results/study.db` |
| Study name | `isogrid_01_v1_tpe` |

TPE splits past trials at each parameter into "good" (low objective) and "bad" (high objective) groups,
then samples from the "good" region. With 8 continuous variables and 200 trials this is well within
TPE's effective range.

---

## 5. Result Extraction

| Quantity | Extractor | Source |
|----------|-----------|--------|
| Mass | `extract_part_mass_material(_i.prt)` | `_temp_part_properties.json` written by `solve_simulation.py` journal |
| Max von Mises | `extract_solid_stress(op2_file, subcase=1)` | OP2 binary from Nastran solve |

Mass is extracted from the **idealized part** (`_fem2_i.prt`), not estimated by the Brain.
The NX journal writes a JSON temp file after each solve; the extractor reads it back.

> ⚠️ NX FEM model uses AL6061 material properties (E≈68.98 GPa, ρ=2.711e-6 kg/mm³).
> Mass extraction is therefore slightly low (~4% underestimate vs AL7075-T6).
> Tracked as Gap G-01 in CONTEXT.md.

---

## 6. Study Structure

```
studies/01_v1_tpe/
├── README.md              ← You are here
├── STUDY_REPORT.md        ← Post-run results (fill after campaign)
├── check_preflight.py     ← Quick validation before running
├── run_optimization.py    ← Main optimization loop
│
├── 1_setup/               ← Model files (working copies — modified by NX each trial)
│   └── model/
│       ├── ACS_Stack_Main_Plate_Iso_Project.prt
│       ├── ACS_Stack_Main_Plate_Iso_project_fem2_i.prt    ← CRITICAL
│       ├── ACS_Stack_Main_Plate_Iso_project_fem2.fem
│       ├── ACS_Stack_Main_Plate_Iso_project_sim2.sim
│       └── adaptive_isogrid_data/
│           ├── geometry_sandbox_1.json   ← Sandbox boundary + holes (from NX)
│           ├── geometry_sandbox_2.json
│           ├── rib_profile_sandbox_1.json   ← Written per trial (current)
│           └── rib_profile_sandbox_2.json
│
├── 2_iterations/          ← Per-trial logs (auto-created at runtime)
│   ├── trial_0001/
│   │   ├── params.json              ← Sampled design variables
│   │   ├── results.json             ← mass, stress, SF, objective
│   │   ├── rib_profile_sandbox_1.json   ← Rib geometry (copy)
│   │   └── rib_profile_sandbox_2.json
│   └── trial_NNNN/
│       └── ...
│
└── 3_results/             ← Optimization database + summary outputs
    └── study.db           ← Optuna SQLite (created on first run)
```

---

## 7. Quick Start

```bash
# Step 1: Verify everything is ready
C:\Users\antoi\anaconda3\envs\atomizer\python.exe \
  projects/isogrid-dev-plate/studies/01_v1_tpe/check_preflight.py

# Step 2: Launch
C:\Users\antoi\anaconda3\envs\atomizer\python.exe \
  projects/isogrid-dev-plate/studies/01_v1_tpe/run_optimization.py
```

See [../../playbooks/01_FIRST_RUN.md](../../playbooks/01_FIRST_RUN.md) for full step-by-step including
monitoring, failure handling, and post-run analysis.

---

## 8. Expected Runtime

| Component | Estimate |
|-----------|----------|
| Brain (triangulation + pockets) | ~3–10 s |
| NX import journal | ~15–30 s |
| NX remesh + Nastran solve | ~60–90 s |
| Extraction (mass + stress) | ~1–3 s |
| **Total per trial** | **~90–120 s** |
| **200 trials** | **~8–10 hours** |

Actual per-trial time will vary with mesh complexity (pocket count affects remesh time).

---

## 9. Success Criteria

| Criterion | Target |
|-----------|--------|
| Mass reduction vs baseline | > 10% (aspirational: > 20%) |
| Feasibility rate | > 80% of trials (σ ≤ 100.6 MPa) |
| Best trial SF | ≥ 5.0 (σ_max ≤ 100.6 MPa) |
| Convergence | Best mass stable over last 50 trials |

Baseline solid plate mass: TBD (Gap G-02 — run `extract_part_mass_material` on unmodified model).

---

## 10. Results

> **[Campaign 01 not yet started]**
>
> Fill in after run. See [STUDY_REPORT.md](STUDY_REPORT.md) for the result template.

| Metric | Value |
|--------|-------|
| Status | 🔴 Not started |
| Trials completed | 0 / 200 |
| Best mass | — |
| Best trial | — |
| Best σ_max | — |
| Best SF | — |
| Feasibility rate | — |
| Runtime | — |