Bug 1 — Journal (solve_simulation.py simple workflow):
Expression lookup for p173 fails silently for derived/measurement
expressions, so _temp_mass.txt was never written. Added MeasureManager
fallback via extract_part_mass() (already used in assembly workflow).
Bug 2 — Extractor (extract_mass_from_expression.py):
Journal writes 'p173=<value>' format but extractor tried float() on
the whole content including 'p173='. Added key=value parsing.
Defense in depth — nx_interface.py:
Added stdout parsing fallback: if _temp_mass.txt still missing, parse
mass from journal output captured via solver.py stdout passthrough.
Files changed:
- optimization_engine/nx/solve_simulation.py — MeasureManager fallback
- optimization_engine/extractors/extract_mass_from_expression.py — key=value parse
- optimization_engine/nx/solver.py — include stdout in result dict
- projects/hydrotech-beam/studies/01_doe_landscape/nx_interface.py — stdout fallback
Tags: hydrotech-beam, mass-extraction
- Journal now extracts p173 mass expression and writes _temp_mass.txt
- history.get_study_summary() called before history.close()
- Optuna nan rejection: fallback to INFEASIBLE_MASS penalty
- pyNastran warning 'nx 2512 not supported' is harmless (reads fine)
Root cause: Path.absolute() on Windows does NOT resolve '..' components.
sim_file_path was reaching NX as '...\studies\01_doe_landscape\..\..\models\Beam_sim1.sim'
NX likely can't resolve referenced parts from a path with '..' in it.
Fixes:
- nx_interface.py: glob from self.model_dir (resolved) not model_dir (raw)
- solver.py: sim_file.resolve() instead of sim_file.absolute()
- solve_simulation.py: os.path.abspath(sim_file_path) at entry point
- Diagnostic logging still in place for next run
Need to see why Parts.Open returns None even from the master model folder.
Logs: basePart1 type/name/path, unloaded parts status, file existence checks.
NX .sim files store absolute internal references to .fem/.prt files.
Copying them to iteration folders breaks these references (Parts.Open
returns None). Instead:
1. Backup master model once at study start
2. Restore from backup before each trial (isolation)
3. Solve on master model in-place (NX references intact)
4. Archive solver outputs (OP2/F06) + params.exp to iterations/iterNNN/
5. params.exp in each iteration: import into NX to recreate any trial
iteration_manager.py kept for future use but not wired in.
- history.db: SQLite append-only, never deleted by --clean
- history.csv: Auto-exported after each trial (live updates)
- Logs: DVs, results, feasibility, status, solve time, iter path
- Cross-study queries: full lineage across all runs/phases
- --clean only resets Optuna DB, history preserved
- Each iteration gets full model files in iterations/iterNNN/ (openable in NX)
- Retention: keep last 10 + best 3 with full models, strip the rest
- Stripped iterations keep solver outputs (OP2, F06, params, results)
- All paths resolved to absolute before passing to NX (fixes reference issue)
- iteration_manager.py: reusable for future studies
- One-time backup of model files at study start (_model_backup/)
- Restore clean state before each trial (files stay in models/, NX refs intact)
- If a trial corrupts the model, next trial starts clean
- Best of both: NX reference integrity + trial isolation
- Each iteration gets: params.json, results.json, OP2, F06, mass files
- Model directory stays clean (no solver output buildup)
- Study folder is self-contained with full trial history
- solve_simulation.py: FEM finder now excludes idealized parts, falls back to loading .fem
- solve_simulation.py: hole_count written as [Constant] not [MilliMeter] in .exp
- run_doe.py: dual logging to console + results/doe_run.log
Complete the NXOpenSolver class in nx_interface.py with production-ready
evaluate() and close() methods, following proven patterns from
M1_Mirror/SAT3_Trajectory_V7.
Pipeline per trial:
1. NXSolver.create_iteration_folder() — HEEDS-style isolation with fresh
model copies + params.exp generation
2. NXSolver.run_simulation() — journal-based solve via run_journal.exe
(handles expression import, geometry rebuild, FEM update, SOL 101)
3. extract_displacement() — max displacement from OP2
4. extract_solid_stress() — max von Mises with auto-detect element type
(tries all solid types first, falls back to CQUAD4 shell)
5. extract_mass_from_expression() — reads _temp_mass.txt from journal,
with _temp_part_properties.json fallback
Key decisions:
- Auto-detect element type for stress (element_type=None) instead of
hardcoding CQUAD4 — the beam model may use solid or shell elements
- Lazy solver init on first evaluate() call for clean error handling
- OP2 fallback path: tries solver result first, then expected naming
convention (beam_sim1-solution_1.op2)
- Mass fallback: _temp_mass.txt -> _temp_part_properties.json
- LAC-compliant close(): only uses session_manager.cleanup_stale_locks(),
never kills NX processes directly
Expression mapping (confirmed from binary introspection):
- beam_half_core_thickness, beam_face_thickness, holes_diameter, hole_count
- Mass output: p173 (body_property147.mass, kg)
Refs: OP_09, OPTIMIZATION_STRATEGY.md §8.2
- Use optimization_engine.nx.updater.NXParameterUpdater for expression updates (.exp import method)
- Use optimization_engine.nx.solver.NXSolver for journal-based solving (run_journal.exe)
- Use optimization_engine.extractors for displacement, stress, and mass extraction
- Displacement: extract_displacement() from pyNastran OP2
- Stress: extract_solid_stress() with cquad4 support (shell elements), kPa→MPa conversion
- Mass: extract_mass_from_expression() reads from temp file written by solve journal
- Support for iteration folders (HEEDS-style clean state between trials)
- Proper error handling with TrialResult(success=False, error_message=...)
- 600s timeout per trial (matching existing NXSolver default)
- Keep stub solver and create_solver() factory working
- Maintain run_doe.py interface compatibility
Archive Management:
- Moved RALPH_LOOP, CANVAS, and dashboard implementation plans to archive/review/ for CEO review
- Moved completed restructuring plan and protocol v1 to archive/historical/
- Moved old session summaries to archive/review/
New HQ Documentation (docs/hq/):
- README.md: Overview of Atomizer-HQ multi-agent optimization team
- PROJECT_STRUCTURE.md: Standard KB-integrated project layout with Hydrotech reference
- KB_CONVENTIONS.md: Knowledge Base accumulation principles with generation tracking
- AGENT_WORKFLOWS.md: Project lifecycle phases and agent handoffs (OP_09 integration)
- STUDY_CONVENTIONS.md: Technical study execution standards and atomizer_spec.json format
Index Update:
- Reorganized docs/00_INDEX.md with HQ docs prominent
- Updated structure to reflect new agent-focused organization
- Maintained core documentation access for engineers
No files deleted, only moved to appropriate archive locations.
New project structure with knowledge base integration:
- kb/ with components, materials, fea, dev generations
- models/ for reference NX files (golden copies)
- studies/ for self-contained optimization campaigns
- deliverables/ for final outputs
- DECISIONS.md decision log (6 decisions tracked)
- BREAKDOWN.md (moved from 1_breakdown/)
- Gen 001 created from intake + technical breakdown
KB extension file: atomizer/shared/skills/knowledge-base-atomizer-ext.md
Refs: DEC-HB-004, DEC-HB-005, DEC-HB-006
- Embed Plotly.js inline for offline viewing (fixes CDN loading issues)
- Add 4x resolution PNG export for all charts via toImageButtonOptions
- Add SAT3_Trajectory_V7 study (TPE warm-start from V5, 86 trials, WS=277.37)
- Include V7 optimization report and configuration
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Generates publication-ready PSD figures from OP2 FEA data:
- psd_multi_angle.png: PSD curves for all elevation angles
- psd_band_decomposition.png: LSF/MSF/HSF bar chart
- psd_40deg_composite.png: WFE surface + PSD side-by-side
- psd_trajectory.png: Band evolution vs elevation
Uses Atomizer's full OPD pipeline for consistency with CDR WFE numbers.
- tools/wfe_psd.py: Quick PSD computation for WFE surfaces
- optimization_engine/insights/wfe_psd.py: Full PSD module with band
decomposition (LSF/MSF/HSF), radial averaging, Hann windowing,
and visualization support
- knowledge_base/lac/session_insights/stopp_command_20260129.jsonl:
Session insight from stopp command implementation
PSD analysis decomposes WFE into spatial frequency bands per Tony Hull's
JWST methodology. Used for CDR V7 to validate that MSF (support
print-through) dominates the residual WFE at 85-89% of total RMS.
- X-axis now auto-ranges from data (was going to 10^21)
- Band annotations clamped to actual data extent
- Legend moved to upper-right (was overlapping data)
- Thicker lines (2.5px), larger axis labels
- dtick=1 for clean log-scale tick marks
- Band RMS now uses direct Parseval: sqrt(sum(|FFT|²) / N⁴ / hann_power)
- Previous approach had dimensional mismatch (cycles/apt vs cycles/mm)
- Results now match Zernike filtered RMS within ~10%:
40° vs 20°: PSD=6.18nm vs Zernike=7.70nm
60° vs 20°: PSD=15.83nm vs Zernike=17.69nm
90° Abs: PSD=27.01nm vs Zernike=22.33nm
- PSD plot curve uses separate normalization (shape, not absolute)
- Refactored compute_surface_psd to return dict with freqs, psd, bands
- Remove compute_spatial_freq_metrics() and _spatial_freq_html()
- Add compute_surface_psd(): 2D FFT + Hann window + radial averaging
- Add compute_psd_band_rms(): gravity/support/HF band decomposition
- Add make_psd_plot(): interactive log-log PSD plot with band annotations
- Add _psd_summary_html(): band RMS cards with % breakdown
- New section in report: Power Spectral Density Analysis
- Zernike details now show only coefficient bar charts (cleaner)
- Methodology: Tony Hull JWST approach for WFE spatial frequency analysis
New tool: tools/generate_optical_report.py
- CDR-ready single HTML report from OP2 results
- Executive summary with pass/fail vs targets
- Per-angle WFE analysis with 3D surface plots
- Zernike trajectory analysis (mode-specific RMS)
- Axial vs lateral sensitivity matrix
- Manufacturing correction metrics
- Collapsible Zernike coefficient bar charts
- Optional study DB integration for design params
- Annular aperture support (default M1 inner R=135.75mm)
- Dark theme, interactive Plotly charts, print-friendly
