Anto01
f13563d7ab
Documentation: - Add docs/06_PHYSICS/ with Zernike fundamentals and OPD method docs - Add docs/guides/CMA-ES_EXPLAINED.md optimization guide - Update CLAUDE.md and ATOMIZER_CONTEXT.md with current architecture - Update OP_01_CREATE_STUDY protocol Planning: - Add DYNAMIC_RESPONSE plans for random vibration/PSD support - Add OPTIMIZATION_ENGINE_MIGRATION_PLAN for code reorganization Insights System: - Update design_space, modal_analysis, stress_field, thermal_field insights - Improve error handling and data validation NX Journals: - Add analyze_wfe_zernike.py for Zernike WFE analysis - Add capture_study_images.py for automated screenshots - Add extract_expressions.py and introspect_part.py utilities - Add user_generated_journals/journal_top_view_image_taking.py Tests & Tools: - Add comprehensive Zernike OPD test suite - Add audit_v10 tests for WFE validation - Add tools for Pareto graphs and mirror data extraction - Add migrate_studies_to_topics.py utility Knowledge Base: - Initialize LAC (Learning Atomizer Core) with failure/success patterns Dashboard: - Update Setup.tsx and launch_dashboard.py - Add restart-dev.bat helper script 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
325 lines
11 KiB
Python
325 lines
11 KiB
Python
"""
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Thermal Field Insight
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Provides visualization of temperature distributions from thermal FEA results.
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Shows temperature contours, gradients, and thermal statistics.
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Applicable to: Thermal analysis and thermo-structural optimization studies.
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"""
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from pathlib import Path
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from datetime import datetime
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from typing import Dict, Any, List, Optional, Tuple
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import numpy as np
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from .base import StudyInsight, InsightConfig, InsightResult, register_insight
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# Lazy imports
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_plotly_loaded = False
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_go = None
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_make_subplots = None
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_Triangulation = None
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_OP2 = None
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_BDF = None
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def _load_dependencies():
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"""Lazy load heavy dependencies."""
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global _plotly_loaded, _go, _make_subplots, _Triangulation, _OP2, _BDF
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if not _plotly_loaded:
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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from matplotlib.tri import Triangulation
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from pyNastran.op2.op2 import OP2
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from pyNastran.bdf.bdf import BDF
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_go = go
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_make_subplots = make_subplots
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_Triangulation = Triangulation
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_OP2 = OP2
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_BDF = BDF
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_plotly_loaded = True
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@register_insight
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class ThermalInsight(StudyInsight):
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"""
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Thermal field visualization.
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Shows:
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- 3D mesh colored by temperature
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- Temperature distribution histogram
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- Hot/cold spot identification
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- Temperature gradient visualization
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"""
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insight_type = "thermal"
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name = "Thermal Analysis"
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description = "Temperature distribution and thermal gradients"
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category = "thermal"
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applicable_to = ["thermal", "thermo-structural", "all"]
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required_files = ["*.op2"]
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def __init__(self, study_path: Path):
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super().__init__(study_path)
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self.op2_path: Optional[Path] = None
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self.geo_path: Optional[Path] = None
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self._node_geo: Optional[Dict] = None
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self._temperatures: Optional[Dict] = None
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def can_generate(self) -> bool:
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"""Check if OP2 file with temperature data exists."""
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search_paths = [
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self.results_path,
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self.study_path / "2_iterations",
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self.setup_path / "model",
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]
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for search_path in search_paths:
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if not search_path.exists():
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continue
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op2_files = list(search_path.glob("**/*solution*.op2"))
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if not op2_files:
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op2_files = list(search_path.glob("**/*.op2"))
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if op2_files:
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self.op2_path = max(op2_files, key=lambda p: p.stat().st_mtime)
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break
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if self.op2_path is None:
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return False
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# Try to find geometry
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try:
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self.geo_path = self._find_geometry_file(self.op2_path)
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except FileNotFoundError:
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pass
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# Verify temperature data exists
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try:
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_load_dependencies()
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op2 = _OP2()
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op2.read_op2(str(self.op2_path))
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# Check for temperature results (various possible attributes)
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return bool(hasattr(op2, 'temperatures') and op2.temperatures)
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except Exception:
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return False
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def _find_geometry_file(self, op2_path: Path) -> Path:
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"""Find BDF/DAT geometry file."""
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folder = op2_path.parent
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base = op2_path.stem
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for ext in ['.dat', '.bdf']:
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cand = folder / (base + ext)
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if cand.exists():
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return cand
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for f in folder.iterdir():
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if f.suffix.lower() in ['.dat', '.bdf']:
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return f
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raise FileNotFoundError(f"No geometry file found for {op2_path}")
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def _load_data(self):
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"""Load geometry and temperature data from OP2."""
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if self._temperatures is not None:
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return
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_load_dependencies()
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# Load geometry if available
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if self.geo_path and self.geo_path.exists():
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bdf = _BDF()
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bdf.read_bdf(str(self.geo_path))
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self._node_geo = {int(nid): node.get_position()
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for nid, node in bdf.nodes.items()}
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else:
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self._node_geo = {}
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# Load temperature data
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op2 = _OP2()
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op2.read_op2(str(self.op2_path))
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self._temperatures = {}
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if hasattr(op2, 'temperatures'):
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for key, temp_obj in op2.temperatures.items():
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if hasattr(temp_obj, 'data'):
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data = temp_obj.data
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if data.ndim == 3:
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data = data[0] # First time step
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ngt = temp_obj.node_gridtype.astype(int) if hasattr(temp_obj, 'node_gridtype') else None
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node_ids = ngt if ngt is not None and ngt.ndim == 1 else (ngt[:, 0] if ngt is not None else None)
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self._temperatures[str(key)] = {
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'node_ids': node_ids,
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'temperatures': data.flatten() if data.ndim > 1 else data,
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}
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def _generate(self, config: InsightConfig) -> InsightResult:
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"""Generate thermal field visualization."""
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self._load_data()
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if not self._temperatures:
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return InsightResult(success=False, error="No temperature data found in OP2")
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_load_dependencies()
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# Configuration
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colorscale = config.extra.get('colorscale', 'Thermal')
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temp_unit = config.extra.get('temp_unit', 'K')
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# Aggregate temperature data
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all_temps = []
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all_node_ids = []
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for key, data in self._temperatures.items():
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temps = data['temperatures']
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if isinstance(temps, np.ndarray):
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all_temps.extend(temps.flatten().tolist())
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if data['node_ids'] is not None:
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all_node_ids.extend(data['node_ids'].flatten().tolist())
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all_temps = np.array(all_temps)
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if len(all_temps) == 0:
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return InsightResult(success=False, error="No valid temperature values found")
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max_temp = float(np.max(all_temps))
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min_temp = float(np.min(all_temps))
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mean_temp = float(np.mean(all_temps))
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temp_range = max_temp - min_temp
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# Build visualization
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fig = _make_subplots(
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rows=2, cols=2,
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specs=[
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[{"type": "scene", "colspan": 2}, None],
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[{"type": "xy"}, {"type": "table"}]
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],
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row_heights=[0.65, 0.35],
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subplot_titles=[
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"<b>Temperature Distribution</b>",
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"<b>Temperature Histogram</b>",
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"<b>Summary Statistics</b>"
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]
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)
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# 3D temperature field
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if self._node_geo and all_node_ids:
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# Build node-to-temp mapping
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temp_map = {}
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for key, data in self._temperatures.items():
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if data['node_ids'] is not None:
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for nid, temp in zip(data['node_ids'].flatten(), data['temperatures'].flatten()):
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temp_map[int(nid)] = temp
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node_ids = list(self._node_geo.keys())
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X = np.array([self._node_geo[nid][0] for nid in node_ids])
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Y = np.array([self._node_geo[nid][1] for nid in node_ids])
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Z = np.array([self._node_geo[nid][2] for nid in node_ids])
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colors = np.array([temp_map.get(nid, mean_temp) for nid in node_ids])
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try:
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tri = _Triangulation(X, Y)
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if tri.triangles is not None and len(tri.triangles) > 0:
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i, j, k = tri.triangles.T
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fig.add_trace(_go.Mesh3d(
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x=X, y=Y, z=Z,
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i=i, j=j, k=k,
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intensity=colors,
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colorscale=colorscale,
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opacity=0.95,
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flatshading=False,
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lighting=dict(ambient=0.5, diffuse=0.7, specular=0.2),
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showscale=True,
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colorbar=dict(title=f"Temp ({temp_unit})",
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thickness=15, len=0.5)
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), row=1, col=1)
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except Exception:
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fig.add_trace(_go.Scatter3d(
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x=X, y=Y, z=Z,
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mode='markers',
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marker=dict(size=4, color=colors, colorscale=colorscale, showscale=True),
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), row=1, col=1)
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else:
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fig.add_annotation(
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text="3D mesh not available",
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xref="paper", yref="paper", x=0.5, y=0.7,
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showarrow=False, font=dict(size=14, color='white')
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)
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fig.update_scenes(
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camera=dict(eye=dict(x=1.5, y=1.5, z=1.0)),
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xaxis=dict(title="X", showbackground=True),
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yaxis=dict(title="Y", showbackground=True),
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zaxis=dict(title="Z", showbackground=True),
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)
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# Temperature histogram
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fig.add_trace(_go.Histogram(
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x=all_temps,
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nbinsx=50,
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marker_color='#f97316',
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opacity=0.8,
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name='Temperature'
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), row=2, col=1)
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fig.update_xaxes(title_text=f"Temperature ({temp_unit})", row=2, col=1)
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fig.update_yaxes(title_text="Count", row=2, col=1)
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# Summary table
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stats_labels = [
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"Maximum Temperature",
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"Minimum Temperature",
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"Mean Temperature",
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"Temperature Range",
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"Number of Nodes",
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]
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stats_values = [
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f"{max_temp:.2f} {temp_unit}",
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f"{min_temp:.2f} {temp_unit}",
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f"{mean_temp:.2f} {temp_unit}",
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f"{temp_range:.2f} {temp_unit}",
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str(len(all_temps)),
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]
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fig.add_trace(_go.Table(
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header=dict(values=["<b>Metric</b>", "<b>Value</b>"],
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fill_color='#1f2937', font=dict(color='white')),
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cells=dict(values=[stats_labels, stats_values],
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fill_color='#374151', font=dict(color='white'))
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), row=2, col=2)
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# Layout
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fig.update_layout(
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width=1400, height=900,
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paper_bgcolor='#111827', plot_bgcolor='#1f2937',
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font=dict(color='white'),
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title=dict(text="<b>Atomizer Thermal Analysis</b>",
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x=0.5, font=dict(size=18)),
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showlegend=False
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)
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# Save HTML
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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output_dir = config.output_dir or self.insights_path
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output_dir.mkdir(parents=True, exist_ok=True)
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html_path = output_dir / f"thermal_{timestamp}.html"
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html_path.write_text(
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fig.to_html(include_plotlyjs='cdn', full_html=True),
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encoding='utf-8'
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)
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return InsightResult(
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success=True,
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html_path=html_path,
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plotly_figure=fig.to_dict(),
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summary={
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'max_temp': max_temp,
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'min_temp': min_temp,
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'mean_temp': mean_temp,
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'temp_range': temp_range,
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'temp_unit': temp_unit,
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}
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)
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