SubAERO

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- - __init__.py
  - biometric_engine.py
  - embedding_service.py
  - face_alignment.py
  - face_crop.py
  - face_encoding.py
  - face_landmarker.task
  - face_verification.py
  - jwt_service.py
  - models.py
  - password_service.py
  - registration.py
  - routes.py
  - services.py
  - verification.py
- - aerospace_intelligence.py
  - data_pipeline.py
  - engine_state.py
  - routes.py
  - test_intelligence.py
- - __init__.py
  - advanced_pipeline.py
  - config.py
  - data.py
  - evaluate.py
  - fast_optimizer.py
  - features.py
  - lookup_row.py
  - model_comparison.py
  - optimize_models.py
  - physics_data.py
  - physics_predict.py
  - predict.py
  - train.py
- __init__.py
- main.py
- test_biometric_engine.py
- test_face_alg.py
- test_neural_face.py
- test_prediction_consistency.py
- test_twin_imports.py
- - efficiency_features.csv
  - overall_engine_features.csv
  - physics_metadata.json
  - physics_station_features.csv
  - residual_dataset.csv
- - ground_truth.csv
  - test.csv
  - train.csv
  - turbojet_complete_dataset.csv
- - Turbojet.glb
- - index.html
  - outer.html
  - Turbojet.blend
  - Turbojet.blend1
  - Turbojet.glb
  - Turbojetdemo.glb
- - App.tsx
  - router.tsx
- - whitebox_models.json
- - AerothonAccuracyPanel.tsx
  - BatchExcelAccuracyCalculator.tsx
  - index.ts
- - designTokens.ts
  - index.ts
  - missionDataset.ts
  - mockData.ts
  - officialHalDataset.ts
  - operationalBaseline.ts
  - turbojetDataset.ts
- - index.ts
- - useAnimatedValue.ts
  - useBackendIntelligence.ts
  - useLiveIndicators.ts
  - useMissionDemo.ts
  - useTypewriter.ts
- - index.ts
  - MainLayout.tsx
- - utils.ts
- - index.ts
  - WorkstationPage.tsx
- - aiService.ts
  - apiClient.ts
  - fleetService.ts
  - index.ts
  - intelligenceLayer.ts
  - maintenanceService.ts
  - missionEngine.ts
  - missionEventBus.ts
  - missionPlaybackEngine.ts
  - mlInferenceEngine.ts
  - socketService.ts
  - telemetryService.ts
  - unityBridgeService.ts
- - index.ts
  - useAircraftStore.ts
  - useAuthStore.ts
  - useMissionStore.ts
  - useTelemetryStore.ts
  - useUiStore.ts
- - ai.ts
  - aircraft.ts
  - alert.ts
  - engine.ts
  - index.ts
  - maintenance.ts
  - mission.ts
  - telemetry.ts
  - unity.ts
- - aerospaceHelpers.ts
  - audioEngine.ts
  - index.ts
- index.css
- main.tsx
- vite-env.d.ts
- CombustorHealth.joblib
- CompressorHealth.joblib
- feature_columns.json
- OverallHealth.joblib
- Thrust_N.joblib
- TSFC_g_N_s.joblib
- TurbineHealth.joblib
.gitignore
.prettierrc
eslint.config.js
evaluate_ps2.py
index.html
package-lock.json
package.json
postcss.config.js
README.md
tailwind.config.js
tsconfig.app.json
tsconfig.json
tsconfig.node.json
vercel.json
vite.config.ts

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**SubAERO** is a high-accuracy Aerospace Digital Twin and Prognostics & Health Management (PHM) platform designed for HAL Tejas single-spool turbojet engines. It combines real-time WebGL 3D visualization, first-principles aero-thermal physics, and machine learning ensembles (**98.7% – 99.9% R² accuracy**).

---

## 🔗 Live Demonstration

[SubAero-DigitalTwin](https://null-pointers-aerothon-2026.vercel.app/)

---

## Key Features

- **Interactive 3D Engine Twin**: Real-time WebGL engine visualization featuring 4-stage compressor, combustor, turbine, and nozzle components with damage hot-spot indicators.
- **Aero-Thermal Physics Validation**: Evaluates total stagnation pressure/temperature ratios ($\text{PR}_{3,2}$, $\text{TR}_{3,2}$), isentropic efficiencies ($\eta_c$, $\eta_t$), and enthalpy rise/drop across stations.
- **High-Accuracy ML Health Predictor**: Multi-target regression predicting `CompressorHealth`, `CombustorHealth`, `TurbineHealth`, `OverallHealth`, `Thrust (N)`, and `TSFC (g/N/s)`.
- **Remaining Useful Life (RUL) & PHM Advisory**: Real-time component degradation velocity tracking, RUL prediction, and prescriptive service recommendations.

---

## Tech Stack

- **Frontend**: React 19, TypeScript, Vite, Three.js, ECharts, Recharts, Tailwind CSS
- **Backend API**: Python, FastAPI, Uvicorn, NumPy, Pandas, Scikit-Learn, SciPy
- **Deployment**: Vercel (Frontend), FastAPI / Uvicorn (Backend)

---

## Quick Start

### 1. Frontend Setup
```bash
npm install
npm run dev      # Starts local dev server at http://localhost:5173
npm run build    # Builds production bundle
```

### 2. Backend Setup
```bash
pip install fastapi uvicorn scikit-learn pandas numpy scipy
python backend/main.py   # Starts FastAPI server at http://localhost:8000
```

### 3. Model Evaluation
```bash
python evaluate_ps2.py   # Runs PS2 model evaluation runner
```

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