Aerospace Operations Stack (AeroOps) 🛰️🚀

Cloud-Native Aerospace Microservices Stack & Mission Control Telemetry Visualizer orchestrated with Docker Compose.

AeroOps acts as a centralized operations hub coordinating 3-DoF computational flight dynamics, real-time SGP4 satellite orbit propagation, physics-informed CFD surrogate aerodynamic modeling, and SWE-bench AI benchmark evaluation harnesses under a unified REST API and mission control user interface.

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🏛️ Microservice Systems Architecture

flowchart TD
    Client([Engineer / Browser / Mission Control]) --> Gateway[AeroOps Gateway & Static Dashboard<br/>FastAPI / Port 8080]

    subgraph DockerMesh[Docker Mesh Bridge Network: aero-mesh]
        Gateway --> FlightEngine[Flight Dynamics Service<br/>3-DoF RK4 Suborbital Solver]
        Gateway --> OrbitEngine[Orbital Telemetry Service<br/>SGP4 / J2 Secular Propagator]
        Gateway --> CFDEngine[CFD Surrogate Service<br/>Vortex Panel & Squire-Young Drag]
        Gateway --> BenchHarness[AeroEval Benchmark Service<br/>SWE-bench LLM Leaderboard]
    end

    FlightEngine --> TelemetryFeed[Telemetry Streams<br/>Altitude, Mach, Dynamic Pressure q]
    OrbitEngine --> GroundTrackFeed[Ground Tracks & Pass Schedules<br/>AOS/LOS, Footprint, RF Doppler]
    CFDEngine --> PolarFeed[Aerodynamic Polar & Cp Distribution]
    BenchHarness --> AIReport[Pass@1 Model Resolution Scores]

    TelemetryFeed --> Gateway
    GroundTrackFeed --> Gateway
    PolarFeed --> Gateway
    AIReport --> Gateway

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🚀 Key Platform Capabilities

1. Unified Mission Control UI (/)

• Real-time Canvas Telemetry Gauges: Instantaneous visual rendering of suborbital altitude, axial acceleration, Mach number, and dynamic pressure Max-Q.
• Interactive Orbital World Map: Dynamic sine ground-track projection displaying satellite subpoint, velocity vector, and ground station elevation coverage circle.
• Aerodynamic Surface Pressure Chart ($C_p$): Chordwise suction loop and stall onset rendering for NACA 4-digit profiles.
• SWE-bench AI Leaderboard: Real-time ranking of frontier coding LLMs (Claude 3.5 Sonnet, GPT-4o, DeepSeek-Coder) across aerospace physics tasks.

2. Multi-Container Orchestration (docker-compose.yml)

• Healthcheck-gated container startup ensuring zero-downtime microservice orchestration.
• Production security sandboxing with slim Python base images and non-root execution.
• Multi-stage caching for minimal container footprint (<150 MB).

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⚡ Quick Start with Docker Compose

1. Launch with Docker Compose

# Clone the repository
git clone https://github.com/perfectalone375/aerospace-ops-stack.git
cd aerospace-ops-stack

# Build and start all services in detached mode
docker compose up --build -d

# Check cluster status
docker compose ps

Visit the Mission Control interface in your browser: 👉 http://localhost:8080

2. Run Locally with Python

pip install -e ".[dev]"
aero-ops
# Or: uvicorn aerospace_ops.gateway:app --port 8080 --reload

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📡 REST API Specification

Endpoint	Method	Tag	Description
/api/health	GET	system	Aggregated microservice health check matrix
/api/simulation/flight	POST	telemetry	Runs 3-DoF suborbital trajectory integration
/api/simulation/orbit	POST	telemetry	Propagates satellite orbit and ground station pass
/api/simulation/cfd	POST	telemetry	Evaluates airfoil pressure distribution and polar
/api/benchmarks	GET	evaluation	Returns SWE-bench AI evaluation standings
/	GET	ui	Serves interactive Mission Control single-page app

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🧪 Testing & Verification

# Run pytest test suite
pytest --cov=aerospace_ops

# Run Ruff code style checks
ruff check .

# Validate Docker Compose syntax
docker compose config

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Engineered with precision by Gopi Naga Vignesh Thirupati.