This project implements a Level-1 Digital Twin framework for an ISS-inspired space system, focusing on system-level modeling, estimation, control logic, and health monitoring.
The Digital Twin combines:
- Electrical (battery) dynamics
- Thermal dynamics
- Life support (ECLSS-inspired) dynamics
- State estimation and fault detection
- Health degradation and Remaining Useful Life (RUL) estimation
The objective is to study how Digital Twin architectures can be built for complex engineering systems, with emphasis on clarity, correctness, and scalability, rather than high-fidelity mission replication.
This project creates a closed-loop simulation environment that:
- Models nonlinear multi-physics system dynamics
- Estimates internal system states using an Extended Kalman Filter (EKF)
- Applies optimization-based control logic (mini-MPC style)
- Detects abnormal behavior using statistical fault detection (NIS)
- Predicts battery health degradation and Remaining Useful Life (RUL)
All components are implemented in a modular and extensible architecture.
The system is represented using a unified nonlinear state vector:
- SOC – Battery state of charge
- V_bus – Electrical bus voltage (simplified)
- T_core – Internal system temperature
- T_radiator – Radiator temperature
- O2_mass – Cabin oxygen mass
- CO2_mass – Cabin CO₂ mass
- P_cabin – Cabin pressure
- H_battery – Battery health index
This structure enables future expansion while remaining computationally simple.
- Nonlinear SOC dynamics
- Charging and discharging efficiency
- Solar input disturbance (orbital sunlight)
- Load variation
- Health degradation linked to SOC stress
- Heat generation from electrical load
- Radiative heat rejection
- Pump-controlled heat transfer
- Core and radiator temperature dynamics
- Oxygen generation and consumption
- CO₂ generation and scrubbing
- Cabin pressure variation
- Crew metabolic load as disturbance
- Estimates SOC and battery health
- Handles nonlinear system behavior
- Includes process and measurement noise modeling
- Uses Normalized Innovation Squared (NIS)
- Detects degradation faults after fault injection
- Evaluates filter consistency statistically
- Continuous battery health degradation modeling
- Smoothed Remaining Useful Life (RUL) estimation
- Suitable for predictive maintenance studies
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Optimization-based control using grid search
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Minimizes:
- SOC deviation
- Health degradation rate
- Control effort
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Enforces safety constraints on SOC and health
This represents a simplified Model Predictive Control (MPC) concept.
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System dynamics are propagated using a Runge–Kutta 4th Order (RK4) solver
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Ensures numerical stability and accuracy
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Clean separation between:
- State definition
- System dynamics
- Integration logic
AI_Digital_Twin/
│
├── main_simulation.py # Full closed-loop simulation
│
├── state_vector.py # 8-state system definition
├── system_dynamics.py # Nonlinear multi-physics model
├── rk4_solver.py # RK4 numerical integrator
│
├── System_Architecture_Level1.md
│
├── requirements.txt
└── README.md
Compiled files (*.cpython-313) are generated artifacts and not part of core logic.
- Python
- NumPy, SciPy
- Matplotlib (visualization)
- Control systems & estimation theory
The focus is on engineering modeling and reasoning, not software complexity.
✔ A research-oriented Digital Twin framework ✔ A system-level engineering study ✔ A learning platform for estimation, control, and health monitoring ✔ Suitable for:
- Final-year projects
- Research internships
- Control systems & Digital Twin portfolios
✘ Not a real ISS model ✘ Not connected to live space data ✘ Not a mission-certified flight system ✘ Not a black-box AI project
- Multi-physics system modeling
- State-space formulation
- EKF implementation
- Fault detection using statistical methods
- Health degradation & RUL estimation
- Digital Twin architecture design
- Higher-fidelity physical models
- Coupled estimation across all states
- Advanced MPC formulations
- Integration with real open telemetry datasets
- Extension to lunar or Mars habitat systems
Tamil Selvan Electrical & Electronics Engineering Interests: Digital Twins • Control Systems • Estimation • Space Systems
MIT License – Free for educational and research use.
This project emphasizes clarity, correctness, and engineering discipline in Digital Twin development, serving as a solid foundation for advanced research and system expansion.