AI Workflow for Dynamic Thermal Control System Operation

By replacing fixed thermostat setpoints with a model-predictive control (MPC) agent, the system anticipates thermal loads from eclipse transitions, instrument usage, and attitude changes. It preemptively adjusts louvers or radiators and uses just-in-time heater pulses, cutting the largest discretionary power drain on the spacecraft bus. This directly extends battery life and frees power for revenue-generating payloads.

Autonomous agents monitor component temperature gradients and dissipation rates against known failure models (e.g., solder joint fatigue, lens delamination). By dynamically re-routing heat via alternative radiators or adjusting operational schedules, the workflow prevents components from exceeding design limits, mitigating a leading cause of premature mission degradation and costly ground-based anomaly response.

Manual thermal analysis and command sequence generation for each mission phase is labor-intensive and slow. A deployed workflow with onboard digital twin simulation autonomously validates every planned activity against thermal constraints before execution. This shifts validation from ground stations to the edge, slashing operator workload and enabling more agile, science-focused mission timelines.

During planetary entry, landing, or occultation, no ground command is possible. The autonomous thermal control workflow, integrating fault detection, diagnostics, and recovery (FDDR) logic, maintains safe temperatures by executing pre-validated contingency scripts. This ensures the vehicle survives critical phases where a thermal fault would be mission-ending, providing absolute mission assurance.

High-resolution imagers and spectroscopic instruments require milli-Kelvin stability for calibration. The workflow uses fine-grained control agents that manage dedicated thermal electric coolers (TECs) and micro-heaters, reacting to minute internal heat leaks. This improves data quality and calibration longevity, directly increasing the scientific value and monetizable data output of the mission.

For fleets of smallsats or sensor platforms, a centralized ground approach to thermal management doesn't scale. This agentic workflow, built on a lightweight orchestration layer (e.g., LangGraph), can be deployed across a constellation. Each satellite manages its own thermal state, reporting only aggregate health, radically reducing the ground segment cost and complexity per asset.

This core agent fuses real-time telemetry from hundreds of temperature sensors, heater currents, and louver positions with a physics-informed digital twin of the spacecraft. It continuously estimates the thermal state of all critical components, predicting future temperature trajectories based on current orbital position (sun angle, eclipse) and planned instrument activity. It serves as the single source of truth for the control system, replacing manual telemetry monitoring and spreadsheet-based thermal modeling.

The primary decision engine. Using the state estimate, it runs rapid, rolling-horizon simulations to evaluate thousands of potential control actions (heater on/off cycles, louver angles) against constraints (min/max temps, power budget). It selects the most fuel- and power-efficient sequence that keeps all components within their safe limits for the predicted environmental cycle. This agent directly replaces rule-based thermostats, which are reactive and suboptimal, with proactive, optimization-driven control.

A guardian agent that monitors the health of the thermal hardware (heater strings, louver actuators, sensors) and the MPC's performance. It detects sensor drift, heater failures, or stuck louvers. Upon a fault, it triggers a safe reconfiguration: recalculating the thermal model, isolating the failed component, re-routing control to redundant systems, and updating the MPC's constraints. This enables autonomous fault recovery without ground intervention, preventing local overheating or uncontrolled cooling.

A critical integration point between the thermal workflow and the broader spacecraft command and data handling (C&DH) system. This interface receives scheduled power loads from the mission planner (e.g., 'high-power radar operation from 1400-1430 Z') and negotiates thermal constraints. It can request activity shifts or power caps if a thermal limit would be breached, or it can pre-heat/cool components in anticipation. This bidirectional coordination prevents thermal violations from derailing the science schedule.

Manages the data downlink burden. Instead of streaming all raw thermal telemetry, this agent compresses normal operation into statistical summaries and high-fidelity model parameters. It isolates and tags any anomalies or control actions for priority downlink. This reduces ground station time required for health assessment and provides engineers with curated, actionable logs, turning a firehose of data into intelligible system performance reports.

The offline testing and training environment. A high-fidelity thermal model of the spacecraft, integrated with a flight software emulator, allows for exhaustive testing of the MPC and fault agents against historical or simulated extreme scenarios (e.g., extended eclipses, instrument failures). This sandbox is used for pre-flight validation, post-anomaly investigation, and continuous retraining of the predictive models using actual flight data, ensuring the onboard agents improve over the mission lifetime.