Data Centers

Every rack has a thermal signature

CFD models and design-time thermal simulations predict airflow for the ideal layout. In practice, every rack, every row, every hot aisle develops its own thermal behavior. Nervous Machine learns it and optimizes cooling in real time.

Thermal models are stale the day they're deployed

Data center thermal models are built from CFD simulations of the designed layout. But workloads shift, racks get reconfigured, blanking panels go missing, and seasonal ambient conditions change. The simulation drifts from reality rack by rack.

Operators compensate by overcooling—running CRAC units harder than necessary to ensure nothing overheats. That's energy waste driven by uncertainty. The model says the room is fine; the operator knows individual racks tell a different story.

Per-zone causal thermal models

Nervous Machine builds causal models for each thermal zone—learning the actual relationships between workload, airflow, ambient conditions, and temperature. When a zone drifts, the system identifies the driver: is it a workload spike, a failed fan, or a recirculation path the CFD model never predicted?

Validated thermal relationships propagate across zones and facilities. A discovery in one data hall—say, a specific rack configuration that creates a hot spot under certain workloads—becomes fleet knowledge, shared as causal vectors (~1KB), not raw sensor streams.

What the causal engine learns

Continuous thermal calibration per zone, per rack, per workload profile—enriching CFD baselines with operational reality.

Real-time thermal attribution

Decompose temperature deviations into individual drivers: workload, airflow, ambient, recirculation. Know which factor is causing the hot spot, not just that it exists.

Cooling optimization

Replace overcooling with precision. The causal model predicts thermal response to workload changes, enabling just-in-time cooling adjustments that reduce PUE without increasing risk.

Cross-facility learning

Thermal discoveries propagate across data halls and campuses. Similar rack configurations and workload profiles benefit from shared causal knowledge. Raw telemetry stays local.

Sensor-level deployment

Runs alongside existing BMS and DCIM infrastructure. No cloud dependency for real-time decisions. Lightweight enough for edge controllers in each zone.

Stop overcooling. Start learning what each zone actually needs.

From single-hall pilots to multi-campus rollouts, Nervous Machine adapts to your thermal environment and your workload.