Power and cooling visibility
Correlate distribution, UPS, cooling, temperature, humidity and rack load to understand capacity and thermal risk.
Industries
Data centers
Unify facility, energy, environmental, capacity and security context for IDC and AIDC operations.
Industry perspective
Data-center reliability depends on facilities, IT and people seeing the same operational reality.
AIDC and high-density data centers face simultaneous pressure across capacity, power, cooling, security and maintenance. X·Neurons connects BMS, DCIM, sensors, alarms, work orders and asset models in a digital-twin context so teams can identify risk before an event and retain learning afterward.
Real digitalization leaves context that the next decision can use.
Operating challenges
Intelligence starts with operating constraints.
- OT and IT data silos
- Capacity, energy and risk are hard to correlate
- Critical events require cross-system coordination
Core use cases
Start with operating problems, not a feature list.
Event correlation and root cause
Combine BMS, DCIM, IT monitoring, access control and work orders on one timeline for faster response.
Facility digital twin
Use DTW to model space, equipment, connections and live state for IDC and AIDC planning and daily operations.
DTW · Digital Twin
Not a static 3D model, but a computable, comparable and continuously calibrated facility twin.
DTW places space, equipment, sensor data and physical conditions in one Twin Model. Operations teams can see the facility as it is, assess hotspots, electrical loading and capacity risk, and compare options before making a real change.
Cold airHot airSensorHotspot
- MODEL Space and assets
- LIVE Live sensing
- FIELD Thermal and airflow
- DECIDE Scenario comparison
Facility and equipment model
Define sites, rooms, hot and cold aisles, rack rows and equipment coordinates. Manage dimensions, rated power, make, model and version for racks, CRAC/CRAH, UPS, PDU, chillers, CDU and XDU assets.
Live sensor binding
Bind field tags for temperature, humidity, power, airflow, pressure differential and equipment state to twin assets. PAC, TagBus and TSDB data become model boundary conditions instead of isolated monitoring points.
Thermal flow, airflow and hotspots
Calculate temperature and velocity fields from rack heat, intake and exhaust, CRAC supply and return, perforated floors and ducts. Show aisles, rack inlet and return temperatures, thermal margin and emerging hotspots.
Electrical loading and redundancy
Use power-flow models to assess load, voltage drop and equipment loading for capacity review and abnormal-load location. Production projects can add A/B paths, phases, transformers and line parameters from the actual single-line diagram.
AIDC liquid-cooling model
Model CDU/XDU capacity, primary and secondary supply and return temperatures, flow, heat-exchange efficiency, coolant and the split between air and liquid cooling so high-density GPU rack heat accounts remain understandable.
2D and 3D field visualization
Present zone results, equipment hotspots, temperature contours, velocity vectors and downsampled 3D fields on the facility model. Keep complete CFD fields as referenced artifacts so large datasets do not slow the operating view.
BASELINE · WHAT-IF · FORECAST
Reproduce the present, compare changes, then forecast what comes next.
Establish a comparable operating baseline
Reproduce current facility state from equipment and sensor data and summarize PUE, maximum rack inlet temperature, thermal margin, IT load, return temperature, zone averages, peaks and hotspots.
Simulate impact before making a change
Compare added load, rack relocation, CRAC outage, airflow and supply-temperature changes. Review KPI deltas, zone-temperature differences and hotspots introduced by each scenario.
Look ahead at capacity and risk
Observe load and temperature across future time slices so teams can plan expansion, maintenance and cooling earlier. Inference speed and precision depend on the selected physics solver or surrogate model.
X·Neurons DATA CHAIN
DTW connects live operational data with facility decisions.
- PACConnect BMS, DCIM, electrical, cooling and sensing equipment
- TagBus／TSDBProvide live boundary conditions and traceable history
- DTWModel, calibrate, simulate, compare and forecast
- DTZPresent KPIs, zones, hotspots and 2D/3D fields
- HFSTurn results into review, response and improvement workflows
Engineering boundary: DTW provides model, job orchestration, scenario comparison, visual data contracts and solver integration. Complete CFD meshes, boundary conditions, electrical single-line diagrams and PhysicsNeMo/ONNX surrogate models must be configured for the actual equipment, measurements and validation objectives of each facility.
Capability path
Move data from signals to organizational learning.
- 01Facility and IT signals
- 02Asset and spatial model
- 03Capacity and risk events
- 04Operational decisions
- 05Incident learning
X·Neurons
Compose around the environment; adopt progressively.
Measure outcomes
Verify capability through operating outcomes.
Baselines and targets are defined together during discovery.
↗Availability and incident recovery time
↗PUE and energy intensity
↗Lead time for capacity and hotspot warnings
↗Alarm noise and root-cause time
Delivery
Begin with one verifiable loop.
- 01
Discover
Define decisions, users, sources and baselines.
- 02
Validate
Build the first loop in a bounded environment.
- 03
Govern
Establish semantic, access and quality governance.
- 04
Scale
Scale proven patterns across sites.
Start with the decision that matters most now.
We begin with constraints, existing systems and accountability boundaries.
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