AI factory simulation
AI factory simulation allows organizations to test and validate power and cooling designs before anything is built, ensuring systems are correctly sized, placed, and coordinated. This helps avoid costly rework and infrastructure constraints.
Using AI‑enabled digital twins, teams can evaluate electrical capacity, thermal behavior, airflow, and control strategies early – before construction begins. By running what‑if scenarios, organizations can validate infrastructure safety, optimize sizing, and generate data‑driven evidence for stakeholder and executive approval.
Simulating AI factory designs is superior to correcting issues after the architecture is already finalized, as it helps minimize potential delays and reduce the costs of enhancements or improvements.
This approach brings together AVEVA’s digital twin and information management capabilities as the system‑level integration layer, using OpenUSD to enable multi‑domain interoperability across design, engineering, and operations. Within that environment, anETAP electrical digital twinmodels grid‑to‑rack power behavior and failure scenarios, NVIDIA Omniverse provides real‑time visualization and collaborative simulation, and EcoStruxure™ IT Design CFDdelivers precise airflow and thermal analysis.
Lifecycle AI factory digital twin
A single, reliable source of truth to align mechanical, electrical, and IT controls, thanks to OpenUSD data-center simulation.
Accurate power system simulations
ETAP electrical digital twin helps teams analyze protection coordination, assess fault and arc-flash risks, and test switching scenarios.
Near-real-time CFD iteration
EcoStruxure IT Design CFD uses cloud GPUs to visualize airflow and temperature and iterate layouts fast, making density decisions practical.AI factories often struggle with unclear power capacity, mixed air and liquid cooling strategies, team workflows disconnected from each other, and slow reviews that lead to delays and costly rework.
Power stability and safety
As AI clusters draw highly variable power, they’re unstable. ETAP and Omniverse simulations let teams safely test fault, protection, and switching scenarios before build to reduce risk.High rack densities cooling
Traditional cooling standards fall short for high‑power racks using mixed air and liquid cooling. To address this, fast CFD simulations show airflow, temperature shifts, and hot spots.Disconnected operating teams
Mechanical, electrical, and control teams often work in silos with mismatched models. To address this, operators can unify teams in one framework using OpenUSD-based digital twins.Timely approvals and costly changes
Late design changes are costly, and approvals take longer without data-driven evidence. Digital twins set clear limits, alarms, and check points to speed reviews and reduce commissioning risk.Simulation reduces the boundary between design and operation by letting teams run operational scenarios before construction starts. A useful AI factory model does more than render a 3D room; it connects geometry, engineering attributes, live or historical operating data, so teams can test placement, power behavior, airflow, cooling capacity, structural constraints, and workload impact before physical changes become expensive.
This matters because AI factories operate close to infrastructure limits, where small design assumptions can reduce usable token output, strand power or cooling capacity, or force conservative operating margins. The practical value comes from testing “operate-like” scenarios while still in design: rack moves, next-generation accelerator swaps, higher power density, different CDU selections, primary-loop changes, adiabatic cooling tower assumptions, and AI workload profiles.
Separate tools work for point analysis, but they break down when multiple personas need a shared view of the same facility, assets, and operating state. AI factory design requires CFD, power simulation, engineering documentation, asset models, IT telemetry, OT telemetry, and operational history to line up against the same physical equipment.
Without a common integration layer, each tool creates its own viewport, naming convention, and data model, which forces teams to reconcile assets manually. OpenUSD provides the standardization layer for connecting 3D geometry to multiple data sources and solvers. Schneider Electric, AVEVA, and ETAP have joined the Alliance for OpenUSD to support software interoperability, industrial simulation, collaborative design, and AI infrastructure systems.
Token output depends on what happens inside the compute stack, so the model needs visibility below the rack level. CPU utilization, memory utilization, GPU utilization, network behavior, disk activity, and workload state help connect IT behavior to OT consequences such as power draw, heat rejection, flow requirements, and thermal risk.
A rack-level abstraction misses whether a specific workload, node, or component behavior is driving the infrastructure condition. This is why AI factory simulation must combine IT and OT data: the facility does not produce value by keeping rooms cold; the facility produces value by enabling expensive compute racks to run at the highest safe utilization. Schneider Electric and NVIDIA reference design work explicitly ties AI factory design to operating points such as MaxP and MaxQ, with MaxQ enabling more tokens per watt when power constraints require optimized compute performance.
CFD simulation can test airflow, temperature, velocity, pressure, and cooling behavior against both baseline and what-if scenarios. The practical use case is straightforward: start with a baseline room condition, apply a heavier AI workload or move thermal load from one rack location to another, and visualize whether cold airflow, cooling capacity, and containment strategy still support the load. For air-cooled or hybrid environments, a what-if move does not always require complete recabling or repiping; the model can shift the kilowatt dissipation profile and test the resulting thermal behavior.
Power simulation can quantify how a failure or maintenance condition affects usable IT capacity. A practical scenario is a failed remote power panel combined with a second element entering maintenance mode. The simulation can show whether the facility still sustains the required redundancy posture and how much IT power must be capped to preserve N+1 behavior.
That output can then inform workload scheduling rules, power caps, or frequency reductions before operations begin. ETAP’s electrical digital twin capability supports advanced electrical system design and simulation, dynamic what-if scenario analysis, real-time electrical infrastructure performance tracking, energy efficiency optimization, predictive maintenance, and infrastructure planning based on power usage.
Simulation can evaluate the impact of replacing one GPU generation with another by testing power, cooling, and structural consequences before procurement or construction. A practical example is selecting a rack in the model and testing a future higher-density, heavier platform to understand whether the floor, pipe sizing, cooling loop, and electrical infrastructure can support the change.
This matters because AI factories refresh faster than traditional facilities, and next-generation rack platforms may alter weight, heat rejection, pipe sizing, flow requirements, and power density.
Simulation is becoming a practical requirement for AI factories because the cost of conservative assumptions or design error is too high. If a high-value rack can only be operated at 90% because the facility team lacks confidence in cooling, power, or redundancy margins, the operator gives up token capacity on the most valuable asset in the building.
AI factories require simulation because facility design, workload behavior, and utilization economics are tightly coupled. ETAP power simulation, CFD, real-time telemetry, and visual what-if scenarios help operators test the conditions that determine whether the AI factory can run close to the edge safely.OpenUSD-based digital twins further address the problem of siloed data and tools by enabling system-level reasoning across design, construction, and operation.
Scaling AI-enabled digital twins with OpenUSD
The white paper explains why Schneider Electric supports OpenUSD and NVIDIA Omniverse for AI‑enabled digital twins across the data center lifecycle.ETAP and Schneider Electric launch AI power digital twin
The press release discusses how ETAP and Schneider Electric partnered to unveil an AI factory digital twin to simulate power needs and boost efficiency.EcoStruxure™ IT design CFD
The overview showcases CFD‑based data center design software used to optimize layouts, airflow, cooling efficiency, and energy consumption.Reimagining data centers and AI for sustainable impact
The report shows how generative AI can enable more efficient systems, but also how it is driving a surge in data‑center energy use.
From grid-to-chip, chip-to-chiller, for scalable AI.
Validated AI data center reference patterns (ANSI/IEC) + selection guide.
800 VDC architectures, rack-level power, and resilient energy distribution.
Direct-to-chip, immersion, and hybrid cooling for high-density AI workloads.
Modular pods and white-space modules (time-to-ready).
DCIM, planning/modeling, alarm management, OT cybersecurity.
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