Digital Twin technology was born out of a potential space disaster but now has very real-world applications for designing and building AI data centers
The concept of the digital twin has been traced back to the early days of the US space program. During the Apollo 13 mission, the now famous (and often misquoted) line, “Okay, Houston, we’ve had a problem here,” prompted urgent activity and innovation on the ground. NASA engineers built what they called a “living model” of the damaged spacecraft using simulations and real‑time data. The resulting virtual representation helped diagnose the causes and develop a solution for the astronauts to implement.
What is a digital twin?
A digital twin (DT) today is understood as a digital replica of a physical object, system, facility, or process that exists in a virtual environment and mirrors real-world conditions. Digital twins enable organizations to test scenarios, predict outcomes, and improve design and operational decisions. The applications for highly complex environments such as data centers, populated with inter-dependent equipment with different tolerances and refresh cycles, are obvious.
Despite the high stakes Apollo use case, DT technology evolved slowly, and simple DT models in the data center sector did not gain any meaningful traction until the early 2010s. In data centers, DTs have historically been used to model and align physical footprints and capacity with IT resources for facilities to meet organizational needs without exceeding power or cooling limits. Designers might use Building Information Modeling (BIM) to create a basic DT, visualizing the equipment to be deployed, referencing performance characteristics from vendor spec sheets, and validating that the facility could support the anticipated load.
However, early data center DTs rarely prioritized fine-grained detail. The industry followed well understood design rules: deploy hot‑aisle containment, overprovision for margin, and maintain predictable airflow. Because traditional data centers operated within stable, uniform parameters, highly detailed system‑level simulations were not typically required.
From capacity planning to real-time simulation
The AI era is transforming the role of DT modeling, making the technology increasingly valuable for designing and operating AI‑ready facilities. According to the Uptime Institute Intelligence Update, 'Digital twins: Reshaping AI infrastructure planning', organizations are now applying advanced DT simulations in three critical areas:
- Understanding the requirements and risks associated with hybrid air‑ and liquid‑cooled systems.
- Identifying and resolving power supply constraints - both from the grid and on‑site systems - as demand increases.
- Analyzing electrical distribution challenges driven by high-performance AI compute.
Vertiv and NVIDIA Omniverse: physically accurate simulation
New DT enabling platforms are emerging with specific relevance to data centers. For example, Vertiv is working with NVIDIA’s Omniverse: a set of libraries and microservices that enable real-time, physically accurate simulation and visualization.
Designers can use Omniverse to build true digital twins: visually and operationally accurate models that respond dynamically to data generated during simulations. This enables shorter development cycles, greater precision, and improved collaboration. Omniverse libraries also support interoperability, physically based rendering, generative AI, and real‑time multi‑user design environments.
With NVIDIA Omniverse, data center teams can create detailed digital twins and run simulations that mirror the realities of AI-intensive operations. They can evaluate how systems may respond to changing load conditions, including an illustrative shift from a lower steady-state 30% load1 to full power during an AI training run. They can also explore representative power, cooling, and resilience scenarios, such as grid interruptions, transitions to on-site generation, liquid cooling stress, scheduled shutdowns, or equipment failures, to identify thermal patterns, response timelines, and potential weak points in the facility’s design.
1Note: The 30% load example is used here to represent changing load conditions.
Designing with confidence, operating with precision
The benefits are substantial. Accurate simulations across dynamic operating conditions can reveal true capacity limitations and pinpoint stressors or failure risks, enabling predictive maintenance strategies. They can identify optimal cooling configurations to maximize energy efficiency and highlight under‑ or overutilized servers, helping operators make better decisions about growth, consolidation, or decommissioning.
After a facility is built and commissioned, operators can feed real-time data into the digital twin to continuously refine its accuracy. This helps optimize performance, improve efficiency, anticipate service needs, and minimize unplanned downtime.
While digital twins are still evolving from static design tools into dynamic operational assets, the trajectory is clear, and the potential is immense. A technology shaped by a near‑disaster may now be on course to become a mission‑critical foundation for planning and operating AI era data centers.
Digital twin-driven design and operations is one of the technology trends reshaping data center infrastructure identified in Vertiv Frontiers.
