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There are many specific challenges facing data centres, but before we delve into the specific challenges across our blog series, let’s start by defining what we mean by dynamic simulation, and how this differs from other approaches typically used to assess data centre performance.
What is Dynamic Simulation?
Dynamic simulation involves the creation of a time-based, physics-driven model that encapsulates the entire data centre facility or campus. It simulates the interactions between IT loads, cooling systems, environmental conditions, rack and control strategies, and building fabric over a full year, using real weather data to predict how the building and systems will perform under different climate scenarios. This method provides detailed insights on key performance metrics, encompassing everything from power and water use, renewable energy/storage potential, and heat sharing feasibility, to carbon outputs and thermal performance, with a resolution down to hourly or sub-hourly time-steps.
How Does This Differ from Static or CFD Only Approaches?
Static or spreadsheet-based approaches present only a snapshot in time, typically assessing performance at peak design conditions. This method fails to account for seasonal variation, part-load behaviour, operational vulnerabilities, and real weather impacts across climate zones. While Computational Fluid Dynamics (CFD) is valuable for examining airflow, temperature hotspots and failure scenarios in data halls, it does not capture whole-facility energy, water use, or annualised behaviour. Combining CFD with dynamic simulation provides a more complete picture of facility performance over time.
Why Does it Matter to Data Centres?
For high density, AI driven environments, dynamic simulation supports confident, high impact planning, design and retrofit decisions, revealing hotspots and risk periods that static methods may overlook. It delivers annualised energy, water, and carbon insights, supporting better operational decisions and ESG planning. In the operational phase, these models also present the foundation of a digital twin for ongoing optimisation, including commissioning checks, performance drift detection, and scenario testing for future load growth or technology changes. Together, these capabilities provide a robust evidence base for more confident design, retrofit, and operational decisions.
Dynamic Simulation is the Best Fit
Single or Large Bulk Zone Models cannot represent air/temperature variation across containment and adjacent areas – and this could miss hot/cold aisle differences, mask hotspots and airflow issues and increase cooling risk and inefficiency. Similarly, CFD-Only Approaches are not built to capture annual energy/water use, resilience, or HVAC-IT-climate interactions. Meaning they only give a snapshot in time and can’t capture annualised or year round performance – which naturally leads to poor forecasting and missed risk. And finally Static or Peak Load Models underestimate seasonal and part-load behaviour and they ignore climate dynamics. This could lead to problems with oversized plant, unexpected hotspots, inefficient cooling, inaccurate PUE/WUE projections and ultimately wasted Capex/Opex.
To learn more about dynamic simulation and how it addresses the key challenges facing modern data centres, download the full IES whitepaper: De-risking High-Performance Data Centres with Dynamic Simulation.
Discover how whole-facility modelling is transforming data centre planning, design, retrofit, and operation in an era of AI-driven infrastructure demands.
