Case Study: Enhancing HVAC Efficiency with Model-Based Engineering
Discover how one engineering team used model-based HVAC simulation to improve energy efficiency, correct system imbalances, and reduce commissioning time—without changing a single pipe on site.
The challenge: fixing an underperforming system—without a redesign
A large academic building was struggling with comfort complaints and excessive energy use—despite recent HVAC upgrades.
Occupants reported uneven heating across zones, frequent control overrides, and long delays before rooms reached setpoint. Meanwhile, the energy manager flagged unusually high gas and pump electricity use.
The system was built to spec—but clearly not performing to plan.
Instead of ripping it apart, the engineering team turned to simulation to reveal the root causes and optimise what was already installed.
Project background
Type: University teaching and office block Age: 10-year-old HVAC retrofit with gas boilers and secondary pumps Complaints: Cold zones, pump noise, slow response Goal: Improve comfort, reduce energy, avoid costly rework
Traditional BMS data gave some clues—but it didn’t explain why the system behaved the way it did under part-load conditions.
Simulating the system to reveal hidden losses
Using Hysopt’s model-based engineering platform, the team created a digital twin of the hydronic system, using as-built data, real building loads, and BMS trends.
Simulation revealed:
Poor valve authority in upper floors
Pump head mismatch causing short-circuiting flows
High return temperatures due to partial bypass during night operation
ΔT collapse under 40% load conditions, reducing boiler efficiency
Optimisation without pipework changes
Instead of redesigning the plantroom, the team turned to the simulation model to guide targeted, low-impact improvements.
They adjusted valve pre-settings and replaced a single actuator, rebalanced flow rates at key risers, updated the control strategy to enable low-load staging, and modified pump sequencing logic within the BMS.
No mechanical changes were needed—just data-driven adjustments informed by the model.
Results: simulated vs. installed performance
The accuracy of simulation predictions provided confidence for future projects, even with hybrid plant and low-carbon upgrades on the horizon.
Lessons learned
“Working to spec” ≠ working in real life
System behaviour must be understood across load ranges—not just at peak
Simulation enables quick wins before costly mechanical changes
A digital twin creates shared understanding between engineer, installer, and operator
BMS data alone doesn’t reveal systemic issues—model-based validation is essential
From guessing to knowing
Simulation didn’t just help optimise this project—it built confidence across the team. Engineers, operators, and decision-makers could all see why the system struggled, and what to do about it.
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