Project Overview
KJ Tait was commissioned to assess the overheating potential in the Service Yard of Lion Yard Shopping Centre, Cambridge. The study was prompted by the continued installation of heat rejection equipment in a confined space with limited ventilation, raising concerns about thermal discomfort and operational inefficiency. The objective was to understand the current and future thermal conditions and provide strategic guidance for mitigating overheating risks.
What We Did
Using IES VE software, we developed a detailed airflow and thermal model of the Service Yard. The modelling incorporated:
- Apache for internal heat gains from heat rejection units.
- Macroflo for natural ventilation airflow.
- Apache HVAC for mechanical supply and extract ventilation.
The Service Yard was divided into three clusters:
- Cluster 1: 36.1 kW of heat rejection
- Cluster 2: 43.6 kW of heat rejection
- Cluster 3: 129.0 kW of heat rejection (most critical zone)
Our team simulated multiple scenarios including increased heat rejection, doubled and quadrupled extract rates, increased air supply, and installation of active cooling coils. A future weather file with a peak temperature of 40.2°C was used to reflect worst-case summer conditions.
Added Value
Our modelling provided our client with:
- A clear understanding of the operational limits for installing additional heat rejection units.
- Evidence-based recommendations to mitigate overheating risks under future climate conditions.
- A framework for planning infrastructure upgrades that balance tenant needs, energy efficiency, and regulatory compliance.
The modelling showed that doubling the extract rate was the most effective and feasible solution, significantly reducing peak temperatures and overheating hours in Cluster 3.
Strategic Impact
This project demonstrates how technical modelling can inform asset management decisions in complex environments. By simulating the thermal behaviour of the service yard under various scenarios, we enabled the client to move from reactive installations to a proactive strategy grounded in robust engineering analysis. The insights gained support sustainable, scalable infrastructure planning and enhance operational assurance for tenants and landlords.