Maximising value in laboratory design

is a whole-project approach, starting with design and working through to assembly.

the design temperature the cabs/racks/CDU require during normal conditions..Peak summer and yearly external temperatures:.

Maximising value in laboratory design

the design temperature the external heat rejection equipment must be rated to, typically there will also be an additional allowance for recirculation for multi-unit installations (usually validated by an external CFD based on extreme temperatures and wind conditions)..In an ideal scenario, the peak summer design temperature would be lower than the required supply air/water temperature in the data hall.This allows the data centre to rely solely on the heat rejection plant and no chillers - minimising energy consumption and resulting in low PUE values.

Maximising value in laboratory design

PUE is dependent upon whether the heat rejection plant operates using adiabatic cooling..However, for most data centres, peak summer temperatures exceed the required supply temperature.

Maximising value in laboratory design

This necessitates mechanical cooling to maintain design conditions.

The larger the temperature difference, the harder the mechanical cooling system must work.how long it took to manufacture all trusses in the schedule) and average daily throughput, for comparison against expected market demand.

We recorded buffer accumulation to test for process bottlenecks, as well as the number of occupied painting stations to check capacity against what had been previously assumed.. Case study - modelling conclusion.The model was able to support several assumptions made for the design of the process, such as the allowances for buffers and painting bays.

Additionally, the model could show that the system was sensitive to assumptions made for travel time of materials between stations, but that the different production schedules and variation in process time only resulted in slight variations in results.. With this simulation able to rapidly test multiple options and scenarios, the model can continue to be used to test further sensitivities in the process or be expanded to include even more of the system.This could include raw material handling, truss packing and shipping, or even wider in the supply chain.. Long term changes supported by simulation and automation.

Previous
Previous

Hear Jaimie Johnston on the Autodesk podcast

Next
Next

Our books on platforms for design and construction (P-DfMA)