Cool Down Analysis Of A HVAC System Using Multi-Zone Cabin Approach

Paper #:
  • 2017-01-0182

Published:
  • 2017-03-28
Abstract:
The intended primary objective of a passenger vehicle air conditioning system is to ensure thermal comfort to the passengers seated inside at all prevailing conditions. Presently 1D analysis plays a major role in determining the conformation of the selected system to achieve the desired results. Virtual analysis thus saves a lot of time, cost and effort in predicting the system performance in the initial development phase of the vehicle HVAC systems. A variety of parameters play an important role in achieving the above thermal comfort. In order to optimize the simulation, predicting the duct losses and cabin interior temperatures plays a vital role. Physical and geometrical parameters of the cabin are accurately modelled by considering all the parameters such as glass and sheet metal surfaces, air gaps, solar angles, solar intensity, instrumentation panel, firewall etc. Focus is being laid out on the prediction of HVAC cabin air temperatures by discretizing the cabin into multiple zones (Driver, Co-Driver and Passenger) and mapping the flow profiles in 1D for these zones from the 3D CFD flow data taken from Star CCM+. The simulations are performed using 1D AMESim software. This enables prediction of the average temperature of each zone individually and in turn helps in measuring the human comfort index for respective zones. Interaction effect of the cool air from the four front vents inside the cabin with the warm cabin air, its effect on the cabin temperatures and return air temperature to the evaporator are studied. Thermal comfort is measured using the Human comfort sensor for all the passengers seated inside by measuring the PMV (Predicted Mean Vote) and PPD (Predicted Percentage Dissatisfied) indices as per ISO7730. In the current scenario the modelling approach is done as per the vehicle drive condition in order to compare the same with the test results and the make 1D model more robust for HVAC system performance predictions. The obtained results are within 5% variation with the test results.
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