Robust 1D modelling for automotive HVAC warmup Prediction using DFSS approach

Paper #:
  • 2017-01-0179

  • 2017-03-28
Abstract In current automotive industry, the necessity of providing quick warm up of the cabin during extreme cold conditions becomes more challenging to the engineers. A Trade-off between development time, cost and desired performance has to be achieved for deciding the right combination of HVAC (Heating ventilating air-conditioning) components to meet the customer satisfaction. In the HVAC, heater system plays a major role during winter condition to provide passenger comforts as well as to clear windshield defogging/deicing. The heater system consists of heater core, engine coolant as inner medium and air as outer medium. The coolant is circulated by engine coolant/water pump carrying heat from engine and flows across the heater core. The HVAC blower provides air to the cabin by taking heat from the heater core through floor duct systems thus warm up the cabin. In order to meet the customer satisfaction the heater system shall be tested physically in severe cold condition to meet the objective performance in wind tunnel and subjective performance in cold weather regions by conducting on road trials. The significance of conducting the virtual simulation is to predict the performance of the HVAC system at early design stage. To build the model more robust there is a primary need of comparing the transient simulation output such as average floor duct out air temperature and average cabin air temperature with respect to surrogate test data in same boundary conditions. Once the model is correlated with surrogate test data, the components and input conditions are changed with respect to future program specific to predict the warm up performance during development stage. In order to correlate the simulation results to the surrogate test data there are certain simulation parameter needs to be adjusted. In this paper, a detailed and more robust way of optimizing virtual analysis for cabin warmup is carried out using 1D (One Dimensional) software KULI®. All the simulation parameter which affects the correlation process has been studied carefully by DFSS (Design for six sigma) methodology. L18 orthogonal array developed and plotted signal to noise ratio to understand the influence of each simulation parameters. Data analysis is carried out from DFSS study output and identified the importance of each simulation parameters which is being adjusted for correlation. Accurately predicts any change in the HVAC heater systems circuit components like heater core, heater core inlet coolant flows, heater core inlet coolant temperatures, heater core airflow etc. This study enhances to reduce the number of physical tests, prototypes and cost involved in it.
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