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Investigation of a Dual HVAC MAC System with Three Row Ducts Using 1D Modeling
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 28, 2017 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
In an automotive air-conditioning (AC) system, upfront prediction of the cabin cool down rate in the initial design stage will help in reducing the overall product development (PD) time. Vehicle having higher seating capacity will have higher thermal load and providing thermal comfort to all passengers uniformly is a challenging task for the automotive HVAC (Heating Ventilation and Air conditioning) industry. Dual HVAC unit is generally used to provide uniform cooling to a large cabin volume. One dimensional (1D) simulation is being extensively used to predict the HVAC performance during the initial stage of PD. The refrigerant loop with components such as compressor, condenser, TXV and evaporator was modeled. The complicated vehicle cabin including the glazing surfaces and enclosures were modeled as a three row duct system using 1D tool AMESim®. The material type, density, specific heat capacity and thermal conductivity of the material were specified. The actual vehicle driving conditions as per test standard were used to validate the transient 1D HVAC performance simulations. The heat gain values of the panel ducts were adjusted to reduce the deviation from test. The simulated results for average cabin temperature and grill outlet temperature were compared against a surrogate vehicle test data. The detailed comparison of test data and simulation results were plotted and identified the simulation parameter which affects the correlation. Studies were carried out to understand the influence of thermal parameters on the performance of dual HVAC system and optimal values were arrived for the system under study.
CitationMuthusamy, V., Sathish Kumar, S., and Sambandan, S., "Investigation of a Dual HVAC MAC System with Three Row Ducts Using 1D Modeling," SAE Technical Paper 2017-01-0164, 2017, https://doi.org/10.4271/2017-01-0164.
Data Sets - Support Documents
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