Chiara Scagliola
Inference of Refrigerant Quantity in Car HVAC Systems: A Predictive Model for Leak Detection.
Rel. Paolo Garza. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2023
Abstract
This thesis addresses a critical issue in the automotive sector: customer dissatisfaction arising from complications with Heating, Ventilation, and Air Conditioning (HVAC) systems, notably Air Conditioning (A/C) performance reduction due to gas leakage. The primary objective of this study was to comprehend the behaviour of the air conditioning system under variable refrigerant levels, which facilitates detection of potential leakage. The initial study focused on understanding the HVAC system’s behaviour under various temperatures and identifying the minimal refrigerant charge that can guarantee performance equivalent to the nominal charge. This was done to establish the suitable range of charges for conducting experiments. The aim is to alert the client about potential HVAC system dysfunction before it occurs, based on this identified charge range.
To compose the training set for a machine learning model, a comprehensive data collection strategy was employed, featuring a Latin Hypercube Design
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