Quantitative assessment of refrigerant impact on vehicle air-conditioning systems via Z-Freq 2D statistical analysis
In this study, the impact of refrigerant types on the performance and efficiency of vehicle air-conditioning (AC) systems was quantitatively assessed using a novel two-dimensional Z-Freq 2D statistical analysis method. Wireless vibration accelerometers, capturing both horizontal and vertical vibra...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Asian Research Publishing Network (ARPN)
2024
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| Online Access: | http://eprints.utem.edu.my/id/eprint/28900/2/0213714052025221901796.pdf http://eprints.utem.edu.my/id/eprint/28900/ https://www.arpnjournals.org/jeas/research_papers/rp_2024/jeas_0924_9501.pdf https://doi.org/10.59018/092442 |
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| Summary: | In this study, the impact of refrigerant types on the performance and efficiency of vehicle air-conditioning (AC)
systems was quantitatively assessed using a novel two-dimensional Z-Freq 2D statistical analysis method. Wireless
vibration accelerometers, capturing both horizontal and vertical vibrations, were utilized to measure the dynamic response of the air-conditioning compressor. Before data collection, meticulous calibration of sensors was conducted to ensure the accuracy and reliability of measurements. The introduction of the Z-Freq 2D statistical analysis technique, developed specifically for this research, allowed for a comprehensive examination of the vibrational data, facilitating a deeper understanding of the effects of different refrigerants on AC system performance. To validate the effectiveness and reliability of the Z-Freq 2D analysis, machine learning techniques were employed. These techniques provided a robust framework for the analysis of the statistical data, with performance evaluation indicators demonstrating the efficacy of the newly developed method. The experimental setup was based on an actual vehicle air-conditioning test rig, designed to simulate real-world operating conditions accurately. The findings of this research offer significant insights into the selection of refrigerants for vehicle AC systems, highlighting the potential for enhanced system performance and efficiency through the application of advanced statistical analysis and machine learning validation techniques. This study not only contributes to the field of automotive thermal management but also paves the way for future research in the optimization of vehicle AC systems. |
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