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In the air conditioning system, flow-induced noise is very disturbing, including the noise generated in the expansion device and the heat exchangers. In the past few decades, most researches related to flow-induced noise focused on the relationship between the flow regimes near the expansion device and the amplitude of flow-induced noise when the measurements are not synched. In this paper, an experimental approach is used to explore the simultaneous relationships between flow-induced noise characteristics and flow regimes at the inlet of TXV of evaporators used in automobiles. A pumped R134a loop with microphones and transparent visualization sections is used to simulate the vapor compression system. Also, the paper evaluates the severity of flow-induced noise from not only the amplitude of noise but also the frequency of noise with a parameter called psychoacoustic annoyance (PA).

Understanding lubrication failures at the shoe/swashplate contact of automotive swashplate compressors will greatly enhance the reliability of the air conditioning system. Maintaining proper lubrication is not always possible during transient conditions. Therefore, a method for detection of lubricant loss is of great interest to the automotive industry. Three methods for detecting lubrication loss were examined: contact resistance, acoustic emission, and dynamic pressure oscillations. A mobile air conditioning test stand capable of recording many system parameters was used. Oil return to the compressor was monitored using an oil separator and a refrigerant/oil concentration sensor. Data were taken during steady oil return rates and after oil shut off. The electrical contact resistance between the shoe and swashplate was used to indicate changes in the lubrication conditions at this critical interface. Measurements were taken at two oil return rates during steady oil return tests.

Reductions of noise in vehicle passenger compartments in recent years have made some previously undetectable noises audible. Expansion devices used in automobile air conditioning systems are known producers of noise. The fact that these devices are mounted very close to the passengers increases the problems associated with the reduction of this noise. The understanding of the propagation mechanisms from the noise generated in the refrigerant by the expansion device, through the tube and evaporator walls, and finally to the outside air is important. This paper will focus on how noise from expansion devices is transmitted through tube walls downstream of the expansion valve.

A PC based survey tool has been used to measure the value of a luxury vehicle as a function of its interior noise level at 70 mph. Two straight lines were required to fit the change in value (measured in dollars) with noise level depending upon whether noise was greater or less than a baseline level of 66 dB(A). The slopes of the lines differed by a factor of two with the value loss curve asociated with a noise increase from the baseline level being the steeper of the two. A difference in the value relationships between gains and losses in this manner is expected from prospect theory. When the noise level in dB(A) was adjusted to represent a sones-like scale using a conversion based upon a broadband noise spectrum, the measurements followed a single straight line reinforcing the view that the sones scale is more nearly a “pure” psychometric scale than dB(A).

Neural networks are massively parallel computational models for knowledge representation and information processing. The capabilities of neural networks, namely learning, noise tolerance, adaptivity, and parallel structure make them good candidates for application to a wide range of engineering problems including diagnostics problems. The general approach in developing neural network based diagnostic methods is described through a case study. The development of an acoustic wayside train inspection system using neural networks is described. The study is aimed at developing a neural network based method for detection defective wheels from acoustic measurements. The actual signals recorded when a train passes a wayside station are used to develop a neural network based wheel defect detector and to study its performance. Signal averaging and scoring techniques are developed to improve the performance of the constructed neural inspection system.