In this paper, a heat pump air conditioning system coupling with the battery cooling system is proposed for the thermal management of electric cars. Based on the ambient temperature and working conditions, the heat generated from the battery is applied to preheat the mixed air on heating mode, to reheat the cold air on cooling mode, and to exhaust to the refrigeration system; as well as to dissipate to the outside air directly. A numerical simulation has been carried out to analyse the performance of the coupling system. The results show that the compressor configuration of coupling system can be kept the same as the sole heat pump system when battery heat is less than 800W under the given condition. The longer on-road time takes much more energy for the air conditioning, especially under the hostile ambient temperature condition.
Laser powder bed fusion (L-PBF) additive manufacturing (AM) is one type of metal-based AM process that is capable of producing high-value complex components with a fine geometric resolution. As melt-pool characteristics such as melt-pool size and dimensions are highly correlated with porosity and defects in the fabricated parts, it is crucial to predict how process parameters would affect the melt-pool size and dimensions during the build process to ensure the build quality. This paper presents a two-level machine learning (ML) model to predict the melt-pool size during the scanning of a multi-track build. To account for the effect of thermal history on melt-pool size, a so-called (pre-scan) initial temperature is predicted at the lower-level of the modeling architecture, and then used as a physics-informed input feature at the upper-level for the prediction of melt-pool size. Simulated data sets generated from the Autodesk's Netfabb Simulation are used for model training and validation. Through numerical simulations, the proposed two-level ML model has demonstrated a high prediction performance and its prediction accuracy improves significantly compared to a naive one-level ML without using the initial temperature as an input feature.
To detect driver fatigue states effectively and in real time, a driver fatigue detection system was built, which take ICETEK-DM6347 module as system core, near-infrared LED as light source, and CCD camera as picture gathering device. An improved PER-NORFACE detection method combined several simple and efficient image processing algorithms was proposed, which based on principle of PERCLOS method and take the human face location as the main detection target. To ensure the ability of real-time processing, the algorithms on the DM6437 DaVinci processor were optimized. Experiments show that the system could complete the driver fatigue states detection accurately and in real time.
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