Papers by Author: Xiao Ni Qi

Abstract: Adsorption refrigeration--without Freon as refrigerant, is an environmental friendly refrigeration technology. A novel exhaust-driven adsorption icemaker on a fishing boat is introduced in this paper. Different with the previous tubular structured adsorption bed, WHCT is made of seamless stainless steel pipe, the adsorbent bed, evaporator / condenser are all placed in one tube. In order to study the cooling loss in the transition section, a simplified mathematical model of the transition section was set upbased on assumptions.

Abstract: Due to salt deposition on the packing and subsequent airflow block, thermal performance of the tower declines after a period of time. Eliminating the fill makes the tower fully empty which is of far-reaching significance in circulating seawater with high temperature, high turbidity. Application of PCTs to industry is not practical due to salt deposition on the packing and subsequent blockage. Analysis of seawater characteristics main includes temperature, salinity, density, specific heat and other properties, which is of great significance in the seawater cooling performance. The results provide necessary theoretical bases for the extensive application of seawater cycling and cooling technology, and at the same time lay foundation for the design of seawater cooling tower.

Abstract: The thermal distortion of YK3610 hobbing machine is analyzed. The concept of clustering analysis is proposed and implemented on the gear hobbing machine. The model was used in the experimental of thermal error compensation. The results show that the thermal error compensation control system can reduce thermal errors significantly and the prediction accuracy of the thermal error model is high enough.

Abstract: This paper proposes a new thermal error modeling methodology called Clustering Regression Thermal Error Modeling which not only improves the accuracy and robustness but also saves the time and cost of gear hobbing machine thermal error model. The major heat sources causing poor machining accuracy of gear hobbing machine are investigated. Clustering analysis method is applied to reduce the number of temperature sensors. Least squares regression modeling approach is used to build thermal error model for thermal error on-line prediction of gear hobbing machine. Model performance evaluation through thermal error compensation experiments shows that the new methodology has the advantage of higher accuracy and robustness.