Design And Analysis Of New Magnesium Alloy LED Lamp Radiators

LED(Light Emitting Diode)is known as the new generation of green lighting source.Compared with traditional light sources,it has the advantages of low energy consumption,environmental protection,small size and long lifetime,so it has been widely used in various living and production fields.With the growth of market demand for LED,it will replace the traditional lighting gradually in future.In order to meet the higher luminous brightness of the market demand,input power and device integration for LED is increasing rapidly,which increases the chip temperature and reduces the lifetime and luminous efficiency significantly,that has severely limit the large-scale development of LED industry.Therefore,it is particularly important for LEDs to have an effective thermal design.Aimed at the heat dissipation of LED,a new heat sink with multi-sized fins in AZ31 magnesium alloy for LED lamps was designed in this paper.A regular morphology heat sink,which had same surface area as multi-sized fins heat sink,was provided as a referential sample.The heat dissipation perfonnance of two above mentioned heat sinks were contrastively analyzed.The results shows that the magnesium alloy heat sink with multi-sized fins has the higher air velocity and lower maximum chip temperature under the same condition,which indicates the magnesium alloy heat sink with multi-sized fins has better heat dissipation performance.Then,the designed heat sink was manufactured,and experimental test system was set up.By comparing the experimental data and the simulation data,it is found that the maximum error of the two heat sinks is not more than 8.6%,and the data are in good agreement with each other,which indicates that the simulation model is reliable and effective.In addition,BP neural network and genetic algorithm were combined to optimize the structure of the magnesium alloy heat sink with multi-sized fins.First of all,the factors that affect heat dissipation performance significantly were found out according to single factor test,including fin thickness,length and height,and then an orthogonal test was designed.Aiming at maximum chip temperature we established a BP neural network prediction model,and then the genetic algorithm is applied to obtain the Pareto optimal solutions of maximum chip temperature and weight by different weight coefficient.The result shows that before optimization the maximum chip temperature is reduced by 4.04%and weight is reduced by 66.67%via compared with the heat sink with multi-sized fins;however,after optimization the optimal maximum chip temperature is reduced by 4.47%and optimal weight decreased by 93.18%eventually by compared with the heat sink with regular morphology fins;it is shown that both of maximum chip temperature and the weight can be effectively reduced further by the double-objective optimization,and the heat dissipation performance of the heat sink has been effectively improved while reducing weight significantly.