The Signal To Noise Ratio Analysis On Saw-tooth Vortex Generator In Vehicle Radiators

As a representative form of enhanced heat transfer,eddy current generator is also widely used in radiators.Its three-dimensional structure and geometric parameters have great influence on fluid heat transfer,which has been widely concerned and studied.In order to guarantee the working stability of radiator of engineering vehicle,the influence of air side eddy current is reduced.The tubular radiator model was simulated and calculated.The simulation results of the original radiator were compa red with the experimental data.The serrated vortex generator is proposed to simulate the radiator unit installed with the generator,and the improved radiator JF factor is calculated to be higher than the original radiator.When the flow rate is 12m?s-1 and,the JF factor of the improved radiator is about 29.9% higher than that of the original radiator.At the same time,the eddy current strength of the heat exchanger tube is weakened.In order to analyze the effect of the parameters of the serrated vortex generator on the performance of the radiators,the sensitivity of each parameter was analyzed by means of orthogonal test and SNR combined analysis.The results show that: the contribution rate of installation position,serrated number,blade height,blade width,airflow attack Angle and serrated height to the comprehensive performance of radiator decreases successively,respectively 30.39%,19.61%,15.69%,13.73%,10.78% and 9.80%.The performance prediction model of radiator with sawtooth vortex generator is established,and the multi-objective genetic algorithm is used to solve the parameter model.Compared with the results of original data and concentration,the difference of heat exchange between the two was very small,and the pressure loss of this gr oup decreased by 5.80%.When the pressure loss of the two did not change significantly,the heat transfer increased by 5.27%.The simulation results are basically consistent with the optimization results,which verifies the reliability of the optimization method.Figure 33;Table 15;References 50.