The Control And Optimization Of VAV System Based On TRNSYS-FLUENT Co-simulation

With the development of society and technology,people pay more and more time in the indoor environment,in that way,the requirement of thermal comfort and energy consumption is becoming strict.The energy consumption of air conditioning system occupies almost 50% of whole energy consumption,especially the resource shortage is quite serious.How to decrease the energy consumption of air conditioning system without damaging thermal comfort is always the focus of researchers relevant.As to the survey of energy consumption,amount of researchers choosing TRNSYS to monitor AC system operating condition and reach its dynamic control.At same time,scholars prefer CFD to analyzing distribution of indoor air temperature and velocity for thermal comfort.However,calculating with single point method is the greatest disadvantage of TRNSYS which means TRNSYS overlooks the difference existence of indoor airflow.In this way,indoor air is taken as the same condition while quite different from actual situation and sometimes it may cause great calculation error.And also CFD simulate only with the stable boundary and that means unable to do numerical calculation under the dynamic weather condition.As to the characteristic of software,proposing TRNSYS-FLUENT co-simulation for analyzing the energy consumption and indoor environment comfort at the same time.Firstly,establishing TRNSYS platform and analyzing energy consumption,also thermal comfort of particular room.Accomplishing TRNSYS-FUENT simulation platform based on interface program.Realizing the multi-sensors optimization of supply air through controlling the temperature difference because of the uneven distribution of temperature in the room.Then,propose and actualize the changeless and changing set point of room temperature and supply air.Analyzing the energy consumption and thermal comfort based on those four control strategies.At last,establishing a new simulation platform based on control of comfort index.Comparing the control effect of new platform and the one based on control of temperature.Achieving the advance to start air conditioning system based on the new platform.At last,deciding proper start-up period through single variable regression.Also,predicting the next day cooling load based on BP neural network and proving the operating condition transportability of this neural network.