Optimization Of Thermoacoustic Stack And Its Matching With Thermoacoustic Engine

In recent years research on thermoacoustic engine(TAE) has attracted significant attention and has been a new research hotspot due to the advantages of its simplicity, non-moving components, high reliability and environmental safety. TAE has been into the stage of application from that of theoretical research for 20 years` development, thus convenient and feasible application model and relevant calculation are imperative. But credible result depends on design research and experiment research. Based on the thermoacoustic theoretical and experimental investigation, an application model and a method to solve engineering application on TAE are gradually formed and put forward. Distributed parameter network model of thermoacoustic stack, hot heat exchanger, cold heat exchanger, resonance tube and resonance cavity is respectively built on basis of thermodynamics theory of stack. Thus the thermoacoustic engine(TAE) can be connected by the network model. And Acoustics theory is introduced into the network model to solve for acoustics characteristic parameters such as acoustic propagation constant and characteristic impedance. The network model described by the acoustic characteristic parameters cannot only become basis for theoretical calculation on TAE but also embody the thermoacoustic essence of TAE and stand out the difference of active source and passive source of TAE opponents.Concept of stack spectrum characteristic is adopted to solve optimum frequency and optimize stack. Network model is used to analyze and compare frequency shift characteristic of stack matrices with different gas and spectrum characteristic of different stack matrices. Research shows that adopting mixed gas is a feasible way to increase the adjustable range of resonance frequency to make frequency of stack easily match with that of TAE system. And research also demonstrates that different stack matrices have different frequency range and the pin-array stack is first chosen due to the widest frequency-band among the thermoacoustic stacks. The method can provide theoretical basis for choosing stack matrices and solving the frequency matching between stack and thermoacoustic engine.Distributed parameter network model is used to make matching calculation on thermoacoustic prime mover(TPM) and thermoacoustic refrigerator(TAR), and then determine effect factors such as stack matrices, stack geometries and positions in TAE on their performances. Thus TAE can be adjusted to match stack with stack and the best performance can be achieved according to these effect factors.Calculating the oscillating complex frequency, one of the most important parameters of thermo-driven TAE, and determining effect factors on the frequency, is the first step to make theoretical calculation on TAE. Then the network model is used to calculate acoustic power of different stacks in TPM under different conditions such as different stack matrices,different oscillating pressure ratios(PR) and stack geometries. Furthermore, the model is used to make calculation on thermo-driven TAR and determine the effect factor on cooling performance. Experiment on measuring oscillating frequency, acoustic power and cooling effect under different conditions is made to validate the accuracy of the model. Experiment shows that the model can be used to accurately make calculation on TAE.Moreover, in order to verify the network model, it is used to optimize transitionally dimensional loudspeaker-driven TAR. In optimization, pin-array stack is chosen as heat-pump stack. Its pin diameter and spacing are respectively optimized in terms of maximum cool power factor and maximum cool power factor as well as minimal dissipation power factor. The length and the position are optimized in terms of coefficient of performance(COP) by network model. The method of transfer matrix-changing resonance cavity is presented and used to measure performance parameters of parallel plate and pin-array. The measuring principle,method and procedure are introduced in detail. The measured results show th...