The successful design of a thermoacoustic engine depends on the appropriate description of the processes involved inside the thermoacoustic core (TAC). This is a difficult task when considering the complexity of both the heat transfer phenomena and the geometry of the porous material wherein the thermoacoustic amplification process occurs. An attempt to getting round this difficulty consists in measuring the TAC transfer matrix under various heating conditions, the measured transfer matrices being exploited afterward into analytical models describing the complete apparatus. In this paper, a method based on impedance measurements is put forward, which allows the accurate measurement of the TAC transfer matrix, contrarily to the classical two-load method. Four different materials are tested, each one playing as the porous element allotted inside the TAC, which is submitted to different temperature gradients to promote thermoacoustic amplification. The experimental results are applied to the modeling of basic standing-wave and traveling-wave engines, allowing the prediction of the engine operating frequency and thermoacoustic amplification gain, as well as the optimum choice of the components surrounding the TAC.

Highlights• A model for the propagation of acoustic waves in a thermoacoustic core is proposed • Geometrical and thermal parameters are adjusted by using an inverse method • Parameters are estimated for a ceramic catalyst, a stack of grids and a carbon foam• The values for geometrical parameters are close to manufacturers' data • The heat diffusion length is higher for the stainless-steel grids stack Abstract This paper deals with the in-situ characterization of open-cell porous materials that might be used as a so-called stack (or regenerator) in a thermoacoustic engine. More precisely, the manuscript presents an inverse method aiming at estimating geometrical and thermal properties of various samples of porous media surrounded by heat exchangers and connected to a thermal buffer tube to form a ThermoAcoustic Core (TAC). This estimation is realized from acoustic measurements, and it is expressed as a minimization problem applied to the squared norm of the difference between experimental and theoretical transfer matrices of the TAC. Experimental data, obtained for different stacks (ceramic catalyst, pile of stainless steel wire meshes, carbon and metallic foams) under various heating conditions, are used in order to fit the theoretical forward model by adjusting geometrical properties of the sample and heat exchange coefficients. Common geometrical properties (porosity and average pore's radius) obtained with the present method are consistent with available data from manufacturers. Moreover, this method allows to estimate the tortuosity of the material which is not given by manufacturers. Estimation of heat coefficients (and their variations with heating) provides global in-formation about anisotropic heat diffusion through the porous material employed as a thermoacoustic stack submitted to a temperature gradient. Among the four characterized samples, it appears that the carbon foam allows to get the highest temperature gradients

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Thermo-acoustic systems can convert thermal energy into acoustic waves and vice-versa. This conversion is due to the thermo-viscous interaction between the acoustically oscillating gas fluid within a porous medium, referred to as a regenerator, and the pore internal walls. The thermo-acoustic approach is proposed in this study as an alternative sustainable solution for addressing the issue of electricity in remote areas of developing countries. This approach is environmentally friendly as it utilises air as the working medium and therefore does not generate harmful emissions. In this study, a two-stage travelling-wave thermo-acoustic engine has been modelled using DeltaEC. The simulation was performed by considering various input heat for both of the engine stages. The heat input for the first stage was set within the range of 359.48 to 455.75W, while in the second stage was within the range of 1307.99 to 1656.35W. Hundred (100) data were generated. This dataset was used to build an Artificial Neural Network (ANN) model. The ANN model was validated using the data extracted from DeltaEC. A good agreement between DeltaEC simulation results and ANN predictions was observed. This study shows that the ANN approach is capable of analysing intricate nonlinear thermoacoustic issues.

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A set of X-ray tomographic images of a highly porous material composed of air-saturated coconut fibers is considered and used to estimate intrinsic characteristics of the material. Two physical properties are of interest: porosity and static airflow resistivity. For the porosity, two-dimensional gray-scale tomography images are obtained and post-processed to produce approximative black and white ones, unambiguously attributing distinct regions to fibers and air locations. The porosity is then directly deduced counting the black and white pixels. Several image processing algorithms are compared and associated porosities range from 0.76 to 0.97, depending on the method employed, while it is estimated to be 0.86 from our analysis. For the airflow resistivity, the idea followed here is to use the pattern of the post-processed images as the lattice in a Lattice Boltzmann Method (LBM) fluid dynamics computation. To our knowledge, the LBM has not been used in this context before. A new 2D implementation of the method is therefore developed and studied. After tuning computational parameters, we have estimated the airflow resistivity using ten images of our sample to be 1382 ± 12P a.s/m 2 . Both porosity and resistivity results are fully consistent with measurements obtained from a porosity-meter and a resistivity-meter, demonstrating the pertinence of X-ray tomography and the associated proposed methods.

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