Prediction of Acoustic Wave Parameters of Thermoacoustic Prime mover through Artificial Neural Network Technique: Practical Approach for Thermoacoustics
A. Rahman, Anas
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Thermoacoustic prime movers are considered new alternative heat engines to 14 traditional ones. For good performance of such a heat engine, a careful apparatus 15 design is required. To predict the acoustic wave parameters responding to 16 geometrical parameters of stack and resonator, is important for such a design. 17 Artificial neural network (ANN) model is first proposed to predict the oscillating 18 frequency and acoustic pressure amplitude, under given resonator length, stack 19 length, stack plate spacing and thickness. ANN models for one standing wave 20 thermoacoustic primemover had been developed based on published experimental 21 data, and evaluated based on some criteria such as least mean square error between 22 the predicted and actual outputs during the testing phase. Concerning oscillating 23 frequency, ANN model with the configuration of 4-4-4-1 was adopted whilst 4-4-1 24 for acoustic pressure amplitude, namely 4 neurons representing the four input design 25 parameters, one or two hidden layers each with optimal four hidden neurons and one 26 neuron representing the output oscillating frequency or acoustic pressure amplitude. 27 Moreover, a statistical analysis has been conducted to show the contribution 28 percentages of the proposed geometrical parameters of resonator and stack where it 29 was found that the resonator length has the largest contribution effect with the 30 approximate percentage of 76 % on the two considered acoustic wave parameters. 31 Compared to both experimental and DeltaEC model results, the determined ANN 32 models had been proven to be desirable in their prediction accuracy with the error 33 percentages of 2.26% and 0.78% for predicted oscillating frequency and acoustic 34 pressure amplitude, respectively. This research work provides a promising practical 35 modeling approach based on ANN technique for complex design problems in 36 thermoacoustic systems.
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