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In this paper, a novel pyrogenic pulser was designed both analytically and numerically and evaluated with empirical tests. The motivation of this study was the need for active control of the aero acoustic pressure oscillations by injecting the secondary flow into the solid rocket motor. First, in brief, pyrotechnic and pyrogenic pulsers have been introduced, and then analytical governing equations have been presented in three transient, sinusoidal and Hercules methods. In order to understand the internal pressure of the pulsar and its plume length, the injection flow field has been evaluated using the ANSYS-Fluent software with both − SST and − Realizable models both at ambient and motor pressure. After that, the design and manufacturing of the pulser hardware and the test process have been described. Finally, analytical, numerical and experimental results have been discussed. The results show that there is a good correlation between the transient analysis in theory and the numerical solution by the k-ω SST model and the empirical test data. In addition, pyrogenic pulsers design depends on various parameters of motor and pulser charge performance prediction. The quality of pulser charge bonding to its insulator and erosion of its throat path due to injection have an important role to obtain a desirable pulser mass flow rate and plume length.

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Pressure oscillations are one of the important challenges of segmented solid rocket motors with high slenderness ratio. The reason for these oscillations can be searched in vortex shedding due to grain burning areas, holes and slots. In this paper, the pattern of four segments grain of space shuttle boosters and structure of Ariane5 subscaled motors have been used for evaluation of aeroacoustic pressure oscillations. First, the related parameters to scale down using Buckingham's Pi-theorem were determined and then a sub-scaled 1:31 motor was designed and manufactured. Going on, Strouhal number in various grain forms and vortex shedding prediction criteria was discussed. Next, for a relative understanding of motor internal flow and vortex shedding formation, steady state computational fluid dynamic calculation was done in seven regression steps and finally, for validation of analysis and simulation, two static tests performed. Results show that various definitions for Strouhal number are useful only for primarily glance on vortex shedding and pressure oscillations and so CFD solution and the test program is inevitable for a correct understanding of the ballistic operational condition of the motor. In addition, despite aggress of pressure test data and grain-burning regression of sub-scaled motor to full-scale motor, the internal flow phenomenon may be different due to small-scale time and dimension with the fullscale motor.

This paper has investigated the semi-analytical analysis of the solid-fluid interaction vibration in the presence of concentrated mass-spring-damper vibration absorber. The nonlinear partial differential equations of motion are derived by considering von Karman-type large deformations and viscoelastic behaviour. Fluid-structure interaction is modelled by using an acceleration coupling model in which a nonlinear Van der Pol oscillator simulates fluctuating nature of the vortex street. The nonlinear equations are discretized via the Galerkin approach, and the obtained equations are numerically solved by applying the Runge–Kutta method. Eventually, the dynamic response, phase plane plots, and variations of maximum amplitude in terms of fluid velocity for different parameters are extracted. The results reveal that utilizing vibration absorber leads to a signifi cant effect on the dynamic characteristics of the system, displaces the lock-in phenomenon, and remarkably reduce the amplitude of the system oscillations.

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In this research, neural network models were used to predict the action of sloshing phenomena in a tank containing fluid under harmonic excitation. A new methodology is proposed in this analysis to test and simulate fluid sloshing behavior in the tank. The sloshing behavior was first modeled using the smooth particle hydrodynamics (SPH) method. The backpropagation of the error algorithm was then used to apply the two multilayer feed-forward neural networks and the recurrent neural network. The findings of the SPH process are employed in the training and testing of neural networks. Input neural network data include the tank position, velocity, and acceleration, neural output data, and fluid sloshing curve wave position. The findings of the neural networks were correlated with the experimental evidence provided in the literature. The findings revealed that neural networks can be used to predict fluid sloshing.

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