Pressure Profile Prediction of Dual‐Thrust Rocket Motors under Uncertainties

Abstract

Mathematical models that simulate the internal ballistics of solid propellant rocket motors are widely used in lieu of the expensive, hazardous, time‐consuming static firing experiments. Since they rely on the input of various measured data, these models are vulnerable to uncertainties that may deteriorate their prediction accuracy. Improving the accuracy of internal ballistics prediction can be achieved by coupling the mathematical models with optimization algorithm. This paper discusses the issues of uncertainties and their impact on prediction accuracy of developed mathematical models. The model handles two types of dual thrust rocket motors, where the impact of uncertainties is more pronounced since they include more geometric and ballistic parameters. The model is developed based on the fundamental principles of internal ballistics and is coupled with a commercial genetic algorithm optimization tool. Two static firing tests are conducted to assess the proposed optimized prediction model. It is found that prediction optimization via tuning the uncertain parameters has led to significantly improving the prediction accuracy. More importantly, the tuned uncertain parameters give better understanding and clearer insight of the phenomena taking place inside dual thrust solid propellant rocket motors.

Hybrid Optimization of Star Grain Performance Prediction Tool

“…The values for ܽ and ݊ are arbitrarily chosen to engulf the baseline values provided by the experimental work [17]. Values of other parameters are specified based on previous experience of the authors [13]. A hybrid optimization technique is used to get the minimum RMSE between the theoretical and experimental pressure time profile using Genetic algorithm [18] globally and simulated annealing [18] locally.…”

“…In this respect, the optimization technique is used to minimize the root mean square error (RMSE) between the desired and computed performance merit. In [10][11][12][13], different optimization methods were used such as complex method, pattern search and genetic algorithm. Sforzini [12] used pattern search for optimizing the computed thrust-time profile of a 3D finocyl grain with 10 parameters.…”

“…He also used genetic algorithm on his case study. Recently, Gawad [13] used genetic algorithm to find the minimum RMSE but on DTRM with tubular grain with two different diameters and sloped grain near its head end with 10 parameters.…”

A preliminary multidisciplinary design procedure for tactical missiles

“…In addition, meeting the thrust profile for a predefined missile mission was discussed in many studies 15–18 using analytical, numerical and CAD burn-back analysis techniques for 3D grain geometrical parameters. The sensitivity of motor performance to the variation of its design was examined 19 using Monte-Carlo method while the impact of uncertainties in design parameters was discussed 20 for dual-thrust motors. A significant increase in missile range can be achieved by increasing the grain volumetric loading by using star 21 or wagon-wheel 22 grain configuration.…”