This paper deals with the results of an experimental investigation of a small laboratory hybrid motor performance using oxidizer particles embedded fuel. A uniform blend of hydroxyl terminated polybutadiene (HTPB) and a small amount of ammonium perchlorate (AP) have been used as the solid fuel. Hydrogen peroxide with mass concentration of 90% was used as liquid oxidizer. The presence of solid oxidizers on the evaporating surface of solid fuel results to a monopropellant combustion on the surface. It changes the burning mechanisms and interior ballistics of the motor. It causes the improvement in regression rate by factor of 2. It is also shown that adding some solid oxidizer reduces the required mass flow rate of liquid oxidizer.
The main purpose of this paper is to present a novel Robust Design Optimisation (RDO) strategy based on an efficient Uncertainty Analysis (UA) approach. To this end, a Progressive Latin Hypercube Sampling (PLHS) method was developed to derive the minimum samples for UA. The required sample size is calculated based on the convergence of the UA results. Therefore, UA is achieved by a variable sample size Design of Experiments (DOE). This systematic approach leads to an efficient, adaptive and fast framework for RDO. The proposed algorithm performance was validated by some numerical simulations methods on a benchmark function. In conclusion, the proposed methodology was utilised to the design of a hydrazine catalyst bed as a case study. The results of applied RDO in catalyst bed design parameters and also the corresponding value of objective functions demonstrates the performance of the developed framework in space applications.
The purpose of this study is to provide an efficient Multi-Objective Multidisciplinary Robust Design Optimization (MOMRDO) framework. To this end, Bi-Level Integrated System Synthesis (BLISS) framework is implemented as a fast Multi-disciplinary Design Optimization (MDO) framework. Progressive Latin Hypercube Sampling (PLHS) is developed as a Design of Experiment (DOE) of the Uncertainty Analysis (UA). This systematic approach leads to a fast, adaptive and efficient framework for Robust Design Optimization (RDO) of complex systems. The accuracy and performance of the proposed algorithm have been evaluated with various tests. Finally, the RDO of a hydrazine monopropellant thruster is defined as a case study. The results show that the proposed method is a fast and efficient method for the multi-objective optimization design of complex systems, and this approach can be used for other engineering applications as well.
The main aim of this paper is to present a novel multi-objective gray wolf optimization (MOGWO) by utilizing the Kriging meta-model.To this end, surrogate models are used in Multi-Objective Gray Wolf Optimizer as the fitness function.The meta-model is obtained based on exact analysis and numerical simulations.Inheritable Latin Hypercube Design (ILHD) is used as the design of experiments for generation and testing the Kriging model.Then, sensitivity analysis is done to evaluate the effect of design parameter on system responses.The sensitivity analysis leads to appropriate selection of optimization design variables.Hence, the MOGWO algorithm is applied to the problem, the set of non-dominated optimal points are obtained as Pareto Front and one optimal point is selected based on the minimum distance approach.The most important purpose of the methodology is to improve the time consuming in multi-objective optimization problems.In conclusion, for the design of hydrazine catalyst bed was utilized from the proposed methodology.In case, design variables are catalyst bed pellet diameter, loading factor, thrust chamber pressure and Reaction efficiency and objective functions are increasing performance and reducing mass and pressure drop.The results of optimal catalyst bed parameters and also corresponding value of objective functions are shown the performance of methodology in the space propulsion system applications.
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