scholarly journals Multidisciplinary Design Optimization and Analysis of Hydrazine Monopropellant Propulsion System

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Amirhossein Adami ◽  
Mahdi Mortazavi ◽  
Mehran Nosratollahi ◽  
Mohammadreza Taheri ◽  
Jalal Sajadi
Keyword(s):  
Design Optimization ◽  
Attitude Control ◽  
Multidisciplinary Design Optimization ◽  
Total Mass ◽  
Dry Mass ◽  
Propulsion System ◽  
Simultaneous Optimization ◽  
Multidisciplinary Design ◽  
Nozzle Geometry ◽  
Design Algorithm

Monopropellant propulsion systems are widely used especially for low cost attitude control or orbit correction (orbit maintenance). To optimize the total propulsion system, subsystems should be optimized. Chemical decomposition, aerothermodynamics, and structure disciplines demand different optimum condition such as tank pressure, catalyst bed length and diameter, catalyst bed pressure, and nozzle geometry. Subsystem conflicts can be solved by multidisciplinary design optimization (MDO) technique with simultaneous optimization of all subsystems with respect to any criteria and limitations. In this paper, monopropellant propulsion system design algorithm is presented and the results of the proposed algorithm are validated. Then, multidisciplinary design optimization of hydrazine propulsion system is proposed. The goal of optimization can be selected as minimizing the total mass (including propellant), minimizing the propellant mass (maximizing the Isp), or minimizing the dry mass. Minimum total mass, minimum propellant mass, and minimum dry mass are derived using MDO technique. It is shown that minimum total mass, minimum dry mass, and minimum propellant mass take place in different conditions. The optimum parameters include bed-loading, inlet pressure, mass flow, nozzle geometry, catalyst bed length and diameter, propellant tank mass, specific impulse (Isp), and feeding mass which are derived using genetic algorithm (GA).

Multi-modular design optimization and multidisciplinary design optimization

2015 ◽  
Vol 3 (2/3) ◽  
pp. 156-170
Author(s):  
Amirhossein Adami ◽  
Mahda Mortazavi ◽  
Mehran Nosratollahi
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Modular Design ◽  
Estimation Error ◽  
Propulsion System ◽  
Performance Estimation ◽  
System Level ◽  
Multidisciplinary Design ◽  
Mass Model ◽  
Content Type

Purpose – For complex engineering problems, multidisciplinary design optimization (MDO) techniques use some disciplines that need to be run several times in different modules. In addition, mathematical modeling of a discipline can be improved for each module. The purpose of this paper is to show that multi-modular design optimization (MMO) improves the design performances in comparison with MDO technique for complex systems. Design/methodology/approach – MDO framework and MMO framework are developed to optimum design of a complex system. The nonlinear equality and inequality constrains are considered. The system optimizers included Genetic Algorithm and Sequential Quadratic Programming. Findings – As shown, fewer design variables (optimization variables) are needed at the system level for MMO. Unshared variables are optimized in the related module when shared variables are optimized at the system level. The results of this research show that MMO has lower elapsed times (14 percent) with lower F-count (16 percent). Practical implications – The monopropellant propulsion upper-stage is selected as a case study. In this paper, the efficient model of the monopropellant propulsion system is proposed. According to the results, the proposed model has acceptable accuracy in mass model (error < 2 percent), performance estimation (error < 6 percent) and geometry estimation (error < 10 percent). Originality/value – The monopropellant propulsion system is broken down into the three important modules including propellant tank (tank and propellant), pressurized feeding (tank and gas) and thruster (catalyst, nozzle and catalysts bed) when chemical decomposition, aerothermodynamics, mass and configuration, catalyst and structure have been considered as the disciplines. The both MMO and MDO frameworks are developed for the monopropellant propulsion system.

scholarly journals Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

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