Optimization of System Parameters for Liquid Rocket Engines with Gas-Generator Cycles

Cai, Guobiao; Fang, Jie; Zheng, Yingcheng; Tong, Xiaoyan; Chen, Jun; Wang, Jue

doi:10.2514/1.40649

Cited by 23 publications

(11 citation statements)

References 5 publications

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“…Examples include the design of launch vehicles [39], rocket engines [40], satellite constellations [147], flight trajectories [72,148], and flight control systems [149], as well as the preliminary design of complete aircraft [17,18] and aircraft family design [150]. Beyond aerospace engineering, CO has been applied to problems involving automobile engines [28], bridge design [22], and railway cars [26], and even the design of a scanning optical microscope [27].…”

Section: Collaborative Optimization (Co)mentioning

confidence: 99%

“…One of the first applications of MDO was aircraft wing design, where aerodynamics, structures, and controls are three strongly coupled disciplines [9,10,11,12,13,14,15,16]. Since then, the application of MDO has been extended to complete aircraft [17,18,19,20,21] and a wide range of other engineering systems, such as bridges [22], buildings [23,24], railway cars [25,26], microscopes [27], automobiles [28,29], ships [30,31], propellers [32,33], rotorcraft [34,35], wind turbines [36,37,38], and spacecraft [39,40].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multidisciplinary Design Optimization: A Survey of Architectures

2013

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Multidisciplinary design optimization (MDO) is a field of research that studies the application of numerical optimization techniques to the design of engineering systems involving multiple disciplines or components. Since the inception of MDO, various methods (architectures) have been developed and applied to solve MDO problems. This paper provides a survey of all the architectures that have been presented in the literature so far. All architectures are explained in detail using a unified description that includes optimization problem statements, diagrams, and detailed algorithms. The diagrams show both data and process flow through the multidisciplinary system and computational elements, which facilitates the understanding of the various architectures, and how they relate to each other.A classification of the MDO architectures based on their problem formulations and decomposition strategies is also provided, and the benefits and drawbacks of the architectures are discussed from both a theoretical and experimental perspective. For each architecture, several applications to the solution of engineering design problems are cited. The result is a comprehensive but straightforward introduction to MDO for non-specialists, and a reference detailing all current MDO architectures for specialists.

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Section: Collaborative Optimization (Co)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multidisciplinary Design Optimization: A Survey of Architectures

2013

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“…It is seen that a collaborative optimization will be achieved in Step-2 by considering the interaction of the temporary public variables with the public variables. It is not difficult to find that with Procedure (3), optimal problems containing several disciplines such as those in Refs [1][2][3][4]26,27] can be readily tackled.…”

Section: Re-formulation Of the Conventional Collaborative Optimizationmentioning

confidence: 99%

An improved collaborative optimization for multidisciplinary problems with coupled design variables

Jia²,

Wen

et al. 2016

Advances in Engineering Software

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In this paper, the collaborative optimization is improved to tackle multidisciplinary problems with coupling design variables within disciplines. The design variables are first distinctly classified into private and public ones according to disciplines. A criterion is then proposed to judge the coupling of private variables with the public variables. For the coupling case, a strategy is suggested to decouple the variables before performing the collaborative optimization.To demonstrate the effectiveness of the improved collaborative optimization, two practical optimization problems are comparatively studied for uncoupled design variables as well as coupled ones. It is shown that the present framework can achieve the better optimal results as compared with other methods. Furthermore, the judge criterion and the decoupling strategy are also validated.

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“…One of the first applications of MDO was aircraft wing design, where aerodynamics, structures, and controls are three strongly coupled disciplines [13]. Since then, the application of MDO has been extended to complete aircraft [14] and a wide range of other engineering systems, such as bridges [15], buildings [16], automobiles [17] and [18], ships [19], and spacecraft [20]. Yifei et al [21] proposed the framework of multidisciplinary energy-saving optimization design for bridge cranes, and realized metal structures level, transmission design level, and electrical system design as well as the optimization design of bridge cranes.…”

Section: Multidisciplinary Design Optimizationmentioning

confidence: 99%

Sensitivity-based Multidisciplinary Optimal Design of a Hydrostatic Rotary Table with Particle Swarm Optimization

Cheng

Zhan

Liu

et al. 2015

SV-JME

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In five-axis machine tools, a rotary table is often used as a means for providing rotational motion and supporting the workpiece. Its rigidity, precision and carrying capacity is directly related to the machining ability and the accuracy of the NC machine tool. Traditional rotary table design is normally performed by teams, each with expertise in a specific discipline, which causes excessive iterations and cannot provide users with products of reliable working performance and bearing capacity. To achieve an optimal design with less cost and better performance, this paper considers the mutual interaction of hydrostatics and structure disciplines involved in the design of hydrostatic rotary tables, and a sensitivity-based multidisciplinary optimal design procedure of a hydrostatic rotary table is proposed. Sensitivity analysis is adopted to identify the key design parameters that have a major influence on the performance of rotary tables to improve the convergence of optimization process. The constrained multi-objective optimization problem is solved by using a particle swarm optimization approach. A hydrostatic rotary table of a five-axis heavy duty machine tool is selected as an illustration example. The results show that the proposed method can realize the multidisciplinary optimization resulting in a rotary table of good rigidity and bearing capacity.

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Optimization of System Parameters for Liquid Rocket Engines with Gas-Generator Cycles

Cited by 23 publications

References 5 publications

Multidisciplinary Design Optimization: A Survey of Architectures

Multidisciplinary Design Optimization: A Survey of Architectures

An improved collaborative optimization for multidisciplinary problems with coupled design variables

Sensitivity-based Multidisciplinary Optimal Design of a Hydrostatic Rotary Table with Particle Swarm Optimization

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