Multidisciplinary Design Optimization Models and Algorithms for Space Launch Vehicles

Castellini, Francesco; Riccardi, Annalisa; Lavagna, Michèle; Büskens, Christof

doi:10.2514/6.2010-9086

Cited by 8 publications

(8 citation statements)

References 16 publications

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“…13 and summarized in Table 3, where maximum absolute value, average and standard deviation of the errors on several design parameters are reported. Errors are evaluated on a database of European, US, Russian and Japanese ELVs, collected from Ref.…”

Section: B Sensitivity Analysesmentioning

confidence: 99%

“…Although more detailed description as well as validation results are given in Ref. 13, a brief overview of the implemented disciplinary models is given here: * All computational times in the paper are referred to a 2.10 GHz single processor, 4 GB DDR2 RAM Downloaded by Stanford University on October 7, 2012 | http://arc.aiaa.org | DOI: 10.2514/6.2011-1901 • Propulsion: the analysis is performed by either picking up an Off-The-Shelf (OTS) Liquid Propulsion (LP) or Solid Propulsion (SP) engine in a database collected mainly from Ref. 16 and the web * , or by designing a new SP or LP system.…”

Section: Elv Conceptual Design Modelsmentioning

confidence: 99%

“…To tackle this issue, the engineering models have been developed in two successive levels of detail, from conceptual to early-preliminary design. Previous papers [11][12][13] describe in detail models and algorithms introduced for the conceptual level step, and show disciplinary methods and optimization algorithms validation results. The present work draws on this experience, and focuses on a critical analysis of the system design results, with a twofold objective: to assess their accuracy, and to identify the most critical modeling aspects to be improved for the successive early-preliminary design step.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Global and Local Multidisciplinary Design Optimization of Expendable Launch Vehicles

Castellini

Riccardi

Lavagna

et al. 2011

52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference

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The paper presents engineering models, optimization algorithms and design results from a Multidisciplinary Design Optimization (MDO) research in the framework of ESA's PRESTIGE PhD program. The application focuses on the conceptual design of classical unmanned Expendable Launch Vehicles, and results are presented from sensitivity studies and validation tests on European launchers (Ariane-5 ECA and VEGA). Relatively simple models and a mixed global/local optimization approach allow obtaining reasonable results with limited computational effort. A critical analysis of the results also leads to the identification of the most critical modeling aspects to be improved to allow for early preliminary design applications. Nomenclature α = engine mixture ratio ε = nozzle expansion ratio θ = pitch angle ψ = yaw angle µ = mean value σ = standard deviation A e = nozzle exhaust area AoA = total angle of attack a = orbit semiaxiscore boosters configuration CpL = cost per launch e = orbit eccentricity GTOW = gross take-off weight i = orbit inclination I sp = specific impulse, nominal conditions (i.e. nozzle optimal expansion) 2 I sp,vac = specific impulse in vacuum I sp,sea = specific impulse at sea level L/D = length over diameter ratio LSP = launch success probability MR = Engine mixture ratio M = Mach number M prop = Propellant mass (usable propellant only) M dry = Dry mass = inert mass + unused propellants mass N s = number of stages N bs = number of booster sets N b,j = number of boosters for j-th boosters set n ax = axial acceleration p cc = chamber pressure PL = payload PLSF = payload scaling factor q dyn = dynamic pressure q heat = heat flux SET = single engine type configuration (i.e. same engine type for all stages) T nom = total thrust, nominal conditions (i.e. nozzle optimal expansion) T ,vac = total thrust in vacuum T sea = total thrust at sea level

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Section: B Sensitivity Analysesmentioning

confidence: 99%

Section: Elv Conceptual Design Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Global and Local Multidisciplinary Design Optimization of Expendable Launch Vehicles

Castellini

Riccardi

Lavagna

et al. 2011

52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference

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“…The system-level optimizer can be chosen among several stochastic global algorithms: a simple single-objective particle swarm optimization (SO-PSO) [14] or one of the double-grid multiobjective PSO (DG-MOPSO, proposed in [15]), NSGA-II [16], MOACOr [17], or their hybridization [18], which was developed ad hoc for ESA's Program in Education for Space, Technology, Innovation and Knowledge research. A previous research work [19] describes in detail the use of such global optimization strategies for the application to trajectory and multidisciplinary designs, including an extensive quantitative comparison on mathematical benchmarks and representative test problems; hence, no further details are reported here.…”

Section: Mdo Architecture and Optimization Approachmentioning

confidence: 99%

Quantitative Assessment of Multidisciplinary Design Models for Expendable Launch Vehicles

Castellini¹,

Lavagna²,

Riccardi³

et al. 2014

Journal of Spacecraft and Rockets

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The research effort described in this paper is centered on the development and quantitative assessment of a multidisciplinary design optimization environment for the early design phases of expendable launch vehicles. The focus of the research is on the engineering modeling aspects, with the goal of evaluating in detail the accuracy of engineering-level methods for launch vehicle design, both in terms of disciplinary errors (e.g., engine specific impulse evaluation) and of system-level sensitivities, to assess their applicability to industrial early design. Although aerospace applications of multidisciplinary design optimization can be found in literature, the systematic assessment of the models' accuracies to the extent described in the present research is a rather new endeavor, which is critical for the advancement of this field. In fact, the widespread industrial application of multidisciplinary design optimization has often been obstructed by the difficulty of finding a suitable compromise between the analysis fidelity and computational cost. Considerable effort was therefore spent on a careful, incremental modeling process, with the purpose of overcoming such an obstacle. As a result, although it is clear that the development and tuning of a reliable multidisciplinary environment is a particularly complex and challenging task, detailed investigations showed that a good compromise can indeed be achieved for the expendable launch vehicles application. In particular, the 1Ïƒ accuracy on the payload performance was assessed to be in the order of 12%for a computational time <2 sper design cycle, allowing one to obtain physically sound design changes through the multidisciplinary design optimization, even exploiting only fast engineering-level methods

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“…MDO has a wide variety of engineering-related applications; many of them related to fluid dynamics, such as wind turbine design [77,78], energy generation systems [79], windfarms [80] or, more particularly related to the subject of this thesis, aerospace engineering [81,82]. For instance, there are works related to the design of space launchers [83][84][85][86][87][88][89][90][91], vehicles [92][93][94] and satellites [95], thermal and power analysis [81], rotorcraft [96], wings [61,67,69,[97][98][99][100][101][102][103][104][105][106][107][108], fuselage [109,110], and complete subsonic [111][112][113], supersonic [114][115][116] and even hypersonic [117] aircraft.…”

Section: Multidisciplinary Design Optimizationmentioning

confidence: 99%

RPAS Design : an MDO Approach

Aliaga-Aguilar¹

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Unmanned aircraft and, particularly, RPAS (Remotely Piloted Aircraft Systems) are nowadays experiencing great growth both in the military and civil industries. This is due to the fact that removing the need to be manned has enabled the improvement of their endurance, range, and overall performance while, at the same time, reducing the risk to which human lives were exposed. In addition, RPAS can be made much smaller. This decrease of mass and size increases the variety of missions they can perform. However, the design process to manufacture such aircraft is often long and costly, which prevents small companies from undertaking it. Multidisciplinary Design Optimization (MDO) is an engineering field whose focus is to solve highly complex problems by the means of optimization techniques. It has been used in the design of commercial aircraft for a long time, but integrating the various engineering disciplines that take part in designing an RPAS within an MDO to simplify the design process is a challenge. In addition, there is an ample variety of architectures for MDO projects and, the reasons to choose a particular one, have to be discussed on a case by case basis. During the last years, distributed architectures have become widespread, given that they can take advantage of parallel computing (even with graphical platforms) and reduce computing time. Adapting the formulation of a problem to a particular vii Dissertation Overview Chapter 1: This is the introduction to the thesis. It presents the evolution of RPAS and the current state of the art both in MDO and in RPAS design methodologies. Chapter 2 introduces the Generic Parameter Penalty Architecture (GPPA): a new, flexible MDO architecture, oriented towards the solution of engineering problems that present different levels of complexity. Chapter 3 introduces the RPAS Advanced MDO Platform (RAMP). A new MDO environment aimed at the design of small RPAS. Chapters 4-7 present RAMP's main analysis models: aerodynamics, structure, economy and pricing, propulsion, and performance. Chapter 8 presents an application case of RAMP to a real-world mission. Chapter 8 does so by limiting RAMP's configuration availability to just classical. This provides the opportunity to compare its results to most commercially available RPAS, while Chapter 9 unleashes RAMP's full capabilities to also consider Blended Wing Body (BWB) and Canard configurations. Chapter 9 presents results for the various tests and hypothesis that are presented in the thesis, while Chapter 10 serves to give shape to the conclusions of the thesis and the future lines of research.

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Multidisciplinary Design Optimization Models and Algorithms for Space Launch Vehicles

Cited by 8 publications

References 16 publications

Global and Local Multidisciplinary Design Optimization of Expendable Launch Vehicles

Global and Local Multidisciplinary Design Optimization of Expendable Launch Vehicles

Quantitative Assessment of Multidisciplinary Design Models for Expendable Launch Vehicles

RPAS Design : an MDO Approach

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