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Journal articles on the topic 'Multidisciplinary analysis and optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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1

Thokala, Praveen, Jim Scanlan, and Andy Chipperfield. "Framework for Aircraft Cost Optimization Using Multidisciplinary Analysis." Journal of Aircraft 49, no. 2 (2012): 367–74. http://dx.doi.org/10.2514/1.c000187.

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2

Chu, X. Z., L. Gao, W. D. Li, H. B. Qiu, and X. Y. Shao. "An Uncertainty Analysis Approach to Multidisciplinary Design Optimization." Concurrent Engineering 17, no. 2 (2009): 121–28. http://dx.doi.org/10.1177/1063293x09105327.

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3

Armellin, Roberto, and Michèle Lavagna. "Multidisciplinary Optimization of Aerocapture Maneuvers." Journal of Artificial Evolution and Applications 2008 (April 7, 2008): 1–13. http://dx.doi.org/10.1155/2008/248798.

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A multidisciplinary-multiobjective optimization of aerocapture maneuvers is presented. The proposed approach allows a detailed analysis of the coupling among vehicle's shape, trajectory control, and thermal protection system design. A set of simplified models are developed to address this analysis and a multiobjective particle swarm optimizer is adopted to obtain the set of Pareto optimal solutions. In order to deal with an unconstrained multiobjective optimization, a two-point boundary value problem is formulated to implicitly satisfy the constraints on the atmospheric exit conditions. The tr

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4

Shin, Moon-Kyun, and Gyung-Jin Park. "Multidisciplinary design optimization based on independent subspaces." International Journal for Numerical Methods in Engineering 64, no. 5 (2005): 599–617. http://dx.doi.org/10.1002/nme.1380.

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5

Sun, Yicheng, and Howard Smith. "Low-boom low-drag optimization in a multidisciplinary design analysis optimization environment." Aerospace Science and Technology 94 (November 2019): 105387. http://dx.doi.org/10.1016/j.ast.2019.105387.

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6

Lin, JiGuan G. "Analysis and Enhancement of Collaborative Optimization for Multidisciplinary Design." AIAA Journal 42, no. 2 (2004): 348–60. http://dx.doi.org/10.2514/1.9098.

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7

Sarma, Hari K., Ramana V. Grandhi, and Ronald F. Taylor. "Expert system for multidisciplinary analysis and optimization using ASTROS." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 4, no. 2 (1990): 117–28. http://dx.doi.org/10.1017/s0890060400002304.

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This paper discusses the framework of a knowledge-based expert system environment to design aerospace structures under structural and aerodynamic constraints using ASTROS (Automated Structural Optimization program). ASTROS is a synthesis tool built around the NASTRAN finite element program. The knowledge base capabilities are discussed for synthesizing in statics, normal mode, steady and unsteady aerodynamic disciplines. A description of the two ASTROS advisor modules, the Editor/Bulk Data generator and Post-processor, is included. Experiences and issues involved in hierarchical representation

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8

Luo, Xiaodong, and R. V. Grandhi. "Astros for reliability-based multidisciplinary structural analysis and optimization." Computers & Structures 62, no. 4 (1997): 737–45. http://dx.doi.org/10.1016/s0045-7949(96)00234-9.

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9

Lee, Jae-Woo, Seok-Min Choi, Nguyen Nhu Van, Ji-Min Kim, and Yung-Hwan Byun. "Multidisciplinary UAV Design Optimization Implementing Multi-Fidelity Analysis Techniques." Journal of the Korean Society for Aeronautical & Space Sciences 40, no. 8 (2012): 695–702. http://dx.doi.org/10.5139/jksas.2012.40.8.695.

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10

Wang, Lei, Chuang Xiong, Xiaojun Wang, Guanhua Liu, and Qinghe Shi. "Sequential optimization and fuzzy reliability analysis for multidisciplinary systems." Structural and Multidisciplinary Optimization 60, no. 3 (2019): 1079–95. http://dx.doi.org/10.1007/s00158-019-02258-y.

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11

Kurdi, Mohammad. "A Structural Optimization Framework for Multidisciplinary Design." Journal of Optimization 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/345120.

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This work describes the development of a structural optimization framework adept at accommodating diverse customer requirements. The purpose is to provide a framework accessible to the optimization research analyst. The framework integrates the method of moving asymptotes into the finite element analysis program (FEAP) by exploiting the direct interface capability in FEAP. Analytic sensitivities are incorporated to provide a robust and efficient optimization search. User macros are developed to interface the design algorithm and analytic sensitivity with the finite element analysis program. To

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12

Habbal, Abderrahmane, Joakim Petersson, and Mikael Thellner. "Multidisciplinary topology optimization solved as a Nash game." International Journal for Numerical Methods in Engineering 61, no. 7 (2004): 949–63. http://dx.doi.org/10.1002/nme.1093.

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13

Xu, Huanwei, Wei Li, Liudong Xing, and Shun-Peng Zhu. "Multidisciplinary design optimization under correlated uncertainties." Concurrent Engineering 25, no. 3 (2017): 262–75. http://dx.doi.org/10.1177/1063293x17697456.

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Uncertainty analysis is a hot research topic in multidisciplinary design optimization for complex mechanical systems. Existing multidisciplinary design optimization works typically assume that uncertainties are uncorrelated of each other. In real-world engineering systems, however, correlations do exist between different uncertainties. The multidisciplinary design optimization methods without considering correlations between uncertainties may cause inaccuracy and thus misleading optimization results. In this article, we make contributions by proposing a new multidisciplinary design optimizatio

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14

Adami, Amirhossein, Mahdi Mortazavi, Mehran Nosratollahi, Mohammadreza Taheri, and Jalal Sajadi. "Multidisciplinary Design Optimization and Analysis of Hydrazine Monopropellant Propulsion System." International Journal of Aerospace Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/295636.

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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

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15

Meng, Xin-Jia, Shi-Kai Jing, Ye-Dong Wang, Jing-Tao Zhou, Li-Xiang Zhang, and Ji-Hong Liu. "Multidisciplinary Inverse Reliability Analysis Based on Collaborative Optimization with Combination of Linear Approximations." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/964238.

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Multidisciplinary reliability is an important part of the reliability-based multidisciplinary design optimization (RBMDO). However, it usually has a considerable amount of calculation. The purpose of this paper is to improve the computational efficiency of multidisciplinary inverse reliability analysis. A multidisciplinary inverse reliability analysis method based on collaborative optimization with combination of linear approximations (CLA-CO) is proposed in this paper. In the proposed method, the multidisciplinary reliability assessment problem is first transformed into a problem of most prob

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16

Chai, Mengjiang, Yongliang Yuan, and Wenjuan Zhao. "An improved particle swarm optimization algorithm for dynamic analysis of chain drive based on multidisciplinary design optimization." Advances in Mechanical Engineering 11, no. 3 (2019): 168781401982961. http://dx.doi.org/10.1177/1687814019829611.

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Chain drive is one of the most commonly used mechanical devices in the main equipment transmission system. In the past decade, scholars focused on basic performance research, but ignore its best performance. In this study, due to the large vibration of the chain drive in the transmission system, the vibration performance and optimization parameters are also considered as a new method to design the chain drive system to obtain the best performance of the chain drive system. This article proposes a new method and takes a chain drive design as a case based on the multidisciplinary design optimiza

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17

Vlahopoulos, N., and C. G. Hart. "A Multidisciplinary Design Optimization Approach to Relating Affordability and Performance in a Conceptual Submarine Design." Journal of Ship Production and Design 26, no. 04 (2010): 273–89. http://dx.doi.org/10.5957/jspd.2010.26.4.273.

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A multidisciplinary design optimization (MDO) framework is used for a conceptual submarine design study. Four discipline-level performances—internal deck area, powering, maneuvering, and structural analysis—are optimized simultaneously. The four discipline-level optimizations are driven by a system level optimization that minimizes the manufacturing cost while at the same time coordinates the exchange of information and the interaction among the discipline-level optimizations. Thus, the interaction among individual optimizations is captured along with the impact of the physical characteristics

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18

Peri, Daniele, and Emilio F. Campana. "Multidisciplinary Design Optimization of a Naval Surface Combatant." Journal of Ship Research 47, no. 01 (2003): 1–12. http://dx.doi.org/10.5957/jsr.2003.47.1.1.

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Whereas shape optimal design has received considerable attention in many industrial contexts, the application of automatic optimization procedures to hydrodynamic ship design has not yet reached the same maturity. Nevertheless, numerical tools, combining together modern computational fluid dynamics and optimization methods, can aid in the ship design, enhancing the operational performances and reducing development and construction costs. This paper represents an attempt of applying a multidisciplinary design optimization (MDO) procedure to the enhancement of the performances of an existing shi

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19

Gray, Justin, Kenneth T. Moore, Tristan A. Hearn, and Bret A. Naylor. "Standard Platform for Benchmarking Multidisciplinary Design Analysis and Optimization Architectures." AIAA Journal 51, no. 10 (2013): 2380–94. http://dx.doi.org/10.2514/1.j052160.

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20

Nikbay, Melike, Levent Öncü, and Ahmet Aysan. "Multidisciplinary Code Coupling for Analysis and Optimization of Aeroelastic Systems." Journal of Aircraft 46, no. 6 (2009): 1938–44. http://dx.doi.org/10.2514/1.41491.

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21

Huang, H. Z., H. Yu, X. Zhang, S. Zeng, and Z. Wang. "Collaborative optimization with inverse reliability for multidisciplinary systems uncertainty analysis." Engineering Optimization 42, no. 8 (2010): 763–73. http://dx.doi.org/10.1080/03052150903443798.

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22

Huang, Hong-Zhong, Xiaoling Zhang, Wei Yuan, Debiao Meng, and Xudong Zhang. "Collaborative Reliability Analysis under the Environment of Multidisciplinary Design Optimization." Concurrent Engineering 19, no. 3 (2011): 245–54. http://dx.doi.org/10.1177/1063293x11420177.

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23

Ma, Rong, Ke-ping Zhou, and Feng Gao. "Stability analysis of underground engineering based on multidisciplinary design optimization." Journal of Coal Science and Engineering (China) 14, no. 4 (2008): 608–12. http://dx.doi.org/10.1007/s12404-008-0422-5.

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24

Wang, Lei, Chuang Xiong, Juxi Hu, Xiaojun Wang, and Zhiping Qiu. "Sequential multidisciplinary design optimization and reliability analysis under interval uncertainty." Aerospace Science and Technology 80 (September 2018): 508–19. http://dx.doi.org/10.1016/j.ast.2018.07.029.

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25

Xu, Huanwei, Wei Li, Mufeng Li, Cong Hu, Suichuan Zhang, and Xin Wang. "Multidisciplinary robust design optimization based on time-varying sensitivity analysis." Journal of Mechanical Science and Technology 32, no. 3 (2018): 1195–207. http://dx.doi.org/10.1007/s12206-018-0223-8.

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26

Mastroddi, Franco, and Stefania Gemma. "Analysis of Pareto frontiers for multidisciplinary design optimization of aircraft." Aerospace Science and Technology 28, no. 1 (2013): 40–55. http://dx.doi.org/10.1016/j.ast.2012.10.003.

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27

Lei, Li, and Zhang Jianrun. "Multidisciplinary Design Optimization of Distribution Cam Mechanism of Diesel Engine." Applied Mathematics & Information Sciences 7, no. 5 (2013): 1957–62. http://dx.doi.org/10.12785/amis/070534.

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28

MIELOSZYK, Jacek, Tomasz GOETZENDORF-GRABOWSKI, and Dawid MIESZALSKI. "RAPID GEOMETRY DEFINITION FOR MULTIDISCIPLINARY DESIGN AND ANALYSIS OF AN AIRCRAFT." Aviation 20, no. 2 (2016): 60–64. http://dx.doi.org/10.3846/16487788.2016.1195066.

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Conceptual and preliminary design level of aircraft design is searching for an easy, flexible and efficient way of computational geometry definition. Accelerating the process of geometry definition is the basic step for acceleration of all computations. It also enables optimization, where changes of numerical model are made automatically according to the optimization algorithms. The geometry definition has to be robust, free from errors and stay feasible.

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29

Zhang, Zhuo, Fei Yu, Bo Xu, Shipeng Du, and Qiuying Wang. "The Analysis and Optimization Design of Thermal-Electrical Coupling System with Consideration of Numerical Noises." Journal of Computational and Theoretical Nanoscience 13, no. 10 (2016): 6906–15. http://dx.doi.org/10.1166/jctn.2016.5646.

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The optimization function for designing is usually not smooth or discontinuous due to numerical noises, which makes the multidisciplinary decoupling and optimization design more difficult. An global multidisciplinary optimization approach with consideration of numerical noises is proposed in this paper. First, the decoupling problem is transferred into optimization in line with the idea of Simultaneous Analysis and Design (SAND). Kriging models are introduced as surrogate models in order to filter the numerical noises, then the location of new samples is determined with the method of Maximum L

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30

Lam, Xuan-Binh. "Multidiscilinary design optimization for aircraft wing using response surface method, genetic algorithm, and simulated annealing." Journal of Science and Technology in Civil Engineering (STCE) - NUCE 14, no. 1 (2020): 28–41. http://dx.doi.org/10.31814/stce.nuce2020-14(1)-03.

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Multidisciplinary Design Optimization (MDO) has received a considerable attention in aerospace industry. The article develops a novel framework for Multidisciplinary Design Optimization of aircraft wing. Practically, the study implements a high-fidelity fluid/structure analyses and accurate optimization codes to obtain the wing with best performance. The Computational Fluid Dynamics (CFD) grid is automatically generated using Gridgen (Pointwise) and Catia. The fluid flow analysis is carried out with Ansys Fluent. The Computational Structural Mechanics (CSM) mesh is automatically created by Pat

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31

Brevault, Loïc, Mathieu Balesdent, and Sébastien Defoort. "Preliminary study on launch vehicle design: Applications of multidisciplinary design optimization methodologies." Concurrent Engineering 26, no. 1 (2017): 93–103. http://dx.doi.org/10.1177/1063293x17737131.

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The design of complex systems such as launch vehicles involves different fields of expertise that are interconnected. To perform multidisciplinary studies, concurrent engineering aims at providing a collaborative environment which often relies on data set exchange. In order to efficiently achieve system-level analyses (uncertainty propagation, sensitivity analysis, optimization, etc.), it is necessary to go beyond data set exchange which limits the capabilities of performance assessments. Multidisciplinary design optimization methodologies is a collection of engineering methodologies to optimi

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32

Yifei, Tong, Ye Wei, Yang Zhen, Li Dongbo, and Li Xiangdong. "Research on Multidisciplinary Optimization Design of Bridge Crane." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/763545.

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Bridge crane is one of the most widely used cranes in our country, which is indispensable equipment for material conveying in the modern production. In this paper, the framework of multidisciplinary optimization for bridge crane is proposed. The presented research on crane multidisciplinary design technology for energy saving includes three levels, respectively: metal structures level, transmission design level, and electrical system design level. The shape optimal mathematical model of the crane is established for shape optimization design of metal structure level as well as size optimal math

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33

Martin, Thomas J., and George S. Dulikravich. "Analysis and Multidisciplinary Optimization of Internal Coolant Networks in Turbine Blades." Journal of Propulsion and Power 18, no. 4 (2002): 896–906. http://dx.doi.org/10.2514/2.6015.

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34

Wang, Li, Boris Diskin, Robert T. Biedron, Eric J. Nielsen, and Olivier A. Bauchau. "High-Fidelity Multidisciplinary Sensitivity Analysis and Design Optimization for Rotorcraft Applications." AIAA Journal 57, no. 8 (2019): 3117–31. http://dx.doi.org/10.2514/1.j056587.

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35

York, Martin A., Berk Öztürk, Edward Burnell, and Warren W. Hoburg. "Efficient Aircraft Multidisciplinary Design Optimization and Sensitivity Analysis via Signomial Programming." AIAA Journal 56, no. 11 (2018): 4546–61. http://dx.doi.org/10.2514/1.j057020.

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36

Sobieszczanski-Sobieski, Jaroslaw. "Sensitivity analysis and multidisciplinary optimization for aircraftdesign - Recent advances and results." Journal of Aircraft 27, no. 12 (1990): 993–1001. http://dx.doi.org/10.2514/3.45973.

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37

KIM, Sangho, Jungkeun PARK, Jeong-Oog LEE, and Jae-Woo LEE. "A Systematic Approach for Quantitative Analysis of Multidisciplinary Design Optimization Framework." TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES 52, no. 178 (2010): 246–54. http://dx.doi.org/10.2322/tjsass.52.246.

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38

Korngold, J. C., and G. A. Gabriele. "Multidisciplinary Analysis and Optimization of Discrete Problems Using Response Surface Methods." Journal of Mechanical Design 119, no. 4 (1997): 427–33. http://dx.doi.org/10.1115/1.2826386.

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The objective of this paper is to present a new algorithm to efficiently optimize multidisciplinary, coupled nonhierarchic systems with discrete variables. The algorithm decomposes the system into contributing disciplines, and uses designed experiments within the disciplines to build local response surface approximations to the discipline analysis. First and second order Global Sensitivity Equations are formulated and approximated by experimental data to build approximations to the global design space. The global approximation is optimized using branch and bound or simulated annealing. Converg

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39

Gray, Justin S., John T. Hwang, Joaquim R. R. A. Martins, Kenneth T. Moore, and Bret A. Naylor. "OpenMDAO: an open-source framework for multidisciplinary design, analysis, and optimization." Structural and Multidisciplinary Optimization 59, no. 4 (2019): 1075–104. http://dx.doi.org/10.1007/s00158-019-02211-z.

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40

Zhang, Jun, and Bing Zhang. "A Collaborative Approach for Multidisciplinary Systems Reliability Design and Optimization." Advanced Materials Research 694-697 (May 2013): 911–14. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.911.

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In order to improve the efficiency and robustness of reliability-based multidisciplinary design optimization (RBMDO), a new collaborative strategy (named C-RBMDO) which integrates performance measure approach (PMA) and concurrent subspace optimization strategy (CSSO) is proposed. Both the mathematical model and optimization procedure are put forward. The traditional triple-level nested flowchart of RBMDO is decoupled with the sequential optimization and reliability assessment (SORA). The deterministic multidisciplinary design optimization and the multidisciplinary reliability analysis are exec

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41

Meng, Debiao, Miao Liu, Shunqi Yang, Hua Zhang, and Ran Ding. "A fluid–structure analysis approach and its application in the uncertainty-based multidisciplinary design and optimization for blades." Advances in Mechanical Engineering 10, no. 6 (2018): 168781401878341. http://dx.doi.org/10.1177/1687814018783410.

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In practical engineering, the choice of blade shape is crucial in the design process of turbine. It is because not only the structural stability but also the aerodynamic performance of turbine depends on the shape of blades. Generally, the design of blades is a typical multidisciplinary design optimization problem which includes many different disciplines. In this study, a fluid–structure coupling analysis approach is proposed to show the application of multidisciplinary design optimization in engineering. Furthermore, a strategy of uncertainty-based multidisciplinary design optimization using

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42

Mohammad Zadeh, Parviz, and Mohadeseh Sadat Shirazi. "Multidisciplinary design optimization architecture to concurrent design of satellite systems." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 231, no. 10 (2016): 1898–916. http://dx.doi.org/10.1177/0954410016665412.

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The design of space systems is a complex and multidisciplinary process with multiple conflicting objectives, large number of design variables, and constraints that limits application of the existing multidisciplinary design optimization architectures to this class of design problems. This paper presents an enhanced multidisciplinary design optimization architecture to concurrent holistic design optimization of a satellite system. The proposed multidisciplinary design optimization architecture extends concepts of multidiscipline feasible and bi-level integrated system synthesis into a unified a

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43

Yuan, Rong, Debiao Meng, and Haiqing Li. "Multidisciplinary reliability design optimization using an enhanced saddlepoint approximation in the framework of sequential optimization and reliability analysis." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 230, no. 6 (2016): 570–78. http://dx.doi.org/10.1177/1748006x16673500.

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For high reliability calculation efficiency and evaluation accuracy, saddlepoint approximation technology has been introduced into design and optimization under uncertainties. When using saddlepoint approximation, there are two prerequisites: all random information is tractable and saddlepoint equations are easy to be solved. However, the above requirements cannot always be met in complex multidisciplinary systems. Random variables sometimes are intractable, or saddlepoint equations are highly nonlinear. To tackle these problems, in this study, an efficient reliability-based multidisciplinary

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Besnard, Eric, Adeline Schmitz, Hamid Hefazi, and Rahul Shinde. "Constructive Neural Networks and Their Application to Ship Multidisciplinary Design Optimization." Journal of Ship Research 51, no. 04 (2007): 297–312. http://dx.doi.org/10.5957/jsr.2007.51.4.297.

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This paper presents a neural network-based response surface method for reducing the cost of computer-intensive optimizations for applications in ship design. In the approach, complex or costly analyses are replaced by a neural network, which is used to instantaneously estimate the value of the function(s) of interest. The cost of the optimization is shifted to the generation of (smaller) data sets used for training the network. The focus of the paper is on the use and analysis of constructive networks, as opposed to networks of fixed size, for treating problems with a large number of variables

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45

Du, Qiang, and Max D. Gunzburger. "A Gradient Method Approach to Optimization-Based Multidisciplinary Simulations and Nonoverlapping Domain Decomposition Algorithms." SIAM Journal on Numerical Analysis 37, no. 5 (2000): 1513–41. http://dx.doi.org/10.1137/s0036142998343087.

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46

Tappeta, R. V., and J. E. Renaud. "Multiobjective Collaborative Optimization." Journal of Mechanical Design 119, no. 3 (1997): 403–11. http://dx.doi.org/10.1115/1.2826362.

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This investigation focuses on the development of modifications to the Collaborative Optimization (CO) approach to multidisciplinary systems design, that will provide solution capabilities for multiobjective problems. The primary goal of this paper is to provide a comprehensive overview and development of mathematically rigorous optimization strategies for Multiobjective Collaborative Optimization (MOCO). Collaborative Optimization strategies provide design optimization capabilities to discipline designers within a multidisciplinary design environment. To date these CO strategies have primarily

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Deng, Jian, Guangming Zhou, and Yu Qiao. "Multidisciplinary design optimization of sandwich-structured radomes." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, no. 1 (2018): 179–89. http://dx.doi.org/10.1177/0954406218757268.

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A multidisciplinary design optimization framework is proposed for sandwich-structured radomes. Radomes ensure the functional operation of antenna systems in adverse environment catering for aerodynamic stresses and payload requirements. The existence of radomes can partially degrade the electromagnetic performance of antenna systems. The electromagnetic performance and mechanical responses are taken into account simultaneously in the optimization design. This is more time-saving and economical compared to the traditional separate considerations on these two aspects. Coupled with multi-island g

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48

Goetzendorf-Grabowski, Tomasz. "Multi-disciplinary optimization in aeronautical engineering." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 231, no. 12 (2017): 2305–13. http://dx.doi.org/10.1177/0954410017706994.

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Nowadays, optimization is a very popular tool used to improve existing projects. The optimization covers different disciplines by linking them into multidisciplinary process of design. Existing software tools allow to very effectively solve particular problems giving high quality solutions which were previously very hard to achieve. Aeronautical engineering is a domain/field which links many disciplines: aerodynamics, stability, control, structural analysis, materials, propulsion systems, avionics, etc. Therefore, the multidisciplinary optimization results in very significant progress not only

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49

Choi, Jung-Sun, and Gyung-Jin Park. "Multidisciplinary design optimization of the flapping wing system for forward flight." International Journal of Micro Air Vehicles 9, no. 2 (2017): 93–110. http://dx.doi.org/10.1177/1756829317691990.

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The success of a flapping wing air vehicle flight is strongly related to the flapping motion and wing structure. Various disciplines should be considered for analysis and design of the flapping wing system. A design process for a flapping wing system is defined by using multidisciplinary design optimization. Unsteady aeroelastic analysis is employed as the system analysis. From the results of the aeroelastic analysis, the deformation of the wing is transmitted to the fluid discipline and the dynamic pressure is conveyed to the structural discipline. In the fluid discipline, a kinematic optimiz

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50

Egorov, Igor, Tatiana Buyukli, Grigorii Popov, Evgeni Goriachkin, Yulia Novikova, and Andrey Volkov. "Experimental Compressor Multidisciplinary Optimization Using Different Parameterization Schemes." MATEC Web of Conferences 220 (2018): 03005. http://dx.doi.org/10.1051/matecconf/201822003005.

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The present-day compressors development is a labor-intensive problem, because compressor structure should meet different requirements to the design characteristics. It’s reasonable to find optimal combination of compressor design parameters using the mathematical optimization resources. In this paper the multi-criteria optimization of the rotor and stator blades of the experimental compressor stage NASA Rotor 37 is carried out. The goal of this work is the analysis of different blade parameterization schemes and determination of optimum number of variable parameters for compressor stage aerody

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