Готові списки джерел за темами / Multi-objective Design

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Добірка наукової літератури з теми "Multi-objective Design"

Автор: Grafiati
Опубліковано: 14 грудня 2024

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Статті в журналах з теми "Multi-objective Design"

1

Min, Xinyuan, Jaap Sok, Feije de Zwart, and Alfons Oude Lansink. "Multi-stakeholder multi-objective greenhouse design optimization." Agricultural Systems 215 (March 2024): 103855. http://dx.doi.org/10.1016/j.agsy.2024.103855.

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2

Freier, Lars, and Eric von Lieres. "Robust multi-objective process design." New Biotechnology 33 (July 2016): S27. http://dx.doi.org/10.1016/j.nbt.2016.06.822.

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3

Sun, Qi, Tinghuan Chen, Siting Liu, Jianli Chen, Hao Yu, and Bei Yu. "Correlated Multi-objective Multi-fidelity Optimization for HLS Directives Design." ACM Transactions on Design Automation of Electronic Systems 27, no. 4 (July 31, 2022): 1–27. http://dx.doi.org/10.1145/3503540.

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High-level synthesis (HLS) tools have gained great attention in recent years because it emancipates engineers from the complicated and heavy hardware description language writing and facilitates the implementations of modern applications (e.g., deep learning models) on Field-programmable Gate Array (FPGA) , by using high-level languages and HLS directives. However, finding good HLS directives is challenging, due to the time-consuming design processes, the balances among different design objectives, and the diverse fidelities (accuracies of data) of the performance values between the consecutive FPGA design stages. To find good HLS directives, a novel automatic optimization algorithm is proposed to explore the Pareto designs of the multiple objectives while making full use of the data with different fidelities from different FPGA design stages. Firstly, a non-linear Gaussian process (GP) is proposed to model the relationships among the different FPGA design stages. Secondly, for the first time, the GP model is enhanced as correlated GP (CGP) by considering the correlations between the multiple design objectives, to find better Pareto designs. Furthermore, we extend our model to be a deep version deep CGP (DCGP) by using the deep neural network to improve the kernel functions in Gaussian process models, to improve the characterization capability of the models, and learn better feature representations. We test our design method on some public benchmarks (including general matrix multiplication and sparse matrix-vector multiplication) and deep learning-based object detection model iSmart2 on FPGA. Experimental results show that our methods outperform the baselines significantly and facilitate the deep learning designs on FPGA.
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YAMASHINA, Hajime, Susumu OKUMURA, and Yoshimasa KONDO. "Parameter Design with Multi Objective Characteristics." Journal of the Japan Society for Precision Engineering 58, no. 3 (1992): 516–20. http://dx.doi.org/10.2493/jjspe.58.516.

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Kor, Jean, Xiang Chen, Zhizhong Sun, and Henry Hu. "Casting Design Through Multi-Objective Optimization." IFAC Proceedings Volumes 44, no. 1 (January 2011): 11642–47. http://dx.doi.org/10.3182/20110828-6-it-1002.01726.

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6

Joseph, Shaine, Hyung W. Kang, and Uday K. Chakraborty. "Lens design as multi-objective optimisation." International Journal of Automation and Control 5, no. 3 (2011): 189. http://dx.doi.org/10.1504/ijaac.2011.042851.

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Sanchis, J., M. Martinez, and X. Blasco. "Multi-objective engineering design using preferences." Engineering Optimization 40, no. 3 (March 2008): 253–69. http://dx.doi.org/10.1080/03052150701693057.

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Eckert, Jony Javorski, Fabio Mazzariol Santiciolli, Ludmila C. A. Silva, and Franco Giuseppe Dedini. "Vehicle drivetrain design multi-objective optimization." Mechanism and Machine Theory 156 (February 2021): 104123. http://dx.doi.org/10.1016/j.mechmachtheory.2020.104123.

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Pelinescu, Diana M., and Michael Yu Wang. "Multi-objective optimal fixture layout design." Robotics and Computer-Integrated Manufacturing 18, no. 5-6 (October 2002): 365–72. http://dx.doi.org/10.1016/s0736-5845(02)00027-3.

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Lim, Dudy, Yew-Soon Ong, Yaochu Jin, Bernhard Sendhoff, and Bu Sung Lee. "Inverse multi-objective robust evolutionary design." Genetic Programming and Evolvable Machines 7, no. 4 (September 16, 2006): 383–404. http://dx.doi.org/10.1007/s10710-006-9013-7.

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Дисертації з теми "Multi-objective Design"

1

Kipouros, Timoleon. "Multi-objective aerodynamic design optimisation." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.614261.

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Nezhadali, Vaheed. "Multi-objective optimization of Industrial robots." Thesis, Linköpings universitet, Maskinkonstruktion, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-113283.

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Анотація:
Industrial robots are the most widely manufactured and utilized type of robots in industries. Improving the design process of industrial robots would lead to further developments in robotics industries. Consequently, other dependant industries would be benefited. Therefore, there is an effort to make the design process more and more efficient and reliable. The design of industrial robots requires studies in various fields. Engineering softwares are the tools which facilitate and accelerate the robot design processes such as dynamic simulation, structural analysis, optimization, control and so forth. Therefore, designing a framework to automate the robot design process such that different tools interact automatically would be beneficial. In this thesis, the goal is to investigate the feasibility of integrating tools from different domains such as geometry modeling, dynamic simulation, finite element analysis and optimization in order to obtain an industrial robot design and optimization framework. Meanwhile, Meta modeling is used to replace the time consuming design steps. In the optimization step, various optimization algorithms are compared based on their performance and the best suited algorithm is selected. As a result, it is shown that the objectives are achievable in a sense that finite element analysis can be efficiently integrated with the other tools and the results can be optimized during the design process. A holistic framework which can be used for design of robots with several degrees of freedom is introduced at the end.
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Liu, Wei. "A multi-objective approach for RMT design." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27149.

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A reconfigurable manufacturing system (RMS) is designed for rapid adjustment of manufacturing capacity and functionality in response to market changes. An RMS consists of a number of reconfigurable machine tools (RMTs) which can process different jobs by quickly changing processing modules. The potential benefits of an RMS may not be achieved if an RMS is not properly designed. Most of the related studies focus on a few individual technical issues, in particular on modularity or configurability of individual RMTs. Other important concerns such as cost and processing accuracy have not been adequately addressed. As a result, many highly reconfigurable manufacturing systems turn out to be unprofitable. For the above reason, this study focuses on optimization of RMT design, including the design of modules and module warehouse, with consideration of three factors: configurability, cost and accuracy. (Abstract shortened by UMI.)
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4

Li, Yinjiang. "Robust multi-objective optimisation in electromagnetic design." Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/415498/.

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In electromagnetic design, optimisation often involves evaluating the finite element method (FEM) – repetitive evaluation of the objective function may require hours or days of computation, making the use of standard direct search methods (e.g. genetic algorithm and particle swarm) impractical. Surrogate modelling techniques are helpful tools in these scenarios. Indeed, their applications can be found in many aspects of engineering design in which a computationally expensive model is involved. Kriging, one of the most widely used surrogate modelling techniques, has become an increasingly active research subject in recent decades. This thesis focuses on four interesting research topics in surrogate-based optimisation: infill sampling efficiency, robust optimisation, and the memory problem encountered in large datasets and multi-objective optimisation. This thesis briefly provides relevant background information and introduces a number of independent novel approaches for each topic, with the aim of increasing efficiency of optimisation process and ability to handle larger datasets.
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Ramadan, Saleem Z. "Bayesian Multi-objective Design of Reliability Testing." Ohio University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1298474937.

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6

El-Sayed, Jacqueline Johnson. "Multi-objective optimization of manufacturing processes design /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9841282.

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Faragalli, Michele. "Multi-objective design optimization of compliant lunar wheels." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=117030.

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The development of the wire-mesh wheel of the Apollo Lunar Roving Vehicle was realized through a time consuming trial and error design process, primarily driven by manufacturability and physical testing. Recent wheel development, motivated by renewed interest in lunar surface exploration, utilizes more sophisticated numerical simulation tools. However, many researchers still employ trial and error or parametric approaches to designing the wheels. This thesis proposes a systematic approach to the design optimization of compliant lunar wheels. The problem is decomposed into system and component level analyses. The system level analysis investigates the effect of elastic wheel behaviour on rover and mission performance metrics. This is realized by optimizing concept independent wheel design variables using multi-disciplinary models coupled with optimization algorithms. Wheel concepts are explored by prototyping and physical testing, as well as numerical modelling. The mobility performance metrics of cellular, segmented and iRings wheels are compared to a baseline rubber wheel. In the component level analysis, a multi-objective optimization algorithm is coupled with numerical simulations of wheel-ground interaction to find optimal cellular wheel designs. The effectiveness of the methodology to optimize cellular wheel concepts is verified, and the limitations of the approach examined. Finally, a discussion to extend the proposed methodology to alternative wheel concepts is provided.
Le développement de la roue treillis métallique de l'Apollo Lunar Roving Vehicle a été réalisé par un processus d'essais et d'erreurs. Les récents développements de roues flexibles, motivé par un regain d'intérêt pour l'exploration lunaire, ont maintenant à leur disposition des outils de simulation numérique plus sophistiqués. Cependant, la majorité des chercheurs emploient toujours des méthodes expérimentales ou paramétriques pour développer leurs roues. Cette thèse propose une nouvelle approche systématique pour l'optimisation de concepts de roues lunaires flexibles. Le problème est décomposé en deux analyses se rapportant au niveau du système et celui des composantes. L'analyse au niveau du système étudie l'effet du comportement de la roue élastique sur des mesures de performance lors d'une mission du rover. Ceci est réalisé en optimisant les paramètres décrivant une roue flexible à l'aide de modèles multidisciplinaires. Différents concepts de roues sont explorés à l'aide de prototypes et d'essais physiques, ainsi que de modélisations numériques. La performance de chacun des concepts de roues flexibles cellulaires, iRings et segmentés sont comparées à un pneu standard. L'analyse au niveau des composantes effectue une optimisation multi-objective afin de déterminer, par le biais de simulations numériques, le concept optimal de roues flexibles cellulaires. L'efficacité de la méthodologie pour optimiser la roue cellulaire est ensuite vérifiée et les limites de cette approche sont examinées en détail. Finalement, une discussion sur l'application de la méthodologie proposée à des concepts de roues arbitraires est abordée.
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Skinner, Benjamin Adam. "Multi-objective evolutionary optimisation of submarine propulsion design." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611230.

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Brown, Nathan C. (Nathan Collin). "Early building design using multi-objective data approaches." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123573.

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Анотація:
Thesis: Ph. D. in Architecture: Building Technology, Massachusetts Institute of Technology, Department of Architecture, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 201-219).
During the design process in architecture, building performance and human experience are increasingly understood through computation. Within this context, this dissertation considers how data science and interactive optimization techniques can be combined to make simulation a more effective component of a natural early design process. It focuses on conceptual design, since technical principles should be considered when global decisions are made concerning the massing, structural system, and other design aspects that affect performance. In this early stage, designers might simulate structure, energy, daylighting, thermal comfort, acoustics, cost, and other quantifiable objectives. While parametric simulations offer the possibility of using a design space exploration framework to make decisions, their resulting feedback must be synthesized together, along with non-quantifiable design goals.
Previous research has developed optimization strategies to handle such multi-objective scenarios, but opportunities remain to further adapt optimization for the creative task of early building design, including increasing its interactivity, flexibility, accessibility, and ability to both support divergent brainstorming and enable focused performance improvement. In response, this dissertation proposes new approaches to parametric design space formulation, interactive optimization, and diversity-based design. These methods span in utility from early ideation, through global design exploration, to local exploration and optimization. The first presented technique uses data science methods to interrogate, transform, and, for specific cases, generate design variables for exploration. The second strategy involves interactive stepping through a design space using estimated gradient information, which offers designers more freedom compared to automated solvers during local exploration.
The third method addresses computational measurement of diversity within parametric design and demonstrates how such measurements can be integrated into creative design processes. These contributions are demonstrated on an integrated early design example and preliminarily validated using a design study that provides feedback on the habits and preferences of architects and engineers while engaging with data-driven tools. This study reveals that performance-enabled environments tend to improve simulated design objectives, while designers prefer more flexibility than traditional automated optimization approaches when given the choice. Together, these findings can stimulate further development in the integration of interactive approaches to multi-objective early building design. Key words: design space exploration, conceptual design, design tradeoffs, interactive design tools, structural design, sustainable design, multi-objective optimization, data science, surrogate modeling
by Nathan C. Brown.
Ph. D. in Architecture: Building Technology
Ph.D.inArchitecture:BuildingTechnology Massachusetts Institute of Technology, Department of Architecture
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10

Paik, Sangwook. "Multi-objective optimal design of steel trusses in unstructured design domains." Thesis, Texas A&M University, 2005. http://hdl.handle.net/1969.1/3124.

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Researchers have applied genetic algorithms (GAs) and other heuristic optimization methods to perform truss optimization in recent years. Although a substantial amount of research has been performed on the optimization of truss member sizes, nodal coordinates, and member connections, research that seeks to simultaneously optimize the topology, geometry, and member sizes of trusses is still uncommon. In addition, most of the previous research is focused on the problem domains that are limited to a structured domain, which is defined by a fixed number of nodes, members, load locations, and load magnitudes. The objective of this research is to develop a computational method that can design efficient roof truss systems. This method provides an engineer with a set of near-optimal trusses for a specific unstructured problem domain. The unstructured domain only prescribes the magnitude of loading and the support locations. No other structural information concerning the number or locations of nodes and the connectivity of members is defined. An implicit redundant representation (IRR) GA (Raich 1999) is used in this research to evolve a diverse set of near-optimal truss designs within the specified domain that have varying topology, geometry, and sizes. IRR GA allows a Pareto-optimal set to be identified within a single trial. These truss designs reflect the tradeoffs that occur between the multiple objectives optimized. Finally, the obtained Pareto-optimal curve will be used to provide design engineers with a range of highly fit conceptual designs from which they can select their final design. The quality of the designs obtained by the proposed multi-objective IRR GA method will be evaluated by comparing the trusses evolved with trusses that were optimized using local perturbation methods and by trusses designed by engineers using a trial and error approach. The results presented show that the method developed is very effective in simultaneously optimizing the topology, geometry, and size of trusses for multiple objectives.
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Книги з теми "Multi-objective Design"

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Liu, Aying. A multi-objective and multi-design evaluation procedure for environmental protection forestry. Portsmouth: University of Portsmouth, Department of Economics, 1997.

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Wang, Lihui, Amos H. C. Ng, and Kalyanmoy Deb, eds. Multi-objective Evolutionary Optimisation for Product Design and Manufacturing. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-652-8.

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Silvano, Cristina, William Fornaciari, and Eugenio Villar, eds. Multi-objective Design Space Exploration of Multiprocessor SoC Architectures. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8837-9.

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C, Ng Amos H., Deb Kalyanmoy, and SpringerLink (Online service), eds. Multi-objective Evolutionary Optimisation for Product Design and Manufacturing. London: Springer-Verlag London Limited, 2011.

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5

United States. National Aeronautics and Space Administration., ed. Multi-objective decision-making under uncertainty: Fuzzy logic methods. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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United States. National Aeronautics and Space Administration., ed. Multi-objective decision-making under uncertainty: Fuzzy logic methods. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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7

S, Rao S. Applications of fuzzy theories to multi-objective system optimization. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1991.

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8

Center, Lewis Research, and United States. National Aeronautics and Space Administration., eds. Multi objective controller design for linear systems via optimal interpolation. [Columbus, Ohio]: Ohio State University, 1996.

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Center, Lewis Research, and United States. National Aeronautics and Space Administration., eds. Multi objective controller design for linear systems via optimal interpolation. [Columbus, Ohio]: Ohio State University, 1996.

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10

Saravanos, D. A. Multi-objective shape and material optimization of composite structures including damping. [Washington, D.C.]: NASA, 1990.

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Частини книг з теми "Multi-objective Design"

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Han, Xu, and Jie Liu. "Micro Multi-objective Genetic Algorithm." In Numerical Simulation-based Design, 153–78. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-10-3090-1_9.

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Chen, Yi, and Yun Li. "Extra‐Numerical Multi‐Objective optimization." In Computational Intelligence Assisted Design, 115–23. Boca Raton, FL : CRC Press, Taylor & Francis Group, 2018. | "A science publishers book.": CRC Press, 2018. http://dx.doi.org/10.1201/9781315153179-8.

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Sun, Jian-Qiao, Fu-Rui Xiong, Oliver Schütze, and Carlos Hernández. "Multi-objective Optimal Control Design." In Cell Mapping Methods, 149–68. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0457-6_10.

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Sun, Jian-Qiao, Fu-Rui Xiong, Oliver Schütze, and Carlos Hernández. "Multi-objective Optimal Structure Design." In Cell Mapping Methods, 169–90. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0457-6_11.

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Sun, Jian-Qiao, Fu-Rui Xiong, Oliver Schütze, and Carlos Hernández. "Multi-objective Optimal Airfoil Design." In Cell Mapping Methods, 191–202. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0457-6_12.

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Han, Xu, and Jie Liu. "Introduction to Multi-objective Optimization Design." In Numerical Simulation-based Design, 141–51. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-10-3090-1_8.

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Parmee, Ian C. "Multi-objective Satisfaction and Optimisation." In Evolutionary and Adaptive Computing in Engineering Design, 177–203. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0273-1_10.

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Jerin Leno, I., S. Saravana Sankar, and S. G. Ponnambalam. "Multi Objective Integrated Layout Design Problem." In Swarm, Evolutionary, and Memetic Computing, 500–508. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35380-2_59.

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D’Errico, Fabrizio. "Multi-Objective Optimization in Engineering Design." In SpringerBriefs in Materials, 33–67. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13030-9_2.

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M’laouhi, Ibrahim, Najeh Ben Guedria, and Hichem Smaoui. "Multi-objective Discrete Rotor Design Optimization." In Condition Monitoring of Machinery in Non-Stationary Operations, 193–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28768-8_20.

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Тези доповідей конференцій з теми "Multi-objective Design"

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Liu, Yiwei, Yiping Liu, Jiahao Yang, Xinyi Zhang, Li Wang, and Xiangxiang Zeng. "Multi-Objective Molecular Design in Constrained Latent Space." In 2024 International Joint Conference on Neural Networks (IJCNN), 1–8. IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651509.

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Jian, Huang, and Wang Yihan. "Asset Optimization Scheme Design with Multi-Objective Optimization." In 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/iciteics61368.2024.10625611.

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Wen, Yi, Wei Ye, and Gang Yu. "A Hybrid Multi-objective Model for Multi-story Warehouse Design: A Case Study in Shenzhen." In CAADRIA 2024: Accelerated Design, 283–92. CAADRIA, 2024. http://dx.doi.org/10.52842/conf.caadria.2024.1.283.

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Zangl, H., and G. Steiner. "Optimal design of multi-objective multi-sensor systems." In Proceedings of the 2005 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement. IEEE, 2005. http://dx.doi.org/10.1109/amuem.2005.1594616.

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Yuan-Chang Chang, Li-Wei Kuo, and Jenq-Lang Wu. "Reliable multi-objective decentralized controller design." In 2010 International Conference on System Science and Engineering (ICSSE). IEEE, 2010. http://dx.doi.org/10.1109/icsse.2010.5551749.

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Wang, Wei, Xin-long Chang, You-hong Zhang, and Chun-wen Wang. "Composite Laminated Multi-Objective Optimization Design." In 2020 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA). IEEE, 2020. http://dx.doi.org/10.1109/aiea51086.2020.00134.

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Keough, Ian, and David Benjamin. "Multi-objective optimization in architectural design." In the 2010 Spring Simulation Multiconference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1878537.1878736.

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8

Rojas, José David, and Victor M. Alfaro. "Multi-objective design of industrial controllers." In 2017 IEEE 3rd Colombian Conference on Automatic Control (CCAC). IEEE, 2017. http://dx.doi.org/10.1109/ccac.2017.8320344.

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Kor, Jean, Xiang Chen, Zhizhong Sun, and Henry Hu. "Casting Design through Multi-objective Optimization." In 2009 Second International Conference on Future Information Technology and Management Engineering (FITME). IEEE, 2009. http://dx.doi.org/10.1109/fitme.2009.156.

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Poian, M., S. Poles, F. Bernasconi, E. Leroux, W. Steffe, and M. Zolesi. "Multi-objective optimization for antenna design." In 2008 IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS). IEEE, 2008. http://dx.doi.org/10.1109/comcas.2008.4562817.

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Звіти організацій з теми "Multi-objective Design"

1

Kuprowicz, Nicholas J. The Integrated Multi-Objective Multi-Disciplinary Jet Engine Design Optimization Program. Fort Belvoir, VA: Defense Technical Information Center, January 1999. http://dx.doi.org/10.21236/ada372032.

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2

Fernandez, Ruben, Hernando Lugo, and Georfe Dulikravich. Aerodynamic Shape Multi-Objective Optimization for SAE Aero Design Competition Aircraft. Florida International University, October 2021. http://dx.doi.org/10.25148/mmeurs.009778.

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Анотація:
The SAE Regular Class Aero Design Competition requires students to design a radio-controlled aircraft with limits to the aircraft power consumption, take-off distance, and wingspan, while maximizing the amount of payload it can carry. As a result, the aircraft should be designed subject to these simultaneous and contradicting objectives: 1) minimize the aerodynamic drag force, 2) minimize the aerodynamic pitching moment, and 3) maximize the aerodynamic lift force. In this study, we optimized the geometric design variables of a biplane configuration using 3D aerodynamic analysis using the ANSYS Fluent. Coefficients of lift, drag, and pitching moment were determined from the completed 3D CFD simulations. Extracted coefficients were used in modeFRONTIER multi-objective optimization software to find a set of non-dominated (Pareto-optimal or best trade-off) optimized 3D aircraft shapes from which the winner was selected based to the desired plane performance.
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3

Wenren, Yonghu, Joon Lim, Luke Allen, Robert Haehnel, and Ian Dettwiler. Helicopter rotor blade planform optimization using parametric design and multi-objective genetic algorithm. Engineer Research and Development Center (U.S.), December 2022. http://dx.doi.org/10.21079/11681/46261.

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In this paper, an automated framework is presented to perform helicopter rotor blade planform optimization. This framework contains three elements, Dakota, ParBlade, and RCAS. These elements are integrated into an environment control tool, Galaxy Simulation Builder, which is used to carry out the optimization. The main objective of this work is to conduct rotor performance design optimizations for forward flight and hover. The blade design variables manipulated by ParBlade are twist, sweep, and anhedral. The multi-objective genetic algorithm method is used in this study to search for the optimum blade design; the optimization objective is to minimize the rotor power required. Following design parameter substitution, ParBlade generates the modified blade shape and updates the rotor blade properties in the RCAS script before running RCAS. After the RCAS simulations are complete, the desired performance metrics (objectives and constraints) are extracted and returned to the Dakota optimizer. Demonstrative optimization case studies were conducted using a UH-60A main rotor as the base case. Rotor power in hover and forward flight, at advance ratio 𝜇𝜇 = 0.3, are used as objective functions. The results of this study show improvement in rotor power of 6.13% and 8.52% in hover and an advance ratio of 0.3, respectively. This configuration also yields greater reductions in rotor power for high advance ratios, e.g., 12.42% reduction at 𝜇𝜇 = 0.4.
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4

Dulikravich, George S., Igor N. Egorov, Vinod K. Sikka, and G. Muralidharan. Alloys-by-Design Strategies Using Stochastic Multi-Objective Optimization: Initial Formulation and Results. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada416083.

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5

Bau, Domenico. Recovery Act: Multi-Objective Optimization Approaches for the Design of Carbon Geological Sequestration Systems. Office of Scientific and Technical Information (OSTI), May 2013. http://dx.doi.org/10.2172/1097612.

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6

Barlow, Gregory J. Design of Autonomous Navigation Controllers for Unmanned Aerial Vehicles Using Multi-Objective Genetic Programming. Fort Belvoir, VA: Defense Technical Information Center, March 2004. http://dx.doi.org/10.21236/ada460111.

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7

Kobayashi, Marcelo H. (HBCU) Development and Application of a Biologically Inspired Methodology for the Optimized, Multi-Disciplinary and Multi-Objective Design of Air Vehicles. Fort Belvoir, VA: Defense Technical Information Center, May 2013. http://dx.doi.org/10.21236/ada584389.

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8

Choi, Yong-Joon, Mohammad M Mostafa Abdo, Yong-Joon Choi, Juan Luque Gutierrez, Jason Hou, Christoper Gosdin, and Jarrett Valeri. Pressurized-Water Reactor Core Design Demonstration with Genetic Algorithm Based Multi-Objective Plant Fuel Reload Optimization Platform. Office of Scientific and Technical Information (OSTI), September 2023. http://dx.doi.org/10.2172/2006453.

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9

Choi, Yong-Joon, Junyung Kim, Mohammad M Mostafa Abdo, Juan Luque Gutierrez, Jason Hou, Christoper Gosdin, and Jarrett Valeri. Pressurized-Water Reactor Core Design Demonstration with Genetic Algorithm Based Multi-Objective Plant Fuel Reload Optimization Platform. Office of Scientific and Technical Information (OSTI), September 2023. http://dx.doi.org/10.2172/2006437.

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10

Allen, Luke, Joon Lim, Robert Haehnel, and Ian Dettwiller. Helicopter rotor blade multiple-section optimization with performance. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41031.

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Анотація:
This paper presents advancements in a surrogate-based, rotor blade design optimization framework for improved helicopter performance. The framework builds on previous successes by allowing multiple airfoil sections to designed simultaneously to minimize required rotor power in multiple flight conditions. Rotor power in hover and forward flight, at advance ratio 𝜇 = 0.3, are used as objective functions in a multi-objective genetic algorithm. The framework is constructed using Galaxy Simulation Builder with optimization provided through integration with Dakota. Three independent airfoil sections are morphed using ParFoil and aerodynamic coefficients for the updated airfoil shapes (i.e., lift, drag, moment) are calculated using linear interpolation from a database generated using C81Gen/ARC2D. Final rotor performance is then calculated using RCAS. Several demonstrative optimization case studies were conducted using the UH-60A main rotor. The degrees of freedom for this case are limited to the airfoil camber, camber crest position, thickness, and thickness crest position for each of the sections. The results of the three-segment case study show improvements in rotor power of 4.3% and 0.8% in forward flight and hover, respectively. This configuration also yields greater reductions in rotor power for high advance ratios, e.g., 6.0% reduction at 𝜇 = 0.35, and 8.8% reduction at 𝜇 = 0.4.
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