Zeitschriftenartikel: „Genetic algorithms. Multidisciplinary design optimization“

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Farshadnia, Reza. „Genetic Algorithms in Optimization and Computer Aided Design“. Journal of Applied Sciences 1, Nr. 3 (15.06.2001): 289–94. http://dx.doi.org/10.3923/jas.2001.289.294.

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KURAPATI, A., und S. AZARM. „IMMUNE NETWORK SIMULATION WITH MULTIOBJECTIVE GENETIC ALGORITHMS FOR MULTIDISCIPLINARY DESIGN OPTIMIZATION“. Engineering Optimization 33, Nr. 2 (Dezember 2000): 245–60. http://dx.doi.org/10.1080/03052150008940919.

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Zhang, Jing. „Multidisciplinary Fuzzy Optimization Design of Planar Linkage Mechanism“. Advanced Materials Research 211-212 (Februar 2011): 1016–20. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.1016.

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Based on the fuzzy theory and an idea of multidisciplinary design optimization, a fuzzy optimization model of multidisciplinary design is established. Fuzzy constraints are changed by a fuzzy comprehensive evaluation and an amplification-coefficient method. Using collaborative optimization and genetic algorithms, the multidisciplinary fuzzy optimum of planar linkage mechanism is achieved and a four-bar mechanism is given as an example. Two disciplines are involved in the design optimization of mechanism, i.e., kinematics and control. The numerical results indicate that the optimized mechanism not only satisfies the mechanism and control constraints, but also synthesizes approximate optimum value, and lays a foundation for the solution of more complex mechanical system.

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Huang, Jingjing, Longxi Zheng und Qing Mei. „Design and Optimization Method of a Two-Disk Rotor System“. International Journal of Turbo & Jet-Engines 33, Nr. 1 (01.01.2016): 1–8. http://dx.doi.org/10.1515/tjj-2014-0033.

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AbstractAn integrated analytical method based on multidisciplinary optimization software Isight and general finite element software ANSYS was proposed in this paper. Firstly, a two-disk rotor system was established and the mode, humorous response and transient response at acceleration condition were analyzed with ANSYS. The dynamic characteristics of the two-disk rotor system were achieved. On this basis, the two-disk rotor model was integrated to the multidisciplinary design optimization software Isight. According to the design of experiment (DOE) and the dynamic characteristics, the optimization variables, optimization objectives and constraints were confirmed. After that, the multi-objective design optimization of the transient process was carried out with three different global optimization algorithms including Evolutionary Optimization Algorithm, Multi-Island Genetic Algorithm and Pointer Automatic Optimizer. The optimum position of the two-disk rotor system was obtained at the specified constraints. Meanwhile, the accuracy and calculation numbers of different optimization algorithms were compared. The optimization results indicated that the rotor vibration reached the minimum value and the design efficiency and quality were improved by the multidisciplinary design optimization in the case of meeting the design requirements, which provided the reference to improve the design efficiency and reliability of the aero-engine rotor.

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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, Nr. 1 (22.01.2020): 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 Patran Command Language. The structural analysis is done by Nastran. Aerodynamic pressure is transferred to finite element analysis model using Volume Spline Interpolation. In terms of optimization algorithms, Response Surface Method, Genetic Algorithm, and Simulated Annealing are utilized to get global optimum. The optimization objective functions are minimizing weight and maximizing lift/drag. The design variables are aspect ratio, tapper ratio, sweepback angle. The optimization results demonstrate successful and desiable construction of MDO framework. Keywords: Multidisciplinary Design Optimization; fluid/structure analyses; global optimum; Genetic Algorithm; Response Surface Method.

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Liu, Yu, Hong Yun Yang und Guo Chao Wang. „Genetic Algorithm Based Multidisciplinary Design Optimization of MEMS Accelerometer“. Applied Mechanics and Materials 101-102 (September 2011): 530–33. http://dx.doi.org/10.4028/www.scientific.net/amm.101-102.530.

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Multidisciplinary design optimization of a biaxial capacitive micro-accelerometer with the crab-leg flexural suspension is discussed. Considering the influence of microstructure design, fabrication process and detection circuit, as well as the constraints of fabrication limitations, damp design and adhesion factor, a multidisciplinary optimization model is developed by global criterion method. Furthermore, genetic algorithm is applied to obtain the multi-objective optimum of the proposed multidisciplinary design model.

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Adami, Amirhossein, Mahda Mortazavi und Mehran Nosratollahi. „Multi-modular design optimization and multidisciplinary design optimization“. International Journal of Intelligent Unmanned Systems 3, Nr. 2/3 (11.05.2015): 156–70. http://dx.doi.org/10.1108/ijius-01-2015-0001.

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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.

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Xiang, Xianbo, Caoyang Yu, He Xu und Stuart X. Zhu. „Optimization of Heterogeneous Container Loading Problem with Adaptive Genetic Algorithm“. Complexity 2018 (01.11.2018): 1–12. http://dx.doi.org/10.1155/2018/2024184.

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This paper studies an optimized container loading problem with the goal of maximizing the 3D space utilization. Based on the characteristics of the mathematical loading model, we develop a dedicated placement heuristic integrated with a novel dynamic space division method, which enables the design of the adaptive genetic algorithm in order to maximize the loading space utilization. We use both weakly and strongly heterogeneous loading data to test the proposed algorithm. By choosing 15 classic sets of test data given by Loh and Nee as weakly heterogeneous data, the average space utilization of our algorithm reaching 70.62% outperforms those of 13 algorithms from the related literature. Taking a set of test data given by George and Robinson as strongly heterogeneous data, the space utilization in this paper can be improved by 4.42% in comparison with their heuristic algorithm.

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Villanueva, Fredy Marcell, He Linshu und Xu Dajun. „Kick Solid Rocket Motor Multidisciplinary Design Optimization Using Genetic Algorithm“. Journal of Aerospace Technology and Management 5, Nr. 3 (27.08.2013): 293–304. http://dx.doi.org/10.5028/jatm.v5i3.225.

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Nosratollahi, M., M. Mortazavi, A. Adami und M. Hosseini. „Multidisciplinary design optimization of a reentry vehicle using genetic algorithm“. Aircraft Engineering and Aerospace Technology 82, Nr. 3 (18.05.2010): 194–203. http://dx.doi.org/10.1108/00022661011075928.

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Liu, Yu, Zhi Yu Wen, Li Chen und Hong Yun Yang. „Multidisciplinary Design Optimization of the 2-D Microaccelerometer“. Key Engineering Materials 483 (Juni 2011): 647–52. http://dx.doi.org/10.4028/www.scientific.net/kem.483.647.

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Multidisciplinary design optimization of a 2-D microaccelerometer is discussed. Considering the influence of microstructure design, fabrication process and detection circuit, as well as the constraints of fabrication limitations, damp design and adhesion factor, a multidisciplinary optimization model is developed by global criterion method, where the sensitivity, resolution, resonant frequency and damping ratio are selected as objective functions. Furthermore, the multi-objective optimum of the model is obtained by genetic algorithm.

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He, Y., und J. McPhee. „A design methodology for mechatronic vehicles: application of multidisciplinary optimization, multibody dynamics and genetic algorithms“. Vehicle System Dynamics 43, Nr. 10 (Oktober 2005): 697–733. http://dx.doi.org/10.1080/00423110500151077.

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Huang, Yi Qi, Gan Wei Cai, Yu Jiang und Zhao Yu Luo. „Method of Genetic Algorithm Optimization Design of Permanent Magnet Retarder“. Applied Mechanics and Materials 215-216 (November 2012): 362–67. http://dx.doi.org/10.4028/www.scientific.net/amm.215-216.362.

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This paper introduced the method of multidisciplinary design optimization based on genetic algorithm. The basic structure and new auxiliary braking mechanism of permanent magnet retarder was analyzed. The influences of magnetic field parameters, structural design parameters, rotor parameters and permanent magnet temperature parameters on the behaviors performance of the permanent magnet retarder were discussed. The conceptual model of permanent magnet retarder was developed to maximize the brake torque of the permanent magnet retarder. The design variables included the radial width and the axis length of permanent magnet, the number of permanent magnet, the radius of rotor, the thickness of rotor, and the air gas. The constraint conditions included permitting temperature of rotor, saturation magnetic flux density of magnet material, and relation of structural geometry. The results of design optimization variables were obtained by applying genetic algorithm. The multidisciplinary design optimization in this paper is an effective method for the global design optimization of the permanent magnet retarder.

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Mahiddini, Brahim, Taha Chettibi, Khaled Benfriha und Amézian Aoussat. „Multidisciplinary design optimization of a gear train transmission“. Concurrent Engineering 27, Nr. 3 (18.07.2019): 268–81. http://dx.doi.org/10.1177/1063293x19862605.

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This article presents a method for multidisciplinary design optimization of a one-stage gear train transmission for an industrial application. The formulation and implementation that enable the integrated design of the gearbox elements (gears, shafts, and bearings) are detailed. The analytical formulation problem is based on four disciplines: product reliability, customer preference, product cost, and structure. The proposed integrated design process takes into account constraints imposed by quality standards. The optimization of the gear train transmission is performed according to a multidisciplinary feasible architecture and uses a population-based evolutionary algorithm (non-dominated sorting genetic algorithm II) to generate Pareto-optimal fronts. Finally, a detailed case study is presented to illustrate the effectiveness of the proposed approach.

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Suwa, Tohru, und Hamid Hadim. „Multidisciplinary Electronic Package Design and Optimization Methodology Based on Genetic Algorithm“. IEEE Transactions on Advanced Packaging 30, Nr. 3 (August 2007): 402–10. http://dx.doi.org/10.1109/tadvp.2007.898506.

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Roshanian, Jafar, Masoud Ebrahimi, Ehsan Taheri und Ali Asghar Bataleblu. „Multidisciplinary design optimization of space transportation control system using genetic algorithm“. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 228, Nr. 4 (26.02.2013): 518–29. http://dx.doi.org/10.1177/0954410013475573.

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Liu, Jing, Qixing Chen und Xiaoying Tian. „Illustration Design Model with Clustering Optimization Genetic Algorithm“. Complexity 2021 (31.01.2021): 1–10. http://dx.doi.org/10.1155/2021/6668929.

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For the application of the standard genetic algorithm in illustration art design, there are still problems such as low search efficiency and high complexity. This paper proposes an illustration art design model based on operator and clustering optimization genetic algorithm. First, during the operation of the genetic algorithm, the values of the crossover probability and the mutation probability are dynamically adjusted according to the characteristics of the population to improve the search efficiency of the algorithm, then the k-medoids algorithm is introduced to optimize the clustering of the genetic algorithm, and a cost function is used to carry out and evaluate the quality of clustering to optimize the complexity of the original algorithm. In addition, a multiobjective optimization genetic algorithm with complex constraints based on group classification is proposed. This algorithm focuses on the problem of group diversity and uses k-means cluster analysis operation to solve the problem of group diversity. The algorithm divides the entire group into four subgroups and assigns appropriate fitness values to reflect the optimal preservation strategy. A large number of computer simulation calculations show that the algorithm can obtain a widely distributed and uniform Pareto optimal solution, the evolution speed is fast, usually only a few iterations can achieve a good optimization effect, and finally the improved genetic algorithm is used to design the random illustration art. The example simulation shows that the improved algorithm proposed in this paper can achieve higher artistic and innovative illustration art design.

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Zhang, Jing, und Bai Lin Li. „Study on Multidisciplinary Design Optimization of 3-RRS Parallel Robot“. Applied Mechanics and Materials 214 (November 2012): 919–23. http://dx.doi.org/10.4028/www.scientific.net/amm.214.919.

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The paper aims to apply the idea of multidisciplinary design optimization to the design of robot system. The main idea of collaborative optimization is introduced. The collaborative optimization frame of 3-RRS parallel robot is analyzed. With the method of genetic algorithm and Sequential Quadratic Programming, the investigation is made on the executing collaborative optimization of working stroke, driving performance and hydraulic components. The numerical results indicate that the collaborative optimization can be successfully applied to dealing with the complex robot system, and lay a foundation to solve more complex mechanical system.

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Liu, Jian, Gaoyuan Yu, Yao Li, Hongmin Wang und Wensheng Xiao. „Multidisciplinary Design Optimization of Crankshaft Structure Based on Cooptimization and Multi-Island Genetic Algorithm“. Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/9596089.

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The feasibility design method with multidisciplinary and multiobjective optimization is applied in the research of lightweight design and NVH performances of crankshaft in high-power marine reciprocating compressor. Opt-LHD is explored to obtain the experimental scheme and perform data sampling. The elliptical basis function neural network (EBFNN) model considering modal frequency, static strength, torsional vibration angular displacement, and lightweight design of crankshaft is built. Deterministic optimization and reliability optimization for lightweight design of crankshaft are operated separately. Multi-island genetic algorithm (MIGA) combined with multidisciplinary cooptimization method is used to carry out the multiobjective optimization of crankshaft structure. Pareto optimal set is obtained. Optimization results demonstrate that the reliability optimization which considers the uncertainties of production process can ensure product stability compared with deterministic optimization. The coupling and decoupling of structure mechanical properties, NVH, and lightweight design are considered during the multiobjective optimization of crankshaft structure. Designers can choose the optimization results according to their demands, which means the production development cycle and the costs can be significantly reduced.

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Wang, Ming Zhi, Gang Chen und Zhi Zhou. „Application of Multidisciplinary Design Optimization in the Casting Process Optimization“. Advanced Materials Research 936 (Juni 2014): 1845–50. http://dx.doi.org/10.4028/www.scientific.net/amr.936.1845.

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Casting process optimization usually includes modifying the mold structure, regulating the pouring temperature, changing pouring time, etc., which involves the mold structure optimization, but also involves optimization of temperature field. In order to get a better process conditions, it will generally make multiple simulation test under the different processes conditions. Many parameters should be set in each simulation test. In order to make casting engineers get liberation from a lot of repetitive work, multidisciplinary design optimization technology is firstly brought into the field of casting, successfully achieved ProCAST integrated into iSIGHT. The method of sample points collected is used in Optimal Latin Hypercube Design, and the building of approximation models is based on Kriging Model. On this basis, the optimization of the objective function is applied to Multi-Island Genetic Algorithm to obtain a global optimal solution. Compared to the verification result of the corresponding simulation, the relative error of the global optimal solution is 0.47%. The result of optimization is ensure average shrinkage rate of casting a smaller value when technological yield of the casting from 54.6% of the existing production case up to 61.1%.

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Huber, Martin, und Horst Baier. „Qualitative Knowledge and Manufacturing Considerations in Multidisciplinary Structural Optimization of Hybrid Material Structures“. Advanced Materials Research 10 (Februar 2006): 143–52. http://dx.doi.org/10.4028/www.scientific.net/amr.10.143.

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An optimization approach is derived from typical design problems of hybrid material structures, which provides the engineer with optimal designs. Complex geometries, different materials and manufacturing aspects are handled as design parameters using a genetic algorithm. To take qualitative information into account, fuzzy rule based systems are utilized in order to consider all relevant aspects in the optimization problem. This paper shows results for optimization tasks on component and structural level.

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Deng, Jian, Guangming Zhou und Yu Qiao. „Multidisciplinary design optimization of sandwich-structured radomes“. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, Nr. 1 (13.02.2018): 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 genetic algorithm, transmission coefficient and boresight error are identified as the objectives. Lateral deformation, material failure, and structural stability are included in mechanical analysis. Three-dimensional ray-tracing technique and physical optics based aperture integration method are employed to address interactions between the antenna and radome. Tsai–Wu and maximum stress criteria are used to predict material failure. Structural stability is analyzed using the linear perturbation of stiffness matrices. The applicability of the electromagnetic model is validated using examples of a hemispheric air and single-layered radome. A numerical experiment is conducted to investigate the utility and feasibility of the multidisciplinary design optimization model. Results show that the optimal section profile brings about considerable improvement in transmission coefficient and boresight error. Mechanical constrains are reasonably subjected to the preset limits. Hence, the proposed multidisciplinary design optimization model is an effective and feasible alternative in the environment of radome design.

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Villanueva, Fredy M., Lin Shu He und Da Jun Xu. „Multidisciplinary Design Optimization of Small Canister-Launched Space Launch Vehicle Using Genetic Algorithm“. Applied Mechanics and Materials 302 (Februar 2013): 583–88. http://dx.doi.org/10.4028/www.scientific.net/amm.302.583.

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A multidisciplinary design optimization approach of a three stage solid propellant canister-launched launch vehicle is considered. A genetic algorithm (GA) optimization method has been used. The optimized launch vehicle (LV) is capable of delivering a microsatellite of 60 kg. to a low earth orbit (LEO) of 600 km. altitude. The LV design variables and the trajectory profile variables were optimized simultaneously, while a depleted shutdown condition was considered for every stage, avoiding the necessity of a thrust termination device, resulting in reduced gross launch mass of the LV. The results show that the proposed optimization approach was able to find the convergence of the optimal solution with highly acceptable value for conceptual design phase.

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Chen, Jianjiang, Yifang Zhong, Renbin Xiao und Jianxun Sun. „The research of the decomposition‐coordination method of multidisciplinary collaboration design optimization“. Engineering Computations 22, Nr. 3 (01.04.2005): 274–85. http://dx.doi.org/10.1108/02644400510588085.

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PurposeTo obtain the global optimum of large‐scale complex engineering systems, the paper proposes a decomposition‐coordination method of multidisciplinary design optimization (MDO).Design/methodology/approachA rational decomposition approach based on artificial neural network (ANN) and genetic algorithms is proposed for partitioning the complex design problem into smaller, more tractable subsystems. Once the problem is decomposed into subsystems, each subsystem may be solved in parallel provided that there is some mechanism to coordinate the solutions in the different subsystems. So the response surface approximation model based on the ANN as a coordination method is described and a MDO framework is presented.FindingsThe proposed method was implemented in the design of a tactical missile. Numerical results show the effectiveness of the decomposition‐coordination method, as indicated by both better performance and lower computational requirements.Originality/valueThis paper adopts a novel MDO method to solve complex engineering problem and offers a potential and efficient MDO framework to researchers.

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Zeeshan, Qasim, Amer Farhan Rafique, Ali Kamran, Muhammad Ishaq Khan und Abdul Waheed. „Multidisciplinary design and optimization of expendable launch vehicle for microsatellite missions“. Aircraft Engineering and Aerospace Technology 93, Nr. 4 (28.05.2021): 660–70. http://dx.doi.org/10.1108/aeat-01-2021-0004.

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Purpose The capability to predict and evaluate various configurations’ performance during the conceptual design phase using multidisciplinary design analysis and optimization can significantly increase the preliminary design process’s efficiency and reduce design and development costs. This research paper aims to perform multidisciplinary design and optimization for an expendable microsatellite launch vehicle (MSLV) comprising three solid-propellant stages, capable of delivering micro-payloads in the low earth orbit. The methodology’s primary purpose is to increase the conceptual and preliminary design process’s efficiency by reducing both the design and development costs. Design/methodology/approach Multidiscipline feasible architecture is applied for the multidisciplinary design and optimization of an expendable MSLV at the conceptual level to accommodate interdisciplinary interactions during the optimization process. The multidisciplinary design and optimization framework developed and implemented in this research effort encompasses coupled analysis disciplines of vehicle geometry, mass calculations, aerodynamics, propulsion and trajectory. Nineteen design variables were selected to optimize expendable MSLV to launch a 100 kg satellite at an altitude of 600 km in the low earth orbit. Modern heuristic optimization methods such as genetic algorithm (GA), particle swarm optimization (PSO) and SA are applied and compared to obtain the optimal configurations. The initial population is created by passing the upper and lower bounds of design variables to the optimizer. The optimizer then searches for the best possible combination of design variables to obtain the objective function while satisfying the constraints. Findings All of the applied heuristic methods were able to optimize the design problem. Optimized design variables from these methods lie within the lower and upper bounds. This research successfully achieves the desired altitude and final injection velocity while satisfying all the constraints. In this research effort, multiple runs of heuristic algorithms reduce the fundamental stochastic error. Research limitations/implications The use of multiple heuristics optimization methods such as GA, PSO and SA in the conceptual design phase owing to the exclusivity of their search approach provides a unique opportunity for exploration of the feasible design space and helps in obtaining alternative configurations capable of meeting the mission objectives, which is not possible when using any of the single optimization algorithm. Practical implications The optimized configurations can be further used as baseline configurations in the microsatellite launch missions’ conceptual and preliminary design phases. Originality/value Satellite launch vehicle design and optimization is a complex multidisciplinary problem, and it is dealt with effectively in the multidisciplinary design and optimization domain. It integrates several interlinked disciplines and gives the optimum result that satisfies these disciplines’ requirements. This research effort provides the multidisciplinary design and optimization-based simulation framework to predict and evaluate various expendable satellite launch vehicle configurations’ performance. This framework significantly increases the conceptual and preliminary design process’s efficiency by reducing design and development costs.

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Deng, Qinghua, Shuai Shao, Lei Fu, Haifeng Luan und Zhenping Feng. „An Integrated Design and Optimization Approach for Radial Inflow Turbines—Part II: Multidisciplinary Optimization Design“. Applied Sciences 8, Nr. 11 (23.10.2018): 2030. http://dx.doi.org/10.3390/app8112030.

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This paper proposes an integrated design and optimization approach for radial inflow turbines consisting of an automated preliminary design module and a flexible three-dimensional multidisciplinary optimization module. The latter was constructed by an evolution algorithm, a genetic algorithm-assisted self-learning artificial neural network and a dynamic sampling database. The 3-D multidisciplinary optimization approach was validated by the original T-100 turbine and the T-100re turbine obtained from the automated preliminary design approach, for maximizing the total-to-static efficiency and minimizing the rotor weight while keeping the mass flow rate constant and stress limitation satisfied. The validation results indicate that the total-to-static efficiency is 89.6%, increased by 1.3%, and the rotor weight is reduced by 0.14 kg (14.6%) based on the T-100re turbine, while the efficiency is 88.2%, increased by 2.2% and the weight is reduced by 0.49 kg (37.4%) based on the original T-100 turbine. Moreover, the T-100re turbine shows better performance at the preliminary design stage and conserves this advantage to the end, though both the aerodynamic performance of the T-100 and the T-100re turbine are improved after 3-D optimization. At the same time, it is implied that the preliminary design plays an essential role in the radial inflow turbine design process, and it is hard for only 3-D optimization to get a further performance improvement.

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Adami, Amirhossein, Mahdi Mortazavi, Mehran Nosratollahi, Mohammadreza Taheri und 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 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).

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Su, Yi, Fa-Yin Wang und Jian-Yu Zhao. „Improved Multidisciplinary Design Optimization Based on Genetic Algorithm and Artificial Neural Networks and Its Application“. Journal of Computational and Theoretical Nanoscience 13, Nr. 10 (01.10.2016): 6501–8. http://dx.doi.org/10.1166/jctn.2016.5593.

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Multidisciplinary Design Optimization (MDO) is an algorithm widely used in the engineering field currently. However, traditional MDO often leads to the failure of convergence or local optimum problems caused by convergence. In such cases, a multidisciplinary design optimization based on genetic algorithm (GA) and artificial neural networks (ANN) (GA-ANN-MDO) is presented in the paper. Under the thought of parallel distribution of traditional MDO, the real sub-disciplinary model is replaced by a highly precise ANN model dependent on the Latin Hypercube experimental design method in the GA-ANN-MDO, so as to reduce the computational cost and smooth the value noise. The GA optimization system level is applied to decline the possibility of partial solution involved in the optimization. As shown from the optimization results of two classic mathematical examples, GA-ANN-MDO is presented good robustness, which could quickly and effectively converge to the global optimal solution. In addition, a project example was employed finally to verify the feasibility of GA-ANN-MDO in the engineering.

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Wang, Ping, und Guangqiang Wu. „Multidisciplinary design optimization of vehicle instrument panel based on multi-objective genetic algorithm“. Chinese Journal of Mechanical Engineering 26, Nr. 2 (März 2013): 304–12. http://dx.doi.org/10.3901/cjme.2013.02.304.

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Gunawan, S., A. Farhang-Mehr und S. Azarm. „On Maximizing Solution Diversity in a Multiobjective Multidisciplinary Genetic Algorithm for Design Optimization“. Mechanics Based Design of Structures and Machines 32, Nr. 4 (31.12.2004): 491–514. http://dx.doi.org/10.1081/sme-200034164.

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WANG, Ping. „Multidisciplinary Design Optimization of Vehicle Body Structure Based on Collaborative Optimization and Multi-objective Genetic Algorithm“. Journal of Mechanical Engineering 47, Nr. 02 (2011): 102. http://dx.doi.org/10.3901/jme.2011.02.102.

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Jafarsalehi, A., HR Fazeley und M. Mirshams. „Spacecraft mission design optimization under uncertainty“. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230, Nr. 16 (08.08.2016): 2872–87. http://dx.doi.org/10.1177/0954406215603416.

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The design of space systems is a complex and multidisciplinary process. In this study, two deterministic and nondeterministic approaches are applied to the system design optimization of a spacecraft which is actually a small satellite in low Earth orbit with remote sensing mission. These approaches were then evaluated and compared. Different disciplines such as mission analysis, payload, electrical power supply, mass model, and launch manifest were properly combined for further use. Furthermore, genetic algorithm and sequential quadratic programming were employed as the system-level and local-level optimizers. The main optimization objective of this study is to minimize the resolution of the satellite imaging payload while there are several constraints. A probabilistic analysis was performed to compare the results of the deterministic and nondeterministic approaches. Analysis of the results showed that the deterministic approaches may lead to an unreliable design (because of leaving little or no room for uncertainties), while using the reliability-based multidisciplinary design optimization architecture, all probabilistic constraints were satisfied.

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Altus, Stephen S., Ilan M. Kroo und Peter J. Gage. „A Genetic Algorithm for Scheduling and Decomposition of Multidisciplinary Design Problems“. Journal of Mechanical Design 118, Nr. 4 (01.12.1996): 486–89. http://dx.doi.org/10.1115/1.2826916.

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Complex engineering studies typically involve hundreds of analysis routines and thousands of variables. The sequence of operations used to evaluate a design strongly affects the speed of each analysis cycle. This influence is particularly important when numerical optimization is used, because convergence generally requires many iterations. Moreover, it is common for disciplinary teams to work simultaneously on different aspects of a complex design. This practice requires decomposition of the analysis into subtasks, and the efficiency of the design process critically depends on the quality of the decomposition achieved. This paper describes the development of software to plan multidisciplinary design studies. A genetic algorithm is used, both to arrange analysis subroutines for efficient execution, and to decompose the task into subproblems. The new planning tool is compared with an existing heuristic method. It produces superior results when the same merit function is used, and it can readily address a wider range of planning objectives.

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Chagraoui, Hamda, und Mohamed Soula. „Multidisciplinary design optimization of stiffened panels using collaborative optimization and artificial neural network“. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, Nr. 20 (08.11.2017): 3595–611. http://dx.doi.org/10.1177/0954406217740164.

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A new method for solving the multidisciplinary design optimization problems with a minimal computational effort is presented. The proposed methodology is based on the combination of artificial neural network model and Improved Multi-Objective Collaborative Optimization. In the artificial neural network–Improved Multi-Objective Collaborative Optimization scheme, the back-propagation algorithm is used for training the artificial neural network metamodel and the Non-dominated Sorting Genetic Algorithm-II is used to search a Pareto optimality set for the objective functions of stiffened panels. The artificial neural network–Improved Multi-Objective Collaborative Optimization algorithm aims firstly to decompose the global optimization problem hierarchically into optimization design problem at system level and several sub-problems at sub-system level and secondly to replace each optimization problem at the system and subsystem levels by artificial neural network model to limit the computational cost. To highlight the efficiency and effectiveness of the proposed artificial neural network–Improved Multi-Objective Collaborative Optimization method, mathematical and engineering examples are presented. Results obtained from the application of the artificial neural network–Improved Multi-Objective Collaborative Optimization approach to an optimization problem of a stiffened panel are compared with those obtained by traditional optimization without using prediction tools. The new method (artificial neural network–Improved Multi-Objective Collaborative Optimization) was proven to be superior to traditional optimization. These results have confirmed the efficiency and effectiveness of the artificial neural network–Improved Multi-Objective Collaborative Optimization method. In addition, it converges at faster rate than traditional optimization. The traditional optimization method converges within 7918 s, while artificial neural network–Improved Multi-Objective Collaborative Optimization requires only 42 s, clearly, the artificial neural network–Improved Multi-Objective Collaborative Optimization method is much more efficient.

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Liu, Yongjun, Jinwei Fan und Donghui Mu. „Reliability allocation method based on multidisciplinary design optimization for electromechanical equipment“. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 229, Nr. 14 (18.11.2014): 2573–85. http://dx.doi.org/10.1177/0954406214560597.

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Reliability design is one important link to assure the product’s quality. Reliability allocation is the main task of reliability design, which is influenced by cost, manufacturing consistency, and other factors. Reliability, cost, and manufacturing consistency are coupled and influenced by each other. To find the global optimal reliability indices, one modeling and solving method of reliability allocation based on multidisciplinary design optimization was proposed in the paper. The influence factors of reliability allocation were analyzed and calculated or fuzzy processed. The reliability allocation optimization models were created, and the models were decomposed by the calculation frame of collaborative optimization. The genetic algorithm of the system-level optimization and the sequential quadratic programming algorithm of the disciplinary-level optimization were adopted. As one typical electromechanical equipment, CNC cylindrical grinder was allocated of reliability by the method proposed in the paper. The results showed that the cost can be dropped 3.45%, 4.76%, 4.72%, and 8.45% compared by equal allocation method, fuzzy allocation method, AGREE allocation method, and actual cost, respectively. The reliability allocation method proposed in the article can be used in design of electromechanical equipment to reduce costs.

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Bo, Ji Kang. „Multidisciplinary Design Optimization of a Hydraulic Poppet Valve Considering Fluid-Solid Coupling“. Advanced Materials Research 308-310 (August 2011): 559–62. http://dx.doi.org/10.4028/www.scientific.net/amr.308-310.559.

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Hydraulic poppet valve often works in fluid-solid coupling state. It is necessary to comprehensively consider the multidisciplinary coupling effects in the design and optimization of poppet valves. This paper presents the fluid-solid coupling model for the optimization of a poppet valve, and the actual flow performance of the poppet valve is analyzed by numerical method. By coordinating the conflicting requirements at the system level, the MDO model of the poppet valve is built. The Multiple-island Genetic Algorithm is employed to solve the optimization problem, and an optimum design of the poppet valve is obtained. The optimization results show that the optimum poppet valve has a better performance on the flow characteristics.

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Grendysa, Wojciech. „Multidisciplinary wing design of a light long endurance UAV“. Aircraft Engineering and Aerospace Technology 91, Nr. 6 (10.06.2019): 905–14. http://dx.doi.org/10.1108/aeat-09-2018-0256.

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Purpose The purpose of this paper is finding the optimal geometric parameters and developing of a method for optimizing a light unmanned aerial vehicle (UAV) wing, maximizing, at the same time, its endurance with the assumed parameters of aircraft mission. Design/methodology/approach The research is based on the experience gained by the author’s contribution to the project of building medium-altitude, long-endurance class, light UAV called “Samonit”. The author was responsible for the structure design, wind tunnel tests and flight tests of the “Samonit” aircraft. Based on the experience, the author was able to develop an optimization process considering various disciplines involved in the whole aircraft design topics such as aerodynamics, flight mechanics, structural stiffness and weight, aircraft stability and maneuverability. The presented methodology has a multidisciplinary nature, as in the process of optimization both aerodynamic aspects and the influence of wing geometric parameters on the wing structure and weight and the aircraft payload were taken into account. The optimal wing configuration was obtained using the genetic algorithms. Findings As a result, a set of wing geometrical parameters has been obtained that allowed for achieving twice as long endurance as compared with the initial one. Practical implications Using the methodology presented in the paper, an aircraft designer can easily find the optimum wing configuration of a designed aircraft, satisfying the mission requirements in a best way. Originality/value An original procedure has been developed, based on the actual design, wind tunnel tests and numerical calculations of “Samonit” aircraft, enabling the determination of optimum wing configuration for a small unmanned aircraft.

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Sen-chun, Miao, Shi Zhi-xiao, Wang Xiao-hui, Shi Feng-xia und Shi Guang-tai. „Impeller meridional plane optimization of pump as turbine“. Science Progress 103, Nr. 1 (16.09.2019): 003685041987654. http://dx.doi.org/10.1177/0036850419876542.

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How to improve efficiency is still a very active research point for pump as turbine. This article comes up with a method for optimal design of pump as turbine impeller meridional plane. It included the parameterized control impeller meridional plane, the computational fluid dynamics technique, the optimized Latin hypercube sampling experimental design, the back propagation neural network optimized by genetic algorithm and genetic algorithm. Concretely, the impeller meridional plane was parameterized by the Pro/E software, the optimized Latin hypercube sampling was used to obtain the test sample points for back propagation neural network optimized by genetic algorithm, and the model corresponding to each sample point was calculated to obtain the performance values by the computational fluid dynamics techniques. Then, back propagation neural network learning and training are carried out by combining sample points and corresponding model performance values. Last but not least, back propagation neural network optimized by genetic algorithm and genetic algorithm were combined to deal with the optimization problem of impeller meridional plane. According to the aforementioned optimization design method, impeller meridional plane of the pump as turbine was optimized. The result manifests that the optimized pump as turbine energy-conversion efficiency was improved by 2.28% at the optimum operating condition, at the same time meet the pressure head constraint, namely the head difference between initial and optimized model is under the set numeric value. This demonstrates that the optimization method proposed in this article to optimize the impeller meridional plane is practicable.

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Nikoui, Hamid Reza. „Digital Circuit Design using Chaotic Particle Swarm Optimization Assisted by Genetic Algorithm“. Indian Journal of Science and Technology 6, Nr. 9 (20.09.2013): 1–7. http://dx.doi.org/10.17485/ijst/2013/v6i9.4.

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Yao, Xiling, Seung Ki Moon und Guijun Bi. „Multidisciplinary design optimization to identify additive manufacturing resources in customized product development“. Journal of Computational Design and Engineering 4, Nr. 2 (26.10.2016): 131–42. http://dx.doi.org/10.1016/j.jcde.2016.10.001.

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Abstract Additive manufacturing (AM) techniques are ideal for producing customized products due to their high design flexibility. Despite the previous studies on specific additive manufactured customized products such as biomedical implants and prostheses, the simultaneous optimization of components, materials, AM processes, and dimensions remains a challenge. Multidisciplinary design optimization (MDO) is a research area of solving complex design problems involving multiple disciplines which usually interact with each other. The objective of this research is to formulate and solve an MDO problem in the development of additive manufactured products customized for various customers in different market segments. Three disciplines, i.e. the customer preference modeling, AM production costing, and structural mechanics are incorporated in the MDO problem. The optimal selections of components, materials, AM processes, and dimensional parameters are searched with the objectives to maximize the functionality utility, match individual customers' personal performance requirements, and minimize the total cost. A multi-objective genetic algorithm with the proposed chromosome encoding pattern is applied to solve the MDO problem. A case study of designing customized trans-tibial prostheses with additive manufactured components is presented to illustrate the proposed MDO method. Clusters of multi-dimensional Pareto-optimal design solutions are obtained from the MDO, showing trade-offs among the objectives. Appropriate design decision can be chosen from the clusters based on the manufacturer's market strategy. Highlights An optimization problem for additive manufactured customized products is solved. Three disciplines are incorporated in multidisciplinary design optimization (MDO). The selection of component, material, additive manufacturing process and dimensions are optimized. A multiobjective genetic algorithm is applied to solve the MDO problem. Pareto-optimal solutions with different utilities and costs are obtained.

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Gorshy, Hesham, Xue Zheng Chu, Liang Gao und Hao Bo Qiu. „Novel Approach to Ship Multidisciplinary Design and Optimization Using Genetic Algorithm and Response Surface Method“. Advanced Materials Research 118-120 (Juni 2010): 967–71. http://dx.doi.org/10.4028/www.scientific.net/amr.118-120.967.

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Ship design is a complex engineering effort required excellent coordination between the various disciplines and essentially applies iteration to satisfy the relevant requirements, such as stability, power, weight, and strengths. Through, all-in-one Multidisciplinary Design Optimization (MDO) approach is proposed to get the optimum performance of the ship considering three disciplines, power of propulsion, ship loads and structure. In this research a Latin Hypercube Sampling (LHS) is employed to improve the space filling property of the design and explore it to sample data for covering the design space. To avoid the problem of huge calculation time and saving the develop time, a quadratic Response Surface Method (RSM) is adopted as an approximation model to study the relation between a set of design variables and the system output for solving the system design problems. A genetic algorithm (GA) is adopted as search technique used in computing to find exact or approximate solutions to optimize and search problems and appropriate design result in MDO in ship design. Finally, the validity of the proposed approach is proven by a case study of a bulk carrier.

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Zhou, Yu, Leishan Zhou, Yun Wang, Zhuo Yang und Jiawei Wu. „Application of Multiple-Population Genetic Algorithm in Optimizing the Train-Set Circulation Plan Problem“. Complexity 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/3717654.

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The train-set circulation plan problem (TCPP) belongs to the rolling stock scheduling (RSS) problem and is similar to the aircraft routing problem (ARP) in airline operations and the vehicle routing problem (VRP) in the logistics field. However, TCPP involves additional complexity due to the maintenance constraint of train-sets: train-sets must conduct maintenance tasks after running for a certain time and distance. The TCPP is nondeterministic polynomial hard (NP-hard). There is no available algorithm that can obtain the optimal global solution, and many factors such as the utilization mode and the maintenance mode impact the solution of the TCPP. This paper proposes a train-set circulation optimization model to minimize the total connection time and maintenance costs and describes the design of an efficient multiple-population genetic algorithm (MPGA) to solve this model. A realistic high-speed railway (HSR) case is selected to verify our model and algorithm, and, then, a comparison of different algorithms is carried out. Furthermore, a new maintenance mode is proposed, and related implementation requirements are discussed.

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Ma, Shi Lei, Fang Yi Li, Yang He und Qing Zhong Xu. „Multidisciplinary Design Optimization of Driving Axle Housing Using Sparse Grid Approach“. Applied Mechanics and Materials 224 (November 2012): 82–86. http://dx.doi.org/10.4028/www.scientific.net/amm.224.82.

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In order to improve the engineering performance of lightweight design on the driving axle housing, lightweight, structural mechanics, fatigue strength and dynamics are applied in the multidisciplinary design optimization. Firstly, finite element model of driving axle housing was established and its accuracy was verified through bench tests. Secondly, driving axle housing system was divided into multiple sub-discipline systems and design variables of multidisciplinary lightweight design were determined, in order to solve the problems of large amount of data transmission and complex calculation, sparse grid approach was used to establish high accuracy approximate model of each discipline. Lastly, mass of driving axle housing and difference values of first six order modal frequencies before and after lightweight design were optimized through Non-dominated Sorted Genetic Algorithm-Ⅱ, the Pareto optimal solution set was obtained. In optimization results, masses of driving axle housing are all decreased compared to the initial design, meanwhile, the dynamic performance, structural static intensity and fatigue life are all ensured.

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Du, Maohua, Zheng Cheng, Yanfei Zhang und Shensong Wang. „Multiobjective Optimization of Tool Geometric Parameters Using Genetic Algorithm“. Complexity 2018 (01.11.2018): 1–14. http://dx.doi.org/10.1155/2018/9692764.

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Tool geometric parameters have a huge impact on tool wear. Up to now, there are only a few researches on tool geometric parameters and optimization, and the single objective function of parameter optimization used by researchers during high-speed machining (HSM) mainly is the minimum cutting force. However, the elevated cutting temperature also greatly affects tool wear due to the numerous cutting heat generation. Thus, to reduce tool wear, it is the most fundamental approach to taking into account the comprehensive control of the cutting force and cutting temperature because they are the two most important physical quantities in metal cutting processes. This work proposes a new optimization idea of the cutting-tool’s multi geometric parameters (three main parameters: rake angle, clearance angle, and cutting edge radius) with two objective functions (the cutting force and the temperature). Based on the response surface method (RSM), we have established the modified functional relation models of the influence of tool geometric parameters on the cutting force and temperature according to the finite element simulation results in high-speed cutting of Ti6Al4V. Then the models are solved by using a genetic algorithm, and the optimal tool geometric parameters values that can concurrently control the two objectives in their minimum values are obtained. The advantages lie in the strategy of the separate models of the cutting force and cutting temperature owing to their different dimensions and the solution of the models through giving the cutting force and cutting temperature different weight coefficients. The optimal results are verified by experiments, which shows that the optimal tool geometric parameters are very effective and vital for ensuring both the cutting force and the cutting temperature not too high. This work is of great significance to the cutting tool design theory and its manufacturing for reducing tool wear.

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Sun, Le Feng, Bai Nan Zhang und Rui Nie. „Multidisciplinary Collaborative Optimization of General Parameters of Low Orbit Spacecraft“. Applied Mechanics and Materials 268-270 (Dezember 2012): 1482–89. http://dx.doi.org/10.4028/www.scientific.net/amm.268-270.1482.

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This article takes the optimization of general parameters of low-orbit spacecraft as object. Three subsystems of attitude and orbit control subsystem, power subsystem and structure subsystem were researched. Coupling relations between subsystems and system were described. Analysis models of the subsystems were provided. Meanwhile, optimization models of the researched problem were established based on collaborative optimization (CO). Sequential quadratic programming (NLPQL) was selected as search strategy of CO subsystem optimizer. While NLPQL, adaptive simulated annealing algorithm (ASA) and multi-island genetic algorithm (MIGA) were respectively selected as the search strategy of CO system-level optimizer to search. Furthermore, multidisciplinary feasible method (MDF) was used to optimize the same problem. NLPQL, ASA, MIGA were respectively selected as the search strategy of MDF system-level optimizer. The optimization result of CO was compared to the optimization result of MDF shows that using CO can get the result which close to the optimal result. That proves CO can be effectively applied to multidisciplinary design optimization of the general parameters of the low-orbit spacecraft.

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Yu, Xiaolin, Yifeng Huang, Yong Yang, Yuxuan Chen, Yufan Luo und Buyu Jia. „Optimal design for the connector of bridge prefabricated concrete barriers“. Science Progress 104, Nr. 3 (Juli 2021): 003685042110363. http://dx.doi.org/10.1177/00368504211036386.

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The current optimization research on the connectors of prefabricated barriers uses experimental comparisons only and lacks theoretically based optimization methods as guidance. The primary objective of this study was to propose an efficient optimization approach to the connector design of prefabricated bridge barriers. This paper presents an efficient two-stage optimization approach to the connector design of prefabricated bridge barriers. In the first stage, the hybrid cellular automaton algorithm is used to perform dynamic topology optimization on the connector, and the best material distribution in the design domain is obtained. In the second stage, a kriging metamodel and genetic algorithm are combined to further optimize the size of the connector structure. With a prefabricated bridge as the engineering background, finite element models of a barrier system under impact load caused by a car crash were established. The above approach is utilized to optimize the design of the barrier connector. Results showed that the optimized connector structure greatly improved the overall performance of the barrier system while reducing the material consumption and costs. The proposed optimization approach can determine the optimal material distribution and size of the connector structure, thus providing guiding significance for the design and construction of connectors of prefabricated components.

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Paek, Sung, Sangtae Kim und Olivier de Weck. „Optimization of Reconfigurable Satellite Constellations Using Simulated Annealing and Genetic Algorithm“. Sensors 19, Nr. 4 (13.02.2019): 765. http://dx.doi.org/10.3390/s19040765.

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Agile Earth observation can be achieved with responsiveness in satellite launches, sensor pointing, or orbit reconfiguration. This study presents a framework for designing reconfigurable satellite constellations capable of both regular Earth observation and disaster monitoring. These observation modes are termed global observation mode and regional observation mode, constituting a reconfigurable satellite constellation (ReCon). Systems engineering approaches are employed to formulate this multidisciplinary problem of co-optimizing satellite design and orbits. Two heuristic methods, simulated annealing (SA) and genetic algorithm (GA), are widely used for discrete combinatorial problems and therefore used in this study to benchmark against a gradient-based method. Point-based SA performed similar or slightly better than the gradient-based method, whereas population-based GA outperformed the other two. The resultant ReCon satellite design is physically feasible and offers performance-to-cost(mass) superior to static constellations. Ongoing research on observation scheduling and constellation management will extend the ReCon applications to radar imaging and radio occultation beyond visible wavelengths and nearby spectrums.

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Kim, Kun-Jung, und Kee-Ho Yu. „Multidisciplinary Design Optimization for a Solar-Powered Exploration Rover Considering the Restricted Power Requirement“. Energies 13, Nr. 24 (16.12.2020): 6652. http://dx.doi.org/10.3390/en13246652.

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The energy requirements of a solar-powered exploration rover constrain the mission duration, traversability, and tractive capability under the given limited usable power. Thus, exploration rover design, more specifically, rover wheel design (related to considerable energy consumption in driving), plays a significant role in the success of exploration missions. This paper describes the modeling of an operational environment and a multi-body dynamics (MBD) simulation tool based on wheel-terrain interaction model to predict the dynamic behavior on a digital elevation model (DEM) map. With these simulation environments, a multidisciplinary optimal wheel design methodology, integrating the MBD simulation tool and non-dominated sorting genetic algorithm-II (NSGA-II), is developed. Design parameters are chosen through sensitivity analysis. These multi-objective optimizations in dynamic states are conducted to obtain the optimal wheel dimension that meet the limited power condition with maximal tractive capability under the given operational environment. Furthermore, numerical and experimental verification using a single wheel testbed on lunar simulant are conducted to convincingly validate the derived optimization results. Finally, these results reveal that the proposed design methodology is an effective approach to deciding the best design parameter among a large variety of candidate design points considering the restricted power requirement.

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Wan, Zhi-Qiang, Xiao-Zhe Wang und Chao Yang. „Integrated aerodynamics/structure/stability optimization of large aircraft in conceptual design“. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, Nr. 4 (11.01.2017): 745–56. http://dx.doi.org/10.1177/0954410016687143.

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The multidisciplinary design optimization is suitable for modern large aircraft, and it has the potential in conceptual phase of aircraft design especially. An integrated optimization method considering the disciplines of aerodynamics, structure and stability for large aircraft design in conceptual phase is presented. The objective is the minimum stiffness of a beam-frame wing structure subject to aeroelasticity, aerodynamics, and stability constraints. The aeroelastic responses are computed by commercial software MSC. Nastran, and the cruise stability is evaluated by the linear small-disturbance equations. A viscous-inviscid iteration method, which is composed of a computational fluid dynamics tool solving the Euler equations and a viscous correction method, is used for computing the flow over the model. The method ensures effective and rapid computation. In this paper, a complete aircraft model is optimized, and all the responses are computed in the trim condition with a fixed maximum takeoff weight. Genetic algorithm is utilized for global optimizations, and the optimal jig shape, the elastic axis positions and the stiffness distribution can be attained adequately. The results show that the method has a value of application in engineering optimizations. For the satisfaction of the total drag and stability constraints, the structure weight usually needs a price to pay. The integrated optimization captures the tradeoff between aerodynamics, structure and stability, and the repeated design can be avoided.

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Yang, Dong, und Peijian Wu. „E-Commerce Logistics Path Optimization Based on a Hybrid Genetic Algorithm“. Complexity 2021 (22.03.2021): 1–10. http://dx.doi.org/10.1155/2021/5591811.

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Based on the problem of e-commerce logistics and distribution network optimization, this paper summarizes the solution ideas and solutions proposed by domestic and foreign scholars and designs a method to optimize the B2C e-commerce logistics and distribution network by taking into account the special traffic conditions in the city. The logistics network optimization model is established and solved by combining various methods. Taking into account the new target requirements constantly proposed in the modern logistics environment, the vehicle path problem under the generalized objective function is studied, and the multidimensional impact maximization problem in this type of problem is proposed and modeled. The problem follows from the path planning for emergency material delivery. Given locations, roads, and multiple classes of supplies in a map, each road allows vehicles to deliver each class of supplies with a certain probability. The goal of the problem is how to select a finite number of locations in the map as centers of supplies so that the number of locations that can be effectively covered by vehicle paths from them is maximized with the desired probability. For the first time, we used a hybrid genetic algorithm to optimize the e-commerce logistics path, and the optimized results are more reasonable than other algorithms.

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