Multi-objective design optimisation of a 3D-rail stamping process using a robust multi-objective optimisation platform (RMOP)

The paper investigates the multi-objective design optimisation of a stamping process to control the final shape and the final quality using advanced high strength steels. The design problem of the stamping process is formulated to minimise the difference between the desired shape and the final geometry obtained by numerical simulation accounting elastic springback. In addition, the final product quality is maximised by improving safety zones without wrinkling, thinning, or failure. Numerical results show that the proposed methodology improves the final product quality while reduces its springback.Peer ReviewedPostprint (published version

Hybrid-Game Strategies for multi-objective design optimization in engineering

A number of Game Strategies (GS) have been developed in past decades. They have been used in the fields of economics, engineering, computer science and biology due to their efficiency in solving design optimization problems. In addition, research in multi-objective (MO) and multidisciplinary design optimization (MDO) has focused on developing robust and efficient optimization methods to produce a set of high quality solutions with low computational cost. In this paper, two optimization techniques are considered; the first optimization method uses multi-fidelity hierarchical Pareto optimality. The second optimization method uses the combination of two Game Strategies; Nash-equilibrium and Pareto optimality. The paper shows how Game Strategies can be hybridised and coupled to Multi-Objective Evolutionary Algorithms (MOEA) to accelerate convergence speed and to produce a set of high quality solutions. Numerical results obtained from both optimization methods are compared in terms of computational expense and model quality. The benefits of using Hybrid-Game Strategies are clearly demonstrate

Robust design optimisation of advance hybrid (fiber-metal) composite structures

Hybrid Composite Structures (HCSs) are consisting of alternating layers of Fiber-Reinforced Polymer and metal sheets. Mechanical properties and responses for off-design conditions of HCSs can be improved using an innovative methodology coupling Multi-Objective Genetic Algorithm and robust design method. The concept of robust design approach ensures that a structure will be tolerant to unexpected loading and operating conditions. In this paper, two applications are considered; the first is to maximise the stiffness of the HCS while minimising its total weight through a Multi-Objective Design Optimisation. The second application considers a Robust Multi-Objective Design Optimisation (RMDO) to minimise total weight of HCS and to minimise both, the normalised mean displacement and the standard deviations of displacement, considering critical load cases. For the optimisation process, a distributed/parallel Multi-Objective Genetic Algorithm in robust multi-objective optimisation platform is used and it is coupled to a Finite Element Analysis based composite structure analysis tool to find the optimal combination of laminates sequences for HCSs. Numerical results show the advantages in mechanical properties of HCS over the metal structures, and also the use of RMDO methodology to obtain higher characteristics of HCS in terms of mechanical properties and its stability at the variability of load cases

Multilayered composite structure design optimisation using distributed/parallel multi-objective evolutionary algorithms

This paper presents a research work on stacking sequence design optimisation for multilayered composite plate using a parallel/distributed evolutionary algorithm. The stacking sequence of fibres has a dramatic influence on the strength of multilayered composite plates. Multiple layers of fibre-reinforced material systems offer versatility in engineering material design due to the fact that the stacking sequence of each orthotropic layer can offer full advantage of superior mechanical properties. Numerical results show that the optimal composite structures have lower weight, higher stiffness and also affordable cost when compared to the extreme and intermediate composite structures. In addition, the benefits of using a parallel optimisation system are also presented

Aerodynamic shape optimization using adaptive remeshing

Adaptive mesh refinement is one of the most important tools in Computational Fluid Dynamics (CFD) for solving complex engineering design problems. The paper investigates two practical transonic aerofoil design optimization problems using a genetic algorithm coupled with an Euler aerodynamic analysis tool. The first problem consists in the minimization of transonic drag whereas the second is a reconstruction transonic problem solved by minimizing the pressure error. In both cases, the solutions obtained with and without adaptive mesh refinement are compared. Numerical results obtained by both drag minimization and reconstruction design clearly show that the use of adaptive mesh refinement reduces the computational cost and also produces a better solution.Postprint (published version

Multi-objective design optimisation of a 3D-rail stamping process using a robust multi-objective optimisation platform (RMOP)

The paper investigates the multi-objective design optimisation of a stamping process to control the final shape and the final quality using advanced high strength steels. The design problem of the stamping process is formulated to minimise the difference between the desired shape and the final geometry obtained by numerical simulation accounting elastic springback. In addition, the final product quality is maximised by improving safety zones without wrinkling, thinning, or failure. Numerical results show that the proposed methodology improves the final product quality while reduces its springback

An Analysis Of Discharge And Water Level Changes Due To Weir (In The Case Of The Waegwan And Nakdong Station In South Korea)

ABSTRACT Our country, Korea, lacks the per capita water endowment because of the high population density and geographical characteristics. Therefore, The 4 Rivers Project has been promoted since 2009. In order to control effectively drought or floods caused by abnormal climate and strong rainfall in summer, it was done and completed in 2013. The Nakdong River is the longest in the 4 Rivers Project, which it was conducted among the country\u27s major rivers, and a lot of hydraulic structures were installed. we selected Nakdong and Weagwan stations which are located in the Nakdong River. Because the change was smaller there with the inflow of tributaries flow than elsewhere. And many hydraulic structures were installed between Nakdong and Weagwan stations, which used T/M machine to collect real-time data. In this study, we compared and analyzed the flow change of real-time and water level data of prior to weir installation on May, June, July, 2009 and completion of weir installation on May, June, July , 2013. The original purpose of installing the weir is well-kept. After installation of Hydraulic structures(weir), the discharge difference between the Nakdong Station and Weagwan Station was reduced , and both of Nakdong and Weagwan stations are almost maintain a constant value of water level without being affected by rainfall. Also, we could confirm that the maximum water-level variation is decreased after installation of the weir. Based on this, we judged that hydrological effects on drought or floods due to abnormal weather were decreased by the time of weir installation. ACKNOWLEDGEMENT This research was supported by a grant(12-TI-C01) from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean governmen

Robust multidisciplinary UAS design optimisation

There are many applications in aeronautical/aerospace engineering where some values of the design parameters states cannot be provided or determined accurately. These values can be related to the geometry(wingspan, length, angles) and or to operational flight conditions that vary due to the presence of uncertainty parameters (Mach, angle of attack, air density and temperature, etc.). These uncertainty design parameters cannot be ignored in engineering design and must be taken into the optimisation task to produce more realistic and reliable solutions. In this paper, a robust/uncertainty design method with statistical constraints is introduced to produce a set of reliable solutions which have high performance and low sensitivity. Robust design concept coupled with Multi Objective Evolutionary Algorithms (MOEAs) is defined by applying two statistical sampling formulas; mean and variance/standard deviation associated with the optimisation fitness/objective functions. The methodology is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. It is implemented for two practical Unmanned Aerial System (UAS) design problems; the flrst case considers robust multi-objective (single disciplinary: aerodynamics) design optimisation and the second considers a robust multidisciplinary (aero structures) design optimisation. Numerical results show that the solutions obtained by the robust design method with statistical constraints have a more reliable performance and sensitivity in both aerodynamics and structures when compared to the baseline design

Optimal mission path planning (MPP) for an air sampling unmanned aerial system

This paper presents advanced optimization techniques for Mission Path Planning (MPP) of a UAS fitted with a spore trap to detect and monitor spores and plant pathogens. The UAV MPP aims to optimise the mission path planning search and monitoring of spores and plant pathogens that may allow the agricultural sector to be more competitive and more reliable. The UAV will be fitted with an air sampling or spore trap to detect and monitor spores and plant pathogens in remote areas not accessible to current stationary monitor methods. The optimal paths are computed using a Multi-Objective Evolutionary Algorithms (MOEAs). Two types of multi-objective optimisers are compared; the MOEA Non-dominated Sorting Genetic Algorithms II (NSGA-II) and Hybrid Game are implemented to produce a set of optimal collision-free trajectories in three-dimensional environment. The trajectories on a three-dimension terrain, which are generated off-line, are collision-free and are represented by using Bézier spline curves from start position to target and then target to start position or different position with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of coupling a Hybrid-Game strategy to a MOEA for MPP tasks. The reduction of numerical cost is an important point as the faster the algorithm converges the better the algorithms is for an off-line design and for future on-line decisions of the UAV

Wideband and Dual-polarized Transparent Antenna-on-Display using Via-less and Single-Layer Topology for mmWave Wireless Communication

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