• Structural Engineering and Mechanics
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• v.86 no.3
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• pp.337-348
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• 2023

The optimization of reinforced concrete (RC) cantilever retaining walls is a complex problem and requires the use of advanced techniques like metaheuristic algorithms. For this purpose, an optimization model must first be developed, which involves mathematical complications, multidisciplinary knowledge, and programming skills. This task has proven to be too arduous and has halted the mainstream acceptance of optimization. Therefore, it is necessary to unravel the complications of optimization into an easily applicable form. Currently, the most commonly used method for designing retaining walls is by following the proportioning limits provided by the ACI handbook. However, these limits, derived manually, are not verified by any optimization technique. There is a need to validate or modify these limits, using optimization algorithms to consider them as optimal limits. Therefore, this study aims to propose updated proportioning limits for the economical design of a RC cantilever retaining wall through a comprehensive parametric investigation using the genetic algorithm (GA). Multiple simulations are run to examine various design parameters, and trends are drawn to determine effective ranges. The optimal limits are derived for 5 geometric and 3 reinforcement variables and validated by comparison with their predecessor, ACI's preliminary proportioning limits. The results indicate close proximity between the optimized and code-provided ranges; however, the use of optimal limits can lead to additional cost optimization. Modifications to achieve further optimization are also discussed. Besides the geometric variables, other design parameters not covered by the ACI building code, like reinforcement ratios, bar diameters, and material strengths, and their effects on cost optimization, are also discussed. The findings of this investigation can be used by experienced engineers to refine their designs, without delving into the complexities of optimization.

• International Journal of CAD/CAM
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• v.2 no.1
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• pp.1-12
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• 2002

Computer-Aided Reliable and Optimal Design (CAROD) system is an efficient tool defining the best compromise between cost and safety. Using the concurrent engineering concept, it can supply the designer with all numerical information in the design process. This system integrates several fields such as multidisciplinary optimization, reliability analysis, finite element analysis, geometrical modeling, sensitivity analysis and concurrent engineering. When integrating these disciplines, many difficulties are found such as model coupling and computational time. In this paper, we propose a new concurrent methodology satisfying the reliability requirement, allowing the coupling of different models and reducing the computational time. Two applications (rotating disk and hook structures) demonstrate that CAROD system can be a practical concurrent engineering application for designers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.93-102
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• 2020

The design of large complex mechanical systems, such as automobile, aircraft, and ship, is a kind of Multidisciplinary Design Optimization (MDO) because it requires both experience and expertise in many areas. With the rapid development of technology and the demand to improve human convenience, the complexity of these systems is increasing further. The design of such a complex system requires an integrated system design, i.e., MDO, which can fuse not only domain-specific knowledge but also knowledge, experience, and perspectives in various fields. In the past, the MDO relied heavily on the designer's intuition and experience, making it less efficient in terms of accuracy and time efficiency. Process integration and the design optimization framework mainly support MDO owing to the evolution of IT technology. This paper examined the procedure and methods to implement an efficient MDO with reasonable effort and time using RCE, an open-source PIDO framework. As a benchmarking example, the authors applied the proposed MDO methodology to a bulk carrier's conceptual design synthesis model. The validity of this proposed MDO methodology was determined by visual analysis of the Pareto optimal solutions.

• Structural Engineering and Mechanics
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• v.44 no.3
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• pp.325-337
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• 2012

In this article, the multi-objective optimization of cylindrical aluminum tubes under axial impact load is presented. The specific absorbed energy and the maximum crushing force are considered as objective functions. The geometric dimensions of tubes including diameter, length and thickness are chosen as design variables. D/t and L/D ratios are constricted in the range of which collapsing of tubes occurs in concertina or diamond mode. The Non-dominated Sorting Genetic Algorithm-II is applied to obtain the Pareto optimal solutions. A back-propagation neural network is constructed as the surrogate model to formulate the mapping between the design variables and the objective functions. The finite element software ABAQUS/Explicit is used to generate the training and test sets for the artificial neural networks. To validate the results of finite element model, several impact tests are carried out using drop hammer testing machine.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.5
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• pp.18-24
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• 2003

The design cycle associated with large engineering systems requires an initial decomposition of the complex system into design processes which are coupled through the transference of output data. Some of these design processes may be grouped into iterative sybcycles. Previous researches predifined the numbers of design processes in groups, but these group sizes should be determined optimally to balance the computing time of each groups. This paper proposes adaptive decomposition method, which determines the group sizes and the order of processes simultaneously to raise design efficiency by expanding the chromosome of the genetic algorithm. Finally, two sample cases are presented to show the effects of optimizing the sequence of processes with the adaptive decomposition method.

• CDE review
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• v.10 no.1
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• pp.48-54
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• 2004

제품설계에 관련된 다양한 공학해석분야, 즉 구조해석, 동역학, 열ㆍ유체ㆍ유동해석, 제어, 전자기장해석 등을 동시에 고려하면서 최적의 설계를 결정하는 것을 다분야통합최적설계(Multidisciplinary Design Optimization: MDO) 기술이라 한다. MDO 프레임적 (framework)은 최적화기술, 컴퓨팅기반구조기술, 통합설계기술이 유기적으로 구현된 소프트웨어 복합체계로서, 분산컴퓨팅 기반구조를 통하며 MDO요소 기술들과 기존의 CAD/CAE 도구들을 연계하여 설계 작업을 통합적으로 관리하고 자동화한다. MDO 프레임칙은 이러한 자동화된 통합관리를 통하여 설계도구 간의 데이터 전달과 변환에 소요되는 설계자의 부담을 경감시키며 다분야 전문가가 참여하는 공통 작업 환경을 제공함으로써 설계 효율성을 증진시킨다. 이 글에서는 현재 최적설계신기술연구센터 (iDOT)에서 개발 중인 MDO 프레임웍인 EMDIOS를 소개하기로 한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.6
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• pp.3661-3666
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• 2015

The paper describes the integrated design system using MDO and approximation technique. In MDO related research, final target is an integrated and automated MDO framework systems. However, in order to construct the integrated design system, the prerequisite condition is how much save computational cost because of iterative process in optimization design and lots of data information in CAD/CAE integration. Therefore, this paper presents that an efficient approximation method, Adaptive approximation, is a competent strategy via MDO framework systems.

• International Journal of Aerospace System Engineering
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• v.2 no.2
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• pp.29-33
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• 2015

The multidisciplinary design optimization (MDO) of a launch vehicle (LV) with a hybrid rocket engine (HRE) was carried out to investigate the ability of an HRE for a single-stage LV. The non-dominated sorting genetic algorithm-II (NSGA-II) was employed to solve two design problems. The design problems were formulated as two-objective cases involving maximization of the downrange distance over the target flight altitude and minimization of the gross weight, for two target altitudes: 50.0 km and 100.0 km. Each objective function was empirically estimated. Several non-dominated solutions were obtained using the NSGA-II for each design problem, and in each case, a trade-off was observed between the two objective functions. The results for the two design problem indicate that economical performance of the LV is limited with the HRE in terms of the maximum downrange distances achievable. The LV geometries determined from the non-dominated solutions were examined.

• Advances in aircraft and spacecraft science
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• v.9 no.5
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• pp.401-414
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• 2022

In the present paper, the optimal selection of batteries for an electric pump-fed hybrid rocket engine is analyzed. A two-stage Mars Ascent Vehicle, suitable for the Mars Sample Return Mission, is considered as test case. A single engine is employed in the second stage, whereas the first stage uses a cluster of two engines. The initial mass of the launcher is equal to 500 kg and the same hybrid rocket engine is considered for both stages. Ragone plot-based correlations are embedded in the optimization process in order to chose the optimal values of specific energy and specific power, which minimize the battery mass ad hoc for the optimized engine design and ascent trajectory. Results show that a payload close to 100 kg is achievable considering the current commercial battery technology.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.10 s.181
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• pp.2568-2580
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• 2000

This paper presents a quasi-static optimization technique for elastic structures under dynamic loads. An equivalent static load (ESL) set is defined as a static load set which generates the same displacement field as that from a dynamic load at a certain time. Multiple ESL sets calculated at every time step are employed to represent the various states of the structure under the dynamic load. They can cover every critical state that might happen at an arbitrary time. Continuous characteristics of dynamic load are simulated by multiple discontinuous ones of static loads. The calculated sets of ESLs are applied as a multiple loading condition in the optimization process. A design cycle is defined as a circulated process between an analysis domain and a design domain. Design cycles are repeated until a design converges. The analysis domain gives a loading condition necessary for the design domain. The design domain gives a new updated design to be verified by the analysis domain in the next design cycle. This iterative process is quite similar to that of the multidisciplinary optimization technique. Even though the global convergence cannot be guaranteed, the proposed technique makes it possible to optimize the structures under dynamic loads. It has also applicability, flexibility, and reliability.