• International Journal of Aeronautical and Space Sciences
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• 제17권2호
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• pp.268-283
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• 2016

Aircraft manufacturing companies have to consider multiple derivatives to satisfy various market requirements. They modify or extend an existing aircraft to meet new market demands while keeping the development time and cost to a minimum. Many researchers have studied the derivative design process, but these research efforts consider baseline and derivative designs together, while using the whole set of design variables. Therefore, an efficient process that can reduce cost and time for aircraft derivative design is needed. In this research, a more efficient design process is proposed which obtains global changes from local changes in aircraft design in order to develop aircraft derivatives efficiently. Sensitivity analysis was introduced to remove unnecessary design variables that have a low impact on the objective function. This prevented wasting computational effort and time on low priority variables for design requirements and objectives. Additionally, uncertainty from the fidelity of analysis tools was considered in design optimization to increase the probability of optimization results. The Reliability Based Design Optimization (RBDO) and Possibility Based Design Optimization (PBDO) methods were proposed to handle the uncertainty in aircraft conceptual design optimization. In this paper, Collaborative Optimization (CO) based framework with RBDO and PBDO was implemented to consider uncertainty. The proposed method was applied for civil jet aircraft derivative design that increases cruise range and the number of passengers. The proposed process provided deterministic design optimization, RBDO, and PBDO results for given requirements.

• 한국CDE학회논문집
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• 제15권1호
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• pp.24-32
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• 2010

This paper introduces a B-spline based branch & bound algorithm for global optimization. The branch & bound is a well-known algorithm paradigm for global optimization, of which key components are the subdivision scheme and the bound calculation scheme. For this, we consider the B-spline hypervolume to approximate an objective function defined in a design space. This model enables us to subdivide the design space, and to compute the upper & lower bound of each subspace where the bound calculation is based on the LHS sampling points. We also describe a search tree to represent the searching process for optimal solution, and explain iteration steps and some conditions necessary to carry out the algorithm. Finally, the performance of the proposed algorithm is examined on some test problems which would cover most difficulties faced in global optimization area. It shows that the proposed algorithm is complete algorithm not using heuristics, provides an approximate global solution within prescribed tolerances, and has the good possibility for large scale NP-hard optimization.

• International Journal of Precision Engineering and Manufacturing
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• 제3권1호
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• pp.13-19
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• 2002

Although the design of magnetic bearing needs a systematic optimization due to several design variables, constraints, geometric limitations, nonlinearities, and so on, the present designs for magnetic bearings have been conducted in the linear region of the characteristics for magnetic materials by trial and error considering design constraints. This article, therefore, provides the possibility of a genetic algorithm(GA) based optimization with two dimensional nonlinear finite element magnetic field analysis for the design of a radial heteropolar magnetic bearing. The magnetic bearing design by GA based optimization makes good agreements with that by a commercial optimization software DOT using the sensitivity analysis.

• 한국정밀공학회지
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• 제16권8호
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• pp.53-58
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• 1999

Although the design of magnetic bearing needs a systematic optimization du e to several design variables, constraints, geometric limitations, nonlinearities, and so on, the present for magnetic bearings have been conducted in the linear region of the characteristics for magnetic by trial and error considering design constraints. This article, therefore, provides the possibility of a genetic algorithm(GA) based optimization with two dimensional-nonlinear finite element magnetic field analysis for design of a radial heteropolar magnetic bearing. The magnetic bearing design by GA based optimization makes good agreements with that by a commercial optimization software DOT using the sensitivity analysis.

• 한국항공운항학회:학술대회논문집
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• 한국항공운항학회 2015년도 추계학술대회
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• pp.25-28
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• 2015

Aircraft conceptual design usually uses low to medium fidelity analysis to determine the basic configuration of an aircraft. Optimum solution is bounded by at least one of the constraints in most cases. This solution has risk to fail at later stage when analyzed with more sophisticated analysis tools. This research uses pre-constructed database to estimate the analysis prediction errors associated with simplified analysis methods. A possibility based design optimization framework is developed to utilize the newly proposed piecewise-linear fuzzy membership functions that compensate the discrepancies caused by simplified analysis. The proposed approach for aircraft design produces the optimum aircraft configurations that are less likely to fall into infeasible region when analyzed using higher fidelity analysis at later design stages.

• International Journal of Internet, Broadcasting and Communication
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• 제16권2호
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• pp.127-135
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• 2024

Recent studies have shown that inverse design using deep learning has the potential to rapidly generate the optimal design that satisfies the target performance without the need for iterative optimization processes. Unlike traditional methods, deep learning allows the network to rapidly generate a large number of solution candidates for the same objective after a single training, and enables the generation of diverse designs tailored to the objectives of inverse design. These inverse design techniques are expected to significantly enhance the efficiency and innovation of design processes in various fields such as aerospace, biology, medical, and engineering. We analyzes inverse design models that are mainly utilized in the nano and chemical fields, and proposes inverse design models based on supervised and unsupervised learning that can be applied to the engineering system. It is expected to present the possibility of effectively applying inverse design methodologies to the design optimization problem in the field of engineering according to each specific objective.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2007년도 정기 학술대회 논문집
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• pp.707-712
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• 2007

Recently, the rapid progress of Internet and Network affects engineering design environment as well as business fields to utilize Web technologies to enhance it's competitiveness in the world. This situation has also caused many companies to seek out the possibility to increase the collaboration between different organizations within their Product design process. Thus. this research shows the web based distributed design system framework which involves Workflow-based framework that enables to automate, integrate and manage the design process, such as CAD, CAE, Optimization tools.

• 한국안전학회지
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• 제27권5호
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• pp.148-157
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• 2012

Reliability-based design optimization (RBDO) problem is usually formulated as an optimization problem to minimize an objective function subjected to probabilistic constraint functions which may include deterministic design variables as well as random variables. The challenging task is that, because the probability models of the random variables are often assumed based on limited data, there exists a possibility of selecting inappropriate distribution models and/or model parameters for the random variables, which can often lead to disastrous consequences. In order to select the most appropriate distribution model from the limited observation data as well as model parameters, this study takes into account a set of possible candidate models for the random variables. The suitability of each model is then investigated by employing performance and risk functions. In this regard, this study enables structural design optimization and fitness assessment of the distribution models of the random variables at the same time. As the first paper of a two-part series, this paper describes a new design method considering probability model uncertainties. The robust performance of the proposed method is presented in Part 2. To demonstrate the effectiveness of the proposed method, an example of ten-bar truss structure is considered. The numerical results show that the proposed method can provide the optimal design variables while guaranteeing the most desirable distribution models for the random variables even in case the limited data are only available.

• Advances in Computational Design
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• 제3권1호
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• pp.35-47
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• 2018

Design of settlement space is a complicated process while reasonable spatial layout bears great significance on the development and resource allocation of a settlement. The study proposes a weighted L-system generation algorithm based on CA (Cellular Automation) model which tags the spatial attributes of cells through changes in their state during the evolution of CA and thus identifies the spatial growth mode of a settlement. The entrance area of the Caidian Botanical and Animal Garden is used a case study for the model. A design method is proposed which starts from the internal logics of spatial generation, explores possibility of spatial rules and realizes the quantitative analysis and dynamic control of the design process. Taking a top-down approach, the design method takes into account the site information, studies the spatial generation mechanism of settlements and further presents a engine for the generation of multiple layout proposals based on different rules. A optimal solution is acquired using GA (Genetic Algorithm) which generates a settlement spatial layout carrying site information and dynamically linked to the surround environment. The study aims to propose a design method to optimize the spatial layout of the complex settlement system based on parametric generation.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2003년도 제21회 추계학술대회 논문집
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• pp.145-150
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• 2003

The present study aims to optimize the performance of the Two-Stage Gas Gun by using the experimentally obtained data. RSM(Response Surface Method) was adopted in the optimization process to find the operating parameter than can maximize the projectile speed with the minimum number of tests. To decide the test points which results can consist of the response surface, 3$^{k}$ full factorial method was used, and the design variables were chosen with piston mass and 2$^{nd}$ driver fill pressure. The response surface was composed by nine test results and consequently the optimization was done with GENOCOP III, inherently GA code, in order to seek the optimal test point. The optimal test condition from the response surface was verified by the experiment. Results showed that the optimization process with response surface can successfully predict the test results fairly well. This study shows the possibility of performance optimization for the experimental facilities using numerical optimization algorithm.