• 한국전산유체공학회:학술대회논문집
• /
• 2005.04a
• /
• pp.187-190
• /
• 2005

In this study, the reliablility based design optimization is peformed for an aircraft wing. The flexiblility of the wing was assumed by considering the interaction modeled by static aeroelasticity between aerodynamic forces and the structure. For a multidisciplinary design optimization the results of aerodynamic analysis and structural analysis were included in the optimization formulation. The First Order Reliability Method(FORM) was employed to consider the uncertainty of the designed points.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• v.y2005m4
• /
• pp.11-15
• /
• 2005

Compared with the conventional ground rocket launching, air-launching has many advantages. However, comprehensive and integrated system design approach is required because the physical geometry of air launch vehicle is quite dependent on the installation limitation of the mother plane. The system design has been performed using two different approaches: the sequential optimization and the multidisciplinary feasible(MDF) optimization method. Analysis modules include mission analysis, staging, propulsion analysis, configuration, weight analysis, aerodynamics analysis and trajectory analysis. MDF optimization shows better result than sequential optimization. As a result of system optimization, a supersonic air launching rocket with total mass of 1244.91 kg, total length of 6.18 m, outer diameter of 0.60 m and the payload mass of 7.5 kg has been successfully designed.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.33 no.12
• /
• pp.26-32
• /
• 2005

Compared with the conventional ground rocket launching, air-launching has many advantages. However, a comprehensive and integrated system design approach is required because the physical geometry of air launch vehicle is quite dependent on the installation limitation of the mother plane. The system design has been performed using two different approaches: the sequential optimization and the multidisciplinary feasible(MDF) optimization method. Analysis modules include mission analysis, staging, propulsion analysis, configuration, weight analysis, aerodynamics analysis and trajectory analysis. MDF optimization shows better results than the sequential optimization. As a result of system optimization, a supersonic air launching rocket with total mass of 1244.91kg, total length of 6.36m, outer diameter of 0.60m and the payload mass of 7.5kg has been successfully designed.

• Structural Engineering and Mechanics
• /
• v.6 no.1
• /
• pp.31-40
• /
• 1998

The research and applications of numerical methods of design optimization on structural dynamic behaviors are presented in this paper. The emphasis is focused on the dynamic design optimization of aerospace structures, particularly those composed of composite laminate and sandwich plates. The methods of design modeling, sensitivity analysis on structural dynamic responses, and the optimization solution approaches are presented. The numerical examples of sensitivity analysis and dynamic structural design optimization are given to demonstrate the effectiveness of the numerical methods.

• Proceedings of the Korea Contents Association Conference
• /
• 2004.05a
• /
• pp.330-334
• /
• 2004

Java bytecode is stack-base code. Stack-base code makes analysis and optimization hardly because use stack access imperative. Therefore, fragment of code that is problem that occur in stack-base code optimization, loss of type information, unnecessary Load and Store can appear. Optimization and analysis of bytecode design bytecode framework by solution way of problem that is difficult. This paper indicates optimization of bytecode and hangup of analysis, and describe research contents about existent byte code optimization technology. This propose byte code framework by the alternative to simplify analysis and optimization of byte code.

• Transactions of the Society of Information Storage Systems
• /
• v.3 no.4
• /
• pp.178-182
• /
• 2007

Topology optimization methods are classified into two methods such as the density method and the homogenization method. Those methods need to consider relationships between the material property and the density of each element in a design domain, the relaxation of the design space, etc. However, it is hard to apply on some cases due to the complexity to compose the design objective and its sensitivity analysis. In this paper, a modified topology optimization is proposed to assist designers who do not have mathematical or theoretical background of the topology optimization. In this study, optimal topology of structures can be achieved by the sequential design of experiment (DOE) and the sensitivity analysis. We conducted the DOE with an orthogonal array and the sensitivity analysis of design variables to determine sensitive variables used for connectivity between elements. The modified topology optimization method has advantages such as freedom from penalizing intermediate values and easy application with basic DOE concept.

• International Journal of Aeronautical and Space Sciences
• /
• v.17 no.2
• /
• pp.268-283
• /
• 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.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.6 no.1
• /
• pp.1-15
• /
• 1998

Currently, computational analysis is a popular technology in automobile engineering. Finite element analysis is an excellent method for body analysis. For finite element analysis, accurate modeling is very important to obtain precise information. Stick modeling is a convenient way in that it is easy and simple. When a stick model is utilized, the joints are modified in the tuning process. A tuning method for the joint has been developed. The joints are modeled by designated beam elements. An optimization method called "Goal Programming" is employed to impose the target values. With the tuned joints, the entire optimization has been carried out. Using the "Recursive Quadratic Programming" algorithm, the optimization process determines the configuration of the entire structure and sizes of all the sections. For example, the structure of an electrical vehicle is modeled and analyzed by the method. The stick model works well since the structure is made of aluminium frames. Although the example handles an electrical vehicle, this method can be applied to general vehicle structures.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.2
• /
• pp.911-917
• /
• 2017

There are generally three folds when developing neural network classifiers. They are as follows: 1) discriminant function; 2) lots of parameters in the design of classifier; and 3) high dimensional training data. Along with this viewpoint, we propose space search optimized polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and simultaneous tuning strategy, which is a balance optimization strategy used in the design of PNNC when running space search optimization. Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions. The overall optimization of PNNC is realized with the aid of so-called structure optimization and parameter optimization with the use of simultaneous tuning strategy. Space search optimization algorithm is considered as a optimize vehicle to help the implement both structure and parameter optimization in the construction of PNNC. Furthermore, principal component analysis and linear discriminate analysis are selected as the data preprocessing techniques for PNNC. Experimental results show that the proposed neural network classifier obtains better performance in comparison with some other well-known classifiers in terms of accuracy classification rate.

• Structural Engineering and Mechanics
• /
• v.36 no.1
• /
• pp.79-94
• /
• 2010

This study shows how uncertainties of data like material properties quantitatively have an influence on structural topology optimization results for dynamic problems, here such as both optimal topology and shape. In general, the data uncertainties may result in uncertainties of structural behaviors like deflection or stress in structural analyses. Therefore optimization solutions naturally depend on the uncertainties in structural behaviors, since structural behaviors estimated by the structural analysis method like FEM need to execute optimization procedures. In order to quantitatively estimate the effect of data uncertainties on topology optimization solutions of dynamic problems, a so-called interval analysis is utilized in this study, and it is a well-known non-stochastic approach for uncertainty estimate. Topology optimization is realized by using a typical SIMP method, and for dynamic problems the optimization seeks to maximize the first-order eigenfrequency subject to a given material limit like a volume. Numerical applications topologically optimizing dynamic wall structures with varied supports are studied to verify the non-stochastic interval analysis is also suitable to estimate topology optimization results with dynamic problems.