• Structural Monitoring and Maintenance
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• v.10 no.1
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• pp.63-86
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• 2023

Structural health monitoring and damage detection are essential for assessing, maintaining, and rehabilitating structures. Most of the existing damage detection approaches compare the current state structural response with the undamaged vibrational structural response, which is unsuitable for old and existing structures where undamaged vibrational responses are absent. One of the approaches for existing structures, numerical model updating/inverse modelling, available in the literature, is limited to numerical studies with high-end software. In this study, an attempt is made to study the effectiveness of the model updating technique, simplify modelling complexity, and economize its usability. The optimization-based detection problem is addressed by using programmable open-sourced code, OpenSees^® and a derivative-free optimization code, NOMAD^®. Modal analysis is used for damage identification of beam-like structures with several damage scenarios. The performance of the proposed methodology is validated both numerically and experimentally. The proposed method performs satisfactorily in identifying both locations and intensity of damage in structures.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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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.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.3
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• pp.111-118
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• 2017

Performance factors such as the EER(Energy Efficiency Ratio) and the COP (Coefficient of Performance) are being replaced by seasonal energy efficiency factors, like the SEER (Seasonal EER) and the SCOP (Seasonal COP) to evaluate the performance of a heat pump by the time of the year. Seasonal performance factors, such as the CSPF (Cooling Seasonal Performance Factor) and the HSPF (Heating Seasonal Performance Factor) are used to describe the heat pump's performance during the cool and hot seasons. In this study, the optimization of all heat pump's operating parameters was experimentally conducted to enhance the SEER based on the EU standard (EN 14825). Moreover, the SEER was improved by the compressor frequency, as well as indoor and outdoor fan speeds. In addition, the performance characteristics of the heat pump were studied under partial load conditions. As a result, the SEER was enhanced by 17% when the compressor frequency was optimized. An additional 2% improvement was achievable with the optimization of indoor and outdoor fan speeds.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.5
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• pp.413-419
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• 2008

Deterministic design optimization (DO) does not explicitly deal with a variety of factors from inherent randomness and uncertainties. Reliability based design optimization(RBDO) is necessary to use in engineering systems in order to guarantee quality and performance of product. In this paper, design variables are considered as random variables. Standard deviation according to change of design variables have changed as much as coefficient of variation. And, if the standard deviation is error of manufacturing, standard deviation-mean relation is concave form. We obtain reliability index using advanced first order second moment method(AFOSM). This paper is examined by solving two examples and the results are compares with DO, RBDO and suggested RBDO.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.24 no.4
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• pp.12-19
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• 2016

In the case of hard problems to find solutions or complx combination problems, there are various optimization algorithms that are used to solve the problem. Among these optimization algorithms, the representative of the optimization algorithm created by imitating the behavior patterns of the organism is the PSO (Particle Swarm Optimization) algorithm. Since the PSO algorithm is easily implemented, and has superior performance, the PSO algorithm has been used in many fields, and has been applied. In particular, PSO-SAPARB (PSO with Swarm Arrangement, Parameter Adjustment and Reflective Boundary) algorithm is an advanced PSO algorithm created to complement the shortcomings of PSO algorithm. In this paper, this PSO-SAPARB algorithm was applied to the longitudinal controller design of a VTOL (Vertical Take-Off and Landing) aircraft that has the advantages of fixed-wing aircraft and rotorcraft among drones which has attracted attention in the field of UAVs. Also, through the introduction and performance of the Korean SBAS (Satellite Based Augmentation System) named KASS (Korea Augmentation Satellite System) which is being developed currently, this paper deals with the availability of algorithm such as the PSO-SAPARB.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.6
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• pp.1-9
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• 2011

In this study, the specifications of the components of the vent vale were optimally determined in order to enhance the performance of the vent valve. Design objective was to minimize fuel leakage while satisfying the design constraints on the performance indices. To obtain the optimum solution based on real experiments, several design techniques available in PIAnO, a commercial PIDO tool, were used. First, an orthogonal array was used to generate training design points and then real experiments were performed to measure the experimental data at the training design points. Next, Kriging metamodels for the objective function and design constraints were generated using the experimental data. Finally, a genetic algorithm was employed to obtain the optimization results using the Kriging models. Fuel leakage of the optimized vent valve was found to be reduced by 95.8% compared to that of the initial one while satisfying all the design constraints.

• International Journal of Fluid Machinery and Systems
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• v.4 no.1
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• pp.14-24
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• 2011

In this paper, design optimization for mixed-flow pump impellers and diffusers has been studied using a commercial computational fluid dynamics (CFD) code and DOE (design of experiments). We also discussed how to improve the performance of the mixed-flow pump by designing the impeller and diffuser. Geometric design variables were defined by the vane plane development, which indicates the blade-angle distributions and length of the impeller and diffusers. The vane plane development was controlled using the blade-angle in a fixed meridional shape. First, the design optimization of the defined impeller geometric variables was achieved, and then the flow characteristics were analyzed in the point of incidence angle at the diffuser leading edge for the optimized impeller. Next, design optimizations of the defined diffuser shape variables were performed. The importance of the geometric design variables was analyzed using $2^k$ factorial designs, and the design optimization of the geometric variables was determined using the response surface method (RSM). The objective functions were defined as the total head and the total efficiency at the design flow rate. Based on the comparison of CFD results between the optimized pump and base design models, the reason for the performance improvement was discussed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.1
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• pp.49-56
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• 2013

In this paper, for constrained optimization problem, one multi-objective optimization algorithm that ensures both performance robustness and constraint feasibility is proposed when uncertainties are involved in design variables. In the proposed algorithm, the gradient index of objective function assisted by design sensitivity with the help of finite element method is applied to evaluate robustness; the reliability calculated by the sensitivity-assisted Monte Carlo simulation method is used to assess the feasibility of constraint function. As a demonstration, the performance and numerical efficiency of the proposed method is investigated through application to the optimal design of TEAM problem 22--a superconducting magnetic energy storage system.

• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.6
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• pp.995-1006
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• 2015

Underwater glider, as a new kind of autonomous underwater vehicles, has many merits such as long-range, extended-duration and low costs. The shape of underwater glider is an important factor in determining the hydrodynamic efficiency. In this paper, a high lift to drag ratio configuration, the Blended-Wing-Body (BWB), is used to design a small civilian under water glider. In the parametric geometric model of the BWB underwater glider, the planform is defined with Bezier curve and linear line, and the section is defined with symmetrical airfoil NACA 0012. Computational investigations are carried out to study the hydrodynamic performance of the glider using the commercial Computational Fluid Dynamics (CFD) code Fluent. The Kriging-based genetic algorithm, called Efficient Global Optimization (EGO), is applied to hydrodynamic design optimization. The result demonstrates that the BWB underwater glider has excellent hydrodynamic performance, and the lift to drag ratio of initial design is increased by 7% in the EGO process.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3864-3877
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• 2022

We propose a new approach called PESA (Prioritized replay Evolutionary and Swarm Algorithms) combining prioritized replay of reinforcement learning with hybrid evolutionary algorithms. PESA hybridizes different evolutionary and swarm algorithms such as particle swarm optimization, evolution strategies, simulated annealing, and differential evolution, with a modular approach to account for other algorithms. PESA hybridizes three algorithms by storing their solutions in a shared replay memory, then applying prioritized replay to redistribute data between the integral algorithms in frequent form based on their fitness and priority values, which significantly enhances sample diversity and algorithm exploration. Additionally, greedy replay is used implicitly to improve PESA exploitation close to the end of evolution. PESA features in balancing exploration and exploitation during search and the parallel computing result in an agnostic excellent performance over a wide range of experiments and problems presented in this work. PESA also shows very good scalability with number of processors in solving an expensive problem of optimizing nuclear fuel in nuclear power plants. PESA's competitive performance and modularity over all experiments allow it to join the family of evolutionary algorithms as a new hybrid algorithm; unleashing the power of parallel computing for expensive optimization.