• Transactions of Materials Processing
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• v.27 no.1
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• pp.28-36
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• 2018

Electromagnetic forming is a type of high-speed forming process to deform a workpiece through a Lorentz force. As the high strain rate in an electromagnetic-forming simulation causes infeasibility in determining constitutive parameters, we employed inverse parameter estimation in the previous study. However, the inverse parameter estimation process required us to spend considerable time, which leads to an increase in computational cost. To overcome the computational obstacle, in this research, we applied two types of surrogate modeling methods and compared them to each other to evaluate which model is best for the electromagnetic-forming simulation. We exploited an artificial neural network and we reduced-order modeling methods. During the construction of a reduced-order model, we extracted orthogonal bases with proper orthogonal decomposition and predicted basis coefficients by utilizing an artificial neural network. After the construction of the surrogate models, we verified the artificial neural network and reduced-order models through training and testing samples. As a result, we determined the artificial neural network model is slightly more accurate than the reduced-order model. However, the construction of the artificial neural network model requires a considerably larger amount of time than that of the reduced-order model. Thus, a reduced order modeling method is more efficient than an artificial neural network for estimating the electromagnetic forming and for the rapid approximation of structural simulations which needs repetitive runs.

• The KSFM Journal of Fluid Machinery
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• v.16 no.6
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• pp.32-39
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• 2013

In the present study, three-dimensional aerodynamic optimization of high pressure turbine nozzle for turbofan engine was performed. For this, Kriging surrogate model was built and refined iteratively by supplying additional experimental points until the surrogate model and CFX result has effective difference on objective function. When the surrogate model satisfied this reliability condition and developed enough, optimum point was investigated. Commercial program PIAnO was used for optimization process and evolutionary algorithm was used for searching optimum point. As a result, difference between estimated value from Kriging surrogate model and CFD result converges within 0.01% and the optimized nozzle shape has 0.83% improved aerodynamic efficiency.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.99-99
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• 2023

기후변화는 물 관리의 가장 큰 리스크 요인이므로 물 관리 계획을 수립하는 과정에서 기후변화의 영향을 고려하는 것이 필수적이다. 기후변화에 대한 수자원 예측 관련 연구가 이루어지고 있으나, 대부분의 연구에는 수문학적 모델링이나 시뮬레이션이 동반되는데, 이 과정에는 시간과 비용이 많이 들어가며, 지역이나 연구목적에 따른 정밀한 매개변수의 보정은 전문지식이 필요하기 때문에 현업에서 연구결과를 의사결정에 활용하기에는 한계가 있다고 볼 수 있다. 이에 따라 수문학적 모델링의 입력 및 출력 결과를 딥러닝의 학습자료로 하여 수문모델을 사용하지 않아도 효율적으로 결과를 도출할 수 있는 딥러닝 기반 Surrogate 모형에 대한 연구가 이루어지고 있으나 수자원 분야에 접목된 사례는 부재한 실정이다. 따라서 이 연구를 통해 국내 유역을 대상으로 Surrogate 모형을 구축한 뒤, 그 성능을 평가하고자 한다. 이를 위한 Surrogate 모형 구축 과정은 다음과 같다. 충주댐 유역을 대상으로 과거 20년간의 강우 및 기온 자료를 수집한 뒤, 이 자료를 바탕으로 기후변화의 영향을 고려한 3,162개의 시나리오를 생성한다. 그 후 장기유출모형 IHACRES에 생성된 시나리오를 입력자료로 하여 유입량 결과를 도출하고, 이 결과를 Python코드 기반의 딥러닝 학습자료로 하여 최적 예측 결과를 도출해내는 Surrogate 모형을 생성한 뒤 기존 장기유출모형과의 성능을 비교하고자 한다. 이와 같은 Surrogate 모형은 추가적인 데이터와 매개변수의 보정 과정이 없어도 장기유출모형과 같은 결과를 짧은 시간내에 상당히 정확하게 모사할 수 있어 시간과 비용을 줄일 수 있으며, 비전문가도 쉽게 사용할 수 있다는 장점을 가진다.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.3 no.3
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• pp.143-150
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• 1999

Mobile source emissions are important inputs to photochemical air quality models. Since most mobile source emissions are calculated at the county-level, these emission should be geographically allocated to the computational grid cells of a photochemical air quality model prior to running the model. The traditional method for the spatial allocation of these emissions has been to use a "spatial surrogate indicator" such as population, since grid-specific emission calculations are very labor-intensive and expensive, plus the necessary data are often not available for such grid resolutions. Accordingly, new spatial surrogate indicators for mobile source emissions(specifically for highway emissions) were developed using Geographic Information Systems(GIS) tools due to the spatially variable nature of mobile source emissions. These newly developed spatial surrogate indicators appear to be more appropriate for the allocation of highway emissions than the population surrogate indicator. It was also revealed that the conventional spatial allocation method underestimates the maximum levels of air pollutant emmissions.mmissions.

• Journal of Convergence for Information Technology
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• v.10 no.7
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• pp.138-146
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• 2020

The paper deals with comparative study of characteristics of surrogate models and sensitivity evaluation using design of experiments in order to enhance and analysis the structure design of an automatic salt collector under various design load conditions. Orthogonal array design based on numerical analysis was used for the design of experiments. The thickness sizing variables of main structure member were considered the design factors, and the output responses were selected from the strength performances as well as the weight. The quantitative effects on responses for each design factor were evaluated from the orthogonal array experiment. Optimum design case was also identified to improve the strength performances with weight minimization. Using the orthogonal array experiment. various surrogate models such as response surface model, Kriging model, and Chebyshev orthogonal polynomial were generated. The orthogonal array experiment results were validated by the surrogate modeling results. The most suitable surrogate model was the response surface model for the exploration of design space of the automatic salt collector.

• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1382-1399
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• 2023

Pressure relief valve (PRV) is one of the important control valves used in nuclear power plants, and its sealing performance is crucial to ensure the safety and function of the entire pressure system. For the sealing performance improving purpose, an explicit function that accounts for all design parameters and can accurately describe the relationship between the multi-design parameters and the seal performance is essential, which is also the challenge of the valve seal design and/or optimization work. On this basis, a surrogate model-based design optimization is carried out in this paper. To obtain the basic data required by the surrogate model, both the Finite Element Model (FEM) and the Computational Fluid Dynamics (CFD) based numerical models were successively established, and thereby both the contact stresses of valve static sealing and dynamic impact (between valve disk and nozzle) could be predicted. With these basic data, the polynomial chaos expansion (PCE) surrogate model which can not only be used for inputs-outputs relationship construction, but also produce the sensitivity of different design parameters were developed. Based on the PCE surrogate model, a new design scheme was obtained after optimization, in which the valve sealing stress is increased by 24.42% while keeping the maximum impact stress lower than 90% of the material allowable stress. The result confirms the ability and feasibility of the method proposed in this paper, and should also be suitable for performance design optimizations of control valves with similar structures.

• Smart Structures and Systems
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• v.22 no.5
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• pp.561-574
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• 2018

In this paper, for efficiently reducing the computational cost of the model updating during the optimization process of damage detection, the structural response is evaluated using properly trained surrogate model. Furthermore, in practice uncertainties in the FE model parameters and modelling errors are inevitable. Hence, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The current work builds a framework for Probability Based Damage Detection (PBDD) of structures based on the best combination of metaheuristic optimization algorithm and surrogate models. To reach this goal, three popular metamodeling techniques including Cascade Feed Forward Neural Network (CFNN), Least Square Support Vector Machines (LS-SVMs) and Kriging are constructed, trained and tested in order to inspect features and faults of each algorithm. Furthermore, three wellknown optimization algorithms including Ideal Gas Molecular Movement (IGMM), Particle Swarm Optimization (PSO) and Bat Algorithm (BA) are utilized and the comparative results are presented accordingly. Furthermore, efficient schemes are implemented on these algorithms to improve their performance in handling problems with a large number of variables. By considering various indices for measuring the accuracy and computational time of PBDD process, the results indicate that combination of LS-SVM surrogate model by IGMM optimization algorithm have better performance in predicting the of damage compared with other methods.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.3
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• pp.74-81
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• 2013

Due to its accuracy and efficiency, reduced kinetic mechanism of diesel surrogate is widely used as fuel model when applying 3-D diesel engine simulation. But for the well-developed prediction of diesel surrogate reduced kinetic mechanism, it is important to know some meaningful factors which affect to ignition delay time. Meanwhile, ignition delay time consists of two parts. One is the chemical ignition delay time related with the chemical reaction, and the other is the physical ignition delay time which is affected by physical behavior of the fuel droplet. Especially for chemical ignition delay time, chemical properties of each fuel were studied for a long time, but researches on their mixtures have not been done widely. So it is necessary to understand the chemical characteristics of their mixtures for more precise and detailed modeling of surrogate diesel oil. And it shows same ignition trend of paraffin mixture with those of single component, and shorter ignition delay at low/high initial temperature when mixing paraffin and toluene.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.397-407
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• 2003

Transactions of updating spatial dta with mobile clients are log transactions because a user disconnected from a server surveys real features and updates them. In this environment, it is appropriate to exploit the optimistic approach based on the validation test in order to control the concurrency of transactions. On the contrary, the pessimistic concurrency control scheme makes transactions wait for a long time due to the lock. In this paper, we propose the surrogate transaction model and implement its manager for the S-S-M(Server-Surrogate PC-Mobile Client) structure which is appropriate for updating spatial data in mobile environments. In the S-S-M structure, the mobile client communicates with the server by the surrogate PC. We extend the validation condition in consideration of spatial relationships between spatial objects in this model. We also present the commit protocol where the user of a surrogate PC adjusts objects of the conflicted surrogate transaction to minimize costs for the abortion of the transaction.

• The KSFM Journal of Fluid Machinery
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• v.16 no.2
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• pp.35-41
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• 2013

Inlet part of a printed circuit heat exchanger has been optimized by using three-dimensional Reynolds-Averaged Navier-Stokes analysis and surrogate modeling techniques. Kriging model has been used as the surrogate model. The objective function for the optimization has been defined as a linear combination of uniformity of mass flow rate and the pressure loss with a weighting factor. For the optimization, the angle of the inlet plenum wall, radius of curvature of the inlet plenum wall, and width of the inlet pipes have been selected as design variables. Twenty six design points are obtained by Latin Hypercube Sampling in design space. Through the optimization, considerable improvement in the objective function has been obtained in comparison with the reference design of PCHE.