• Journal of Korean Institute of Industrial Engineers
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• v.35 no.4
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• pp.257-265
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• 2009

This paper considers the hybrid flow shop scheduling problem for the objective of minimizing the number of tardy jobs. In hybrid flow shops, each job is processed through multiple production stages in series, each of which has multiple identical parallel machines. The problem is to determine the allocation of jobs to the parallel machines at each stage as well as the sequence of the jobs assigned to each machine. Due to the complexity of the problem, we suggest search heuristics, tabu search and simulated annealing algorithms with a new method to generate neighborhood solutions. In particular, to evaluate and select neighborhood solutions, three surrogate objectives are additionally suggested because not much difference in the number of tardy jobs can be found among the neighborhoods. To test the performances of the surrogate objective based search heuristics, computational experiments were performed on a number of test instances and the results show that the surrogate objective based search heuristics were better than the original ones. Also, they gave the optimal solutions for most small-size test instances.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.05a
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• pp.41-42
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• 2018

This study is about the prediction of ship's roll motion characteristic which has been used for evaluating ship's seakeeping performance. In order to obtain the ship's roll RAO during voyage, this paper utilized machine learning-based surrogate model. By comparing the prediction result data of surrogate model with test data, we suggest the best approximation technique and data sampling interval of the surrogate model appropriate for predicting the ships' roll motion characteristic.

• Smart Structures and Systems
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• v.29 no.2
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• pp.321-336
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• 2022

Model uncertainty is a key factor that could influence the accuracy and reliability of numerical model-based analysis. It is necessary to acquire an appropriate updating approach which could search and determine the realistic model parameter values from measurements. In this paper, the Bayesian model updating theory combined with the transitional Markov chain Monte Carlo (TMCMC) method and K-means cluster analysis is utilized in the updating of the structural model parameters. Kriging and polynomial chaos expansion (PCE) are employed to generate surrogate models to reduce the computational burden in TMCMC. The selected updating approaches are applied to three structural examples with different complexity, including a two-storey frame, a ten-storey frame, and the national stadium model. These models stand for the low-dimensional linear model, the high-dimensional linear model, and the nonlinear model, respectively. The performances of updating in these three models are assessed in terms of the prediction uncertainty, numerical efforts, and prior information. This study also investigates the updating scenarios using the analytical approach and surrogate models. The uncertainty quantification in the Bayesian approach is further discussed to verify the validity and accuracy of the surrogate models. Finally, the advantages and limitations of the surrogate model-based updating approaches are discussed for different structural complexity. The possibility of utilizing the boosting algorithm as an ensemble learning method for improving the surrogate models is also presented.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4181-4194
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• 2022

Main steam safety valves are commonly used in nuclear power plants to provide final protections from overpressure events. Blowdown and dynamic stability are two critical characteristics of safety valves. However, due to the parameter sensitivity and multi-parameter features of safety valves, using traditional method to design and/or optimize them is generally difficult and/or inefficient. To overcome these problems, a surrogate model-based valve design optimization is carried out in this study, of particular interest are methods of valve surrogate modeling, valve parameters global sensitivity analysis and valve performance optimization. To construct the surrogate model, Design of Experiments (DoE) and Computational Fluid Dynamics (CFD) simulations of the safety valve were performed successively, thereby an ensemble surrogate model (E-AHF) was built for valve blowdown and stability predictions. With the developed E-AHF model, global sensitivity analysis (GSA) on the valve parameters was performed, thereby five primary parameters that affect valve performance were identified. Finally, the k-sigma method is used to conduct the robust optimization on the valve. After optimization, the valve remains stable, the minimum blowdown of the safety valve is reduced greatly from 13.30% to 2.70%, and the corresponding variance is reduced from 1.04 to 0.65 as well, confirming the feasibility and effectiveness of the optimization method proposed in this paper.

• Structural Engineering and Mechanics
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• v.84 no.3
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• pp.323-335
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• 2022

The main idea of the framework is to seamlessly combine a reasonably accurate and fast surrogate model with the importance sampling strategy. Developing a surrogate model for predicting structures' dynamic responses is challenging because it involves high-dimensional inputs and outputs. For this purpose, a novel surrogate model based on cutting-edge deep learning architectures specialized for capturing temporal relationships within time-series data, namely Long-Short term memory layer and Transformer layer, is designed. After being properly trained, the surrogate model could be utilized in place of the finite element method to evaluate structures' responses without requiring any specialized software. On the other hand, the importance sampling is adopted to reduce the number of calculations required when computing the failure probability by drawing more relevant samples near critical areas. Thanks to the portability of the trained surrogate model, one can integrate the latter with the Importance sampling in a straightforward fashion, forming an efficient framework called TTIS, which represents double advantages: less number of calculations is needed, and the computational time of each calculation is significantly reduced. The proposed approach's applicability and efficiency are demonstrated through three examples with increasing complexity, involving a 1D beam, a 2D frame, and a 3D building structure. The results show that compared to the conventional Monte Carlo simulation, the proposed method can provide highly similar reliability results with a reduction of up to four orders of magnitudes in time complexity.

• Clinical and Experimental Reproductive Medicine
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• v.39 no.4
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• pp.138-143
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• 2012

While Korea does not have any legal statement on surrogacy, treatments are carried out in practice. As a result, every Institutional Review Board (IRB) of each fertility clinic faces an ethical predicament in reviewing each case. There is a need to arrange the institutions' own standards of surrogate pregnancy procedures before the establishment of national or professional regulation. This article examines the legal, social, and medical issues of surrogacy to help IRBs to judge their cases.

• Journal of the Chungcheong Mathematical Society
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• v.27 no.3
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• pp.433-438
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• 2014

A semi-infinite optimization problem involving a quasi-convex objective function and infinitely many convex constraint functions with data uncertainty is considered. A surrogate duality theorem for the semi-infinite optimization problem is given under a closed and convex cone constraint qualification.

• International Journal of Fluid Machinery and Systems
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• v.2 no.1
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• pp.1-12
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• 2009

Surrogate modeling is applied to a compressor blade shape optimization to modify its stacking line and thickness to enhance adiabatic efficiency and total pressure ratio. Six design variables are defined by parametric curves and three objectives; efficiency, total pressure and a combined objective of efficiency and total pressure are considered to enhance the performance of compressor blade. Latin hypercube sampling of design of experiments is used to generate 55 designs within design space constituted by the lower and upper limits of variables. Optimum designs are found by formulating a PRESS (predicted error sum of squares) based averaging (PBA) surrogate model with the help of a gradient based optimization algorithm. The optimum designs using the current variables show that, to optimize the performance of turbomachinery blade, the adiabatic efficiency objective is improved substantially while total pressure ratio objective is increased a very small amount. The multi-objective optimization shows that the efficiency can be increased with the less compensation of total pressure reduction or both objectives can be increased simultaneously.

• International Journal of Fluid Machinery and Systems
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• v.8 no.3
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• pp.209-219
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• 2015

A robust multi-fidelity optimization methodology has been developed, focusing on efficiently handling industrial runner design of hydraulic Francis turbines. The computational task is split between low- and high-fidelity phases in order to properly balance the CFD cost and required accuracy in different design stages. In the low-fidelity phase, a physics-based surrogate optimization loop manages a large number of iterative optimization evaluations. Two derivative-free optimization methods use an inviscid flow solver as a physics-based surrogate to obtain the main characteristics of a good design in a relatively fast iterative process. The case study of a runner design for a low-head Francis turbine indicates advantages of integrating two derivative-free optimization algorithms with different local- and global search capabilities.

• The KSFM Journal of Fluid Machinery
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• v.10 no.2 s.41
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• pp.22-31
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• 2007

This paper describes the shape optimization of rotor blade in a transonic axial compressor rotor. Three surrogate models, Kriging, radial basis neural network and response surface methods, are introduced to find optimum blade shape and to compare the characteristics of object function at each optimal design condition. Blade sweep, lean and skew are considered as design variables and adiabatic efficiency is selected as an objective function. Throughout the shape optimization of the compressor rotor, the predicted adiabatic efficiency has almost same value for three surrogate models. Among the three design variables, a blade sweep is the most sensitive on the object function. It is noted that the blade swept to backward and skewed to the blade pressure side is more effective to increase the adiabatic efficiency in the axial compressor Flow characteristics of an optimum blade are also compared with the results of reference blade.