• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.5
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• pp.414-420
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• 2010

This paper presents a validation of the accuracy of a reduced order model(ROM) and the efficiency of the design optimization using a Proper Orthogonal Decomposition(POD) to transonic wing/fuselage system. Three dimensional Euler equations are solved to extrude snapshot data of the full order aerodynamic analysis, and then a set of POD basis vectors reproducing the behavior of flow around the wing/fuselage system is calculated from these snapshots. In this study, reduced order model constructed through this procedure is applied to several validation cases, and then it is confirmed that the ROM has the capability of the prediction of flow field in the space of interest. Additionally, after the design optimization of the wing/fuselage system with the ROM is performed, results of the ROM are compared with results of the design optimization using response surface model(RSM). From these, it can be confirmed that the design optimization with the ROM is more efficient than RSM.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1296-1309
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• 2024

The aim of this work is to explore the effect of the double subdiffusion on the stability in BWRs. A BWR novel reduced order model with double subdiffusion effects: reduced order fractional model (DS-F-ROM) to describe the neutron and heat transfer processes was proposed for this study. The double subdiffusion was developed with a fractional-order two-equation model, and with different fractional-orders and relaxation times. The stability analysis was carried out using the root-locus method and change from the s to the W domain and were confirmed using the time-domain evolution of neutron flux for a unit step change in reactivity. The results obtained using the reduced fractional-order model are presented for different anomalous diffusion coefficient values. Results are compared with normal diffusion and P1 equations, which are obtained straightforwardly with DS-ROM when relaxation time tends to zero, and when the anomalous diffusion coefficient tends to one, respectively.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.1
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• pp.9-16
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• 2024

Using the proper orthogonal decomposition (POD) based intrusive reduced order model (ROM), the total degrees of freedom of the structural system can be significantly reduced and the critical time step satisfying the conditional stability increases in the explicit time integrations. In this study, therefore, the changes in the critical time step in the explicit time integrations are investigated using both the POD-ROM and Voronoi-cell lattice model (VCLM). The snapshot matrix is composed of the data from the structural response under the arbitrary dynamic loads such as seismic excitation, from which the POD-ROM is constructed and the predictive capability is validated. The simulated results show that the significant reduction in the computational time can be achieved using the POD-ROM with sufficiently ensuring the numerical accuracy in the seismic analyses. In addition, the validations show that the POD based intrusive ROM is compatible with the Voronoi-cell lattice based explicit dynamic analyses. In the future study, the research results will be utilized as an elemental technology for the developments of the real-time predictive models or monitoring system involving the high-fidelity simulations of structural dynamics.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1825-1834
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• 2022

Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.579-584
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• 1998

This paper presents an efficient algorithm which determines the parameters of discrete PID controller. The proposed algorithm is an algebraic method to obtain controller parameters using ROM(Reduced Order Model), which can not only make design procedure simple but also reduce the computational burden required for controller implementation. Also, by solving a set of linear equations based on least squares method, the proposed method can make the controller design procedure systematic. Simple examples are given to demonstrate the effectiveness of our method when compared with widely-used conventional method.

• KIPE Magazine
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• v.25 no.4
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• pp.44-49
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• 2020

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.36-48
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• 2022

As a valid numerical method to obtain a high-resolution result of a flow field, computational fluid dynamics (CFD) have been widely used to study coolant flow and heat transfer characteristics in fuel rod bundles. However, the time-consuming, iterative calculation of Navier-Stokes equations makes CFD unsuitable for the scenarios that require efficient simulation such as sensitivity analysis and uncertainty quantification. To solve this problem, a reduced-order model (ROM) based on proper orthogonal decomposition (POD) and machine learning (ML) is proposed to simulate the flow field efficiently. Firstly, a validated CFD model to output the flow field data set of the rod bundle is established. Secondly, based on the POD method, the modes and corresponding coefficients of the flow field were extracted. Then, an deep feed-forward neural network, due to its efficiency in approximating arbitrary functions and its ability to handle high-dimensional and strong nonlinear problems, is selected to build a model that maps the non-linear relationship between the mode coefficients and the boundary conditions. A trained surrogate model for modes coefficients prediction is obtained after a certain number of training iterations. Finally, the flow field is reconstructed by combining the product of the POD basis and coefficients. Based on the test dataset, an evaluation of the ROM is carried out. The evaluation results show that the proposed POD-ROM accurately describe the flow status of the fluid field in rod bundles with high resolution in only a few milliseconds.

• Design & Manufacturing
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• v.17 no.3
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• pp.1-7
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• 2023

Blow molding is a manufacturing process in which thermoplastic preforms are preheated and then pneumatically expanded within a mold to produce hollow products of various shapes. The two-step process, a type of blow molding method, requires the output of multiple infrared lamps to be adjusted individually, so the process of finding initial conditions hinders productivity. In this study, digital twin technology was applied to solve this problem. A blow molding simulation technique was established and simulation-based metadata was generated. A response surface ROM (Reduced Order Model) was built using the generated metadata. Then, a dynamic ROM was constructed using the results of 3D heat transfer analysis. Through this, users can quickly check the product wall thickness uniformity according to changes in the control value of the heating lamp for products of various shapes, and at the same time, check the temperature distribution of the preform in real time.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.1
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• pp.1-9
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• 2017

In this paper, we present and verify an aerodynamic reduced-order model (ROM) based on a quasi-steady flow method to reduce the computational cost of supersonic aeroelastic analysis. For supersonic flows, especially when the characteristic time scale of the flow is small compared to that of the structural motion, the unsteadiness of flow can be negligible, and quasi-steady solutions can be used instead of the unsteady solutions for the aeroelastic analysis. Kriging method is used to build the ROM of the aerodynamics. The surface solutions from the ROM are used as the boundary conditions for the structural analysis at each time-step. The ROM is validated against the unsteady solutions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.10
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• pp.4411-4417
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• 2012

The present study proposes the optimization of wing structure base on reduced model which assures the solution accuracy and computational efficiency. Well-constructed reduced model assures the accurate result in the eigenvalue problem, dynamic analysis or sensitivity of design optimization. Reduced system is classified into the reduce-order model based on structural modes and the reduced system based on degrees of freedom. Because this study uses the reduced system based on degrees of freedom, it is important to select the dominant degrees of freedom properly. For this work, robust selection method, two-level selection scheme, is employed and IRS(Improved Reduced System) is applied to construct the final reduced system. In the optimization process based on the reduced system, all of the equivalent stress, eigenvalue and design sensitivities are calculated from the reduced system. Through a numerical example, it is shown that the present optimization methodology based on the reduction method can provide an optimal results for objective function satisfying constraint condition.