• 한국항공우주학회지
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• 제38권5호
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• pp.414-420
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• 2010

본 연구에서는 천음속 날개/동체 모텔에 대한 축소모델 (Reduced Order Model; ROM)의 정확성을 검증하고, Proper Orthogonal Decomposition(POD)을 이용한 최적설계를 통해 그 효율성을 검토하였다. full order 공력해석을 통한 Snapshot을 추출하기 위해 삼차원 오일러 방정식을 이용하였으며, 이들 Snapshot들을 통해 날개/동체 모델 주위 유동장의 거동을 모사하는 POD의 기저벡터를 계산 하였다. 이러한 과정을 거쳐 구축된 축소모텔은 6개의 Case들로 검증하였으며, 그 결과 ROM을 이용해 관심영역에 대한 유동장의 예측을 할 수 있다는 것을 확인하였다. 그리고 ROM을 통한 날개/동체 모델의 최적설계를 수행 하였으며, 그 결과는 반응면모델 (Response Surface Model; RSM)을 이용한 최적설계 결과와 비교 하였다. 이를 통해 ROM을 바탕으로한 최적설계가 RSM을 이용한 것보다 효율적임을 확인하였다.

• Nuclear Engineering and Technology
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• 제56권4호
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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.

• 한국전산구조공학회논문집
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• 제37권1호
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• pp.9-16
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• 2024

일반적으로 적합직교분해(proper orthogonal decomposition, POD) 기반의 침습적(intrusive) 차수축소모델(reduced order model, ROM)을 활용하면 구조 시스템의 전체 자유도를 크게 줄이고 외연적 시간 적분법에서 해의 안정성을 만족하는 임계 시간 간격을 증가시킬 수 있다. 따라서 본 연구에서는 POD-ROM을 활용하여 Voronoi-cell 격자 요소로 이산화된 구조 시스템의 축소와 이에 따른 외연적 시간 적분법의 임계 시간 간격 및 해석 정확도 변화를 살펴보았다. 또한 지진하중과 같은 불규칙한 하중 이력을 받는 구조물 응답 해석에 POD-ROM을 적용하였다. 해석 결과 ROM을 통해 해의 정확도를 충분히 확보하면서 연산 시간을 크게 단축할 수 있음을 확인하였다. 또한 POD-ROM과 VCLM의 연계 방안의 적절성을 확인하였다. 향후 해당 연구는 고정밀 대용량 동적 구조해석의 실용성을 높이고, 설계 변수에 따른 구조물 동적 거동의 실시간 예측을 위한 기반 연구로 활용될 수 있다.

• Nuclear Engineering and Technology
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• 제54권5호
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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.

• 제어로봇시스템학회논문지
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• 제4권5호
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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.

• 전력전자학회지
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• 제25권4호
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• pp.44-49
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• 2020

• Nuclear Engineering and Technology
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• 제54권1호
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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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• 제17권3호
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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.

• 한국항공우주학회지
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• 제45권1호
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• pp.1-9
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• 2017

본 논문은 초음속 공탄성(aeroelastics) 해석 시간 절약을 위한 준-정상 축약 모델을 제안하고 이를 검증하였다. 유동이 초음속이고 유동의 특성시간이 구조변형의 특성시간에 비해 작을 경우, 유동의 비정상성이 작아 비정상 유동해 대신 정상 유동해를 이용하여 공탄성 해석을 할 수 있다. 크리깅 기법을 적용하여 유동의 축약모델을 구축하였다. 매 시간 축약모델로부터 예측된 표면해가 구조해석의 경계조건으로 사용되었다. 크리킹 기법을 적용한 축약모델을 비정상해석 결과와 비교하여 검증하였다.

• 한국산학기술학회논문지
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• 제13권10호
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• pp.4411-4417
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• 2012

본 연구에서는 축소모델을 기반으로 비행체 날개를 최적화하는 기법을 제안한다. 잘 구축된 축소모델은 고유치 문제나 동적 해석 시 정확한 해석결과를 제공하며, 최적화 과정에서 필요한 민감도 계산에서도 정확한 결과를 제공할 수 있다. 이러한 축소모델은 모드기반으로 구축되는 축소차수모델(Reduce Order Model)과 자유도기반으로 구축되는 축소시스템(Reduced System)으로 구분되는데, 본 연구에서 사용하는 자유도 기반 축소시스템은 구조물의 거동에 지배적인 자유도를 적절히 선정하는 것이 중요하므로, 이를 위하여 기존 연구에서 신뢰성이 검증된 2단계 축소방법을 사용하였고, IRS(Improved Reduced System)에 의해 최종시스템을 구축하였다. 수치예제에서 최적화 과정에서 계산되는 등가응력, 고유치 및 설계민감도는 모두 축소시스템 기반으로 구해지며, 축소시스템을 통해 구속조건을 잘 만족하면서 목적함수에 대한 최적 결과를 얻을 수 있음을 보인다.