• 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.

• 대한기계학회논문집A
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• 제30권6호
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• pp.684-690
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• 2006

This study proposes and validates a new column type electric power steering system (EPS-TT). It is driven by a uni-directional motor and two electro-magnetic clutches. The assist motor produces assist torque in only one direction and two clutches transmit the torque to the column of steering system in either left or right direction with respect to the steering input. A full order and a reduced order models are developed to evaluate the EPS-TT. Models are also used to investigate the vehicle responses. A PID control logic is designed to control the torque of the assist motor. A driver model is applied to the system and the resulting performances are analyzed. The results show that the performances of the full order model are similar to those of reduced order model. The results also prove that the performances achieved by the EPS-TT are improved compared to those of a conventional EPS-TT across the frequency domain.

• 전기의세계
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• 제30권3호
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• pp.167-171
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• 1981

In this paper, the mathematical model of influenza derived by the state space method induced a new model by using normal distribution curve of incubation period and researched the effect of vaccination. The important results are as follows. (1) A new model represents accurate spread curve. (2) The standard deviation period in Korea is about 1.5 degree. (3) The number of carries of influenza since put in practice to the vaccination 20% is reduced by average 9.8% degree, the period of spread increase 4 days degree. (4) The vaccination at early put in operation was far surperior and the period of spread grow longer more or less. (5) In the first stage of an attack of disease a case increase since reducing. (6) The number of carries at night is reduced by average 5.468% than in the daytime.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2001년도 추계학술발표논문집 (상)
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• pp.363-366
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• 2001

It is very difficult to maintain the performance of estimation models for the new breed of projects since the computing environment changes so rapidly in terms of programming languages, development tools, and methodologies. So, we propose to use the relevant cases for a neural network model, whose cost is the decreased number of cases. To balance the relevance and data availability, the qualitative input factors are used as criteria of data classification. With the data sets that have the same value for certain qualitative input factors, we can eliminate the factors from the model making reduced neural network models. So we need to seek the optimally reduced neural network model among them. To find the optimally case-selective neural network, we propose the search techniques and sensitivity analysis between data points and search space.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2011년도 춘계학술대회 논문집
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• pp.443-444
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• 2011

This paper discusses a model order reduction for large order rotor dynamics systems results from the finite element discretization. Typical rotor systems consist of a rotor, built-on parts, and a support system, and require prudent consideration in their dynamic analysis models because they include unsymmetric stiffness, localized nonproportional damping and frequency dependent gyroscopic effects. When the finite element model has a very large number of degrees of freedom because of complex geometry, repeated dynamic analyses to investigate the critical speeds, stability, and unbalanced response are computationally very expensive to finish within a practical design cycle. In this paper, the Krylov-based model order reduction via moment matching significantly speeds up the dynamic analyses necessary to check eigenvalues and critical speeds of a Nelson-Vaugh rotor system. With this approach the dynamic simulation is efficiently repeated via a reduced system by changing a running rotational speed because it can be preserved as a parameter in the process of model reduction. The Campbell diagram by the reduced system shows very good agreement with that of the original system. A 3-D finite element model of the Nelson-Vaugh rotor system is taken as a numerical example to demonstrate the advantages of this model reduction for rotor dynamic simulation.

• 한국항공운항학회지
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• 제17권4호
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• pp.8-16
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• 2009

An experimental study was carried out to investigate aerodynamic characteristics on reduced frequency of flapping wings. The half span of the wing is 28cm, and the mean chord length of wing is 10cm. In flight, the Reynolds Number range of birds is about $10^4$, and the reduced frequency during a level flight is 0.25. The experimental variables of present study were set to have similar conditions with the bird flight's one. The freestream velocities in a wind tunnel were 2.50, 3.75 and $5.00^m/s$, and the corresponding Reynolds numbers were $1.7{\times}10^4$, $2.5{\times}10^4$ and $3.3{\times}10^4$, respectively. The wing beat frequencies of an experimental model were 2, 3 and 4Hz, and the corresponding reduced frequency was decided between 0.1 and 0.5. Aerodynamic forces of an experimental flapping model were measured by using 2 axis load-cell. Inertial forces measured in a vacuum chamber were removed from measuring forces in the wind tunnel in order to acquire pure aerodynamic forces. Hall sensors and laser trigger were used to make sure the exact position of wings during the flapping motion. Results show that the ratio of downstroke in a wing beat cycle is increased as a wing beat frequency increases. The instantaneous lift coefficient is the maximum value at the end of downstroke of flapping wing model. It is found that a critical reduced frequency with large lift coefficient is existed near k=0.25.

• 전산구조공학
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• 제6권2호
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• pp.95-102
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• 1993

노후 구조물의 안전진단을 위하여 동적재하시험을 수행하고 그 결과를 유한요소모델과 같은 해석적 모델과 결합하여 기존구조물의 강성평가 및 파손부위 색출에 적용하고 있는데 측정점의 제한성과 유한요소모델의 많은 자유도가 측정데이타를 유한요소모델과 연계하는데 커다란 문제점으로 대두된다. 본 연구에서는 유한요소모델과 같이 많은 자유도를 갖는 구조계의 해석적 모델을 측정데이타와 결합하기 위하여 축약된 좌표계에서 구조계의 동강성행렬(dynamic stiffness matrix)표현방법을 제시하였다. 유한요소모델로부터 좌표계를 축약시 필연적으로 발생되는 주파수의존성(frequency dependency)을 고려하기 위하여 주파수영역에서 Chebyshev다항식으로 축약된 동강성행렬을 표시하였고 특이점에서 발생되는 악조건(ill-condition)을 극복하기 위하여 특이해분리(singular value decomposition)기법을 사용하였다. 제시된 방법의 검증을 위하여 간단한 구조계에 대하여 시뮬레이션을 수행하였으며 본 방법으로 수립된 구조계의 동적모델은 축약이전의 전체계에 대한 동적특성을 비교적 정확히 유지하고 있고 일반적으로 사용되는 정적축약 형태의 수학적 모델보다 우수함을 알 수 있었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1984년도 하계학술회의논문집
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• pp.84-85
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• 1984

The accurate description of many physical processes leads to high number of different equations which are very difficult to handle for simulation or control purposes. The reduction of high-order, linear, time-invariant systems to lower-order ones has been investigated by many researchers. In this paper, a model technique among these methods is used. This technique has been developed here as if it were extensions of Davison's original method (1), its modification having been made to provide, among other things, steady state agreement between the original large-scale and reduced-order model. The advantage of the modal analysis approach is that only matrix operations have to be executed. Here, it is very simple to obtain a reduced model. An example of illustration is shown using the model method.

• International Journal of Aeronautical and Space Sciences
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• 제18권4호
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• pp.651-661
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• 2017

A truss-braced wing (TBW) aircraft has recently received increasing attention due to higher aerodynamic efficiency compared to conventional cantilever wing aircraft. For conceptual TBW aircraft design, we developed a propulsion-and-airframe integrated design environment by replacing a semi-empirical turbofan engine model with a thermodynamic cycle-based one built upon the numerical propulsion system simulation (NPSS). The constructed NPSS model benefitted TBW aircraft design study, as it could handle engine installation effects influencing engine fuel efficiency. The NPSS model also contributed to broadening TBW aircraft design space, for it provided turbofan engine design variables involving a technology factor reflecting progress in propulsion technology. To effectively consolidate the NPSS propulsion model with the TBW airframe model, we devised a rapid, approximate substitute of the NPSS model by reduced-order modeling (ROM) to resolve difficulties in model integration. In addition, we formed an artificial neural network (ANN) that associates engine component attributes evaluated by object-oriented weight analysis of turbine engine (WATE++) with engine design variables to determine engine weight and size, both of which bring together the propulsion and airframe system models. Through propulsion-andairframe design space exploration, we optimized TBW aircraft design for fuel saving and revealed that a simple engine model neglecting engine installation effects may overestimate TBW aircraft performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.929-944
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• 2019

Many critical applications require accurate real-time human action recognition. However, there are many hurdles associated with capturing and pre-processing image data, calculating features, and classification because they consume significant resources for both storage and computation. To circumvent these hurdles, this paper presents a recognition machine learning (ML) based system model which uses reduced data structure features by projecting real 3D skeleton modality on virtual 2D space. The MMU VAAC dataset is used to test the proposed ML model. The results show a high accuracy rate of 97.88% which is only slightly lower than the accuracy when using the original 3D modality-based features but with a 75% reduction ratio from using RGB modality. These results motivate implementing the proposed recognition model on an embedded system platform in the future.