• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.12-12
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• 2000

This paper consider a new weighted model reduction using block diagonal solutions of Lyapunov inequalities. With the input and/or output weighting function, the stability of reduced order system is quaranteed and a priori error bound is proposed. to achieve this, after finding the solutions of two Lyapunov inequalities and balancing the full order system, we find the reduced order systems using the direct truncation and the singular perturbation approximation. The proposed method is compared with other existing methods using numerical example.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.1193-1196
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• 2003

Noise reduction effect of a pine tree which was used to reinforce a noise barrier was studied by using reduced scale model test. The result show that the pine tree itself was less effective but the combination of pine tree and noise barrier was good for reducing the noise and sight.

• Structural Engineering and Mechanics
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• v.57 no.5
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• pp.921-936
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• 2016

In this study, a model order reduction technique is applied to solve the transient responses of submerged floating tunnel (SFT) from Mokpo to Jeju under seismic excitations. Because the SFT is a very long structure as well as a transient response analysis requires large amount of computational resources, the model order reduction is mandatory in the design stage of the SFT. Thus, we apply a model order reduction based on Krylov subspace to the simplified finite element model of the SFT. The responses of the reduced order model are compared with those of the full order model and also are verified by referring a previous work. In conclusion, the computational resources are dramatically reduced with an acceptable accuracy by using the model order reduction, which eventually is useful for designing the full-scale model of SFTs.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.83.2-83
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• 2002

1. Introduction 2. Twin-Drive Geared Mechanical System 3. Reduced-Order Control Model 4. Model-Based Control System 5. Suppression of Transient Vibration 6. Effects of Model-Based Control 7. Conclusions

• Computational Structural Engineering
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• v.6 no.2
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• pp.95-102
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• 1993

A modeling method is described to provide a smaller structural dynamic model which can be used to compare finite element model of a structure with its experimental counterpart. A structural dynamic model is assumed to be represented by dynamic stiffness matrix. To validate a finite element model, it is often necessary to condense a large degrees of freedom (dofs) to a relatively small number of dofs. For these purpose, static reduction techniques are widely used. However, errors in these techniques are caused by neglecting frequency dependent terms in the functions relating slave dofs and master dofs. An alternative method is proposed in this paper in which the frequency dependent terms are considered by expressing the reduced dynamic stiffness matrix with orthogonal polynomials. The reduced model has finally a minimum set of dofs, such as sensors and excitation points and it is under the same condition as the physical system. It is proposed that the reduced model can be derived from finite element model. The procedure is applied to example structure and the results are discussed.

• 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.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.04a
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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.

• 전기의세계
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• v.30 no.3
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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.

• Phonetics and Speech Sciences
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• v.7 no.4
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• pp.3-8
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• 2015

In this paper, we investigate an input dimension reduction method using continuous word vector in deep neural network language model. In the proposed method, continuous word vectors were generated by using Google's Word2Vec from a large training corpus to satisfy distributional hypothesis. 1-of-${\left|V\right|}$ coding discrete word vectors were replaced with their corresponding continuous word vectors. In our implementation, the input dimension was successfully reduced from 20,000 to 600 when a tri-gram language model is used with a vocabulary of 20,000 words. The total amount of time in training was reduced from 30 days to 14 days for Wall Street Journal training corpus (corpus length: 37M words).