• Journal of the Society of Naval Architects of Korea
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• v.61 no.1
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• pp.8-18
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• 2024

The present study concerns reduced order modeling of a marine diesel engine, which can be used for outlier detection in status monitoring and carbon intensity index calculation. Principal Component Analysis (PCA) is introduced for the reduced order modeling, focusing on the feasibility of detecting and treating nonlinear variables. By cross-correlation, it is found that there are seven non-linear data channels among 23 data channels, i.e., fuel mode, exhaust gas temperature after the turbocharger, and cylinder coolant temperatures. The dataset is handled so that the mean is located at the nominal continuous rating. Polynomial presentation of the dataset is also applied to reflect the linearity between the engine speed and other channels. The first principal mode shows strong effects of linearity of the most data channels to show the linearity of the system. The non-linear variables are effectively explained by other modes. second mode concerns the temperature of the cylinder cooling water, which shows small correlation with other variables. The third and fourth modes correlates the fuel mode and turbocharger exhaust gas temperature, which have inferior linearity to other channels. PCA is proven to be applicable to data given in binary type of fuel mode selection, as well as numerical type data.

• 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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• 2004.11a
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• pp.647-650
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• 2004

The modeling of an active vibration isolation system is accomplished by using the equivalent spring constant, mass and rotational Inertia of each component. The detailed model of the actuation module is successful for describing its frequency-domain performance but also too complicated to implement it to actual system for control so that the order of the model is reduced up to the degree that preserves its characteristic in the low frequency range. The reduced model is suitable for identifying the unknown system parameters such as damping constants of components. The overall isolation system is described by using the reduced model of the actuation module. The accurate model ing and system parameter identification that is essential for the control of the active vibration isolation system is attained successfully.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.6
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• pp.439-444
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• 2002

In this paper, a new model reduction method is proposed to obtain a reduced order model in the frequency domain. The method is developed based on the second-order plus dead time modeling technique. The initial value of the reduced model parameters can be obtained using this method coinciding four point(0, -$\pi$/2, -$\pi$, -3$\pi$/2) on the Nyquist curve. The optimal parameters of the reduced model is obtained through calculation procedure with three steps. It is shown that Nyquist curves and unit step responses of the reduced models of numerical examples closely agree with those of original models.

• 한국연소학회:학술대회논문집
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• 2012.11a
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• pp.309-310
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• 2012

Modeling of biodiesel combustion on compression ignition engine was conducted by using the KIVA3v-Release 2 code coupled with Chemkin chemistry solver2. In order to calculate the chemical kinetics of combustion of biodiesel, a reduced mechanism of methyl decanoate and methyl 9-decanoate was used. It is composed of 123 species and 394 reactions. Also, the experiments were performed on a single-cylinder engine. The simulation results agreed well with experiments results. And soot concentrations of biodiesel were lower than those of diesel.

• Proceedings of the KIEE Conference
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• summer
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• pp.345-347
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• 2004

In small signal stability analysis of power systems, eigenvalue analysis is the most useful method and the detailed modeling of generator has an important effect to the eigenvalues. Generator full model is used for precise dynamic analysis of generators and controllers while two-axis model is used for multi-machine systems because of the reduced order of the state matrix. Also, the eigenvalue sensitivity coefficients are used for optimizing controller parameters to improve system stability. This paper compare the first and second order eigenvalue sensitivity coefficients of controllers using generator full model with those of two-axis model. As a result of an example, the estimated eigenvalues using the first and the second eigenvalue sensitivity coefficients using generator full model is very close to those of state matrix. Also the error ratios throughout a wide range of controller parameters is less than $1\%$.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.5
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• pp.275-282
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• 2018

Using isogeometric analysis(IGA) gives more accurate results for higher order mode in eigenvalue problem than using the finite element method(FEM). This is because the FEM has $C^0$ continuity between elements, whereas IGA guarantee $C^{P-1}$ between elements for p-th order basis functions. In this paper, a mode based reduced model is constructed by using IGA and dynamic behavior analysis is performed using this advantage. Craig-Bampton(CB) method is applied to construct the reduced model. Several numerical examples were performed to compare the eigenvalue analysis results for various order of element basis function by applying the IGA and FEM to simple rod analysis. We have confirmed that numerical error increases in the higher order mode as the continuity between elements decreases in the IGA by allowing internal knots multiplicity. The accuracy of the solution can be improved by using the IGA with high inter-element continuity when high-frequency external force acts on the reduced model for dynamic behavior analysis.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.3
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• pp.27-33
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• 2008

This paper deals with the assessment of potential interference between individual grounding electrodes using an Electrolytic Tank Modeling method. When a test current was passed through a grounding electrode, potential rise was measured and analyzed using an electrolytic tank in real time. In order to analyze the potential interference between grounding electrodes, a reduced scale modeling method was studied. Potential interference between isolated grounding electrodes was evaluated as a function of the separation distance between grounding electrodes and the configuration of grounding electrode to be induced. It was found that the separation distance between grounding electrodes was a major factor in reducing the potential interference.

• Nuclear Engineering and Technology
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• v.51 no.4
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• pp.968-976
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• 2019

This paper presents a number of verification case studies for a recently developed sensitivity/uncertainty code package. The code package, ROMUSE (Reduced Order Modeling based Uncertainty/Sensitivity Estimator) is an effort to provide an analysis tool to be used in conjunction with reactor core simulators, in particular the Virtual Environment for Reactor Applications (VERA) core simulator. ROMUSE has been written in C++ and is currently capable of performing various types of parameter perturbations and associated sensitivity analysis, uncertainty quantification, surrogate model construction and subspace analysis. The current version 2.0 has the capability to interface with the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) code, which gives ROMUSE access to the various algorithms implemented within DAKOTA, most importantly model calibration. The verification study is performed via two basic problems and two reactor physics models. The first problem is used to verify the ROMUSE single physics gradient-based range finding algorithm capability using an abstract quadratic model. The second problem is the Brusselator problem, which is a coupled problem representative of multi-physics problems. This problem is used to test the capability of constructing surrogates via ROMUSE-DAKOTA. Finally, light water reactor pin cell and sodium-cooled fast reactor fuel assembly problems are simulated via SCALE 6.1 to test ROMUSE capability for uncertainty quantification and sensitivity analysis purposes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.12
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• pp.2315-2325
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• 2011

We propose a novel reduced-state maximum-likelihood sequence detection (MLSD) structure using the Viterbi algorithm based on the second-order Volterra kernel modeling nonlinear distortion due to square law detection of multipath channels commonly occurring in chromatic dispersion (CD) or polarization mode dispersion (PMD) in optical communication systems. While all existing MLSD methods for square-law detection receivers are based on direct computation of branch metrics, the proposed algorithm provides an efficient and structured way to implement reduced-state MLSD with almost the same complexity of a MLSD for linear channels. As a result, the proposed algorithm reduces the number of parameters to be estimated and the complexity of computation.