• Environmental Engineering Research
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• 제24권1호
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• pp.24-30
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• 2019

The present study explores the applicability of response surface methodology (RSM) in conjunction with central composite design (CCD) matrix to statistically optimize ettringite crystallization process for the removal of sulphate from landfill leachate. A three factor-five coded level CCD with 20 runs, was performed to estimate the best fitted model. The RSM results indicated that the fitted quadratic regression model could be appropriate to predict sulfate removal efficiency. The pH was identified as the most dominant parameter affecting sulphate removal. 61.6% of maximum sulphate removal efficiency was obtained at pH of 11.06 for a 1.87 of $Ca/SO_4$ and 0.51 of $Al/SO_4$ molar ratios. The operating cost for ettringite crystallization at optimized conditions was calculated to be 0.52 $/$m^3$. The significance of independent variables and their interactions were tested by analysis of variance. Scanning electron microscope (SEM) and SEM coupled with energy dispersive X-Ray spectroscopy results confirmed the formation of ettringite crystal and were used to describe its morphology features.

• Nuclear Engineering and Technology
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• 제50권8호
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• pp.1306-1313
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• 2018

The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper.

• Nuclear Engineering and Technology
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• 제55권12호
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• pp.4431-4446
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• 2023

A Multiphysics coupling framework, MPCORE, has been developed to analyze safety parameters using the best estimate codes. The framework contains neutron kinetics (NK), thermal hydraulics (TH), and fuel performance (FP) codes to analyze fuel burnup, radial power distribution, and coolant temperature (T_bc). Shuffling and rotation capabilities have been verified on the Watts Bar reactor for three cycles. This study focuses on two coupling approaches for TH and FP modules. The one-way coupling approach involves coupling the FP code with the NK code, providing no data to the TH modules but getting T_bc as boundary condition from TH module. The two-way coupling approach exchanges information from FP to TH modules, so that the simplified heat conduction solver of the TH module is not used. The power profile in both approaches does not differ significantly, but there is an impact on coolant and cladding parameters. The one-way coupling approach tends to over-predict the cladding hydrogen concentration (CHC). This research highlights the difference between one-way and two-way coupling on critical boron concentration, T_bc, CHC, oxide surface temperature, and pellet centerline temperature. Overall, MPCORE framework with two-way coupling provides a more accurate and reliable analysis of safety parameters for nuclear reactors.

• Steel and Composite Structures
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• 제51권4호
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• pp.417-440
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• 2024

Artificial neural networks (ANN) have been the focus of several studies when it comes to evaluating the pile's bearing capacity. Nonetheless, the principal drawbacks of employing this method are the sluggish rate of convergence and the constraints of ANN in locating global minima. The current work aimed to build four ANN-based prediction models enhanced with methods from the black hole algorithm (BHA), league championship algorithm (LCA), shuffled complex evolution (SCE), and symbiotic organisms search (SOS) to estimate the carrying capacity of piles in cold climates. To provide the crucial dataset required to build the model, fifty-eight concrete pile experiments were conducted. The pile geometrical properties, internal friction angle 𝛗 shaft, internal friction angle 𝛗 tip, pile length, pile area, and vertical effective stress were established as the network inputs, and the BHA, LCA, SCE, and SOS-based ANN models were set up to provide the pile bearing capacity as the output. Following a sensitivity analysis to determine the optimal BHA, LCA, SCE, and SOS parameters and a train and test procedure to determine the optimal network architecture or the number of hidden nodes, the best prediction approach was selected. The outcomes show a good agreement between the measured bearing capabilities and the pile bearing capacities forecasted by SCE-MLP. The testing dataset's respective mean square error and coefficient of determination, which are 0.91846 and 391.1539, indicate that using the SCE-MLP approach as a practical, efficient, and highly reliable technique to forecast the pile's bearing capacity is advantageous.

• Journal of Mechanical Science and Technology
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• 제17권6호
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• pp.825-835
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• 2003

Previous studies have demonstrated the importance of joint angle errors mainly due to skin artifact and measurement errors during gait analysis. Joint angle errors lead to unreliable kinematics and kinetic analyses in the investigation of human motion. The purpose of this paper is to present the Joint Averaging Coordinate System (JACS) method for human gait analysis. The JACS method is based on the concept of statistical data reduction of anatomically referenced marker data. Since markers are not attached to rigid bodies, different marker combinations lead to slightly different predictions of joint angles. These different combinations can be averaged in order to provide a "best" estimate of joint angle. Results of a gait analysis are presented using clinically meaningful terminology to provide better communication with clinical personal. In order to verify the developed JACS method, a simple three-dimensional knee joint contact model was developed, employing an absolute coordinate system without using any kinematics constraint in which thigh and shank segments can be derived independently. In the experimental data recovery, the separation and penetration distance of the knee joint is supposed to be zero during one gait cycle if there are no errors in the experimental data. Using the JACS method, the separation and penetration error was reduced compared to well-developed existing methods such as ACRS and Spoor & Veldpaus method. The separation and penetration distance ranged up to 15 mm and 12 mm using the Spoor & Veldpaus and ACRS method, respectively, compared to 9 mm using JACS method. Statistical methods like the JACS can be applied in conjunction with existing techniques that reduce systematic errors in marker location, leading to an improved assessment of human gait.

• 대한기계학회논문집A
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• 제36권8호
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• pp.921-927
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• 2012

신뢰성 기반 최적설계는 설계변수들의 변동을 평균이나 분산 등의 통계적 특성으로 고려하여 설계자가 원하는 신뢰도를 만족하는 해를 구한다. 신뢰도를 구하기 위한 기존의 신뢰성해석 기법들은 변수들이 연속함수로 정의되는 특정 확률분포를 따른다는 가정을 하지만 실제 문제에서 변수들은 한정적인 이산정보의 형태인 경우가 많기 때문에 변수들에 대한 가정을 하지 않고 이산정보로부터 신뢰성해석을 수행하는 것은 매우 중요하다. 본 연구에서는 후보 분포들 중에서 이산정보를 가장 잘 추정하는 분포를 결정하는 기법인 Akaike 정보척도를 이용하여 신뢰성해석 및 신뢰성 기반 최적설계를 수행하는 기법을 제안한다. 수학예제를 통해 정확성을 검증하고 철도차량 용접대차의 신뢰성 기반 최적설계에 적용하여 제안한 기법의 유용성을 확인한다.

• 정보처리학회논문지D
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• 제9D권4호
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• pp.603-612
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• 2002

소프트웨어공학에서 소프트웨어 측정분야는 30년 이상 수많은 연구가 있어 왔으나 아직까지 구체적인 소프트웨어 개발노력과 비용 추정 모델이 거의 없는 실정이다. 만약 소프트웨어 개발노력과 비용을 측정하려면 소프트웨어 규모를 추정해야 한다. 소프트웨어의 규모를 측정하기 위한 많은 소프트웨어 척도가 개발되었지만 가장 일반적인 척도가 LOC(line of code)와 FPA(Function Point Analysis)이다. FPA는 소프트웨어 규모를 측정하는데 LOC를 사용할 때의 단점을 극복할 수 있는 기법이다. 본 논문은 FP(Function Point)로 측정된 소프트웨어 규모로 소프트웨어 개발노력을 추정하는 단순회귀모델을 제안한다. 실험에 사용된 데이터들은 다양한 개발환경과 개발방법을 적용한 최근의 789개 소프트웨어 개발 프로젝트들이다. 실험 데이타들에 대한 산점도를 그려 개발노력과 FP의 적합한 관계로부터 단순회귀분석 모델을 유도하였다. 또한, 다양한 소프트웨어 개발환경과 개발방법 등을 고려해 개발된 최근의 대용량 프로젝트에 대해 제안된 단순회귀분석 모델이 기존의 회귀분석 모델들보다 개발노력 추정 정확성이 월등히 좋음을 보였다.

• Nuclear Engineering and Technology
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• 제16권3호
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• pp.155-160
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• 1984

표준 핵연료집합체나 최적 핵연료집합체로 구성된 원자력 1호기 원자로심의 열적여유도를 기존 열설계 방법과 통계적 열설계 방법을 이용하여 분석하였다. 통계적 열설계 방법은 노심내 운전변수들의 불확실도를 통계적으로 처리함으로써 기존 방법에 비하여 열적여유도를 증가시킨다. 계산을 위하여 정상상태와 과도시 열수력분석 전산코드인 COBRA-IV-i를 사용하였다. 계산결과 통계적 설계방법은 열적여유도를 크게 증가시키며, 표준 핵 연료집 합체는 물론 최적 핵 연료집 합체가 장전된 원자력 1호기의 열설계기준을 만족시키는 것으로 밝혀졌다. 그러나 기존 열설계 방법은 원자력 1호기 노심에 최적 핵연료집합체가 장전된 경우 열설계기준을 만족시키지 못하는 것으로 밝혀졌다.

• 한국음향학회지
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• 제38권1호
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• pp.73-81
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• 2019

본 논문에서는 선박 수중방사소음의 셉스트럼(cepstrum) 분석을 이용한 음향역산법을 제안하였다. 셉스트럼 분석을 통해 수중 청음기에서 계측된 선박 소음으로부터 직접 도달파와 해수면과 해저면에서 반사파와의 간섭에서 기인한 음파의 다중반사 구조를 추출할 수 있다. 음향학적 역산은 계측 신호의 셉스트럼과 모의 신호의 셉스트럼을 비교하여 최적의 역산인자를 찾는 방식으로 구성되었다. 본 논문에서 제안된 역산기법을 대한해협에서 계측한 선박 수중방사소음 데이터에 적용하여 대상 선박의 음원중심과 수중청음기의 위치를 추정하였다.

• Nuclear Engineering and Technology
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• 제55권10호
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• pp.3775-3786
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• 2023

Mixing Vane Grid (MVG) is one of the most important structures in fuel assembly due to its high performance in mixing the coolant and ultimately increasing Critical Heat Flux (CHF), which avoids the temperature rising suddenly of fuel rods. To evaluate the mixing performance of the MVG, a Total Diffusion Coefficient (TDC) mixing coefficient is defined in the subchannel analysis code. Conventionally, the TDC of the spacer grid is obtained from the combination of experiments and subchannel analysis. However, the processing of obtaining and determine a reasonable TDC is much challenging, it is affected by boundary conditions and MVG geometries. In is difficult to perform all the large and costing rod bundle tests. In this paper, the CFD method was applied in TDC analysis. A typical 5 × 5 MVG was simulated and validated to estimate the mixing performance of the MVG. The subchannel code was used to calculate the TDC. Firstly, the CFD method was validated from the aspect of pressure drop and lateral temperature distribution in the subchannels. Then the effect of boundary conditions including the inlet temperature, inlet velocities, heat flux ratio between hot and cold rods and the arrangement of hot and cold rods on MVG mixing and TDC were studied. The geometric effects on mixing are also carried out in this paper. The effect of vane pattern on mixing was investigated to determine which one is the best to represent the grid's mixing performance.