• The KSFM Journal of Fluid Machinery
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• v.19 no.4
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• pp.35-38
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• 2016

A PFA lined ball valve, which is machined with fluorinated resin PFA to its inner part for improving corrosion resistance, non-stickness, heat-resistance, has been widely used in semiconductor/LCD manufacturing processes with the high purity chemicals as working fluid. Due to the safety concerns, the experiments for measuring the flow coefficient of a PFA lined ball valve should be conducted with water at room temperature according to IEC standards. However, it is required to know the real flow coefficient with the real working fluid, because the flow coefficient is critical to correctly design valves in piping system. In this study, we calculated the flow coefficient of a PFA lined ball valve 40A with hydrochloric acid ($40^{\circ}C$ 36% HCl) as the working fluid using a commercial CFD package, ANSYS CFX v15. The computational results had a good agreement with the measured data and showed a little difference between water and hydrochloric acid as the working fluid of a PFA lined ball valve.

• Nuclear Engineering and Technology
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• v.43 no.6
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• pp.499-508
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• 2011

The paper briefs a fuel performance code, COSMOS, which can be utilized for an analysis of the thermal behavior and fission gas release of fuel, up to a high burnup. Of particular concern are the models for the fuel thermal conductivity, the fission gas release, and the cladding corrosion and creep in $UO_2$ fuel. In addition, the code was developed so as to consider the inhomogeneity of MOX fuel, which requires restructuring the thermal conductivity and fission gas release models. These improvements enhanced COSMOS's precision for predicting the in-pile behavior of MOX fuel. The COSMOS code also extends its applicability to the instrumented fuel test in a research reactor. The various in-pile test results were analyzed and compared with the code's prediction. The database consists of the $UO_2$ irradiation test up to an ultra-high burnup, power ramp test of MOX fuel, and instrumented MOX fuel test in a research reactor after base irradiation in a commercial reactor. The comparisons demonstrated that the COSMOS code predicted the in-pile behaviors well, such as the fuel temperature, rod internal pressure, fission gas release, and cladding properties of MOX and $UO_2$ fuel. This sufficient accuracy reveals that the COSMOS can be utilized by both fuel vendors for fuel design, and license organizations for an understanding of fuel in-pile behaviors.

• Geomechanics and Engineering
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• v.28 no.6
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• pp.599-611
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• 2022

Tendon reinforced cemented soil is applied extensively in foundation stabilisation and improvement, especially in areas with soft clay. To solve the deterioration problem led by steel corrosion, the glass fiber-reinforced polymer (GFRP) tendon is introduced to substitute the traditional steel tendon. The interface bond strength between the cemented soil matrix and GFRP tendon demonstrates the outstanding mechanical property of this composite. However, the lack of research between the influence factors and bond strength hinders the application. To evaluate these factors, back propagation neural network (BPNN) is applied to predict the relationship between them and bond strength. Since adjusting BPNN parameters is time-consuming and laborious, the particle swarm optimisation (PSO) algorithm is proposed. This study evaluated the influence of water content, cement content, curing time, and slip distance on the bond performance of GFRP tendon-reinforced cemented soils (GTRCS). The results showed that the ultimate and residual bond strengths were both in positive proportion to cement content and negative to water content. The sample cured for 28 days with 30% water content and 50% cement content had the largest ultimate strength (3879.40 kPa). The PSO-BPNN model was tuned with 3 neurons in the input layer, 10 in the hidden layer, and 1 in the output layer. It showed outstanding performance on a large database comprising 405 testing results. Its higher correlation coefficient (0.908) and lower root-mean-square error (239.11 kPa) were obtained compared to multiple linear regression (MLR) and logistic regression (LR). In addition, a sensitivity analysis was applied to acquire the ranking of the input variables. The results illustrated that the cement content performed the strongest influence on bond strength, followed by the water content and slip displacement.

• Structural Engineering and Mechanics
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• v.81 no.5
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• pp.565-574
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• 2022

The infrastructures such as offshore, bridges, power plant, oil and gas piping and aircraft operate in a harsh environment during their service life. Structural integrity of engineering components used in these industries is paramount for the reliability and economics of operation. Two regression models based on the concept of Gaussian process regression (GPR) and Minimax probability machine regression (MPMR) were developed to predict stress intensity factor range (𝚫K). Both GPR and MPMR are in the frame work of probability distribution. Models were developed by using the fatigue crack growth data in MATLAB by appropriately modifying the tools. Fatigue crack growth experiments were carried out on Eccentrically-loaded Single Edge notch Tension (ESE(T)) specimens made of API 5L X65 Grade steel in inert and corrosive environments (2.0% and 3.5% NaCl). The experiments were carried out under constant amplitude cyclic loading with a stress ratio of 0.1 and 5.0 Hz frequency (inert environment), 0.5 Hz frequency (corrosive environment). Crack growth rate (da/dN) and stress intensity factor range (𝚫K) values were evaluated at incremental values of loading cycle and crack length. About 70 to 75% of the data has been used for training and the remaining for validation of the models. It is observed that the predicted SIF range is in good agreement with the corresponding experimental observations. Further, the performance of the models was assessed with several statistical parameters, namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Efficiency (E), Root Mean Square Error to Observation's Standard Deviation Ratio (RSR), Normalized Mean Bias Error (NMBE), Performance Index (ρ) and Variance Account Factor (VAF).

• Structural Engineering and Mechanics
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• v.84 no.2
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• pp.143-154
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• 2022

Corrosion of steel reinforcement is considered as the main cause of concrete structures deterioration, especially those under humid environmental conditions. Hence, fiber reinforced polymer (FRP) bars are being increasingly used as a replacement for conventional steel owing to their non-corrodible characteristics. However, predicting the shear strength of beams reinforced with FRP bars still challenging due to the lack of robust shear theory. Thus, this paper aims to develop an explicit data driven based model to predict the shear strength of FRP reinforced beams using multi-objective evolutionary polynomial regression analysis (MOGA-EPR) as data driven models learn the behavior from the input data without the need to employee a theory that aid the derivation, and thus they have an enhanced accuracy. This study also evaluates the accuracy of predictive models of shear strength of FRP reinforced concrete beams employed by different design codes by calculating and comparing the values of the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²), and percentage of prediction within error range of ±20% (a20-index). Experimental database has been developed and employed in the model learning, validation, and accuracy examination. The statistical analysis illustrated the robustness of the developed model with MAE, RMSE, 𝜇, 𝜎, R², and a20-index of 14.6, 20.8, 1.05, 0.27, 0.85, and 0.61, respectively for training data and 10.4, 14.1, 0.98, 0.25, 0.94, and 0.60, respectively for validation data. Furthermore, the developed model achieved much better predictions than the standard predictive models as it scored lower MAE, RMSE, and 𝜎, and higher R² and a20-index. The new model can be used in future with confidence in optimized designs as its accuracy is higher than standard predictive models.

• Annual Conference of KIPS
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• 2023.11a
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• pp.685-686
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• 2023

상수도관의 수질 및 부식도 검사에는 파이프에 손상을 입히지 않고 지속적인 방법이 필요하다. 초음파는 이를 만족하면서 상태를 확인할 수 있고 주파수가 높을수록 해상도가 좋아져 정밀한 측정이 가능하다는 장점이 있다. 이러한 특성을 이용해 상수도관 모니터링 시스템으로 초음파 기반의 Scanning Acoustic Microscopy(SAM)과 Convolutional Neural Network(CNN)을 사용하는 새로운 방법을 제안한다. 기존의 Non-Destructive Testing(NDT)방식의 단점을 보완하면서 더 높은 해상도로 상수도관을 점검하는 방식으로, SAM 을 이용하여 부식으로 인한 파이프 두께 변화와 부유물의 여부 및 수질을 동시에 감지하고 얻은 데이터를 CNN 으로 분석했다. CNN 의 높은 정확도 결과로 이 시스템의 파이프 부식도 및 수질 모니터링에 대한 적합성을 보여주었다.

• Journal of the Korea institute for structural maintenance and inspection
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• v.11 no.3
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• pp.116-122
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• 2007

The concrete structures in Metropolitan city are usually exposed to carbonation and corrosion of embedded steel occurs due to the carbonation. In inspection and diagnosis of concrete structures, carbonation depth in sound concrete is mainly evaluated and service life for concrete structure is predicted based on the result. Generally, however, mass concrete structures such as columns have construction joint for suitable placing and also have cracks in early-age. In this study, carbonation depth in RC columns used for 20 years in metropolitan city is evaluated and also analyzed by considering the local conditions like sound, cracked, and joint area. The carbonation depth in cracked and joint area is more rapid than that in sound area, and it is thought to be more desirable to consider this effect in concrete structures with small cover depth. Furthermore, the technique for carbonation prediction in cracked concrete is derived in terms of crack width and the results from this technique are verified by comparing those from previous research.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.3
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• pp.119-126
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• 2008

In 1980 and 1990's most of residential buildings were constructed with relatively low strength concrete of 18 MPa. And, columns were designed considering only vertical loads. In this study, compressive strength tests for low strength RC columns retrofitted by carbon fiber sheets were carried out. Carbon fiber sheet provides constructability and high tensile strength as well as good corrosion resistance characteristics. A pair of carbon sheets were wrapped with ${\pm}60^{\circ}$ angle with respect to longitudinal direction of RC column to increase structural capacity against axial and lateral load simultaneously. Strength and strain patterns and failure modes of specimens were analyzed and prediction equation of increased compressive strength of RC column confined by carbon fiber sheet was proposed based on regression analysis.

• Journal of the Korea Concrete Institute
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• v.24 no.4
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• pp.481-490
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• 2012

Evaluation of chloride penetration is very important, because induced chloride ion causes corrosion in embedded steel. Diffusion coefficient obtained from rapid chloride penetration test is currently used, however this method cannot provide a correct prediction of chloride content since it shows only ion migration velocity in electrical field. Apparent diffusion coefficient of chloride ion based on simple Fick's Law can provide a total chloride penetration magnitude to engineers. This study proposes an analysis technique to predict chloride penetration using apparent diffusion coefficient of chloride ion from neural network (NN) algorithm and time-dependent diffusion phenomena. For this work, thirty mix proportions with the related diffusion coefficients are studied. The components of mix proportions such as w/b ratio, unit content of cement, slag, fly ash, silica fume, and fine/coarse aggregate are selected as neurons, then learning for apparent diffusion coefficient is trained. Considering time-dependent diffusion coefficient based on Fick's Law, the technique for chloride penetration analysis is proposed. The applicability of the technique is verified through test results from short, long term submerged test, and field investigations. The proposed technique can be improved through NN learning-training based on the acquisition of various mix proportions and the related diffusion coefficients of chloride ion.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.2
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• pp.87-95
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• 2016

Turbidity is a key indicator to the user that the 'Discolored Water' phenomenon known to be caused by corrosion of the pipeline in the water supply system. 'Discolored Water' is defined as a state with a turbidity of the degree to which the user visually be able to recognize water. Therefore, this study used data mining techniques in order to estimate turbidity changes in water supply system. Decision tree analysis was applied in data mining techniques to develop estimation models for turbidity changes in the water supply system. The pH and residual chlorine dataset was used as variables of the turbidity estimation model. As a result, the case of applying both variables(pH and residual chlorine) were shown more reasonable estimation results than models only using each variable. However, the estimation model developed in this study were shown to have underestimated predictions for the peak observed values. To overcome this disadvantage, a high-pass filter method was introduced as a pretreatment of estimation model. Modified model using high-pass filter method showed more exactly predictions for the peak observed values as well as improved prediction performance than the conventional model.