• Nuclear Engineering and Technology
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• v.42 no.6
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• pp.690-703
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• 2010

Sampling based uncertainty analysis was carried out to quantify uncertainty in predictions of best estimate code RELAP5/MOD3.2 for a thermal hydraulic test (10% hot leg break LOCA) performed in the Large Scale Test Facility (LSTF) as a part of an IAEA coordinated research project. The nodalisation of the test facility was qualified for both steady state and transient level by systematically applying the procedures led by uncertainty methodology based on accuracy extrapolation (UMAE); uncertainty analysis was carried out using the Latin hypercube sampling (LHS) method to evaluate uncertainty for ten input parameters. Sixteen output parameters were selected for uncertainty evaluation and uncertainty band between $5^{th}$ and $95^{th}$ percentile of the output parameters were evaluated. It was observed that the uncertainty band for the primary pressure during two phase blowdown is larger than that of the remaining period. Similarly, a larger uncertainty band is observed relating to accumulator injection flow during reflood phase. Importance analysis was also carried out and standard rank regression coefficients were computed to quantify the effect of each individual input parameter on output parameters. It was observed that the break discharge coefficient is the most important uncertain parameter relating to the prediction of all the primary side parameters and that the steam generator (SG) relief pressure setting is the most important parameter in predicting the SG secondary pressure.

• Nuclear Engineering and Technology
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• v.45 no.4
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• pp.439-458
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• 2013

REX-10 is a fully-passive small modular reactor in which the coolant flow is driven by natural circulation, the RCS is pressurized by a steam-gas pressurizer, and the decay heat is removed by the PRHRS. To confirm design decisions and analyze the transient responses of an integral PWR such as REX-10, a thermal-hydraulic system code named TAPINS (Thermal-hydraulic Analysis Program for INtegral reactor System) is developed in this study. Based on a one-dimensional four-equation drift-flux model, TAPINS incorporates mathematical models for the core, the helical-coil steam generator, and the steam-gas pressurizer. The system of difference equations derived from the semi-implicit finite-difference scheme is numerically solved by the Newton Block Gauss Seidel (NBGS) method. TAPINS is characterized by applicability to transients with non-equilibrium effects, better prediction of the transient behavior of a pressurizer containing non-condensable gas, and code assessment by using the experimental data from the autonomous integral effect tests in the RTF (REX-10 Test Facility). Details on the hydrodynamic models as well as a part of validation results that reveal the features of TAPINS are presented in this paper.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2709-2716
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• 2020

Compositions of large nuclear cores (e.g. boiling water reactors) are highly heterogeneous in terms of fuel composition, control rod insertions and flow regimes. For this reason, they usually lack high order of symmetry (e.g. 1/4, 1/8) making it difficult to estimate their neutronic parameters for large spaces of possible loading patterns. A detailed hyperparameter optimization technique (a combination of manual and Gaussian process search) is used to train and optimize deep neural networks for the prediction of three neutronic parameters for the Ringhals-1 BWR unit: power peaking factors (PPF), control rod bank level, and cycle length. Simulation data is generated based on half-symmetry using PARCS core simulator by shuffling a total of 196 assemblies. The results demonstrate a promising performance by the deep networks as acceptable mean absolute error values are found for the global maximum PPF (~0.2) and for the radially and axially averaged PPF (~0.05). The mean difference between targets and predictions for the control rod level is about 5% insertion depth. Lastly, cycle length labels are predicted with 82% accuracy. The results also demonstrate that 10,000 samples are adequate to capture about 80% of the high-dimensional space, with minor improvements found for larger number of samples. The promising findings of this work prove the ability of deep neural networks to resolve high dimensionality issues of large cores in the nuclear area.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.49-58
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• 2021

The resistance of soil to the tractive force of flowing water is one of the essential parameters for the stability of the soil when directly exposed to the movement of water such as in rivers and ocean beds. Biopolymers, which are new to sustainable geotechnical engineering practices, are known to enhance the mechanical properties of soil. This study addresses the surface erosion resistance of river-sand treated with several biopolymers that originated from micro-organisms, plants, and dairy products. We used a state-of-the-art erosion function apparatus with P-wave reflection monitoring. Experimental results have shown that biopolymers significantly improve the erosion resistance of soil surfaces. Specifically, the critical shear stress (i.e., the minimum shear stress needed to detach individual soil grains) of biopolymer-treated soils increased by 2 to 500 times. The erodibility coefficient (i.e., the rate of increase in erodibility as the shear stress increases) decreased following biopolymer treatment from 1 × 10-2 to 1 × 10-6 times compared to that of untreated river-sands. The scour prediction calculated using the SRICOS-EFA program has shown that a height of 14 m of an untreated surface is eroded during the ten years flow of the Nakdong River, while biopolymer treatment reduced this height to less than 2.5 m. The result of this study has demonstrated the possibility of cross-linked biopolymers for river-bed stabilization agents.

• Wind and Structures
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• v.32 no.4
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• pp.293-308
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• 2021

For the past three decades a significant amount of research has been conducted on bridge flutter. Wind tunnel tests for a 2000 m class twin-box suspension bridge have revealed that a twin-box deck carrying 4 m tall 50% open area ratio wind screens at the deck edges achieved higher critical wind speeds for onset of flutter than a similar deck without wind screens. A result at odds with the well-known behavior for the mono-box deck. The wind tunnel tests also revealed that the critical flutter wind speed increased if the bridge deck assumed a nose-up twist relative to horizontal when exposed to high wind speeds - a phenomenon termed the "nose-up" effect. Static wind tunnel tests of this twin-box cross section revealed a positive moment coefficient at 0° angle of attack as well as a positive moment slope, ensuring that the elastically supported deck would always meet the mean wind flow at ever increasing mean angles of attack for increasing wind speeds. The aerodynamic action of the wind screens on the twin-box bridge girder is believed to create the observed nose-up aerodynamic moment at 0° angle of attack. The present paper reviews the findings of the wind tunnel tests with a view to gain physical insight into the "nose-up" effect and to establish a theoretical model based on numerical simulations allowing flutter predictions for the twin-box bridge girder.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3599-3619
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• 2019

In human activity recognition system both static and motion information play crucial role for efficient and competitive results. Most of the existing methods are insufficient to extract video features and unable to investigate the level of contribution of both (Static and Motion) components. Our work highlights this problem and proposes Static-Motion fused features descriptor (SMFD), which intelligently leverages both static and motion features in the form of descriptor. First, static features are learned by two-stream 3D convolutional neural network. Second, trajectories are extracted by tracking key points and only those trajectories have been selected which are located in central region of the original video frame in order to to reduce irrelevant background trajectories as well computational complexity. Then, shape and motion descriptors are obtained along with key points by using SIFT flow. Next, cholesky transformation is introduced to fuse static and motion feature vectors to guarantee the equal contribution of all descriptors. Finally, Long Short-Term Memory (LSTM) network is utilized to discover long-term temporal dependencies and final prediction. To confirm the effectiveness of the proposed approach, extensive experiments have been conducted on three well-known datasets i.e. UCF101, HMDB51 and YouTube. Findings shows that the resulting recognition system is on par with state-of-the-art methods.

• Nuclear Engineering and Technology
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• v.50 no.7
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• pp.1173-1183
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• 2018

Transient fluid velocity and pressure fields in a pressurized water reactor (PWR) steam generator (SG) secondary side during the blowdown period of a feedwater line break (FWLB) accident were numerically simulated employing the saturated water flashing model. This model is based on the assumption that compressed water in the SG is saturated at the beginning and decompresses into the two-phase region where saturated vapor forms, creating a mixture of steam bubbles in water by bulk boiling. The numerical calculations were performed for two cases of which the outflow boundary conditions are different from each other; one is specified as the direct blowdown discharge to the atmosphere and the other is specified as the blowdown discharge to an extended calculation domain with atmospheric pressure on its boundary. The present simulation results obtained using the two different outflow boundary conditions were discussed through a comparison with the predictions using a simple non-flashing model neglecting the effects of phase change. In addition, the applicability of each of the non-flashing water discharge and saturated water flashing models for the confirmatory assessments of new SG designs was examined.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.36 no.3
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• pp.169-176
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• 2020

The authors developed a nodal network model that simulates the flow of moist air and the thermal behavior of a target area. The nodal network model was enhanced using a parameter estimation technique based on the measured temperature, humidity, and schedule data. However, the nodal model is not good enough for predicting humidity of the target space, having 55.6% of CVRMSE. It is because re-evaporation effect could not be modeled due to uncertain factors in the field measurement. Hence, a data-driven model was introduced using an artificial neural network (ANN). It was found that the data-driven model is suitable for predicting the condensation compared to the nodal model satisfying ASHRAE Guideline with 3.36% of CVRMSE for temprature, relative humidity, and surface temperature on average. The model will be embedded in automated devices for real-time predictive control, to minimize the risk of surface condensation at dressroom in an apartment housing.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.17-24
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• 2023

Based on the problem of 'Tatanic - Machine Learning from Disaster', a representative competition of Kaggle that presents challenges related to data science and solves them, we want to see how data preprocessing and model construction affect prediction accuracy and score. We compare and analyze the features by selecting seven top-ranked solutions with high scores, except when using redundant models or ensemble techniques. It was confirmed that most of the pretreatment has unique and differentiated characteristics, and although the pretreatment process was almost the same, there were differences in scores depending on the type of model. The comparative analysis study in this paper is expected to help participants in the kaggle competition and data science beginners by understanding the characteristics and analysis flow of the preprocessing methods of the top score participants.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.75-85
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• 2021

The broadband noise can be dominant or important for total characteristics for marine propeller noise representing the minimum base of self-noise. Accurate prediction of such noise is crucial for survivability of underwater military vessels. While the FW-H Formulation 1B can be used to predict broadband trailing edge noise, the method required experiment measurements of surface pressure correlations, showing its limitations in generality. Therefore, in this study, the methods are developed to utilize wall pressure spectrum models to overcome those limitations. Chase model is adopted to represent surface pressure along with the developed formulations to reproduce pressure statistics. Newly developed method is validated with the experiments of airfoils at different velocities. Thereafter, with its feasibility and generality, the procedure incorporating computational fluid dynamics is established and performed for a propeller behind submarine hull. The results are compared with the experiments conducted at Large Cavitation Tunnel, thus showing its usability and robustness.