• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.8
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• pp.823-833
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• 2010

For the study of thermophysical properties of kerosene for the liquid rocket and aviation fuels, the surrogate models are investigated. The density distributions based on the real gas equations of state(Soave modification of Redlich-Kwong and Peng-Robinson equation of state) and NIST SUPERTRAPP(extended corresponding state principle) are compared with the previous experimental results at supercritical conditions. The error range of thermophysical properties analyzed for the surrogate models as well. Peng-Robinson equation of state and extended corresponding state principle are especially accurate for the hydrocarbon fuels but the appropriate surrogate models need to be chosen to the operation conditions such as pressure and temperature.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2010.11a
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• pp.19-23
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• 2010

Injection characteristics of a kerosene swirl injector of liquid rocket engine operating at supercritical environment have been investigated. Kerosene surrogate models are proposed to model the kerosene properties. Turbulent numerical model is based on large eddy simulation and contains Soave modification of Redlich-Kwong equation of state and Chung's model. Numerical analysis results at supercritical environment are compared with the one at transcritical condition. Differences of density and viscosity are analyzed at both liquid film and core gas in the swirl injector.

• Earthquakes and Structures
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• v.20 no.1
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• pp.109-122
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• 2021

Significant progress in the oil and gas industry advances the application of pipeline into an intelligent era, which poses rigorous requirements on pipeline safety, reliability, and maintainability, especially when crossing seismic zones. In general, strike-slip faults are prone to induce large deformation leading to local buckling and global rupture eventually. To evaluate the performance and safety of pipelines in this situation, numerical simulations are proved to be a relatively accurate and reliable technique based on the built-in physical models and advanced grid technology. However, the computational cost is prohibitive, so one has to wait for a long time to attain a calculation result for complex large-scale pipelines. In this manuscript, an efficient and accurate surrogate model based on machine learning is proposed for strain demand prediction of buried X80 pipelines subjected to strike-slip faults. Specifically, the support vector regression model serves as a surrogate model to learn the high-dimensional nonlinear relationship which maps multiple input variables, including pipe geometries, internal pressures, and strike-slip displacements, to output variables (namely tensile strains and compressive strains). The effectiveness and efficiency of the proposed method are validated by numerical studies considering different effects caused by structural sizes, internal pressure, and strike-slip movements.

• Smart Structures and Systems
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• v.22 no.5
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• pp.561-574
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• 2018

In this paper, for efficiently reducing the computational cost of the model updating during the optimization process of damage detection, the structural response is evaluated using properly trained surrogate model. Furthermore, in practice uncertainties in the FE model parameters and modelling errors are inevitable. Hence, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The current work builds a framework for Probability Based Damage Detection (PBDD) of structures based on the best combination of metaheuristic optimization algorithm and surrogate models. To reach this goal, three popular metamodeling techniques including Cascade Feed Forward Neural Network (CFNN), Least Square Support Vector Machines (LS-SVMs) and Kriging are constructed, trained and tested in order to inspect features and faults of each algorithm. Furthermore, three wellknown optimization algorithms including Ideal Gas Molecular Movement (IGMM), Particle Swarm Optimization (PSO) and Bat Algorithm (BA) are utilized and the comparative results are presented accordingly. Furthermore, efficient schemes are implemented on these algorithms to improve their performance in handling problems with a large number of variables. By considering various indices for measuring the accuracy and computational time of PBDD process, the results indicate that combination of LS-SVM surrogate model by IGMM optimization algorithm have better performance in predicting the of damage compared with other methods.

• Journal of Radiation Protection and Research
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• v.25 no.2
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• pp.99-107
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• 2000

Through the results of off-gas analysis at 3 sampling points in Plasma Arc Melting vitrification pilot plant, it was evaluated the partitioning of spiked materials in off-gas and the decontamination characteristic of off-gas treatment system. Spiked materials are hazard_us heavy metals(Pb, Cd, Hg), radioactive surrogate(Co, Cs) and radioactive materials($^{60}Co,\;^{137}Cs$). Through the Trial burn tests, Decontamination factor of spiked materials in off-gas treatment system is calculated.

• 한국전산유체공학회:학술대회논문집
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• 2010.05a
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• pp.187-194
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• 2010

Shape optimization of an upper plenum of PBMR type gas cooled nuclear reactor has been performed by using three-dimensional Reynolds-Averaged Navier-Stokes (RANS) analysis and surrogate modeling technique. The objective function is defined as a linear combination of uniformity of flow distribution in the core and pressure drop in the upper plenum and the core. The ratio of thickness of slot to diameter of rising channels, ratio of height of upper plenum to diameter of rising channels, and ratio of eight of the slot at inlet to outlet, are used as design variables for optimization. Design points are selected through Latin-hypercube sampling. The optimal point is determined through surrogate-based optimization method which uses 3-D RANS analyses at design points. The results show that the optimum shape represent remarkably improved performance in flow uniformity and friction loss than the reference shape.

• Journal of computational fluids engineering
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• v.15 no.3
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• pp.16-23
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• 2010

Shape optimization of an upper plenum of a PBMR type gas cooled nuclear reactor has been performed by using three-dimensional Reynolds-Averaged Navier-Stokes (RANS) analysis and surrogate modeling technique. The objective function is defined as a linear combination of uniformity of flow distribution in the core and pressure drop in the upper plenum and the core. The ratio of thickness of slot to diameter of rising channels, ratio of height of upper plenum to diameter of rising channels, and ratio of height of the slot at inlet to outlet, are used as design variables for optimization. Design points are selected through Latin-hypercube sampling. The optimal point is determined through surrogate-based optimization method which uses 3-D RANS analyses at design points. The results show that the optimum shape represent remarkably improved performance in flow uniformity and friction loss than the reference shape.

• Journal of ILASS-Korea
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• v.29 no.2
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• pp.83-90
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• 2024

Understanding the fundamental characteristics of supersonic hydrogen jets is important for the optimization of combustion in hydrogen engines. Previous studies have used helium as a surrogate gas to characterize the hydrogen jet characteristics due to potential explosion risks of hydrogen. It was based on the similarity of hydrogen and helium jet structures in supersonic conditions that has been confirmed using hole-type injectors and large-cone-angle pintle-type injectors. However, the validity of using helium as a surrogate gas has not been examined for recent small-cone-angle pintle-type injectors applied to direct-injection hydrogen engines, which form a supersonic hollow cone near the nozzle and experience the jet collapse downstream. Differences in the physical properties of hydrogen and helium could alter the jet development characteristics that need to be investigated and understood. This study compares supersonic jet structures of hydrogen and helium injected by a small-cone-angle (50°) pintle-type hydrogen injector and discusses their differences and related mechanisms. Jet penetration length and dispersion angle are measured using the Schlieren imaging method under engine-like injection conditions. As a result, the penetration length of hydrogen and helium jets showed a slight difference of less than 5%, and the dispersion angle showed a maximum of 10% difference according to the injection condition.

• Transactions of the Korean hydrogen and new energy society
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• v.29 no.2
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• pp.219-225
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• 2018

This study was carried out to investigate the injection characteristics of 800 kPa compressed natural gas compressed natural gas (CNG) injector developed in Korea. The CNG injector with multi-holes, employed in this experiment, was designed to inject CNG in the manifold at high pressure of 800 kPa. The spray macroscopic visualization test was carried out via Schlieren photography to study fuel-air mixing process. The fundamental spray characteristics, such as spray penetration, spray cone angle and spray velocity, were evaluated in the constant volume combustion chamber (CVCC) with varying the constant back pressure in CVCC from 0 to 1.8 bar. For the safety reason, nitrogen ($N_2$) and an acetone tracer were utilized as a surrogate gas fuel instead of CNG. The surrogate gas fuel pressures were controlled at 3, 5.5, and 8 bar, respectively. Injection durations were set at 5 ms throughout the experiment. The simulating events of the low engine speed were arranged at 1,000 rpm. The spray images were recorded by using a high-speed camera with a frame rate of 10,000 f/s at $512{\times}256pixels$. The spray characteristics were analyzed by using the image processing (Matlab). The results showed the significant difference that higher injection pressure had more effect on the spray shape than the lower injection pressure. When the injection pressure was increased, the longer spray penetration occurred. Moreover, the linear relation between speed and time are dependent on the injection pressure as well.

• Journal of the Korean Society of Propulsion Engineers
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• v.17 no.6
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• pp.48-58
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• 2013

Characteristics of thermophysical properties and flow structures in a swirl injector at supercritical pressure have been investigated using the kerosene surrogate consisting of four species and various ideal and real gas equations of state. The quantitative comparisons of thermophysical properties for equations of state have been performed. Also, a large eddy simulation and preconditioning technique for getting an effective convergence rate are applied to analyze turbulent flow in a swirl injector. The flow characteristics in the injector has significantly different behaviors depending on the equations of state due to the different thermophysical properties in the injector. The Redlich-Kwong-Peng-Robinson equation of state provides the most suitable results in the wide range of temperature.