• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.4
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• pp.221-228
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• 2019

Experimental investigation was performed to analyze the flow field characteristics and power generation performance for a shroud tidal power generation system. Electrical power output was compared with the rotational speed of the turbine blade and electric load connected to the generator for various flow velocity. As the electrical load decreased, the speed of the turbine increased rapidly and reached by about 2 times. The power output also increased remarkably with the decrease of load, and then decreased after maximum power point. In addition, the maximum power point appeared at high electrical loads as the experimental flow velocity increased. These results of the flow field characteristics and power generation performance analysis of the shroud tidal power generation system variation with the flow velocity conditions and electrical load are expected to be the basic data necessary for the development of efficient shroud tidal power generation system.

• Advances in aircraft and spacecraft science
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• v.9 no.5
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• pp.415-431
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• 2022

Secondary flows have a huge impact on losses generation in modern low pressure gas turbines (LPTs). At design point, the interaction of the blade profile with the end-wall boundary layer is responsible for up to 40% of total losses. Therefore, predicting accurately the end-wall flow field in a LPT is extremely important in the industrial design phase. Since the inlet boundary layer profile is one of the factors which most affects the evolution of secondary flows, the first main objective of the present work is to investigate the impact of two different inlet conditions on the end-wall flow field of the T106A, a well known LPT cascade. The first condition, labeled in the paper as C1, is represented by uniform conditions at the inlet plane and the second, C2, by a flow characterized by a defined inlet boundary layer profile. The code used for the simulations is based on the Discontinuous Galerkin (DG) formulation and solves the Reynolds-averaged Navier-Stokes (RANS) equations coupled with the Spalart Allmaras turbulence model. Secondly, this work aims at estimating the influence of viscosity and turbulence on the T106A end-wall flow field. In order to do so, RANS results are compared with those obtained from an inviscid simulation with a prescribed inlet total pressure profile, which mimics a boundary layer. A comparison between C1 and C2 results highlights an influence of secondary flows on the flow field up to a significant distance from the end-wall. In particular, the C2 end-wall flow field appears to be characterized by greater over turning and under turning angles and higher total pressure losses. Furthermore, the C2 simulated flow field shows good agreement with experimental and numerical data available in literature. The C2 and inviscid Euler computed flow fields, although globally comparable, present evident differences. The cascade passage simulated with inviscid flow is mainly dominated by a single large and homogeneous vortex structure, less stretched in the spanwise direction and closer to the end-wall than vortical structures computed by compressible flow simulation. It is reasonable, then, asserting that for the chosen test case a great part of the secondary flows details is strongly dependent on viscous phenomena and turbulence.

• Journal of Korea Water Resources Association
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• v.31 no.6
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• pp.779-793
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• 1998

In this study, for the purpose of water supply planning, we propose a sophisticated multi-period mixed integer programming model that can coordinate the behavior of multi-reservoir operation, minimizing unnecessary spill. It can simulate the system with operating rules which are self- generated by the optimization engine in the algorithm. It is an optimization model in structure, but it indeed simulates the coordinating behavior of multi-reservoir operation. It minimizes the water shortfalls in demand requirements, maintaining flood reserve volume, minimizing unnecessary spill, maximizing hydropower generation release, keeping water storage levels high for efficient hydroelectric turbine operation. This optimization model is a large scale mixed integer programming problem that consists of 3.920 integer variables and 68.658 by 132.384 node-arc incidence matrix for 28 years of data. In order to handle the enormous amount of data generated by a big mathematical model, the utilization of DBMS (data base management system)seems to be inevitable. It has been tested with the Han River multi-reservoir system in Korea, which consists of 2 large multipurpose dams and 3 hydroelectric dams. We demonstrated successfully that there is a good chance of saving substantial amount of water should it be put to use in real time with a good inflow forecasting system.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.211-218
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• 2012

Fossil fuel is used all over the world and it produces greenhouse gases due to fossil fuel use. Therefore, it cause global warming and is serious environmental pollution. In order to decrease the environmental pollution, we should use renewable energy which is clean energy. Among several renewable energy, wind energy is the most promising one. Wind power generation is does not produce environmental pollution and could not be exhausted. However, due to wind power generation has irregular power output, it is important to predict generated electrical energy accurately for smoothing wind energy supply. There, we consider use ramp characteristic to forecast accurate wind power output. The ramp increase and decrease rapidly wind power generation during in a short time. Therefore, it can cause problem of unbalanced power supply and demand and get damaged wind turbine. In this paper, we make prediction models using power ramp rate as well as wind speed and wind direction to increase prediction accuracy. Prediction model construction algorithm used multilayer neural network. We built four prediction models with PRR, wind speed, and wind direction and then evaluated performance of prediction models. The predicted values, which is prediction model with all of attribute, is nearly to the observed values. Therefore, if we use PRR attribute, we can increase prediction accuracy of wind power generation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of the Korean Society of Propulsion Engineers
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• v.23 no.4
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• pp.104-111
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• 2019

Staged combustion cycle engines exhibit higher combustion performance compared with open cycle engines with a gas generator. An advanced research of the staged combustion cycle engine is going on for the next program following the KSLV-II program. Various experiments have been carried out for the technology demonstration model, TDM0A and TDM0B. The experiments on the combustion performance are aimed to understand the engine start condition and combustion characteristics. They also aim to develop the oxidizer-rich pre-burner and the combustor of the staged combustion cycle engine. The engine-shaped model, TDM1A is fabricated based on the experimental data. The combustion experiment of the TDM1A shows that the combustion pressure of the combustor is approximately 91 bar and the turbine rotation is approximately 28,00 rpm. The result is stable and satisfies the development requirements. The present paper reports on the development process and characteristics of engine models from TDM0A to TDM1A.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.12
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• pp.963-969
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• 2021

The wind data measured from local meteorological masts is used to evaluate wind speed distribution and energy production in the specified site for wind farm However, wind data measured from meteorological masts often contain missing information or insufficient desired height or data length, making it difficult to perform wind turbine control and performance simulation. Therefore, long-term continuous wind data is very important to assess the annual energy production and the capacity factor for wind turbines or wind farms. In addition, if seasonal influences are distinct, such as on the Korean Peninsula, wind data with seasonal characteristics should be considered. This study presents methodologies for generating synthetic wind that take into account fluctuations in both wind speed and direction using the hidden Markov model, which is a statistical method. The wind data for statistical processing are measured at Maldo island in the Kokunnsan-gundo, Jeonbuk Province using the Automatic Weather System (AWS) of the Korea Meteorological Administration. The synthetic wind generated using the hidden Markov model will be validated by comparing statistical variables, wind energy density, seasonal mean speed, and prevailing wind direction with measurement data.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.23 no.6
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• pp.489-495
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• 2011

Fossil fuel energy has become a worldwide environmental issue due to its effect on global warming and depletion in its supply. Therefore, the interest in developing alternative energy source has been rising. Ocean energy, especially, has gained strength as an alternative energy source for its unlimited supply with low secondary risks. Among all the ocean energy, the west coast of Korea holds the field of large-scale energy development because of its distinctive tidal range. Tidal power plant construction at the sea may expedite multi development effects such as bridge roles, tourism resource effects and adjustability of flood inundation at the inner bay. This study introduces the validity of tidal power plant construction at Garilim Bay in west coast of Korea by examining anticipated hydraulic characteristics using EFDC model. Through EFDC numerical simulations, the feasibility of Garolim Bay as a tidal power plant field has been proved. And the most effective tidal power plant construction would be to install hydraulic turbine in the west side of bay entrance where ebb current is stronger, and install water gate in the east side of bay entrance where the flood current is superior.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.21 no.7
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• pp.849-861
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• 1997

In this study, the effect of hole geometry of the cooling system on the flow and temperature field was numerically calculated. The finite volume method was employed to discretize the governing equation based on the non-orthogonal coordinate with non-staggered variable arrangement. The standard k-.epsilon. turbulence model was used and also the predicted results were compared with the experimental data to validate numerical modeling. The predicted results showed good agreement in all cases. To analyze the effect of the discharge coefficient for slots of different length to width, the inlet chamfering and radiusing holes were considered. The discharge coefficient was increased with increment of the chamfering ratio, radiusing ratio and slot length to width and also the effect of radiusing showed better result than chamfering in all cases. In order to analyze the difference between the predicted results with plenum region and without plenum region, the velocity profiles of jet exit region for a various flow conditions were calculated. The normal velocity components of jet exit showed big difference for the low slot length to width and high blowing rate cases. To analyze the flow phenomena injected from a row of inclined holes in a real turbine blade, three dimensional flow and temperature distribution of the region including plenum, hole and cross stream with flow conditions were numerically calculated. The results have shown three-dimensional flow characteristics, such as the development of counter rotating vortices, jetting effect and low momentum region within the hole in addition to counter rotating vortex structure in the cross stream.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• v.y2005m4
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• pp.274-280
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• 2005

Experimental investigations were carried out in an atmospheric pressure, optically accessible and laboratory-scale dump combustor. The objective of this study is to obtain the phase-resolved gas temperatures at different phases of the oscillating pressure cycle during unstable combustion. To see the effect of incomplete fuel-air mixing on phase-resolved temperature characteristics, CARS temperature measurements were performed. Results including phase-resolved averaged temperature, normalized standard deviation and temperature probability distribution functions (PDFs) were provided in this paper. It could be found that the profile of mean temperature showed the in-phase relationship with pressure cycle. Temperature PDFs give an insight on the flame behavior as well as NOx emission characteristics. These results would be expected to play an important role in better understanding of driving mechanisms and thermo-acoustic interactions.