• Journal of Advanced Marine Engineering and Technology
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• v.16 no.4
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• pp.88-101
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• 1992

Properties of water and steam are very important for the steam ejector CAD program as a subroutine and design of the Shell & Tube type steam condenser. Present formular programs are based on the Skeleton Table of ASME, and are able to calculate the thermodynamoc properties of water and steam throughout the whole of the region that extend in pressure from 0 to 1000 bar and temperature from 0.01 to 80$0^{\circ}C$. When comparing calculated values for specific volume, enthalpy and entropy with the Skeleton Table 1967 and IAPS Skeleton Table 1984, values fell well within tolerances specified except near the extremes of the range of interest at the critical point and triple point, where deviations were slightly larger.

• Nuclear Engineering and Technology
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• v.35 no.4
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• pp.286-298
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• 2003

A steam explosion module, STX, has been developed using the mechanistic steam explosion analysis code, TEXAS-V, in order to estimate the dynamic load with steam explosion by implementing the module to the integrated safety analysis code, MELCOR. One of the difficulties in using mechanistic steam explosion codes is that they do not have any obvious criteria for defining some uncertain parameters such as triggering timing, triggering magnitude, mesh axial length and mesh cross-sectional area. These parameters have been user decision parts in the past. Steam explosion sample calculations and sensitivity studies on uncertain parameters were conducted to investigate those uncertain parameters. The TEXAS-V simulations were summarized in the format of a look-up table and a linear interpolation technique was adopted to calculate the steam explosion load between the data points in the table. The STX-module merged with MELCOR showed the same results as the original MELCOR and additionally it could estimate the steam explosion load in the reactor cavity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.459-466
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• 2006

Numerical values of thermodynamic properties such as temperature, pressure, dryness, volume, enthalpy and entropy are required in numerical analysis on evaluating the thermal performance. But the steam table itself cannot be used without modelling. From this point of view the neural network with function approximation characteristics can be an alternative. the multi-layer neural networks were made for saturated vapor region and superheated vapor region separately. For saturated vapor region the neural network consists of one input layer with 1 node, two hidden layers with 10 and 20 nodes each and one output layer with 7 nodes. For superheated vapor region it consists of one input layer with 2 nodes, two hidden layers with 15 and 25 nodes each and one output layer with 3 nodes. The proposed model gives very successful results with ${\pm}0.005%$ of percentage error for temperature, enthalpy and entropy and ${\pm}0.025%$ for pressure and specific volume. From these successful results, it is confirmed that the neural networks could be powerful method in function approximation of the steam table.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.11
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• pp.2008-2013
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• 2006

In numerical analysis on evaluating the thermal performance of the thermal equipment, numerical values of thermodynamic properties such as temperature, pressure, specific volume, enthalpy and entropy are required. But the steam table itself cannot be used without modelling. In this study applicability of neural networks in modelling the wide temperature range of wet saturated vapor region was examined. the multi-layer neural network consists of a input layer with 1 node, two hidden layers with 10 and 20 nodes respectively and a output layer with 6 nodes. Quadratic and cubic spline interpoations methods were also applied for comparison. Neural network model revealed similar percentage error to spline interpolation. From these results, it is confirmed that the neural networks could be powerful method in modelling the wide range of the steam table.

• Journal of the Korean Society of Mechanical Technology
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• v.20 no.6
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• pp.872-878
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• 2018

Steam tables including superheated, saturated and compressed region were simultaneously modeled using the neural networks. Pressure and temperature were used as two inputs for superheated and compressed region. On the other hand Pressure and dryness fraction were two inputs for saturated region. The outputs were specific volume, specific enthalpy and specific entropy. The neural network model were compared with the linear interpolation model in terms of the percentage relative errors. The criterion of judgement was selected with the percentage relative error of 1%. In conclusion the neural networks showed better results than the interpolation method for all data of superheated and compressed region and specific volume of saturated region, but similar for specific enthalpy and entropy of saturated region.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.205-208
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• 2008

In numerical analysis numerical values of thermodynamic properties such as temperature, pressure, specific volume, enthalpy and entropy are required. But most of the thermodynamic properties of the steam table are determined by experiment. Therefore they are supposed to have measurement errors. In order to make noised thermodynamic properties corresponding to errors, random numbers are generated, adjusted to appropriate magnitudes and added to original thermodynamic properties. the neural networks and quadratic spline interpolation method are introduced for function approximation of these modified thermodynamic properties in the saturated water based on pressure. It was proved that the neural networks give smaller percentage error compared with quadratic spline interpolation. From this fact it was confirmed that the neural networks trace the original values of thermodynamic properties better than the quadratic interpolation method.

• Nuclear Engineering and Technology
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• v.36 no.5
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• pp.388-402
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• 2004

A phenomena identification and ranking table(PIRT) was developed for a main steam line break (MSLB) event for the Advanced Power Reactor-1400 (APR-1400). The selectee event was a double-ended steam line break at full power, with the reactor coolant pump running. The developmental panel selected the fuel performance as the primary safety criterion during the ranking process. The plant design data, the results of the APR-1400 safety analysis, and the results of an additional best-estimate analysis by the MARS computer code were used in the development of the PIRT. The period of the transient was composed of three phases: pre-trip, rapid cool-down, and safety injection. Based on the relative importance to the primary evaluation criterion, the ranking of each system, component, and phenomenon/process was performed for each time phase. Finally, the knowledge-level for each important process for certain components was ranked in terms of existing knowledge. The PIRT can be used as a guide for planning cost-effective experimental programs and for code development efforts, especially for the quantification of those processes and/or phenomena that are highly important, but not well understood.

• Proceedings of the Korean Nuclear Society Conference
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• 2003.05a
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• pp.128.1-128.1
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• 2003

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.246-249
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• 2007

In numerical analysis for phase change material, numerical values of thermodynamic properties such as temperature, pressure, specific volume, enthalpy and entropy are required. But the steam table or diagram itself cannot be used without modelling. In this study applicability of neural networks in modelling superheated vapor region of water was examined by comparing with the quadratic spline. neural network consists of an input layer with 2 nodes, two hidden layers and an output layer with 3 nodes. Quadratic spline interpoation method was also applied for comparison. Neural network model revealed smaller percentage error to quadratic spline interpolation. From these results, it is confirmed that the neural networks could be powerful method in modelling the superheated range of the steam table.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.677-680
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• 2005

In numerical analysis on the thermal performance of the heat exchanger with phase change fluids, the numerical values of thermodynamic properties are needed. But the steam table should be modeled properly as the direct use of thermodynamic properties of the steam table is impossible. In this study the function approximation characteristics of neural networks was used in modeling the saturated vapor region of refrigerant R12. The neural network consists of one input layer with one node, two hidden layers with 10 and 20 nodes each and one output layer with 7 nodes. Input can be both saturation temperature and saturation pressure and two cases were examined. The proposed model gives percentage error of ${\pm}$0.005% for enthalpy and entropy, ${\pm}$0.02% for specific volume and ${\pm}$0.02% for saturation pressure and saturation temperature except several points. From this results neural network could be a powerful method in function approximation of saturated vapor region of R12.