5,959 research outputs found
Critical heat flux of water in vertical round tubes at low pressure and low flow conditions
No full textAn experimental study on critical heat flux (CHF) has been performed for water flow in vertical round tubes under low pressure and low flow (LPLF) conditions to provide a systematic data base and to investigate parametric trends. Totally 513 experimental data have been obtained with Inconel-625 tube test sections in the following conditions: diameter of 6, 8, 10 and 12 mm; heated length of 0.3 similar to 1.77 m; pressure of 106 similar to 951 kPa; mass flux of 20 similar to 277 kg m(-2) s(-1): and inlet subcooling of 50 similar to 654 kJ kg(-1), thermodynamic equilibrium critical quality of 0.323 similar to 1.251 and CHF of 108 similar to 1598 kW m(-2). Flow regime analysis based on Mishima Bi Ishii's flow regime map indicates that most of the CHF occurred due to liquid film dryout in annular-mist and annular flow regimes. Parametric trends are examined from two different points of view: fixed inlet conditions and fixed exit conditions. The parametric trends are generally consistent with previous understandings except for the complex effects of system pressure and tube diameter. Finally, several prediction models are assessed with the measured data; the typical mechanistic liquid him dryout model and empirical correlations of (Shah, M.M., 1987. Heat Fluid Flow 8 (4), 326-335; Baek, W.P., Kim, H.G., Chang, S.H., 1997. KAIST critical heat flux correlation for water flow in vertical round tubes, NUTHOS-5, Paper No. AA5 show good predictions. The measured CHF data are listed in Appendix B for future reference. (C) 2000 Published by Elsevier Science S.A. All rights reserved
FAST INFERENTIAL ADAPTIVE OPTIMIZATION OF A CONTINUOUS YEAST CULTURE BASED ON CARBON-DIOXIDE EVOLUTION RATE
No full textEXPERIMENTAL AND SIMULATION STUDIES OF MULTIVARIABLE ADAPTIVE OPTIMIZATION OF CONTINUOUS BIOREACTORS USING BILEVEL FORGETTING FACTORS
No full textDevelopment of a back propagation network for one-step transient DNBR calculations
No full textOne of the key safety parameters during the transient of pressurized water reactor is the departure from nucleate boiling ratio (DNBR). In the transient analysis caused by the anticipated operational occurrences or accidents, the DNBR is predicted by three steps: firstly, a system transient analysis code, secondly, a heat flux calculation code and finally a detailed DNBR calculation code should be used. This tandem procedure is time consuming and very cumbersome. In this paper, the back propagation network (BPN) algorithm is incorporated into the system transient analysis code for the one-step transient DNBR calculation, thus, to eliminate the tandem procedure and to increase calculation speed. The BPN is trained with the DNBR training data selected by latin hypercube sampling technique. After the completion of training, performance is evaluated. Results show that the system transient analysis code with a multi-layer BPN algorithm can quickly calculate the transient DNBR with the reasonable accuracy even in case of axial flux shape changes. (C) 1997 Elsevier Science Ltd
Static characteristics of a continuous flow bioreactor containing antibiotic‐resistant recombinant cells
No full textOne of authors (YKC) was supported in part by a David Ross Fellowship
Efficacy of light therapy on nonseasonal depression among elderly adults: a systematic review and meta-analysis [Corrigendum]
No full textCH Chang, CY Liu, SJ Chen, HC Tsai. Neuropsychiatr Dis Treat. 2018;14:3091–3102.The authors have advised that they listed the second affiliation in the author list of the paper incorrectly. The current affiliation number 2 “Department of Psychiatry & Brain Disease Research Center, China Medical University Hospital, Taichung, Taiwan” should instead appear as “An Nan Hospital, China Medical University, Tainan, Taiwan”.Read the original articl
DEVELOPMENT OF REAL-TIME CORE MONITORING-SYSTEM MODELS WITH ACCURACY-ENHANCED NEURAL NETWORKS
No full textCore monitoring models have been developed with use of neural networks for prediction of the core parameters for pressurized water reactors. The neural network model has been shown to be successful for the conservative and accurate prediction of the departure from nucleate boiling ratio (DNBR). Several variations of the neural network technique have been proposed and compared based on numerical experiments. The neural network can be augmented by use of a functional link to improve the performance of the network model. Use of two-fold weight sets or weighted system error backpropagation was very effective for improving the network model accuracy further. Uncertainty factor as a function of output DNBR is used to obtain the conservative DNBR for actual applications. The predictions by the network model need to be supported by extensive training of network and statistical treatment of the data. Studies for further improvements are suggested for the actual applications in the future
ADAPTIVE OPTIMIZATION OF A CONTINUOUS CULTURE WITH THE USE OF A BILEVEL FORGETTING FACTOR
No full textAn independent assessment of Groeneveld et al.s 1995 CHF look-up table
No full textThe prediction capability of the 1995 CHF look-up table (Groeneveld D.C., et al., Nucl. Eng. Des. 163 (1996) 1-23) is independently assessed based on the KAIST data base consisting of 10 822 data for uniformly-heated, vertical, round tubes. This confirms the error statistics for the heat balance method reported by Groeneveld et al. and shows overall average and RMS errors of 4.2 and 36.7%, respectively, for the direct substitution method. The new 1995 table shows better prediction capability than the 1986 AECL-UO table (Groeneveld et al., 1986), especially for the low-pressure, low-flow region. The error analysis indicates the length effect even for significantly long tubes. (C) 1997 Elsevier Science S.A
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