Heat transfer characteristics of carbon dioxide cross flow over tube bundles at supercritical pressures

Gao, Weikai; Zhao, Jiaqing; Li, Xiaowei; Zhao, Houjian; Zhang, Yiyang; Wu, Xinxin

doi:10.1016/j.applthermaleng.2019.113786

Cited by 14 publications

(3 citation statements)

References 48 publications

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“…1, the corresponding Ma of primary-side hot helium is 0.039, so it can be treated as an incompressible viscous fluid with constant properties. Helium properties are calculated using the NIST real gas package which is a material database based on the Thermodynamic and Transport Properties of Refrigerants and Refrigerant Mixtures Database (REFPROP v9.1) provided by the National Institute of Standards and Technology (NIST) (Zhao et al, 2017;Gao et al, 2019). Table 2 lists the helium properties, where 5 significant digits are reserved.…”

Section: Physical Modelsmentioning

confidence: 99%

Numerical Investigation of Hot Helium Flow Homogenizer on Inter-Unit Flow Rate Uniformity of HTGR Once Through Steam Generator

Qin

Luo

et al. 2022

Front. Energy Res.

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High working temperature is a major feature of the high temperature gas-cooled reactor (HTGR). The helical tube once through steam generator (OTSG) should maintain appropriate temperature uniformity. The temperature non-uniformity of the HTGR OTSG includes the in-unit and inter-unit temperature non-uniformity, while the latter is mainly induced by the inter-unit flow rate non-uniformity of primary-side hot helium, which is significantly affected by the inlet structure. In this work, a new inlet structure with a hot helium flow homogenizer is designed, and its flow distribution characteristics are numerically investigated. Accordingly, the optimal geometrical parameters are determined, such as the circular hole diameter on the end wall, the square hole size, and arrangement on the cylinder wall. Increasing the resistance of the flow homogenizer with non-uniformly arranged square holes (NUASHs) can improve inter-unit flow rate uniformity because it decreases the effect of static pressure difference caused by dynamic pressure. Two design parameters (resistance coefficient and flow area ratio of the square hole on both sides) are introduced to evaluate the structure effect of the hot helium flow homogenizer on inter-unit flow rate distribution. They are recommended within the ranges of (7.81–22.42) and (0.53–1.64), respectively. In these recommended ranges, the suction phenomenon near the hot helium inlet can be effectively suppressed, with the critical resistance coefficient of 7.63. By coupling with 19 heat exchange units, the overall performance of the hot helium flow homogenizer is better than that of the current inlet structure with a baffle, with the maximum inter-unit flow rate deviation decreased from 2.97% to 0.30%. This one-magnitude enhancement indicates that the hot helium flow homogenizer with NUASHs is a promising solution to improve inter-unit flow rate uniformity of the HTGR OTSG.

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Section: Physical Modelsmentioning

confidence: 99%

Numerical Investigation of Hot Helium Flow Homogenizer on Inter-Unit Flow Rate Uniformity of HTGR Once Through Steam Generator

Qin

Luo

et al. 2022

Front. Energy Res.

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“…A cha is the area of hot airflow channel which is stacked by two bricks together, A cha = L × D × N, where N is the number of bricks in a row. Based on the Nusselt-Number N u , the heat transfer coefficient h conv can be calculated by the Zukauskas formula [24]. λ is the thermal conductivity.…”

Section: Thermal Convectionmentioning

confidence: 99%

“…In heat convection, multiple round holes can be approximately regarded as the tube bundle. The average heat transfer performance of the tube bundle is related to the flow Reynolds-number (R e ) [24].…”

Section: Thermal Convectionmentioning

confidence: 99%

Electricity Consumption Prediction of Solid Electric Thermal Storage with a Cyber–Physical Approach

Yang

Wang

et al. 2019

Energies

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This paper proposes a cyber-physical approach to enhance the prediction accuracy of electricity consumption of solid electric thermal storage (SETS) system, which integrates a physical model and a data-based cyber model. In the cyber-physical model, the prediction error of the physical model is used as an input of the cyber model to further calibrate the prediction error. Firstly, customers' behavior characteristics are extracted by the integration of K-means and one-versus-one support vector machine. Secondly, based on the behavior characteristics and ambient temperature, the physical model is developed to predict daily electricity consumption. Finally, the error levels of physical model are classified, together with the temperature and prediction values of the physical model, are selected as the inputs of the cyber model using the back propagation (BP) neural network to calibrate the results of the physical model. The effectiveness of the proposed cyber-physical model (CPM) is verified by a 1 MW SETS system. The simulation results show that, compared with the physical model (PM) and cyber model (CM), the maximum relative errors (MRE) with the CPM are reduced to 25.4% and 4.8%, respectively.Energies 2019, 12, 4744 2 of 18 forced-air electric furnaces is built to predict the thermal energy storage, which is an early application of SETS [5]. This work does not take into account the effect of continuously changing the ambient temperature on thermal energy storage. A PM is integrated into the TRNSYS calculation tool to evaluate the optimal thermal energy storage of forced-air electric furnaces with changing ambient temperature [6]. Therefore, it is necessary to improve the basis of the existing methods, and enhance the prediction accuracy. However, the customers' behavior characteristics of SETS are not considered in the above-mentioned PMs, which is very important to improve the accuracy of prediction.In [7], a sparse continuous conditional random fields method was proposed to predict electric load with the identification of behavior. The data from advanced metering infrastructure is used to understand the power consumption patterns to improve the load forecasting accuracy in [8].The prediction accuracy would be significantly enhanced with the consideration of behavior. However, the working mode of SETS are completely different from those of conventional electric loads [9]. SETS is charged by the off-peak electricity, and its thermal energy is released all-day. During the off-peak hours, usually from 21:00 to 6:00, the heating elements quickly heat the dense bricks to a high temperature owning to its cheaper electricity prices. During the peak period from 6:00 to 21:00, the heating elements are switched off, and SETS continues to release its thermal energy to warm the rooms. Many behavior characteristics of SETS directly affect the heat load demand, such as all-day continuous work (e.g., convenience store), and holiday and non-holiday period (e.g., star hotel), which need to be considered. The conventional models o...

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Numerical investigation on heat transfer characteristics of liquid metal cross flow over tube bundles

Xie

Zhao

et al. 2023

Annals of Nuclear Energy

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Heat transfer characteristics of carbon dioxide cross flow over tube bundles at supercritical pressures

Cited by 14 publications

References 48 publications

Numerical Investigation of Hot Helium Flow Homogenizer on Inter-Unit Flow Rate Uniformity of HTGR Once Through Steam Generator

Numerical Investigation of Hot Helium Flow Homogenizer on Inter-Unit Flow Rate Uniformity of HTGR Once Through Steam Generator

Electricity Consumption Prediction of Solid Electric Thermal Storage with a Cyber–Physical Approach

Numerical investigation on heat transfer characteristics of liquid metal cross flow over tube bundles

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