Numerical analysis of a counter-flow wet cooling tower and its plume

Zargar, Arash; Kodkani, A.; Peris, A. Delgado; Clare, E.; Cook, Julian; Karupothula, Prashanth; Vickers, B.; Flynn, M. R.; Secanell, Marc

doi:10.1016/j.ijft.2022.100139

Cited by 18 publications

(4 citation statements)

References 38 publications

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“…The enthalpy difference method, which is based on the Merkel equation, is commonly used for thermodynamic calculations in cooling towers. The basic principle behind this method is that, according to the conservation of energy, the heat released when the temperature of circulating water with mass flow rate Q drops from t wi to t wo is equal to the enthalpy increase of air with mass flow rate G, as shown in Equation (1).…”

Section: Derivation Of the Performance Equationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The cooling towers are general equipments that discharge waste heat from process systems to air through evaporation and convective heat transfer of circulating cooling water. 1 First, they can be divided into natural draft cooling towers and mechanical draft cooling towers based on their ventilation system. Second, they can be classified into countercurrent cooling towers with water flowing downward and air flowing upward, and crossflow cooling towers with water flowing downward and air flowing horizontally.…”

Section: Introductionmentioning

confidence: 99%

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Research of cooling tower filler based on radial basis function artificial neural network (RBF ANN)

Zhang

Chen

Zhao

et al. 2023

Energy Science & Engineering

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The filler is the core component of the cooling tower, filler performance refers to both its thermal and flow resistance characteristics, which use empirical formulas of tower characteristic N, volumetric mass transfer coefficient βxv, and pressure drop ΔP obtained through experimentation under specific conditions. However, the performance equations for identical countercurrent fillers can vary at different heights or seawater concentrations. Linear interpolation is the conventional method for obtaining the filler performance under different conditions, but its uncertainty limits the application. This paper explores the use of the radial basis function artificial neural network (RBF ANN) to analyze filler performance based on existing performance equations. The data set is generated by the filler performance equations. The results demonstrate that RBF ANN has a preferable prediction effect with high correlation (the determination coefficient R2 > 0.99) and prediction accuracy (the proportion of relative error within 10% N10 > 90%). Furthermore, the predicted results are consistent with the experimental results of the filler performance. Therefore, RBF ANN can accurately predict filler performance at varying heights and seawater concentrations, making it universal and providing a basis for cooling tower design.

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Section: Derivation Of the Performance Equationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research of cooling tower filler based on radial basis function artificial neural network (RBF ANN)

Zhang

Chen

Zhao

et al. 2023

Energy Science & Engineering

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“…Indicators that assess the thermal effectiveness of cooling towers have been widely applied for decades [10][11][12][13]. However, these indicators, such as the Merkel number [14], the effectiveness Number of Transfer Units, 'e-NTU' [15], and the Merkel number refined by Poppe [16,17], quantify heat transfer effectiveness, independent of resource consumption and the required cooling service.…”

Section: Previous Literature and Indicatorsmentioning

confidence: 99%

Catalyzing Cooling Tower Efficiency: A Novel Energy Performance Indicator and Functional Unit including Climate and Cooling Demand Normalization

Wenzel,

Fensterle,

Radgen

2023

Sustainability

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Energy and climate targets necessitate efficiency indicators to reflect resource-saving potentials. Prevailing indicators for cooling towers, however, often omit the effect of outside conditions. Hence, this study introduces an innovative indicator grounded in the energy efficiency ratio. Our proposed metric is the cost–benefit ratio between electricity demand and the thermodynamic minimum airflow. Thus, we call the novel indicator the airflow performance indicator. To validate its feasibility, we apply the indicator first to an extensive dataset encompassing 6575 cooling tower models and second to a year-long case study involving a data center’s wet cooling system. As a result, the energy performance indicator demonstrates that dry cooling requires eight times more minimum airflow at the median than evaporative cooling would, directly correlating to the fan power. Furthermore, efficiency benchmarks derived from the dataset of 6575 cooling tower models provide a comparative assessment of the case study. Defining the quantified benefit as minimum airflow additionally underscores the limitations of free cooling as the wet cooling system only partly covers the cooling demand, requiring chillers additionally. In conclusion, the indicator empowers the identification of energy-saving potentials in the selection, design, and operation of cooling towers. Moreover, the functional unit definition provides a foundation for future life cycle assessments of cooling towers, enhancing cooling tower efficiency and sustainability.

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Investigation of a method to decrease water consumption and enhance productivity in wet cooling towers using dynamic time-related modeling for industrial experimental applications

Seidabadi,

Ghadamian,

Jafari

et al. 2024

Heat Mass Transfer

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Numerical analysis of a counter-flow wet cooling tower and its plume

Cited by 18 publications

References 38 publications

Research of cooling tower filler based on radial basis function artificial neural network (RBF ANN)

Research of cooling tower filler based on radial basis function artificial neural network (RBF ANN)

Catalyzing Cooling Tower Efficiency: A Novel Energy Performance Indicator and Functional Unit including Climate and Cooling Demand Normalization

Investigation of a method to decrease water consumption and enhance productivity in wet cooling towers using dynamic time-related modeling for industrial experimental applications

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