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Research Papers

Cloud Formation in the Plumes of Solar Chimney Power Generation Facilities: A Modeling Study

[+] Author and Article Information
Timothy M. VanReken

Laboratory for Atmospheric Research, Department of Civil & Environmental Engineering, Washington State University, P.O. Box 642910, Pullman, WA, 99164-2910vanreken@wsu.edu

Athanasios Nenes

School of Earth & Atmospheric Sciences, and School Chemical and Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, GA 30332-0340

www.enviromission.com.au

http://www.bom.gov.au/climate/averages/tables/cw̱047053.shtml

J. Sol. Energy Eng 131(1), 011009 (Jan 07, 2009) (10 pages) doi:10.1115/1.3028041 History: Received April 10, 2007; Revised November 21, 2007; Published January 07, 2009

The solar chimney power facility has the potential to become a valuable technology for renewable energy production. Its financial viability depends on a thorough understanding of the processes affecting its performance, particularly because of the large startup costs associated with facility design and construction. This paper describes the potential impacts on plant capacity resulting from cloud formation within or downwind of the solar chimney. Several proposed modifications to the basic concept of the solar chimney power facility have the potential to cause significant additions of water vapor to the air passing through the collector. As the air continues up through and out of the chimney, this excess water can condense to form cloud. This possibility is explored using a cloud parcel model initialized to simulate the range of expected operating conditions for a proposed solar chimney facility in southwestern Australia. A range of temperatures and updraft velocities is simulated for each of four seasonal representations and three levels of water vapor enhancement. Both adiabatic environments and the effects of entrainment are considered. The results indicate that for very high levels of water vapor, enhancement cloud formation within the chimney is likely; at more moderate levels of water vapor enhancement, the likelihood of plume formation is difficult to fully assess as the results depend strongly on the choice of entrainment rate. Finally, the impacts of these outcomes on facility capacity are estimated.

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Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 1

Schematic of a solar chimney power generation facility. Air enters the solar collector (a) at ambient temperature and relative humidity. As air passes through the collector (b), it is heated by solar radiation and its water vapor content may also increase for reasons described in the text. At (c), the parcel passes through a turbine, where part of the kinetic energy is converted to electricity. The parcel, driven by buoyancy, then proceeds up the chimney (d).

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Figure 2

Typical vertical profiles from an adiabatic cloud parcel simulation. The results here are taken from a LoRH simulation for the October seasonal representation, with w=15ms−1 and ΔT=30K. (a) The temperature decreases as the parcel rises, with a change in the rate of decrease at the onset of condensation. (b) The supersaturation profile. As the parcel cools, it surpasses its saturation point and condensation begins. The peak supersaturation occurs at cloud base. (c) Water mixing ratios. The water vapor mixing ratio is the short dashed trace, the liquid water mixing ratio is the long dashed trace, and the total water is solid. The liquid water mixing ratio is zero until cloud base is reached, and total water is conserved save for a minor numerical drift at the start of the simulation. In the figure, the solid black horizontal line indicates the level of the chimney top (1000m) and the dashed black horizontal line indicates the cloud base level at approximately 2700m.

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Figure 3

Results from the adiabatic LoRH simulations for each of the four seasonal representations. The data are grouped along the bottom axis by temperature increase. Within each group, the updraft velocities (1ms−1, 5ms−1, 10ms−1, 15ms−1, and 20ms−1) increase from left to right for all data. Cloud base heights are indicated by triangles in the top graph for each season; the chimney top is indicated by the dashed line. The lower graph for each season includes the final liquid water content (bars) and the maximum supersaturation (squares, with the saturation point indicated by a horizontal dotted line).

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Figure 4

Same as Fig. 3, but for the HiRH adiabatic simulations

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Figure 5

Evolution of the cloud droplet distribution during a “HiRH” simulation. This example is from the October seasonal representation, with w=15ms−1 and ΔT=30K. In this example, cloud formation begins at a height of ∼600m, and the chimney top is indicated by a dashed line.

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Figure 6

Vertical profiles from cloud parcel simulation with entrainment starting at 1000m (the chimney top height). The entrainment rate is 30%km−1. As in Fig. 2, the results here are taken from a LoRH simulation for the October seasonal representation, with w=15ms−1 and ΔT=30K. Plots (a)–(c) are as in Fig. 2. Black traces are from the entrainment simulation, and gray traces reproduce for comparison the results of the adiabatic simulations. Below the 1000m chimney top height, the results of the two simulations are identical. However, above chimney top there are substantial differences between them, including a more rapidly decreasing temperature, an increase in the cloud base height, a decrease in the peak supersaturation, and drastic changes to the water mixing ratios. See the text for a more complete explanation.

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Figure 7

Evolution of the cloud droplet distribution for a LoRH simulation with entrainment. These results are from the same simulation as Fig. 6.

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Figure 8

Results from the LoRH simulations with entrainment for each of the four seasonal representations. The grouping and panels of each graph are as in Fig. 3. Note that the scale for the liquid water content has been reduced by a factor of 4. Cloud formation does not occur in any of the January simulations, nor for one of the October simulations; due to entrainment the parcel never becomes supersaturated in these simulations.

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