Operational cooling tower model (CTTOOL V1.0)

Abstract

SRNL-STI-2015-00039 Revision 0 Program Unit Description thermal imagery must make several additional calculations in addition to computing the temperature drop of the cooling water as it passes through the tower. These additional calculations are needed to convert the remotely measured cooling tower throat or area-weighted temperature into a cooling water inlet temperature. The CTTool model thus has two main components: 1) the inner model, which computes the performance of a counterflow or cross-flow cooling tower during induced draft (fan on) or wind-aided/natural draft (fan off), and 2) the outer model, which takes a remotely measured throat or area-weighted that iterates on the inlet water temperature to match the target temperature of interest Files Reads superfile and opens files for input/output. Cmshr Prints the configuration management header. ReadInput Reads and processes all CTTool input parameters and data. Initialization Initializes deck ray tracing arrays. Initializes cooling tower variables and arrays. Observations Loops over observations or images. SurfMetrRSData Updates surface meteorology and remote sensing data for each observation or image. DayTime Converts zday and ztime to local time. DeckRayTracing Assigns material ids to water decks (XFCT), solid deck, exposed throat, deck hidden by fan shroud and throat hidden by fan shroud using ray tracing from sensor line-of-sight to cooling tower. The fractional areas are then computed for each material type. ROI Region-of-Interest. See Appendix A. CFCoolingTower Models the performance of an induced draft or natural draft/wind-aided wet counterflow cooling tower. XFCoolingTower Models the performance of an induced draft or natural draft/wind-aided wet cross-flow cooling tower. DeckHeatTransfer Computes the steady-state deck top surface temperature using an energy balance. ShroudHeatTransfer Computes the steady-state fan shroud inner and outer surface temperatures. The following sections will describe the MDCT models in more detail. SRNL-STI-2015-00039 Revision 0 2.1.1 Inner Model A general numerical model, for predicting the steady-state thermal performance of wet mechanical draft cooling towers, has been developed and is herein described. The model handles both cross-flow and counterflow type cooling towers. With the fan turned-off, a cooling tower can operate in the natural draft/wind-aided mode, and the model can also predict the performance in this mode of operation. It is generally a one-dimensional model, though the heat and mass transfer in the fill sections of a cross-flow cooling tower are treated two-dimensionally. Detailed derivations and descriptions of the governing equations and constitutive relations are presented. The numerical algorithms are also described. The model is written in FORTRAN 95 with a modular structure and dynamic memory allocation. New cooling tower designs can be easily added to the software framework.

Predictive Modeling of a Paradigm Mechanical Cooling Tower Model: II. Optimal Best-Estimate Results with Reduced Predicted Uncertainties

“…, q w ; and (iv) the outlet air relative humidity, RH (1) . Except for the water outlet flow rate m (50) w , these responses have been measured experimentally [7,8], and the first four moments of their respective statistical distributions have been quantified in [1].…”

Predictive Modeling of a Buoyancy-Operated Cooling Tower under Unsaturated Conditions: Adjoint Sensitivity Model and Optimal Best-Estimate Results with Reduced Predicted Uncertainties

“…Because of the aforementioned similarities with the partially saturated case in [2], the constitutive equation system governing the cooling tower model of interest is closely comparable with the one provided in Equations (2)-(15) in [2]. For this unsaturated analysis, the governing equations within the total of I = 49 control volumes represented in Figure 2 are as follows [1]:…”

“…The amount of heat released into the external environment can be quantified by using a numerical simulation model of the physical processes that take place within the respective cooling tower, complemented by experimentally measured data that characterize external conditions such following (four) measured quantities: (i) Exhaust air temperature according to the "Tidbit" sensor, which will be denoted in the following as T a,out(Tidbit) ; (ii) Exhaust air temperature according to the "Hobo" sensor, which will be denoted in the following as T a,out(Hobo) ; (iii) Outlet water temperature, which will be denoted in the following as T meas w,out ; (iv) Exhaust air relative humidity, which will be referred to as RH meas . For a data set to be intended as unsaturated is required that the value of the exhaust air relative humidity (RH), computed by making use of the SNRL simulation code CTTool [1], is smaller than 100%, while the threshold is set on correspondence of the saturation line (RH = 100%). With this standard set, 6717 data sets among the total 8079 measured have matched the requirement above and have been therefore identified as "unsaturated", leading to include them in the present study.…”

“…A numerical method, which has been presented in detail in [2], has been implemented to improve both the accuracy and the efficiency in the solution of the constitutive equations system governing the counter-flow cooling tower model. The cooling tower model analyzed in this work has been originally presented in [1] and has been thoroughly described in [2], from which Figures 1-3 have been taken. Natural draft air enters the tower just below the fill section, through which it passes flowing upwards along the height of the tower finally exiting at the top, through an exhaust comprising a fan.…”

“…. For a data set to be intended as unsaturated is required that the value of the exhaust air relative humidity ( ) RH , computed by making use of the SNRL simulation code CTTool [1], is smaller than 100%, while the threshold is set on correspondence of the saturation line…”