Method for predicting heat and mass transfer performance of dry-wet combined cooling tower based on numerical simulation

Abstract

This invention relates to the field of design simulation technology, specifically to a method for predicting the heat and mass transfer performance of a combined wet and dry cooling tower based on numerical simulation. The method includes the following steps: collecting structural parameters, operating parameters, and physical property parameters of the combined wet and dry cooling tower, and establishing a coupled numerical equation for energy balance. This invention obtains the current number of segments, calculates the variance of the segmented heat flux density values ​​based on the structural parameters and heat transfer values; dynamically adjusts the number of segments according to the variance of the segmented heat flux density values ​​to obtain the adjusted number of segments, and calculates the outlet temperature difference; then performs numerical iterative convergence based on the outlet temperature difference. By abandoning the traditional model of a fixed number of segments and dynamically adjusting the segments based on the real-time heat flux density distribution, the calculation accuracy is improved in areas with large heat flux fluctuations, and the calculation efficiency is improved in areas with small fluctuations, thereby improving the iterative convergence speed and prediction accuracy.

Description

Technical Field

[0001] This invention relates to the field of design simulation technology, and more specifically, to a method for predicting the heat and mass transfer performance of a combined dry and wet cooling tower based on numerical simulation. Background Technology

[0002] The method for predicting the heat and mass transfer performance of dry and wet combined cooling towers based on numerical simulation is mainly used to accurately calculate key indicators such as total heat exchange, cooling efficiency, defogging performance, and water saving rate during the operation of the cooling tower. It quantifies the heat and mass transfer law of dry / wet channels, and provides data support for the design optimization of cooling towers (such as structural parameters) and adaptive control of operating conditions (such as switching between summer and winter operating conditions). Ultimately, it achieves the coordinated achievement of cooling effect, defogging performance, and water saving rate.

[0003] However, current traditional technologies for predicting the heat and mass transfer performance of combined dry and wet cooling towers mainly include two core solutions:

[0004] First, the fundamental differences in heat and mass transfer mechanisms between dry and wet channels under summer and winter seasonal conditions are not fully considered. During summer operation, there is significant water evaporation and latent heat transfer in the wet channel, resulting in a strong mass transfer effect. However, under winter conditions, the wet channel is often closed or operates only in dry mode, with evaporation mass transfer being almost zero and heat transfer mainly consisting of sensible heat exchange. Traditional heat and mass transfer performance prediction techniques for combined dry and wet cooling towers use a unified heat and mass transfer correlation or a fixed parameter set to describe these two drastically different physical processes, leading to severe coupling distortion of model parameters. This distortion results in inaccurate prediction of white fog elimination effect under winter conditions (leading to residual white fog or packing icing) and high prediction deviation of cooling efficiency under summer conditions, thus causing insufficient cooling capacity or excessive water consumption.

[0005] Second, the numerical iterative algorithm is rigid and cannot adapt to the non-uniform distribution of heat flux density. Traditional heat and mass transfer performance prediction techniques for combined dry and wet cooling towers mostly adopt a discretization strategy with a fixed number of segments to divide the cooling tower area and use a uniform heat flux assumption in each segment. However, in actual operation, due to the non-uniformity of air-water two-phase flow, the influence of packing layout and ambient wind speed, the heat flux density exhibits significant spatial fluctuations: some areas (such as near the air inlet and the packing concentration area) have intense heat and mass exchange and large parameter gradients, while other areas are relatively flat. This makes it impossible for the fixed segmentation mode to achieve local mesh refinement to improve calculation accuracy in areas with large heat flux fluctuations, while in flat areas, excessive discretization reduces calculation efficiency. Ultimately, this leads to slow iteration convergence speed and insufficient prediction accuracy, making it difficult to support refined design and optimization.

[0006] Therefore, we provide a method for predicting the heat and mass transfer performance of combined dry and wet cooling towers based on numerical simulation. Summary of the Invention

[0007] The purpose of this invention is to provide a method for predicting the heat and mass transfer performance of a combined dry and wet cooling tower based on numerical simulation, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, this invention provides a method for predicting the heat and mass transfer performance of a combined dry and wet cooling tower based on numerical simulation, comprising the following steps:

[0009] S1. Collect the structural, operating condition and physical property parameters of the dry-wet combined cooling tower and construct a linear model. Calculate the latent heat of vaporization of water using the linear model.

[0010] S2. Based on the latent heat of vaporization of water and the numerical calculation of structural and operating parameters, the heat transfer value is calculated and combined with the numerical values ​​of the latent heat of vaporization of water, operating conditions and physical property parameters to establish a coupled numerical equation for energy balance.

[0011] S3. Obtain the current segment number value, calculate the variance of the segment heat flux density value by combining the structural parameters and heat transfer value, and dynamically adjust the segment number by combining it with the set variance threshold to obtain the adjusted segment number value.

[0012] S4. Based on the adjusted segmented number values, the subsequent inlet parameters are substituted into the energy balance coupling numerical equation to calculate the wet channel water outlet temperature value, and then calculate the outlet temperature difference value. The numerical iterative convergence is judged by comparing it with the known iterative convergence tolerance value in the database.

[0013] S5. After numerical convergence, the cooling water temperature drop is calculated based on the operating parameters and the wet channel water outlet temperature. At the same time, the cooling efficiency performance is predicted based on the operating conditions and physical property parameters, and used as the predicted value.

[0014] S6. Collect the measured values ​​of the operating parameters of the dry-wet combined cooling tower and use them as actual values. Calculate the performance error value based on the predicted value and the actual value.

[0015] S7. Set the transfer performance error threshold and correct the air convection heat transfer coefficient of the dry / wet channel with the performance error value.

[0016] As a further improvement to this technical solution, the construction of the linear model and the calculation of the latent heat of vaporization of water include the following method steps:

[0017] Select water temperature within the temperature range The latent heat of vaporization was extracted and used as sample data. A linear model was then constructed based on the sample data. ,in, for The baseline value for the latent heat of vaporization of water;

[0018] The water temperature linearity coefficient of the linear model is solved using the least squares method. The linear coefficient of water temperature, Substituting the latent heat of vaporization of water and the water temperature into the linear model, the linear coefficients are obtained.

[0019] Based on linear coefficients, combined with The latent heat of vaporization of water is calculated using the baseline value of the latent heat of vaporization and the numerical values ​​of its physical properties.

[0020] As a further improvement to this technical solution, the calculation of the heat transfer value includes the following method steps:

[0021] Calculate the mass flow rate of water outlet in the wet channel and the moisture content of air outlet in the wet channel based on the operating parameters.

[0022] Based on operating conditions, physical property parameters, and The latent heat of vaporization of water is used to calculate the enthalpy of the air inlet in the dry channel, and then the structural parameter values ​​are used as characteristic dimensions to determine whether the air flow is laminar or turbulent.

[0023] Select the corresponding classical convective heat transfer correlation based on the flow state, calculate the Nusselt number of the dry / wet channel, and deduce the air convective heat transfer coefficient of the dry / wet channel by combining the structural parameter values.

[0024] The overall heat transfer coefficient is calculated based on the air convection heat transfer coefficient of the dry / wet channel and the structural parameters, and then the logarithmic mean temperature difference is calculated based on the operating parameters.

[0025] Using the overall heat transfer coefficient Structural parameter values ​​and logarithmic mean temperature difference calculation of heat transfer values The enthalpy of the air outlet in the dry channel is calculated by combining the enthalpy value of the air inlet in the dry channel.

[0026] As a further improvement to this technical solution, the establishment of the energy balance coupled numerical equation includes the following steps:

[0027] A coupled numerical equation for the air energy balance in the dry channel is established using the total inflow / outflow of air energy. The specific formula is as follows: ;in, This refers to the heat transfer value. and These refer to the mass flow rates at the inlet and outlet of the dry channel air, respectively. and These refer to the enthalpy values ​​of the air inlet and outlet in the dry channel, respectively.

[0028] Based on the mass flow rate values ​​of the wet channel inlet / outlet and Specific heat at constant pressure of water Inlet / outlet temperature of wet channel and Operating condition indicator parameters Water evaporation value The latent heat of vaporization of water A coupled numerical equation for the energy balance of water in the wet channel is established, and the specific formula is as follows: ;

[0029] Based on the dry air inlet / outlet mass flow rate values ​​of the wet channel and Enthalpy values ​​of air inlet / outlet in the humid passage and The latent heat of vaporization of water A coupled numerical equation for the air energy balance in the wet channel is established, and the specific formula is as follows: .

[0030] As a further improvement to this technical solution, the method for calculating the variance of the segmented heat flux density values ​​to obtain the adjusted number of segments includes the following steps:

[0031] Get the current segment number value The heat flux density of each segment is calculated based on the structural parameters and heat transfer values. Then, the average heat flux density of each segment is calculated. Finally, the variance of the heat flux density of each segment is calculated by combining the segment heat flux density with the current number of segments. ;

[0032] Determine the variance threshold The number of segments is dynamically adjusted based on the variance of the segmented heat flux density values ​​to obtain the adjusted number of segments. .

[0033] As a further improvement to this technical solution, the calculation of the wet channel water outlet temperature includes the following steps:

[0034] Based on the adjusted segmentation values, the heat exchanger is numerically divided along the co-current flow direction into segments. The segment is divided into sections, and the exit parameters of the first section are used as the entry parameters of the second section.

[0035] Substitute the inlet parameters of the downstream section into the coupled numerical equation of energy balance in the dry / wet channel to calculate the new heat transfer and water evaporation values, and calculate the total heat transfer and total evaporation values ​​of each segment respectively.

[0036] Based on the algorithm formula for calculating the enthalpy value of the dry channel air outlet, the dry channel air outlet temperature value is obtained.

[0037] The algorithm formula for calculating the mass flow rate of the water outlet in the wet channel is substituted into the established coupled numerical equation for the energy balance of the water in the wet channel. The water outlet temperature value of the wet channel is obtained by rearranging the equation, and the outlet temperature difference is calculated by comparing it with the air outlet temperature value of the dry channel.

[0038] As a further improvement to this technical solution, the numerical iteration convergence determination includes the following method steps:

[0039] The value of the outlet temperature difference and the value of the iteration convergence tolerance are used to determine whether to perform numerical iteration convergence. When the outlet temperature difference is less than the value of the iteration convergence tolerance, it is determined that numerical iteration convergence should be performed.

[0040] When the outlet temperature difference is greater than or equal to the iteration convergence tolerance value, it is determined that the process has not converged. The current segment number value is updated, and the heat flux density value of the segment is recalculated.

[0041] As a further improvement to this technical solution, the calculation of cooling water temperature drop and prediction of cooling efficiency performance values ​​includes the following method steps:

[0042] The cooling water temperature drop is calculated based on the operating parameters and the wet channel water outlet temperature. The cooling efficiency performance is then predicted by combining the operating parameters, physical property parameters, and the total heat transfer value of each segment.

[0043] Predicting defogging performance values ​​involves the following steps:

[0044] Obtain atmospheric pressure values and water saturated vapor pressure By combining the moisture content value of the air outlet in the wet channel with the moisture content value of the air, the relative humidity value of the air outlet in the wet channel is obtained, and then the fog-removing performance value is predicted.

[0045] As a further improvement to this technical solution, the predicted value also includes a water-saving performance value, and the prediction method includes the following steps:

[0046] The evaporation rate of traditional cooling towers is extracted from the database, and the water-saving performance value is predicted by combining the total evaporation rate value. The anti-fogging performance value, water-saving rate value and cooling efficiency value are used as predicted values.

[0047] Calculate the actual cooling efficiency performance value, actual defogging performance value, and actual water saving rate performance value based on the collected measured values, and use these as the actual values. ;

[0048] Using predicted values and actual value Calculate the three performance error values ​​respectively. .

[0049] As a further improvement to this technical solution, the correction of the air convection heat transfer coefficient in the dry / wet channel includes the following steps:

[0050] A transmission performance error threshold is set. Three performance error values ​​are compared to this threshold to determine whether a correction coefficient needs adjustment. When the performance error value exceeds the transmission performance error threshold, and the error exceeds 1% for each subsequent 1% change, the correction coefficient is adjusted. Adjust step size The adjustment yields the adjusted correction coefficient. ;

[0051] The correction is based on the adjusted correction factor and the air convection heat transfer coefficient of the dry / wet channel.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0053] 1. This numerical simulation-based method for predicting the heat and mass transfer performance of a combined wet and dry cooling tower obtains the current number of segments, calculates the heat flux density of each segment by combining structural parameter values ​​and heat transfer values, and then calculates the variance of the segment heat flux density values. A variance threshold is set, and the number of segments is dynamically adjusted based on the variance of the segment heat flux density values ​​to obtain the adjusted number of segments. The heat exchanger is divided into numerical categories according to the flow direction. part, The parameters of the front-end outlet are used as the parameters of the rear-end inlet and substituted into the energy balance coupled numerical equation to calculate the air outlet temperature of the dry channel and the water outlet temperature of the wet channel, and then calculate the outlet temperature difference. The known iteration convergence tolerance values ​​in the database are used for judgment. If the outlet temperature difference is less than the iteration convergence tolerance value, iteration convergence is performed. By abandoning the traditional mode of fixed number of segments, the segment step size is dynamically adjusted based on the real-time heat flux density distribution. The calculation accuracy is improved in the region with large heat flux fluctuations and the calculation efficiency is improved in the region with small fluctuations, thereby improving the iteration convergence speed and prediction accuracy.

[0054] 2. This numerical simulation-based method for predicting the heat and mass transfer performance of a combined dry and wet cooling tower calculates the cooling water temperature drop based on operating parameters and the water outlet temperature in the wet channel; predicts the cooling efficiency performance based on operating parameters and physical property parameters; substitutes atmospheric pressure values ​​from the IAPWSIF97 standard database into the wet air property equation to obtain the relative humidity at the wet channel outlet and predicts the defogging performance; uses the cooling efficiency and defogging performance values ​​as predicted values; collects measured operating parameters of the combined dry and wet cooling tower, calculates the actual cooling efficiency and defogging performance values ​​respectively, and uses them as actual values; calculates the performance error value based on the predicted and actual values; sets a transfer performance error threshold, and corrects the air convection heat transfer coefficient in the dry / wet channels based on the performance error value; adjusts the correction coefficient through performance error feedback to correct the convection heat transfer coefficient in the dry / wet channels, avoiding parameter coupling prediction distortion caused by a unified model, and improving the calculation accuracy of mass and heat transfer parameters across operating conditions. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating the overall method steps of the present invention. Detailed Implementation

[0056] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Example 1

[0058] This invention provides a method for predicting the heat and mass transfer performance of a combined dry and wet cooling tower based on numerical simulation. Please refer to [link to relevant documentation]. Figure 1 The method includes the following steps:

[0059] S1 includes the following method steps:

[0060] S1.1 Collect the structural parameters, operating parameters, and physical property parameters of the combined wet and dry cooling tower; the structural parameter values ​​include the heat exchange area of ​​the indirect heat exchanger. Dry / wet channel clearance , thickness of the isolation plate and thermal conductivity ;

[0061] Operating parameters include environmental parameters (summer: dry channel air inlet temperature). Dry channel air outlet temperature ), wet channel inlet temperature Temperature at the outlet of the wet channel Dry / wet channel fluid mass flow rate (dry channel air inlet mass flow rate value) Mass flow rate of air at the dry channel outlet Mass flow rate of water inlet in the wet channel Moisture content of air at the dry channel inlet Moisture content at the dry channel air outlet Water evaporation value Moisture content of air at the inlet of the wet channel Mass flow rate of dry air inlet in the wet channel The dry channel air inlet / outlet mass flow rate refers to the mass of air flowing into and out of the dry channel of the combined wet and dry cooling tower per unit time, expressed in units of... The core of the system reflects the air flow rate and mass transfer scale in the dry channel of the combined dry and wet cooling tower. The mass flow rate of the air inlet in the dry channel is obtained by collecting the air mass flow meter installed at the air inlet end of the dry channel, and the mass flow rate of the air outlet in the dry channel is derived based on the law of conservation of mass.

[0062] The wet passage water inlet mass flow rate refers to the mass of cooling water flowing into the wet passage of the combined wet and dry cooling tower per unit time, expressed in units of... It is the core operating parameter that describes the scale of water flow in the wet channel of the dry-wet combined cooling tower. It directly participates in the calculation of key performance indicators such as total heat exchange and cooling efficiency, and is the basic data for evaluating the heat exchange capacity and water-saving effect of the dry-wet combined cooling tower.

[0063] The moisture content of dry air at the inlet / outlet refers to the mass of water vapor contained in a unit mass of dry air at the inlet / outlet of the dry channel, and the unit is 1. (Dry air) is used to quantify the humidity state of the air in the dry channel. It is a core physical property parameter for calculating the enthalpy of moist air and the amount of sensible heat exchange, and directly participates in the numerical simulation and performance calculation of the heat and mass transfer process in the dry channel.

[0064] The moisture content at the air inlet of the wet passage refers to the mass of water vapor contained in a unit mass of dry air at the inlet of the wet passage, and the unit is 1000 m³ / s. (Dry air) is a core physical property parameter that describes the humidity state of the air at the inlet of the wet channel. It directly participates in the calculation of the heat and mass transfer process between the cooling water and the air in the wet channel (such as the latent heat of evaporation and the sensible heat exchange). It is a key basic data for evaluating the cooling efficiency and water saving rate performance indicators of the cooling tower.

[0065] The dry air inlet mass flow rate of the wet passage refers to the mass of pure dry air flowing into the wet passage of the combined wet and dry cooling tower per unit time, and the unit is _____. This parameter focuses on the dry air composition and is used to eliminate the mass effect of water vapor in the air. It is a key operating parameter that describes the scale of dry air flow in the wet channel. It directly participates in the core calculation of the moisture content at the air outlet of the wet channel and the energy balance equation of the wet channel air. It is the basic data for quantifying the heat and mass transfer in the wet channel (such as water evaporation and latent heat exchange) and predicting cooling efficiency and water saving rate.

[0066] The physical property parameters include the specific heat of dry air at constant pressure obtained from the database. Specific heat of water vapor at constant pressure Specific heat at constant pressure of water The actual temperature of water ;

[0067] According to the industrial standards of the International Association for the Properties of Water and Steam, the latent heat of vaporization is essentially the difference in specific enthalpy between saturated water vapor and saturated water at the same temperature and pressure. Under these conditions, the enthalpy of saturated water is taken as a thermodynamic reference value. The saturated vapor specific enthalpy is calculated using the standard formula as follows: The difference between the two is To simplify engineering calculations, this value is rounded down to the nearest integer. Latent heat of vaporization of water (reference value) ;

[0068] S1.2 Select the actual operating water temperature of the combined dry and wet cooling tower Concentrated The temperature range within which the latent heat of vaporization of water at normal pressure exhibits a good linear decrease with increasing water temperature was selected for fitting. This range was chosen to ensure a high degree of match between the calculation results and engineering conditions. Based on the International Association for Water and Steam (IAPWSIF) 97 standard data, the following data was extracted... , , , , , The precise latent heat of vaporization values ​​for 6 key temperature points are as follows: , , , , , As sample data, a linear model is constructed based on the sample data. ,in, for The latent heat of vaporization of water is used as a reference value, and the water temperature linearity coefficient of the linear model is solved by the least squares method. The linear coefficient of water temperature, The latent heat of vaporization of water and water temperature were substituted into the linear model for calculation, and the result was the fitted value. With the objective of minimizing the sum of squared residuals between the fitted value and the IAPWS standard value, the least squares formula was applied to the sample data to obtain the initial slope value. Considering the dual requirements of accuracy and practicality in the heat and mass transfer calculation of the wet-dry combined cooling tower, the initial slope value was optimized and corrected to balance the overall fitting error within the temperature range with the simplicity of the linear model calculation. Minor deviations were eliminated to finally determine the linear coefficient of the latent heat of vaporization of water with temperature at atmospheric pressure. , combined The latent heat of vaporization of water is calculated using the baseline value of latent heat of vaporization and the actual temperature of water. Where 1000 represents the conversion ratio between kilojoules (kJ) and joules (J), and the latent heat of vaporization of water is dynamically calculated with temperature.

[0069] S2 includes the following method steps:

[0070] S2.1. Under steady-state operation of the combined dry and wet cooling tower, the interior of the dry channel is selected as the control volume. The air mass within the control volume will not accumulate or dissipate over time. According to the law of conservation of mass, the air mass flowing into the control volume per unit time must be equal to the air mass flowing out of the control volume per unit time. Therefore, the inlet and outlet mass flow rates of the dry channel are strictly equal. ;

[0071] Since the mass of dry air in the dry channel remains constant, and the mass of water vapor it carries also does not increase or decrease, the ratio between the two remains constant. Therefore, the moisture content of the air does not change as it flows through the dry channel, indicating that the moisture content at the air inlet and outlet of the dry channel is equal. Then set the operating condition indicator parameters. (1 = summer, 0 = winter), and the mass flow rate of the wet channel water inlet. And water evaporation value Calculate the mass flow rate at the outlet of the wet channel. Then, based on the operating condition marking parameters and the humidity content of the air inlet in the wet channel... The mass flow rate of dry air at the wet channel inlet and the water evaporation rate. Numerical calculation of moisture content at the air outlet of the wet channel ;

[0072] Based on the specific heat at constant pressure of dry air Dry channel inlet air temperature Moisture content of air at the dry channel inlet Specific heat of water vapor at constant pressure and Latent heat of vaporization of water Calculate the enthalpy value of the air inlet in the dry channel. With the gap between dry / wet channels Using the characteristic dimension, the cross-sectional velocity of the air within the channel is calculated from the mass flow rate of the fluid in the dry / wet channel. Then, combined with air physical properties, the Reynolds number is calculated. Determine whether the airflow is laminar or turbulent, select the corresponding classical convective heat transfer correlation based on the flow state, and calculate the Nusselt number of the dry channel. Nusselt number of wet channels Nusselt number of the trunk channel Nusselt number of wet channels Dry / wet channel clearance and thermal conductivity Substitute into the reverse formula: and Thus, the convective heat transfer coefficients of the dry / wet channels can be obtained. , Combined with the thickness of the isolation plate thermal conductivity Calculate the overall heat transfer coefficient Then, the temperature difference at both ends is calculated according to the co-current arrangement: based on the temperature of the wet channel inlet. Dry channel air outlet temperature Calculate the temperature difference at the hot end Then, based on the temperature of the wet channel outlet... and dry channel air inlet temperature Calculate the cold junction temperature difference The logarithmic mean temperature difference between the wet and dry channels is calculated based on the temperature difference at the hot and cold ends. Utilizing the overall heat transfer coefficient and the heat exchange area of ​​the indirect heat exchanger The logarithmic mean temperature difference between the dry and wet channels is used to calculate the heat transfer value through the partition plate in the dry / wet channels. ;

[0073] S2.2, Based on heat transfer numerical values Enthalpy value of air inlet in dry channel Mass flow rate of air inlet in the dry channel Calculate the enthalpy value of the air outlet in the dry channel. Mass flow rate of air through the dry channel inlet Enthalpy value of air inlet in dry channel Calculate the enthalpy of air energy inflow in the dry channel The total amount of air energy flowing into the dry channel is calculated based on the enthalpy and heat transfer values ​​of the air energy inflow. Then, based on the mass flow rate value of the dry channel air outlet... Enthalpy value of air outlet in dry channel Calculate the total air energy outflow from the dry channel A coupled numerical equation for the balance of air energy in the main channel is established using the total inflow and outflow of air energy in the main channel. The specific formula is as follows: Because the dry channel is designed as a closed, independent space that exchanges heat only with the wet channel through an isolation plate, it has no external heat source, heat dissipation, or other energy input. The only external energy source is the heat transfer between the dry and wet channels through the isolation plate. According to the steady-state energy conservation principle that "total air energy inflow into the dry channel = total air energy outflow into the dry channel," the total air energy inflow into the dry channel consists of two parts: the enthalpy of air energy inflow into the dry channel and the heat transfer between the dry and wet channels through the isolation plate. These two together constitute the total air energy inflow into the dry channel. If the heat transfer between the dry and wet channels through the isolation plate is omitted, the numerical equation for the dry channel air energy balance will have missing input terms, violating the first law of thermodynamics and failing to reflect the true energy balance. Therefore, the heat transfer between the dry and wet channels through the isolation plate is used as the air energy inflow term in the dry channel to achieve a closed loop of overall energy conservation. This ensures that the numerical equation for the dry channel air energy balance strictly conforms to the actual heat transfer law, accurately calculates the enthalpy rise and temperature rise of air in the dry channel, and avoids prediction deviations in cooling efficiency due to missing energy terms.

[0074] Based on the mass flow rate of the wet channel water inlet Specific heat at constant pressure of water , wet channel inlet temperature Mass flow rate of water outlet in wet channel Temperature at the outlet of the wet channel Operating condition indicator parameters Water evaporation value The latent heat of vaporization of water and heat transfer values A coupled numerical equation for the energy balance of water in the wet channel is established, and the specific formula is as follows: When a wet-dry combined cooling tower is in steady-state operation, the total energy within the tower will not accumulate or dissipate over time, adhering to the rigid physical constraint that total input energy equals total output energy. This is due to the numerical value of the dry air inlet mass flow rate in the wet channel. Equal to the mass flow rate of dry air at the wet channel outlet Based on the specific heat of dry air at constant pressure Dry channel air inlet temperature Moisture content of air at the inlet of the wet channel Specific heat of water vapor at constant pressure and Latent heat of vaporization of water Calculate the enthalpy value of the air inlet in the wet passage. The enthalpy value of the air outlet in the wet channel is not calculated directly from temperature and humidity, but rather relies on the wet channel air energy balance equation, combined with the law of conservation of mass. Deformation and reverse propulsion: Then, based on the mass flow rate of the dry air inlet in the wet channel... Mass flow rate of dry air at the wet channel outlet Enthalpy value of air inlet in the humid channel Operating condition indicator parameters Water evaporation value The latent heat of vaporization of water Humidity channel air outlet enthalpy value A coupled numerical equation for the air energy balance in the wet channel is established, and the specific formula is as follows: To address the differences in heat and mass transfer mechanisms between summer and winter operating conditions, operating condition identification parameters are introduced. By constructing dynamically coupled numerical equations for mass, energy, and heat transfer, numerical simulation is used to achieve adaptive switching of operating conditions, avoiding numerical prediction distortion caused by traditional fixed models.

[0075] S3 includes the following steps:

[0076] Get the current segment number value Then, based on the heat exchange area of ​​the indirect heat exchanger... and heat transfer values The heat flux density values ​​of each segment in the current iteration are calculated. ,in, It refers to the first The heat flux density value of the segment, It refers to the first A heat transfer value, , It refers to the first Given the segmented heat transfer area, and knowing the heat flux density of each segment, the average heat flux density of all segments is calculated based on the segment's heat flux density and the current number of segments. ,in, The unit is Then, based on the segmented heat flux density values, the current segment number, and the average segmented heat flux density values, the variance of the heat flux density values ​​for all segments is calculated. ;

[0077] Get the Segmented Real Heat Flux Density (Without iteration error), the theoretically accurate total heat transfer is calculated based on the actual heat flux density of each segment, the heat transfer area of ​​each segment, and the current number of segments. Then, based on the theoretically accurate total heat transfer and the heat transfer values, the absolute difference in heat transfer is calculated. Retrieve known iteration convergence tolerance values ​​from the database. (Relative difference), the iterative convergence tolerance value is the relative difference.

[0078] The value selection criteria are dynamically determined based on the operating condition parameter values: Summer operating condition parameter values ​​(wet channel inlet water temperature 37℃, wet channel water inlet mass flow rate). Large temperature and flow rate fluctuations Winter operating parameters (wet channel inlet water temperature 30℃, wet channel water inlet mass flow rate) Small fluctuations require higher precision. To adapt to the accuracy requirements of different working conditions, a new maximum iteration limit of 200 times has been added, and a divergence handling mechanism has been set: if convergence is not achieved after 200 iterations, the number of segments will be automatically adjusted. Within a reasonable range, restart the iteration 50 times; if the convergence condition is still not met, output an algorithm divergence warning, suggesting optimization of structural parameters (such as adjusting the gap between dry / wet channels) or checking the operating parameter values ​​to avoid infinite loops and ensure algorithm stability. The maximum allowable relative error for total heat transfer is equal to the iteration convergence tolerance. Based on the current number of segments, the allowable relative error of the total heat transfer is mapped to the discrete allowable range of the segmented heat flux density:

[0079] Based on the theoretically accurate total heat transfer and the heat transfer area of ​​the indirect heat exchanger. Calculate the true average heat flux density Then, based on the values ​​of the true average heat flux density and the segmented average heat flux density, the maximum allowable relative error of the total heat transfer is calculated. / The variance formula for calculating the heat flux density values ​​of all segments is used. Combining the Cauchy-Schwarz inequality, we can derive the following: Considering the nonlinear effect of heat flux distribution, a correction factor is introduced. ( (Fitting under operating conditions) to obtain the maximum allowable relative error of total heat transfer. This allows us to inversely deduce the upper limit of the variance of the segmented heat flux density values: Engineering parameters (such as...) Substitute In the formula, the calculation is as follows By using the error propagation formula, the theoretical upper limit of the heat flux density variance is derived. The variance threshold must fall within this theoretical benchmark range to avoid the total heat transfer exceeding tolerance due to the accumulation of discrete errors. Therefore, engineering specifications and literature on the segmented heat transfer calculation of similar heat exchange equipment such as wet-dry combined cooling towers, indirect heat exchangers, and closed cooling towers are retrieved to extract mature threshold ranges: the general range for the segmented heat flux density variance threshold of such equipment is as follows. Then exclude extreme values ​​within the interval: lower limit This can lead to iterative redundancy, increased computation time, and an upper limit. Localized heat load calculations are prone to deviations exceeding tolerances; therefore, the candidate calibration interval is determined as follows. To obtain the heat transfer load fluctuation range of the combined wet and dry cooling tower under summer and winter conditions, the accuracy is calculated based on the current segment number, iterative convergence tolerance, total heat transfer coefficient, and logarithmic mean temperature difference between the wet and dry channels, within the candidate calibration interval. Within, select the median value. This value satisfies the requirement that the maximum allowable relative error of the total heat transfer is less than or equal to While meeting the accuracy requirements, it also keeps the number of iterations in each segment within a reasonable range to avoid invalid calculations. This is achieved when the variance of the segmented heat flux density values ​​is less than or equal to... When checking the total heat transfer, the maximum allowable relative error is less than or equal to Furthermore, the iteration convergence speed shows no significant delay; if the variance of the piecewise heat flux density values ​​is greater than... At that time, the maximum allowable relative error of the total heat transfer is likely to exceed the iterative convergence tolerance value, confirming... Variance threshold ;

[0080] The number of segments is dynamically adjusted based on the variance threshold and the variance of the segmented heat flux density values, resulting in the adjusted number of segments. The basis for adopting 1.2 is as follows: The coefficient is set to a moderate increment, which can quickly reduce the dispersion of heat flux density, bringing the variance of the segmented heat flux density values ​​to within the variance threshold. It avoids slow adjustment and redundant iterations due to an excessively small coefficient (e.g., 1.05), and also avoids a surge in computational load due to an excessively large coefficient (e.g., 2x). This approach is suitable for the dual objectives of iteration efficiency and accuracy under summer and winter conditions in combined dry and wet cooling towers. The rationale for using a reduction coefficient of 0.8 is as follows: a small reduction coefficient of 0.8 can prevent the loss of local heat flux details caused by a sudden reduction in the number of segments, avoid over-averaging of local heat transfer characteristics in the dry / wet channel gaps and the heat conduction of the partition plate, and ensure that the maximum allowable relative error of the total heat transfer is always controlled within a certain range. Within a certain range, without exceeding the convergence accuracy requirement; the basis for using a hysteresis coefficient of 0.5: if directly using As a variance threshold, when the variance of the piecewise heat flux density values ​​is within a certain range... When there are minor fluctuations nearby, an oscillation problem occurs where the subdivision and merging process frequently switches, disrupting the stability of the iteration. Setting... The hysteresis interval can be used to divide the heat flow fluctuation into periods of excessive heat flow. Moderate fluctuations The system identifies three states with minimal fluctuations, thereby ensuring the stability of the dynamically adjusted segmented logic.

[0081] Test condition range with a mild increment coefficient of 1.2: statistical analysis of high-fluctuation summer conditions ( The structural parameters are fixed as follows: heat exchange area of ​​the indirect heat exchanger. Dry / wet channel clearance , thickness of the isolation plate and thermal conductivity Operating parameters: Mass flow rate of water inlet in the wet channel. , wet channel inlet temperature Mass flow rate of air inlet in the dry channel Physical property parameters are taken according to the IAPWSIF97 standard; Boundary conditions: initial number of segments. Variance threshold Iterative convergence tolerance value Evaluation indicators: The number of iterations required to converge to the variance threshold, computational cost (based on the grid computation cost of a single iteration), and convergence stability (no oscillations) were measured. The testing process included:

[0082] Using the established numerical simulation model (based on the finite volume method, with a second-order upwind discretization scheme), input the structural, operating condition, and physical property parameters. Set the piecewise adjustment coefficients to 1.05, 1.2, 1.5, and 2.0 respectively, keeping other parameter values ​​unchanged. Then start the iterative convergence calculation, recording the results in each round. until Stop timing and record the iteration convergence round; statistics The number of iterations that first meet the variance threshold is averaged after removing outliers (such as extreme values ​​caused by initial fluctuations). Using a single computational cost of 2.0 as a benchmark (set to 100%), the relative proportion of each group is calculated based on the number of iterations and the number of grid cells per iteration. When the average number of iterations with a piecewise adjustment coefficient of 1.2 is between 3 and 5, it is 40% less than 1.05 (average 7 iterations), and the computational cost is 60% of that with 2.0, with no convergence oscillations.

[0083] Test condition range with a reduction factor of 0.8: low-fluctuation winter conditions ( The structural parameters are the same as those for the summer operating conditions; operating condition parameters: mass flow rate of water inlet in the wet channel. , wet channel inlet temperature Mass flow rate of air inlet in dry channel Physical property parameters are taken according to the IAPWSIF97 standard; Boundary conditions: initial number of segments. Variance threshold Iterative convergence tolerance value Evaluation metrics: Local heat transfer characteristic error (compared to actual heat flux distribution), maximum allowable relative error of total heat transfer, iteration time; Test process:

[0084] Using the same numerical simulation model as the summer test, winter operating parameters were input, and low-fluctuation heat flux was simulated by adjusting the ambient humidity. Then, the reduction coefficients were set to 0.7, 0.8, and 0.9 respectively, while keeping other parameter values ​​unchanged. After the iteration converged, the heat flux density values ​​of each segment were extracted and compared with the actual heat flux density distribution to calculate the local heat transfer characteristic error. The relative error of the total heat transfer and the total iteration time were recorded simultaneously. Each segment adjustment coefficient was tested in three parallel tests.

[0085] Take all segments The maximum value; iteration time: based on the time taken with a reduction factor of 0.9 (set to 100%), the relative proportion of each group is calculated; when the segmented reduction factor is 0.8, the local heat transfer characteristic error of the 0.8 segmented reduction factor is less than or equal to 3%, and the maximum allowable relative error of the total heat transfer is less than or equal to The iteration time is reduced by 30% compared to 0.9, which is better than 0.7 (local heat transfer characteristic error is greater than or equal to 5%).

[0086] Test conditions with a hysteresis coefficient of 0.5: based on 100 different operating conditions (heat exchange area of ​​indirect heat exchanger). Dry / wet channel clearance Operating parameters: Summer / winter switching, wet channel water inlet mass flow rate. ), core control at variance threshold nearby Fluctuation, boundary conditions: initial number of segments Iterative convergence tolerance value The three states of heat flow fluctuation are defined as follows: excessive fluctuation ( Moderate fluctuations ), fluctuations are too small ( Evaluation metrics: detailed down to oscillation frequency (unit: Hz, i.e., number of switches per second) and state division accuracy; testing process:

[0087] A test set was constructed based on the above 100 sets of different working conditions. The numerical simulation model was input, and the hysteresis coefficients were set to 0.4, 0.5, and 0.6 respectively. Then, adjustments were made. It fluctuates slightly around 500 (adjusted every 5 iterations). ±20), run continuously for 1000 rounds, record the segmented adjustment actions (subdivision / merging / holding) in each round, and count the number of subdivision to merging switching times per unit time (oscillation frequency); compare the preset state with the numerical simulation model division results, count the number of times the three states were correctly divided, and find that the oscillation frequency of 0.5 hysteresis coefficient decreased from 0.3Hz to 0.05Hz, and the state division accuracy reached 95%, which is better than 0.4 (accuracy 82%, state division is fuzzy) and 0.6 (oscillation frequency 0.2Hz, insufficient suppression);

[0088] Due to the subtle differences in the gaps between dry and wet channels, the thickness of the partition plates, and the local convective heat transfer coefficients, when the number of segments is less than 30, the segment averaging effect becomes too strong, failing to capture the local heat flux distribution characteristics. This leads to a significant increase in the calculation errors of the overall heat transfer coefficient and the logarithmic mean temperature difference between dry and wet channels, failing to meet the high-precision convergence requirements of the iterative convergence tolerance values. When the number of segments exceeds 200, the improvement in the representation accuracy of the heat flux density distribution is minimal (diminishing marginal effects). Instead, numerical rounding errors and iterative cumulative errors gradually become prominent, reducing the calculation accuracy of the heat transfer values ​​and violating the convergence accuracy design goal of the iterative convergence tolerance values. Thus, the minimum segment value is determined. and minimum segment value ;

[0089] S4 includes the following steps:

[0090] S4.1. Divide the heat exchanger into numerical segments along the co-current flow direction according to the adjusted segment number values. The process involves dividing the channel into sections, then using the outlet parameters of the first section as the inlet parameters of the second section. The inlet parameter values ​​of the second section are then substituted into the coupled numerical equations for air energy balance in the dry channel and water energy balance in the wet channel to calculate the new heat transfer value. And the value of fresh water evaporation According to the new heat transfer values Calculate the total heat exchange value for all segments. ,in, It refers to the first A new heat transfer value, and then based on the new water evaporation value. Calculate the total evaporation value ,in, It refers to the first A new water evaporation rate value;

[0091] The algorithm formula for calculating the enthalpy of the air outlet in the main channel is as follows: The algorithm formula for calculating the enthalpy of the air inlet in the humid channel is as follows: Furthermore, the mass flow rates of the air inlet and outlet in the dry channel are strictly equal. Substituting the algorithm formula for calculating the enthalpy of the air inlet in the wet channel into the algorithm formula for calculating the enthalpy of the air inlet in the dry channel, the air outlet temperature value in the dry channel can be solved. Record number and The dry channel air outlet temperature value obtained by round calculation , ;

[0092] S4.2. Based on the established numerical equation for the water energy balance in the wet channel, the specific formula is as follows: The algorithm formula for calculating the mass flow rate at the outlet of the wet channel is as follows: Substituting the algorithm formula for calculating the mass flow rate at the outlet of the wet channel into the established coupled numerical equation for the energy balance of the wet channel, the numerical value of the outlet temperature of the wet channel is obtained by rearranging the equation. Record number and The calculated wet channel water outlet temperature value , ;

[0093] Using dry channel air outlet temperature values , Water outlet temperature value of the wet channel , Calculate the difference in outlet temperature between the current iteration and the previous iteration. The algorithm then uses the outlet temperature difference and the iteration convergence tolerance value to determine whether numerical convergence has occurred. When the outlet temperature difference is less than the iteration convergence tolerance value, convergence is determined. When the outlet temperature difference is greater than or equal to the iteration convergence tolerance value, convergence is determined. The current number of segments is updated, and the heat flux density values ​​of each segment in the current iteration are recalculated. Since traditional numerical simulation algorithms use a fixed number of segments (such as 100 segments), they cannot adapt to the differences in heat flux density value distribution under summer and winter conditions (the heat flux density value of the wet channel fluctuates greatly in summer and less in winter). This solution dynamically adjusts the number of segments based on the real-time heat flux density values ​​of each segment. In areas with large gradients in heat flux density values, segments are subdivided, and in areas with small gradients, segments are merged. Through numerical iteration, the accuracy and efficiency are optimized in a coordinated manner.

[0094] S5 includes the following method steps:

[0095] When it is determined that the numerical iteration convergence is achieved, the temperature at the wet channel inlet is used as the basis. Water outlet temperature value of the wet channel Calculate the cooling water temperature drop value Combining the total heat exchange values ​​of all segments Mass flow rate of water inlet in the wet channel Specific heat at constant pressure of water Predicted cooling efficiency performance values ;

[0096] Atmospheric pressure values ​​were obtained from the IAPWSIF97 standard database. and water saturated vapor pressure atmospheric pressure value saturated vapor pressure of water Moisture content of air at the outlet of the humidification channel Substituting into the equation for the physical properties of moist air, the specific formula is: The relative humidity value at the outlet of the wet channel is obtained by solving. Set the defogging threshold By searching engineering design specifications for combined wet and dry cooling towers and closed-circuit cooling towers (such as the "Cooling Tower Design Code" GB / T50102-2014) and industry cases, it was found that the general range of relative humidity threshold for fog removal function is as follows: While a lower limit of 70% for the defogging threshold results in a more thorough defogging effect, it significantly increases the cost of heat exchange or dehumidification within the combined dry and wet cooling tower (e.g., by increasing the heat exchange area and fan power), leading to increased equipment investment and operating energy consumption. While an upper limit of 80% for the defogging threshold reduces costs, it approaches the condensation threshold, resulting in poor defogging stability and susceptibility to fluctuations in ambient temperature and humidity. 75% represents the midpoint of this range, representing the optimal balance between defogging effectiveness and economy. It is also the preferred threshold for defogging design in similar equipment, consistent with the design logic of this case, which emphasizes both accuracy and practicality. Therefore, a defogging threshold of less than 75% is chosen as the design requirement for defogging in the combined dry and wet cooling tower.

[0097] The relative humidity value at the wet aisle outlet and the defogging threshold are used to determine whether the defogging performance of the combined dry and wet cooling tower meets the design requirements. When the relative humidity value at the wet aisle outlet is less than the defogging threshold, the design requirements for defogging of the combined dry and wet cooling tower are considered to be met. Simultaneously, the defogging performance value is predicted based on the relative humidity value at the wet aisle outlet. Then, extract the evaporation rate data of traditional cooling towers from the database. Combined with the total evaporation value Predicted water-saving performance values The anti-fogging performance value Water saving rate and cooling efficiency values As predicted value .

[0098] S6 and S7 include the following method steps:

[0099] Collect measured values ​​of water inlet mass flow rate in the wet channel. Specific heat at constant pressure of water Measured temperature at the wet channel inlet Measured values ​​of water outlet temperature in the wet channel Operating condition indicator parameters Measured values ​​of water evaporation The latent heat of vaporization of water Measured heat transfer values ​​through the dry / wet channels and the isolation plate Calculate the actual total heat exchange value during operation of the combined wet and dry cooling tower. Based on the collected measured values, calculate the actual cooling efficiency performance value, actual defogging performance value, and actual water saving rate performance value, and use these as the actual values. Using predicted values and actual value Calculate the three performance error values ​​respectively. Set the transmission performance error threshold The three performance error values ​​and the transmission performance error threshold are used to determine whether to adjust the correction coefficient. (Initial value is 1). When the performance error value is greater than the propagation performance error threshold, and the error value exceeds the propagation performance error threshold by 1% for each subsequent 1% increase, the correction factor will be adjusted. Adjust step size The adjustment yields the adjusted correction coefficient. Among them, the transmission performance error threshold is The basis for this is that the water-saving rate and cooling efficiency of combined wet and dry cooling towers are affected by environmental temperature and humidity, water quality, and equipment aging, which inherently have limitations. Natural fluctuations The transmission performance error threshold can cover such fluctuations in actual working conditions, avoiding misjudging the model as unqualified due to slight actual fluctuations. Small adjustments to the step size can avoid over-correction or under-correction, thereby improving the subsequent prediction accuracy.

[0100] Adjust step size The rationale is as follows: Combined wet and dry cooling towers exhibit natural fluctuations due to environmental temperature and humidity, and equipment aging. The transfer performance error threshold already covers these fluctuations. To avoid over-correction or under-correction of the model due to small fluctuations, the adjustment step size is set to a specific proportion of the error increment. Adjustments are made for every 1% increase in error, with the adjustment step size set at 0.5%, meaning a coefficient change of 0.005 for every 1% increase in error. Setting the adjustment step size to 0.05 (i.e., a 5% correction for every 1% increase in error) results in a large single adjustment, potentially causing drastic fluctuations in the correction coefficient and reducing model stability. For example, if the error only exceeds 1%, a direct jump to 1.05 or 0.95 in the correction coefficient could easily lead to over-correction and compromise prediction accuracy. Since the transfer performance error of combined wet and dry cooling towers is affected by multiple factors such as environmental temperature and humidity and equipment aging, natural fluctuations are usually small (e.g., ...). When the adjustment step size is 0.005 (0.5%), it can not only achieve incremental fine-tuning, but also respond to the trend of performance error transmission and suppress noise interference. Therefore, an adjustment step size of 0.005 means that only 0.5% of the correction coefficient is adjusted for every 1% error, which meets the requirements of small adjustment, ensures a smooth correction process, and avoids model convergence difficulties or oscillations due to excessive adjustment step size.

[0101] Let the upper and lower limits of the correction coefficient be [ To avoid over-correction that could distort the model;

[0102] Introducing damping factor Adjust the step size interval to This slows down the correction rate.

[0103] Add a lag judgment: Adjustment is only made when the performance error value exceeds the transmission performance error threshold and continues for two rounds;

[0104] When the performance error value exceeds the transmission performance error threshold, and for every 1% exceeding the threshold, the correction coefficient is adjusted according to the above rules, taking into account both the timeliness and stability of the correction, to ensure the convergence of the calculation for the dry-wet combined cooling tower under all operating conditions.

[0105] By adjusting the correction factor and the dry / wet channel air convection heat transfer coefficient , After correction, the corrected dry / wet channel air convection heat transfer coefficients are obtained. , Then, based on the corrected air convection heat transfer coefficients of the dry / wet channels, the core values ​​of the overall heat transfer coefficient, heat transfer, wet channel water outlet temperature, and wet channel air outlet moisture content are recalculated. These recalculated core values ​​are then used to re-predict the defogging performance, water saving rate, and cooling efficiency. When the performance error value is less than or equal to the transfer performance error threshold, the predicted defogging performance, water saving rate, and cooling efficiency values ​​are considered reliable. Numerical graphs (curves showing the changes in temperature, humidity, and mass flow rate) are generated for all segments, including the total heat transfer value, cooling water temperature drop value, wet channel outlet relative humidity value, defogging performance value, water saving rate, and cooling efficiency value. When the wet channel outlet relative humidity value is greater than or equal to the defogging threshold, the design requirements for defogging in the combined dry and wet cooling tower are not met. The structural parameters are automatically adjusted (e.g., increasing the heat exchange area of ​​the partition heat exchanger and the gap between the dry and wet channels) until the design requirements for defogging in the combined dry and wet cooling tower are met.

[0106] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for predicting the heat and mass transfer performance of a combined dry and wet cooling tower based on numerical simulation, characterized in that: The methods and steps include the following: S1. Collect the structural parameters, operating parameters, and physical property parameters of the combined dry and wet cooling tower, and construct a linear model to calculate the latent heat of vaporization of water using the linear model. S2. Based on the latent heat of vaporization of water and the numerical calculation of structural and operating parameters, the heat transfer value is calculated and combined with the numerical values ​​of the latent heat of vaporization of water, operating conditions and physical property parameters to establish a coupled numerical equation for energy balance. S3. Obtain the current segment number value, calculate the variance of the segment heat flux density value by combining the structural parameters and heat transfer value, and dynamically adjust the segment number by combining it with the set variance threshold to obtain the adjusted segment number value. S4. Based on the adjusted segmented number values, the subsequent inlet parameters are substituted into the energy balance coupling numerical equation to calculate the wet channel water outlet temperature value, and then calculate the outlet temperature difference value. The numerical iterative convergence is judged by comparing it with the known iterative convergence tolerance value in the database. When the outlet temperature difference value is less than the iterative convergence tolerance value, it is judged as converged. S5. After numerical convergence, the cooling water temperature drop is calculated based on the operating parameters and the wet channel water outlet temperature. At the same time, the cooling efficiency performance is predicted based on the operating conditions and physical property parameters, and used as the predicted value. S6. Collect the measured values ​​of the operating parameters of the dry-wet combined cooling tower and use them as actual values. Calculate the performance error value based on the predicted value and the actual value. S7. Set the transfer performance error threshold and correct the air convection heat transfer coefficient of the dry / wet channel with the performance error value.

2. The method for predicting the heat and mass transfer performance of a combined dry and wet cooling tower based on numerical simulation according to claim 1, characterized in that: The construction of the linear model and the calculation of the latent heat of vaporization of water include the following steps: Select water temperature within the temperature range The corresponding latent heat of vaporization was extracted and used as sample data. A linear model was then constructed based on the sample data. ,in, for The baseline value for the latent heat of vaporization of water; The water temperature linearity coefficient of the linear model is solved using the least squares method. The linear coefficient of water temperature, Substituting the latent heat of vaporization of water and the water temperature into the linear model, the linear coefficients are obtained. Based on linear coefficients, combined with The latent heat of vaporization of water is calculated using the baseline value of the latent heat of vaporization and the numerical values ​​of its physical properties.

3. The method for predicting the heat and mass transfer performance of a combined dry and wet cooling tower based on numerical simulation according to claim 1, characterized in that: The calculation of the heat transfer value includes the following steps: Calculate the mass flow rate of water outlet in the wet channel and the moisture content of air outlet in the wet channel based on the operating parameters. Based on operating conditions, physical property parameters, and The latent heat of vaporization of water is used to calculate the enthalpy of the air inlet in the dry channel, and then the structural parameter values ​​are used as characteristic dimensions to determine whether the air flow is laminar or turbulent. Select the corresponding classical convective heat transfer correlation based on the flow state, calculate the Nusselt number of the dry / wet channel, and deduce the air convective heat transfer coefficient of the dry / wet channel by combining the structural parameter values. The overall heat transfer coefficient is calculated based on the air convection heat transfer coefficient of the dry / wet channel and the structural parameters, and then the logarithmic mean temperature difference is calculated based on the operating parameters. Using the overall heat transfer coefficient Structural parameter values ​​and logarithmic mean temperature difference calculation of heat transfer values The enthalpy of the air outlet in the dry channel is calculated by combining the enthalpy value of the air inlet in the dry channel.

4. The method for predicting the heat and mass transfer performance of a combined dry and wet cooling tower based on numerical simulation according to claim 1, characterized in that: The establishment of the coupled numerical equations for energy balance includes the following steps: A coupled numerical equation for the air energy balance in the dry channel is established using the total inflow / outflow of air energy. The specific formula is as follows: ;in, This refers to the heat transfer value. and These refer to the mass flow rates at the inlet and outlet of the dry channel air, respectively. and These refer to the enthalpy values ​​of the air inlet and outlet in the dry channel, respectively. Based on the mass flow rate values ​​of the wet channel inlet / outlet and Specific heat at constant pressure of water Inlet / outlet temperature of wet channel and Operating condition indicator parameters Water evaporation value The latent heat of vaporization of water The numerical equations for the water energy balance in the wet channel are established, and the specific formulas are as follows: ; Based on the dry air inlet / outlet mass flow rate values ​​of the wet channel and Enthalpy values ​​of air inlet / outlet in the humid passage and The latent heat of vaporization of water A coupled numerical equation for the air energy balance in the wet channel is established, and the specific formula is as follows: .

5. The method for predicting the heat and mass transfer performance of a combined dry and wet cooling tower based on numerical simulation according to claim 1, characterized in that: The method for calculating the variance of the segmented heat flux density values ​​to obtain the adjusted number of segments includes the following steps: Get the current segment number value The heat flux density of each segment is calculated based on the structural parameters and heat transfer values. Then, the average heat flux density of each segment is calculated. Finally, the variance of the heat flux density of each segment is calculated by combining the segment heat flux density with the current number of segments. ; Determine the variance threshold The number of segments is dynamically adjusted based on the variance of the segmented heat flux density values ​​to obtain the adjusted number of segments. .

6. The method for predicting the heat and mass transfer performance of a combined dry and wet cooling tower based on numerical simulation according to claim 1, characterized in that: The calculation of the wet channel water outlet temperature includes the following steps: Based on the adjusted segmentation values, the heat exchanger is numerically divided along the co-current flow direction into segments. The parameters of the first segment are then used as the input parameters of the second segment. Substitute the inlet parameters of the downstream section into the coupled numerical equation of energy balance in the dry / wet channel to calculate the new heat transfer and water evaporation values, and calculate the total heat transfer and total evaporation values ​​of each segment respectively. Based on the algorithm formula for calculating the enthalpy of the dry channel air outlet, the temperature of the dry channel air outlet is obtained. The algorithm formula for calculating the mass flow rate of the water outlet in the wet channel is substituted into the established coupled numerical equation for the energy balance of the water in the wet channel. The water outlet temperature value of the wet channel is obtained by rearranging the equation, and the outlet temperature difference is calculated by comparing it with the air outlet temperature value of the dry channel.

7. The method for predicting the heat and mass transfer performance of a combined wet and dry cooling tower based on numerical simulation according to claim 1, characterized in that: The numerical iteration convergence determination includes the following steps: The value of the outlet temperature difference and the value of the iteration convergence tolerance are used to determine whether numerical iteration convergence is required. When the outlet temperature difference is less than the value of the iteration convergence tolerance, it is determined to be converged. When the outlet temperature difference is greater than or equal to the iteration convergence tolerance value, it is determined that the process has not converged. The current segment number value is updated, and the heat flux density value of the segment is recalculated.

8. The method for predicting the heat and mass transfer performance of a combined dry and wet cooling tower based on numerical simulation according to claim 1, characterized in that: The calculation of cooling water temperature drop and prediction of cooling efficiency performance values ​​include the following methods and steps: The cooling water temperature drop is calculated based on the operating parameters and the wet channel water outlet temperature. The cooling efficiency performance is then predicted by combining the operating parameters, physical property parameters, and the total heat transfer value of each segment. Predicting defogging performance values ​​involves the following steps: Obtain atmospheric pressure values and water saturated vapor pressure The relative humidity at the outlet of the wet channel is substituted into the wet air property equation to obtain the relative humidity at the outlet of the wet channel and to predict the defogging performance value.

9. The method for predicting the heat and mass transfer performance of a combined dry and wet cooling tower based on numerical simulation according to claim 1, characterized in that: The predicted value also includes a water-saving performance value, and the prediction method includes the following steps: The evaporation rate of traditional cooling towers is extracted from the database, and the water-saving performance value is predicted by combining the total evaporation rate value. The anti-fogging performance value, water-saving rate value and cooling efficiency value are used as predicted values. Calculate the actual cooling efficiency performance value, actual defogging performance value, and actual water saving rate performance value based on the collected measured values, and use these as the actual values. ; Using predicted values and actual value Calculate the three performance error values ​​respectively. .

10. The method for predicting the heat and mass transfer performance of a combined wet and dry cooling tower based on numerical simulation according to claim 1, characterized in that: The correction of the air convection heat transfer coefficient in the dry / wet channel includes the following steps: A transmission performance error threshold is set. Three performance error values ​​are compared to this threshold to determine whether a correction coefficient needs adjustment. When the performance error value exceeds the transmission performance error threshold, and the error exceeds 1% for each subsequent 1% change, the correction coefficient is adjusted. Adjust step size The adjustment yields the adjusted correction coefficient. ; The correction is based on the adjusted correction factor and the air convection heat transfer coefficient of the dry / wet channel.

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