CN109190327B - Method, device and equipment for analyzing and optimizing organic Rankine cycle system - Google Patents
Method, device and equipment for analyzing and optimizing organic Rankine cycle system Download PDFInfo
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Abstract
The application relates to an organic Rankine cycle system analysis optimization method, device and equipment, which comprises the following steps: acquiring system operation parameters and system design parameters; carrying out thermodynamic calculation according to the initial turbine efficiency to obtain system thermodynamic parameters; obtaining the dynamic turbine efficiency; when the relative error between the dynamic turbine efficiency and the initial turbine efficiency is greater than or equal to the preset error value, updating the value of the initial turbine efficiency into the value of the dynamic turbine efficiency; when the relative error between the dynamic turbine efficiency and the initial turbine efficiency is smaller than the preset error value, calculating thermodynamic and economic indexes according to system operation parameters, system design parameters and thermodynamic parameters; and optimizing system operation parameters and system design parameters through a multi-objective optimization model according to the thermodynamic and economic indexes and the system operation parameters. The dynamic turbine efficiency is adopted in the whole analysis optimization process, so that the error between the result obtained in the whole analysis process and the actual condition is smaller, and the analysis result is more accurate.
Description
Technical Field
The application relates to the technical field of waste heat utilization, in particular to an analysis optimization method, device and equipment for an organic Rankine cycle system.
Background
With rapid energy consumption and increasingly serious environmental problems, development and utilization of low-grade heat energy (such as solar energy, geothermal energy, biomass energy and industrial waste heat) are receiving wide attention. Because of the advantages of simple structure, low maintenance cost, environmental friendliness and the like, the Rankine cycle provides a feasible method for recycling low-grade waste heat. The Organic Rankine Cycle (ORC) can directly recycle low-temperature waste heat for power generation and utilization by utilizing the characteristic of low boiling point of an Organic working medium.
The ORC mainly comprises a waste heat boiler (or a heat exchanger), a turbine, a condenser and a working medium pump, wherein an organic working medium absorbs heat from waste heat flow in the heat exchanger to generate steam with certain pressure and temperature, and the steam enters the turbine to expand and do work so as to drive a generator or drag other power machines. The steam discharged from the turbine releases heat to cooling water in the condenser, condenses into liquid, and finally returns to the heat exchanger again by means of the working medium pump, so that the steam is continuously circulated, and the utilization of waste heat is realized.
The efficiency of the turbine is assumed to be a fixed value in most of current analytical methods related to the organic rankine cycle system, but in practical situations, the turbine efficiency is greatly different due to different working medium types and operating parameters, and therefore, according to the existing analytical methods, the result of analysis has an error if the turbine efficiency is assumed to be a fixed value.
Disclosure of Invention
In order to overcome the problems in the related art at least to a certain extent, the application provides an analysis optimization method, device and equipment of an organic Rankine cycle system.
According to a first aspect of the application, an organic rankine cycle system analysis optimization method is provided and comprises the following steps:
acquiring system operation parameters and system design parameters;
carrying out thermodynamic calculation according to the initial turbine efficiency to obtain system thermodynamic parameters;
inputting the system operation parameters, the system design parameters and the system thermodynamic parameters into a pre-designed one-dimensional centripetal turbine efficiency calculation model to obtain dynamic turbine efficiency;
updating the value of the initial turbine efficiency to the value of the dynamic turbine efficiency when the relative error between the dynamic turbine efficiency and the initial turbine efficiency is greater than or equal to a preset error value;
when the relative error between the dynamic turbine efficiency and the initial turbine efficiency is smaller than a preset error value, calculating thermodynamic and economic indexes according to the system operation parameters, the system design parameters and the thermodynamic parameters;
and optimizing system operation parameters and system design parameters through a multi-objective optimization model according to the thermodynamic and economic indexes and the system operation parameters.
Optionally, the inputting the system operation parameters, the system design parameters, and the system thermodynamic parameters into a pre-designed one-dimensional centripetal turbine efficiency calculation model to obtain the dynamic turbine efficiency includes:
determining turbine design parameters, wherein the turbine design parameters comprise a speed ratio, a reaction degree, a nozzle speed coefficient, a movable blade speed coefficient, a wheel diameter ratio, a movable blade inlet absolute airflow angle and a movable blade outlet relative airflow angle;
calculating loss according to the turbine design parameters;
calculating the dynamic turbine efficiency based on the losses.
Optionally, the losses include nozzle losses, bucket losses, lost velocities, friction losses, and leakage losses;
the calculating losses from the turbine design parameters includes:
calculating nozzle loss according to a first formula
In which ξ n It is an indication of the loss of the nozzle,
representing the nozzle velocity coefficient, and omega representing the reaction degree;
calculating the loss coefficient according to a second formula
In which ξ r Indicating bucket loss, w 2 Representing the relative speed of the bucket outlet, Δ h s Expressing ideal enthalpy drop of the turbine, and psi expressing the speed coefficient of the movable blade;
calculating the residual speed loss according to a third formula, wherein the third formula isIs of the formula
In which ξ e Represents the loss of residual speed, c 2 Representing the absolute speed of a rotor blade outlet;
calculating the friction loss according to a fourth formula
Wherein ξ f Denotes the friction loss, D 1 Indicating the bucket inlet diameter, u 1 Indicating the inlet peripheral speed, v, of the rotor blade 1 Representing the specific volume of the inlet of the rotor blade, m f Representing the mass flow of the working medium;
calculating leakage loss according to a fifth formula
Any real number between 0.01 and 0.20, wherein xi 1 Denotes leakage loss, δ is tip clearance, D 2 Is the diameter of the outlet of the rotor blade, /) 2 The height of the outlet of the movable vane is the height of the outlet of the movable vane.
Optionally, the calculating the dynamic turbine efficiency according to the loss includes:
calculating the dynamic turbine efficiency according to a sixth formula, wherein the sixth formula is eta tur ＝1-ξ n -ξ r -ξ e -ξ f -ξ 1 Wherein eta tur Representing the dynamic turbine efficiency.
Optionally, the multi-objective optimization model adopts a multi-objective wolf algorithm.
According to a second aspect of the present application, there is provided an organic rankine system analysis optimization device, including:
the acquisition module is used for acquiring system operation parameters and system design parameters;
the first calculation module is used for carrying out thermodynamic calculation according to the initial turbine efficiency to obtain system thermodynamic parameters;
the second calculation module is used for inputting the system operation parameters, the system design parameters and the system thermodynamic parameters into a pre-designed one-dimensional centripetal turbine efficiency calculation model to obtain dynamic turbine efficiency;
the updating module is used for updating the value of the initial turbine efficiency to the value of the dynamic turbine efficiency when the relative error between the dynamic turbine efficiency and the initial turbine efficiency is larger than or equal to a preset error value;
the third calculation module is used for calculating thermodynamic and economic indexes according to the system operation parameters, the system design parameters and the thermodynamic parameters when the relative error between the dynamic turbine efficiency and the initial turbine efficiency is smaller than an error preset value;
and the optimization module is used for optimizing system operation parameters and system design parameters through a multi-objective optimization model according to the thermodynamic and economic indexes and the system operation parameters.
Optionally, the second calculating module includes:
the determining unit is used for determining turbine design parameters, and the turbine design parameters comprise a speed ratio, a reaction degree, a nozzle speed coefficient, a movable blade speed coefficient, a wheel diameter ratio, a movable blade inlet absolute airflow angle and a movable blade outlet relative airflow angle;
a first calculation unit for calculating a loss according to the turbine design parameter;
a second calculation unit for calculating the dynamic turbine efficiency based on the loss.
Optionally, the losses include nozzle losses, bucket losses, lost velocities, friction losses, and leakage losses;
the first calculation unit includes:
a first calculating subunit for calculating the nozzle loss according to a first formula
In which ξ n It is an indication of the loss of the nozzle,
representing the nozzle velocity coefficient, and omega representing the reaction degree;
a second calculating subunit for calculating the loss coefficient according to a second formula
In which ξ r Indicating bucket loss, w 2 Representing the relative speed of the bucket outlet, Δ h s Expressing ideal enthalpy drop of the turbine, and psi expressing the speed coefficient of the movable blade;
a third calculating subunit for calculating the residual speed loss according to a third formula
In which ξ e Represents the loss of residual speed, c 2 Representing the absolute speed of the outlet of the bucket;
a fourth calculating subunit for calculating the friction loss according to a fourth formula
Wherein xi is f Denotes the friction loss, D 1 Indicating the bucket inlet diameter, u 1 Indicating the inlet peripheral speed, v, of the rotor blade 1 Representing the specific volume of the inlet of the rotor blade, m f Representing the mass flow of the working medium;
a fifth calculating subunit, configured to calculate the leakage loss according to a fifth formula
Is any real number between 0.01 and 0.20, wherein xi 1 Denotes leakage loss, δ is tip clearance, D 2 Is the diameter of the outlet of the rotor blade, /) 2 The height of the outlet of the movable vane is higher.
Optionally, the second calculating unit includes:
a sixth calculating subunit usingCalculating the dynamic turbine efficiency according to a sixth formula, wherein the sixth formula is eta tur ＝1-ξ n -ξ r -ξ e -ξ f -ξ 1 Wherein eta tur Representing the dynamic turbine efficiency.
According to a third aspect of the present application, there is provided an organic rankine system analysis optimization apparatus including:
a processor, and a memory coupled to the processor;
the memory is configured to store a computer program for performing at least the organic rankine system analysis optimization method of the first aspect of the present application;
the processor is used for calling and executing the computer program in the memory.
The technical scheme provided by the application can comprise the following beneficial effects: the method comprises the steps of firstly obtaining system operation parameters and system design parameters, then carrying out thermodynamic calculation according to initial turbine efficiency to obtain thermodynamic parameters, inputting the thermodynamic parameters, the system operation parameters and the system design parameters into a pre-designed one-dimensional centripetal turbine efficiency calculation model to obtain dynamic turbine efficiency, updating the value of the initial turbine efficiency to the value of the dynamic turbine efficiency when the relative error between the dynamic turbine efficiency and the initial turbine efficiency is larger than or equal to an error preset value, calculating thermodynamic and economic indexes according to the system operation parameters, the system design parameters and the thermodynamic parameters when the relative error between the dynamic turbine efficiency and the initial turbine efficiency is smaller than the error preset value, and finally optimizing the system operation parameters and the system design parameters through a multi-objective optimization model according to the thermodynamic and economic indexes and the system operation parameters. Based on the dynamic turbine efficiency, the turbine efficiency adopted in the whole analysis optimization process is the dynamic turbine efficiency calculated through the one-dimensional centripetal turbine efficiency calculation model according to the system operation parameters, the system design parameters and the system thermodynamic parameters, and the dynamic turbine efficiency is more matched with the current system, so that the error between the result obtained in the whole analysis process and the actual situation is smaller, and the analysis result is more accurate.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a schematic flowchart of an analysis optimization method of an organic rankine cycle system according to a first embodiment of the present application.
Fig. 2 is a schematic structural diagram of an analysis and optimization device of an organic rankine cycle system provided in a second embodiment of the present application.
Fig. 3 is a schematic structural diagram of an organic rankine system analysis and optimization device provided in the third embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The organic Rankine cycle system has the advantages of simple structure, low maintenance cost, environmental friendliness and the like, so that the organic Rankine cycle provides a feasible method for recycling low-grade waste heat. In most of the current organic Rankine cycle analysis methods, the turbine efficiency is assumed to be a fixed value, but in the actual situation, the turbine efficiency is greatly different due to different working medium types and operation parameters, so that a one-dimensional turbine efficiency calculation model is adopted to calculate the turbine efficiency, the dynamic turbine efficiency is adopted to replace the fixed turbine efficiency in the traditional organic Rankine cycle analysis, the thermal performance and the economic performance of an organic Rankine cycle system are comprehensively considered, and the system performance is analyzed by adopting a multi-objective optimization algorithm.
Example one
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating an analysis optimization method of an organic rankine cycle system according to an embodiment of the present disclosure.
As shown in fig. 1, the analysis and optimization method for an organic rankine cycle system provided by this embodiment includes:
and step 16, optimizing system operation parameters and system design parameters through a multi-objective optimization model according to the thermodynamic and economic indexes and the system operation parameters.
The method comprises the steps of firstly obtaining system operation parameters and system design parameters, then carrying out thermodynamic calculation according to initial turbine efficiency to obtain thermodynamic parameters, inputting the thermodynamic parameters, the system operation parameters and the system design parameters into a pre-designed one-dimensional centripetal turbine efficiency calculation model to obtain dynamic turbine efficiency, updating the value of the initial turbine efficiency to the value of the dynamic turbine efficiency when the relative error between the dynamic turbine efficiency and the initial turbine efficiency is larger than or equal to an error preset value, calculating thermodynamic and economic indexes according to the system operation parameters, the system design parameters and the thermodynamic parameters when the relative error between the dynamic turbine efficiency and the initial turbine efficiency is smaller than the error preset value, and finally optimizing the system operation parameters and the system design parameters through a multi-objective optimization model according to the thermodynamic and economic indexes and the system operation parameters. Based on the dynamic turbine efficiency, the turbine efficiency adopted in the whole analysis optimization process is the dynamic turbine efficiency calculated through the one-dimensional centripetal turbine efficiency calculation model according to the system operation parameters, the system design parameters and the system thermodynamic parameters, and the dynamic turbine efficiency is more matched with the current system, so that the error between the result obtained in the whole analysis process and the actual situation is smaller, and the analysis result is more accurate.
It should be noted that the system operation parameters may include heat source parameters, cold source parameters, system parameters, and the like, and specifically may include heat source temperature, cold source temperature, steam temperature, condensation temperature, evaporation pressure, and the like; system design parameters may include heat exchanger area, pinch temperature, turbine type selection, etc.
Further, step 13 may include:
determining turbine design parameters, wherein the turbine design parameters comprise a speed ratio, a reaction degree, a nozzle speed coefficient, a movable blade speed coefficient, a wheel diameter ratio, a movable blade inlet absolute airflow angle and a movable blade outlet relative airflow angle;
calculating loss according to turbine design parameters;
and calculating the dynamic turbine efficiency according to the loss.
Wherein the losses may include nozzle losses, bucket losses, excess speed losses, friction losses, and leakage losses, and calculating the dynamic turbine efficiency from the losses may include:
calculating nozzle loss according to a first formula
Xi therein n It is an indication of the loss of the nozzle,
represents a nozzle speed coefficient, and Ω represents a reaction degree;
calculating the loss coefficient according to a second formula
In which ξ r Indicating bucket loss, w 2 Representing the relative speed of the bucket outlet, Δ h s Expressing ideal enthalpy drop of the turbine, and psi expressing a speed coefficient of the rotor blade;
calculating the residual speed loss according to a third formula
Xi therein e Represents the loss of residual speed, c 2 Representing the absolute speed of a rotor blade outlet;
calculating the friction loss according to a fourth formula
Wherein ξ f Denotes the friction loss, D 1 Indicating the bucket inlet diameter, u 1 Indicating the inlet peripheral speed, v, of the rotor blade 1 Representing the specific volume of the inlet of the rotor blade, m f Representing the mass flow of the working medium;
according to a fifth formulaLeakage loss of the fifth formula
Any real number between 0.01 and 0.20, wherein xi 1 Denotes leakage loss, δ is tip clearance, D 2 Is the diameter of the outlet of the rotor blade, /) 2 The height of the outlet of the movable vane is the height of the outlet of the movable vane.
Further, calculating the dynamic turbine efficiency based on the losses may include:
calculating the dynamic turbine efficiency according to a sixth formula, wherein the sixth formula is eta tur ＝1-ξ n -ξ r -ξ e -ξ f -ξ 1 Wherein eta tur Representing the dynamic turbine efficiency.
In addition, the thermodynamic performance and the economic performance of the organic rankine cycle system are comprehensively considered in the embodiment, wherein the thermodynamic and economic indicators may include net output power, thermal efficiency, investment cost, and system product
The unit price.
In step 16, the investment cost and system product of the unit output power system is selected
The unit price is an objective function, the evaporation temperature and the condensation temperature are optimization variables, and the organic Rankine cycle system coupled with the dynamic turbine efficiency is optimized. The multi-objective optimization model is represented as follows:
wherein SIC is the investment cost of unit output power system, c p,total As a system product
Is monovalent.
The constraints may include:
the evaporation temperature is lower than the heat source temperature and lower than the critical temperature of the working medium, and the narrow point temperature difference of the evaporator is larger than the designed minimum heat transfer temperature difference (the value is 5K in the example);
the condensation temperature should be higher than the ambient temperature and lower than the evaporation temperature, and in this embodiment, 303.15K-323.15K is taken as the condensation temperature.
The constraints may be expressed as follows:
wherein, T heat Indicating heat source temperature, T critical Representing the critical temperature, Δ T, of the working medium heat Denotes the narrow point temperature, T, of the evaporator amb Indicating the heat source temperature.
It should be noted that, in the embodiment, a multi-target grey wolf algorithm is adopted to perform multi-target optimization on the organic rankine cycle system, and the grey wolf algorithm is a meta-heuristic algorithm for simulating collective hunting behavior of wolf clusters to expand, and has the advantages of simple mechanism, high convergence rate and the like.
The social level, surrounding prey and hunting behavior of the gray wolf are respectively established by the following mathematical models:
firstly, the social level is adopted, and in the process of designing the grey wolf algorithm, in order to simulate the social level of the grey wolf, the grey wolf individuals in a wolf group are divided into four different types according to the fitness value: the individuals with the best fitness are defined as alpha wolves, the individuals with the second best fitness and the third best fitness are defined as beta wolves and delta wolves, respectively, and the remaining individuals are designated as omega wolves. In the gray wolf algorithm, the optimization process is mainly finished by guiding alpha, beta and delta wolfs, and the omega wolf is responsible for following the three former wolfs to find the optimal solution.
Secondly, the hunting objects are surrounded, the gray wolf needs to surround the hunting objects in the hunting process, the distance between the gray wolf and the hunting objects is firstly determined during the surrounding, and the formula is
Wherein t represents the currentThe number of iterations;
a position vector representing the prey is determined,
a position vector representing the grey wolf is shown,
in order to be a wobble factor,
can be expressed as
Therein are
Is [0,1]A random vector of (1).
The gray wolf carries out position updating according to the distance between the gray wolf and the prey:
wherein
Represents a convergence coefficient vector, and
is [0,1]The random vector of (a) is selected,
the number of bands increases from 2 to 0 during the iteration, for example, when the number of bands is 5, it is 2, 1.5, 1, 0.5, 0.
Finally, the hunting behavior is determined, in the grey wolf algorithm, the grey wolf does not know the specific position of the prey (optimal solution), in order to simulate the hunting behavior, three wolfs with better fitness are determined to be closest to the prey, and the prey is positioned according to the position information of the three wolfs. Therefore, three wolfs with the best fitness alpha, beta and delta wolfs are saved in each iteration process, and the positions of other wolfs (omega wolfs) are updated according to the position information of the three wolfs, and the mathematical model of the three wolfs is as follows:
the specific flow of the gray wolf algorithm is as follows:
initializing the quantity of the wolfs, the number of variables, the maximum iteration times and the pareto archive number; randomly generating and storing an initial population, and initializing parameters a, C, A and t; calculating the fitness of all wolf individuals and the position vectors of the wolf individuals; determining a non-dominant solution and saving it in a factual pareto archive; the crowding distance of each pareto archive individual is calculated, and three wolfs are selected: α, β, δ wolf; according to the formula
Calculating the position vectors of three wolfs; according to the formula
And formulas
Updating the current gray wolf position vector; calculating the fitness of all updated wolf individuals, determining a new non-dominant solution, storing the solution in a pareto archive, and deleting the solution dominated in the pareto archive; calculating a crowding distance for each pareto archive individual; bengba (Chinese character of 'Dangba')When the cumulative storage file size is larger than the allowable size, the pareto storage file size is cut to the allowable size according to the congestion distance, then non-dominated sorting is carried out according to the congestion distance, and the global optimal solution is selected and updated; when the pareto archive size is not larger than the allowable size, directly performing non-dominated sorting according to the congestion distance, and selecting and updating a global optimal solution; and after non-domination sorting is carried out according to the crowding distance, and the global optimal solution is selected and updated, judging whether an iteration criterion is met, if so, outputting the pareto optimal solution, and if not, returning to the step of determining the non-domination solution and storing the non-domination solution in a Parto archive for working.
Wherein, the pareto optimal solution refers to a series of investment costs and system products of the unit output power system
A monovalent combination.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of an analysis and optimization apparatus of an organic rankine cycle system according to a second embodiment of the present disclosure.
As shown in fig. 2, the analysis and optimization apparatus for an organic rankine cycle system according to the present embodiment includes:
an obtaining module 21, configured to obtain system operation parameters and system design parameters;
the first calculation module 22 is used for performing thermodynamic calculation according to the initial turbine efficiency to obtain system thermodynamic parameters;
the second calculation module 23 is configured to input the system operation parameters, the system design parameters, and the system thermodynamic parameters into a pre-designed one-dimensional centripetal turbine efficiency calculation model to obtain the dynamic turbine efficiency;
an updating module 24, configured to update the value of the initial turbine efficiency to the value of the dynamic turbine efficiency when a relative error between the dynamic turbine efficiency and the initial turbine efficiency is greater than or equal to a preset error value;
the third calculating module 25 is configured to calculate thermodynamic and economic indexes according to the system operation parameters, the system design parameters and the thermodynamic parameters when the relative error between the dynamic turbine efficiency and the initial turbine efficiency is smaller than the error preset value;
and the optimization module 26 is used for optimizing the system operation parameters and the system design parameters through a multi-objective optimization model according to the thermodynamic and economic indexes and the system operation parameters.
Further, the second calculation module includes:
the determining unit is used for determining turbine design parameters, and the turbine design parameters comprise a speed ratio, a reaction degree, a nozzle speed coefficient, a movable blade speed coefficient, a wheel diameter ratio, a movable blade inlet absolute airflow angle and a movable blade outlet relative airflow angle;
a first calculation unit for calculating a loss according to a turbine design parameter;
and the second calculation unit is used for calculating the dynamic turbine efficiency according to the loss.
Further, the losses include nozzle losses, bucket losses, lost velocities, friction losses, and leakage losses;
the first calculation unit includes:
a first calculating subunit for calculating the nozzle loss according to a first formula
In which ξ n It is an indication of the loss of the nozzle,
represents a nozzle speed coefficient, and Ω represents a reaction degree;
a second calculating subunit for calculating the loss coefficient according to a second formula
In which ξ r Indicating bucket loss, w 2 Representing the relative speed of the bucket outlet, Δ h s Expressing ideal enthalpy drop of the turbine, and psi expressing the speed coefficient of the movable blade;
a third calculating subunit for calculating the residual speed loss according to a third formula
In which ξ e Represents the loss of residual speed, c 2 Representing the absolute speed of the outlet of the bucket;
a fourth calculating subunit for calculating the friction loss according to a fourth formula
Wherein xi is f Denotes the friction loss, D 1 Representing the bucket inlet diameter, u 1 Representing the inlet peripheral speed, v, of the rotor blade 1 Representing the specific volume of the inlet of the rotor blade, m f Representing the mass flow of the working medium;
a fifth calculating subunit for calculating the leakage loss according to a fifth formula
Is any real number between 0.01 and 0.20, wherein xi 1 Denotes leakage loss, δ is tip clearance, D 2 Is the diameter of the outlet of the rotor blade, /) 2 The height of the outlet of the movable vane is the height of the outlet of the movable vane.
Further, the second calculation unit includes:
a sixth calculating subunit for calculating the dynamic turbine efficiency according to a sixth formula, wherein the sixth formula is eta tur ＝1-ξ n -ξ r -ξ e -ξ f -ξ 1 Wherein eta tur Representing the dynamic turbine efficiency.
EXAMPLE III
Referring to fig. 3, fig. 3 is a schematic structural diagram of an organic rankine system analysis optimization apparatus provided in a third embodiment of the present application.
The organic rankine system analysis and optimization device provided by the embodiment comprises:
a processor 31, and a memory 32 connected to the processor;
the memory is used for storing a computer program, and the computer program is at least used for executing the organic Rankine system analysis and optimization method in the first embodiment of the application;
the processor is used to call and execute the computer program in the memory.
With regard to the apparatus in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be described in detail here.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present application, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware that is related to instructions of a program, and the program may be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer-readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. An organic Rankine system analysis optimization method is characterized by comprising the following steps:
acquiring system operation parameters and system design parameters;
performing thermodynamic calculation according to the initial turbine efficiency to obtain system thermodynamic parameters;
inputting the system operation parameters, the system design parameters and the system thermodynamic parameters into a pre-designed one-dimensional centripetal turbine efficiency calculation model to obtain dynamic turbine efficiency;
updating the value of the initial turbine efficiency to the value of the dynamic turbine efficiency when the relative error between the dynamic turbine efficiency and the initial turbine efficiency is greater than or equal to a preset error value;
when the relative error between the dynamic turbine efficiency and the initial turbine efficiency is smaller than a preset error value, calculating thermodynamic and economic indexes according to the system operation parameters, the system design parameters and the thermodynamic parameters;
and optimizing system operation parameters and system design parameters through a multi-objective optimization model according to the thermodynamic and economic indexes and the system operation parameters.
2. The method of claim 1, wherein said inputting said system operating parameters, system design parameters and system thermodynamic parameters into a pre-designed one-dimensional centripetal turbine efficiency calculation model to obtain a dynamic turbine efficiency comprises:
determining turbine design parameters, wherein the turbine design parameters comprise a speed ratio, a reaction degree, a nozzle speed coefficient, a movable blade speed coefficient, a wheel diameter ratio, a movable blade inlet absolute airflow angle and a movable blade outlet relative airflow angle;
calculating loss according to the turbine design parameters;
calculating the dynamic turbine efficiency from the losses.
3. The method of claim 2, wherein the losses include nozzle losses, bucket losses, lost velocities, friction losses, and leakage losses;
the calculating the loss according to the turbine design parameter includes:
calculating nozzle loss according to a first formula
In which ξ n It is an indication of the loss of the nozzle,
representing the nozzle velocity coefficient, and omega representing the reaction degree;
calculating a loss coefficient according to a second formula
Xi therein r Indicating bucket loss, w 2 Representing the relative speed of the bucket outlet, Δ h s Expressing ideal enthalpy drop of the turbine, and psi expressing the speed coefficient of the movable blade;
calculating the residual speed loss according to a third formula
In which ξ e Represents the loss of residual speed, c 2 Representing the absolute speed of a rotor blade outlet;
calculating the friction loss according to a fourth formula
Wherein xi is f Denotes the friction loss, D 1 Indicating the bucket inlet diameter, u 1 Indicating the inlet peripheral speed, v, of the rotor blade 1 Representing the specific volume of the inlet of the rotor blade, m f Representing the mass flow of the working medium;
calculating leakage loss according to a fifth formula
Between 0.01 and 0.20Any real number of (1), where ξ 1 Denotes leakage loss, δ is tip clearance, D 2 Is the diameter of the outlet of the rotor blade, /) 2 The height of the outlet of the movable vane is the height of the outlet of the movable vane.
4. The method of claim 3, wherein said calculating said dynamic turbine efficiency from said losses comprises:
calculating the dynamic turbine efficiency according to a sixth formula, wherein the sixth formula is eta tur ＝1-ξ n -ξ r -ξ e -ξ f -ξ 1 Wherein eta tur Representing the dynamic turbine efficiency.
5. The method according to any one of claims 1 to 4, wherein the multi-objective optimization model employs a multi-objective gray wolf algorithm.
6. An organic rankine system analysis optimization device, comprising:
the acquisition module is used for acquiring system operation parameters and system design parameters;
the first calculation module is used for performing thermodynamic calculation according to the initial turbine efficiency to obtain system thermodynamic parameters;
the second calculation module is used for inputting the system operation parameters, the system design parameters and the system thermodynamic parameters into a pre-designed one-dimensional centripetal turbine efficiency calculation model to obtain dynamic turbine efficiency;
the updating module is used for updating the value of the initial turbine efficiency into the value of the dynamic turbine efficiency when the relative error between the dynamic turbine efficiency and the initial turbine efficiency is greater than or equal to an error preset value;
the third calculation module is used for calculating thermodynamic and economic indexes according to the system operation parameters, the system design parameters and the thermodynamic parameters when the relative error between the dynamic turbine efficiency and the initial turbine efficiency is smaller than an error preset value;
and the optimization module is used for optimizing system operation parameters and system design parameters through a multi-objective optimization model according to the thermodynamic and economic indexes and the system operation parameters.
7. The apparatus of claim 6, wherein the second computing module comprises:
the determining unit is used for determining turbine design parameters, and the turbine design parameters comprise a speed ratio, a reaction degree, a nozzle speed coefficient, a movable blade speed coefficient, a wheel diameter ratio, a movable blade inlet absolute airflow angle and a movable blade outlet relative airflow angle;
a first calculation unit for calculating a loss according to the turbine design parameter;
a second calculation unit for calculating the dynamic turbine efficiency based on the loss.
8. The apparatus of claim 7, wherein the losses include nozzle losses, bucket losses, lost afterspeed losses, friction losses, and leakage losses;
the first calculation unit includes:
a first calculating subunit for calculating the nozzle loss according to a first formula
In which ξ n It is an indication of the loss of the nozzle,
representing the nozzle velocity coefficient, and omega representing the reaction degree;
a second calculating subunit for calculating the loss coefficient according to a second formula
Xi therein r Indicating bucket loss, w 2 Representing the relative speed of the bucket outlet, Δ h s Expressing ideal enthalpy drop of the turbine, and psi expressing the speed coefficient of the movable blade;
a third calculation subunit for calculating according to a third formulaResidual velocity loss, the third formula is
In which ξ e Represents the loss of residual speed, c 2 Representing the absolute speed of the outlet of the bucket;
a fourth calculating subunit for calculating the friction loss according to a fourth formula
Wherein ξ f Denotes the friction loss, D 1 Representing the bucket inlet diameter, u 1 Representing the inlet peripheral speed, v, of the rotor blade 1 Representing the specific volume of the inlet of the rotor blade, m f Representing the mass flow of the working medium;
a fifth calculating subunit, configured to calculate the leakage loss according to a fifth formula
Any real number between 0.01 and 0.20, wherein xi 1 Denotes leakage loss, δ is tip clearance, D 2 Is the diameter of the outlet of the rotor blade, /) 2 The height of the outlet of the movable vane is the height of the outlet of the movable vane.
9. The apparatus of claim 8, wherein the second computing unit comprises:
a sixth calculating subunit, configured to calculate the dynamic turbine efficiency according to a sixth formula, where η is the sixth formula tur ＝1-ξ n -ξ r -ξ e -ξ f -ξ 1 Wherein eta tur Representing the dynamic turbine efficiency.
10. An organic rankine system analysis optimization apparatus, comprising:
a processor, and a memory coupled to the processor;
the memory for storing a computer program for performing at least the organic Rankine system analysis optimization method of any one of claims 1-5;
the processor is used for calling and executing the computer program in the memory.
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