CN109190327A - Organic Rankine Cycle system analysis optimization method, device and equipment - Google Patents
Organic Rankine Cycle system analysis optimization method, device and equipment Download PDFInfo
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Abstract
This application involves a kind of organic rankine cycle system analysis optimization method, device and equipments, comprising: obtains system operational parameters and system design parameters；Calculation of thermodynamics is carried out according to initial efficiency of turbine, obtains systems thermodynamics parameter；Obtain dynamic efficiency of turbine；When the relative error of dynamic efficiency of turbine and initial efficiency of turbine is greater than or equal to error preset value, the value of initial efficiency of turbine is updated to the value of dynamic efficiency of turbine；When the relative error of dynamic efficiency of turbine and initial efficiency of turbine is less than error preset value, heating power and economic index are calculated according to system operational parameters, system design parameters, thermodynamic parameter；It is optimized the system operation parameter and system design parameters according to heating power and economic index and system operational parameters by Model for Multi-Objective Optimization.It is dynamic efficiency of turbine that entire analysis optimization process, which uses, therefore the entire obtained result of analytic process and practical situation error are smaller, and analysis result is more accurate.
Description
Technical field
This application involves technical field of waste heat utilization more particularly to a kind of organic rankine cycle system analysis optimization method,
Device and equipment.
Background technique
As the energy sharply consumes, environmental problem is got worse, develop and using low grade heat energy (such as solar energy, underground heat,
Biomass energy and industrial exhaust heat) it has received widespread attention.It is bright since structure is simple, maintenance cost is low and advantages of environment protection
Agree circulation and provides a kind of feasible method for low grade residual heat recycling.Wherein, Organic Rankine Cycle (Organic
Rankine Cycle, ORC) using the lower feature of organic working medium boiling point, recycling power generation benefit directly can be carried out to low temperature exhaust heat
With.
ORC is mainly made of waste heat boiler (or heat exchanger), turbine, condenser and the big portion of working medium pump four set, organic working medium
Heat is absorbed from residual heat stream in heat exchanger, generates the steam of tool certain pressure and temperature, steam enters turbomachinery expansion
Acting, to drive generator or the other dynamic power machines of dragging.The steam being discharged from turbine in condenser to cooling water heat release,
Liquid is condensed into, finally comes back to heat exchanger by working medium pump, so constantly circulation is gone down, to realize the benefit to waste heat
With.
Wherein, critical component of the turbine as Organic Rankine Cycle, current most of about organic rankine cycle system
In analysis method, efficiency is assumed to be fixed value, but in a practical situation, efficiency of turbine is because of working medium type and operating parameter
Difference and have biggish difference, therefore, according to existing analysis method, efficiency of turbine, which is assumed to be fixed value, can to analyze
Result there is error.
Summary of the invention
To be overcome the problems, such as present in the relevant technologies at least to a certain extent, the application provides a kind of Organic Rankine Cycle
Network analysis optimization method, device and equipment.
According to a first aspect of the present application, a kind of organic rankine cycle system analysis optimization method is provided, comprising:
Obtain system operational parameters and system design parameters；
Calculation of thermodynamics is carried out according to initial efficiency of turbine, obtains systems thermodynamics parameter；
The input of the system operational parameters, system design parameters and systems thermodynamics parameter is pre-designed one-dimensional centripetal
Efficiency of turbine computation model obtains dynamic efficiency of turbine；
When the relative error of the dynamic efficiency of turbine and the initial efficiency of turbine is greater than or equal to error preset value,
The value of the initial efficiency of turbine is updated to the value of the dynamic efficiency of turbine；
When the relative error of the dynamic efficiency of turbine and the initial efficiency of turbine is less than error preset value, according to institute
It states system operational parameters, system design parameters, thermodynamic parameter and calculates heating power and economic index；
Optimized according to the heating power and economic index and the system operational parameters by Model for Multi-Objective Optimization and is
Operating parameter of uniting and system design parameters.
Optionally, described to set the input of the system operational parameters, system design parameters and systems thermodynamics parameter in advance
The one-dimensional radial-inward-flow turbine efficiency calculation model of meter obtains dynamic efficiency of turbine, comprising:
Determine that turbine design parameter, the turbine design parameter include speed ratio, degree of reaction, nozzle velocity coefficient, movable vane speed
It spends coefficient, impeller diameter ratio, movable vane entrance absolute air flow angle and movable vane and exports relative wind angle；
It is lost according to the turbine calculation of design parameters；
According to dynamic efficiency of turbine described in the costing bio disturbance.
Optionally, the loss includes nozzle loss, moving blade loss, leaving loss, friction loss and leakage loss；
It is described to be lost according to the turbine calculation of design parameters, comprising:
Nozzle loss is calculated according to the first formula, first formula isWherein ξnIt indicates
Nozzle loss,Indicate that nozzle velocity coefficient, Ω indicate degree of reaction；
Loss coefficient is calculated according to the second formula, second formula isWherein ξrIt indicates
Moving blade loss, w2Indicate that movable vane exports relative velocity, Δ hsIndicate that turbine isentropic enthalpy drop, ψ indicate movable vane velocity coeffficient；
Leaving loss is calculated according to third formula, the third formula isWherein ξeIndicate leaving loss,
c2Indicate that movable vane exports absolute velocity；
Friction loss is calculated according to the 4th formula, the 4th formula isWherein, ξfIndicate friction loss, D1Indicate movable vane inlet diameter, u1Table
Show movable vane entrance rim velocity, v1Indicate movable vane import specific volume, mfIndicate working medium mass flow；
Leakage loss is calculated according to the 5th formula, the 5th formula is It is 0.01
Any real number between~0.20, wherein ξ1Indicate leakage loss, δ is tip clearance, D2For movable vane outlet diameter, l2For movable vane
It is high to export leaf.
Optionally, the dynamic efficiency of turbine according to the costing bio disturbance, comprising:
The dynamic efficiency of turbine is calculated according to the 6th formula, the 6th formula is ηtur=1- ξn-ξr-ξe-ξf-ξ1,
Middle ηturIndicate dynamic efficiency of turbine.
Optionally, the Model for Multi-Objective Optimization uses multiple target grey wolf algorithm.
According to a second aspect of the present application, a kind of organic Rankine network analysis optimization device is provided, comprising:
Module is obtained, for obtaining system operational parameters and system design parameters；
First computing module obtains systems thermodynamics parameter for carrying out calculation of thermodynamics according to initial efficiency of turbine；
Second computing module, for inputting the system operational parameters, system design parameters and systems thermodynamics parameter
The one-dimensional radial-inward-flow turbine efficiency calculation model being pre-designed obtains dynamic efficiency of turbine；
Update module is greater than or equal to for the relative error when the dynamic efficiency of turbine and the initial efficiency of turbine
When error preset value, the value of the initial efficiency of turbine is updated to the value of the dynamic efficiency of turbine；
Third computing module is less than for the relative error when the dynamic efficiency of turbine and the initial efficiency of turbine and misses
When poor preset value, heating power is calculated according to the system operational parameters, system design parameters, thermodynamic parameter and economy refers to
Mark；
Optimization module, for excellent by multiple target according to the heating power and economic index and the system operational parameters
Change model optimization system operational parameters and system design parameters.
Optionally, second computing module includes:
Determination unit, for determining that turbine design parameter, the turbine design parameter include speed ratio, degree of reaction, nozzle speed
It spends coefficient, movable vane velocity coeffficient, impeller diameter ratio, movable vane entrance absolute air flow angle and movable vane and exports relative wind angle；
First computing unit, for being lost according to the turbine calculation of design parameters；
Second computing unit is used for the dynamic efficiency of turbine according to the costing bio disturbance.
Optionally, the loss includes nozzle loss, moving blade loss, leaving loss, friction loss and leakage loss；
First computing unit includes:
First computation subunit, for calculating nozzle loss according to the first formula, first formula isWherein ξnIndicate nozzle loss,Indicate that nozzle velocity coefficient, Ω indicate degree of reaction；
Second computation subunit, for calculating loss coefficient according to the second formula, second formula isWherein ξrIndicate moving blade loss, w2Indicate that movable vane exports relative velocity, Δ hsIndicate that turbine is ideal
Enthalpy drop, ψ indicate movable vane velocity coeffficient；
Third computation subunit, for calculating leaving loss according to third formula, the third formula is
Wherein ξeIndicate leaving loss, c2Indicate that movable vane exports absolute velocity；
4th computation subunit, for calculating friction loss according to the 4th formula, the 4th formula isWherein, ξfIndicate friction loss, D1Indicate movable vane inlet diameter, u1Table
Show movable vane entrance rim velocity, v1Indicate movable vane import specific volume, mfIndicate working medium mass flow；
5th computation subunit, for calculating leakage loss according to the 5th formula, the 5th formula is For any real number between 0.01~0.20, wherein ξ1Indicate leakage loss, δ is tip
Gap, D2For movable vane outlet diameter, l2It is high that leaf is exported for movable vane.
Optionally, second computing unit includes:
6th computation subunit, for calculating the dynamic efficiency of turbine according to the 6th formula, the 6th formula is ηtur
=1- ξn-ξr-ξe-ξf-ξ1, wherein ηturIndicate dynamic efficiency of turbine.
According to the third aspect of the application, a kind of organic Rankine network analysis optimization equipment is provided, comprising:
Processor, and the memory being connected with the processor；
For storing computer program, the computer program is at least used to execute the application first aspect the memory
The organic Rankine network analysis optimization method；
The processor is for calling and executing the computer program in the memory.
Technical solution provided by the present application can include the following benefits: acquisition system operational parameters and system first
Then design parameter carries out calculation of thermodynamics according to initial efficiency of turbine, thermodynamic parameter is obtained, by thermodynamic parameter, system
Operating parameter and system design parameters are input in the one-dimensional radial-inward-flow turbine efficiency calculation model being pre-designed and obtain dynamic turbine
Efficiency, when the relative error of dynamic efficiency of turbine and initial efficiency of turbine is greater than or equal to error preset value, by initial turbine
The value of efficiency is updated to the value of dynamic efficiency of turbine, when the relative error of the dynamic efficiency of turbine and the initial efficiency of turbine
When less than error preset value, heating power and economy are calculated according to the system operational parameters, system design parameters, thermodynamic parameter
Property index, finally system is run by Model for Multi-Objective Optimization according to heating power and economic index and system operational parameters and is joined
Several and system design parameters optimize.Based on this, efficiency of turbine used by entire analysis optimization process is transported according to system
Row parameter, system design parameters and systems thermodynamics parameter are calculated dynamic by one-dimensional radial-inward-flow turbine efficiency calculation model
State efficiency of turbine, dynamic efficiency of turbine are more matched with current system, therefore the entire obtained result of analytic process and reality
The case where border, error was smaller, and analysis result is more accurate.
It should be understood that above general description and following detailed description be only it is exemplary and explanatory, not
The application can be limited.
Detailed description of the invention
The drawings herein are incorporated into the specification and forms part of this specification, and shows the implementation for meeting the application
Example, and together with specification it is used to explain the principle of the application.
Fig. 1 is a kind of process signal for organic rankine cycle system analysis optimization method that embodiments herein one provides
Figure.
Fig. 2 is a kind of structural representation for organic rankine cycle system analysis optimization device that embodiments herein two provides
Figure.
Fig. 3 is a kind of structural schematic diagram for organic Rankine network analysis optimization equipment that embodiments herein three provides.
Specific embodiment
Example embodiments are described in detail here, and the example is illustrated in the accompanying drawings.Following description is related to
When attached drawing, unless otherwise indicated, the same numbers in different drawings indicate the same or similar elements.Following exemplary embodiment
Described in embodiment do not represent all embodiments consistent with the application.On the contrary, they be only with it is such as appended
The example of the consistent device and method of some aspects be described in detail in claims, the application.
Since organic rankine cycle system has, structure is simple, maintenance cost is low, advantages of environment protection, it is organic
Rankine cycle provides a kind of feasible method for low grade residual heat recycling.It is analyzed in current most of Organic Rankine Cycles
In method, efficiency of turbine is assumed to be fixed value, but in a practical situation, and efficiency of turbine is because of working medium type and operating parameter
Different and have bigger difference, therefore, the application calculates efficiency of turbine using one-dimensional efficiency of turbine computation model, using dynamic turbine
Efficiency replaces the fixation efficiency of turbine in traditional Organic Rankine Cycle analysis, comprehensively considers the heating power of organic rankine cycle system
Performance and economic performance analyze system performance using multi-objective optimization algorithm.
Embodiment one
Referring to Fig. 1, Fig. 1 is a kind of organic rankine cycle system analysis optimization method that embodiments herein one provides
Flow diagram.
As shown in Figure 1, organic rankine cycle system analysis optimization method provided in this embodiment includes:
Step 11 obtains system operational parameters and system design parameters；
Step 12 carries out calculation of thermodynamics according to initial efficiency of turbine, obtains systems thermodynamics parameter；
Step 13, by system operational parameters, system design parameters and systems thermodynamics parameter input be pre-designed it is one-dimensional
Radial-inward-flow turbine efficiency calculation model obtains dynamic efficiency of turbine；
Step 14, when the relative error of dynamic efficiency of turbine and initial efficiency of turbine is greater than or equal to error preset value,
The value of initial efficiency of turbine is updated to the value of dynamic efficiency of turbine；
Step 15, when the relative error of dynamic efficiency of turbine and initial efficiency of turbine is less than error preset value, according to being
Operating parameter, system design parameters, the thermodynamic parameter of uniting calculate heating power and economic index；
Step 16 passes through Model for Multi-Objective Optimization optimization system according to heating power and economic index and system operational parameters
Operating parameter and system design parameters.
System operational parameters and system design parameters are obtained first, and thermodynamics meter is then carried out according to initial efficiency of turbine
Calculate, obtain thermodynamic parameter, thermodynamic parameter, system operational parameters and system design parameters are input to be pre-designed it is one-dimensional
Dynamic efficiency of turbine is obtained in radial-inward-flow turbine efficiency calculation model, when the relative error of dynamic efficiency of turbine and initial efficiency of turbine
When more than or equal to error preset value, the value of initial efficiency of turbine is updated to the value of dynamic efficiency of turbine, when dynamic turbine is imitated
When rate and the relative error of initial efficiency of turbine are less than error preset value, according to system operational parameters, system design parameters, heating power
It learns parameter and calculates heating power and economic index, more mesh are finally passed through according to heating power and economic index and system operational parameters
Mark Optimized model optimizes system operational parameters and system design parameters.Based on this, entire analysis optimization process is used
Efficiency of turbine be to be imitated according to system operational parameters, system design parameters and systems thermodynamics parameter by one-dimensional radial-inward-flow turbine
The dynamic efficiency of turbine that rate computation model is calculated, dynamic efficiency of turbine are more matched with current system, therefore entire point
The obtained result of analysis process and practical situation error are smaller, and analysis result is more accurate.
It should be noted that system operational parameters may include Heat-Source Parameters, cold source parameter, system parameter etc., specifically may be used
To be heat source temperature, sink temperature, vapor (steam) temperature, condensation temperature, evaporating pressure etc.；System design parameters may include heat exchanger
Area, pinch temperatures, turbine type selecting etc..
Step 12 is to the process that step 15 is to a loop iteration of efficiency of turbine, firstly, the original of initial efficiency of turbine
Initial value can be the assumed value rule of thumb set, then seek systems thermodynamics parameter according to initial efficiency of turbine value,
Systems thermodynamics parameter is input in one-dimensional radial-inward-flow turbine efficiency calculation model, dynamic efficiency of turbine is obtained, then judgement is dynamic
Whether the relative error of state efficiency of turbine and initial efficiency of turbine is less than error preset value, and wherein error preset value can be set to
0.01, when it is no for determining result, the value of initial efficiency of turbine is updated to the value of dynamic efficiency of turbine, then further according to new
Initial efficiency of turbine computing system thermodynamic parameter obtains new dynamic turbine by one-dimensional radial-inward-flow turbine efficiency calculation model and imitates
After rate, then judge whether dynamic efficiency of turbine and the relative error of initial efficiency of turbine are less than error preset value, until judgement is tied
Fruit be it is yes, dynamic efficiency of turbine at this time is final efficiency of turbine, and systems thermodynamics parameter at this time is final system heating power
Parameter is learned, heating power and economic index are finally calculated according to system operational parameters, system design parameters, thermodynamic parameter.
Further, step 13 may include:
Determine that turbine design parameter, turbine design parameter include speed ratio, degree of reaction, nozzle velocity coefficient, movable vane speed system
Number, impeller diameter ratio, movable vane entrance absolute air flow angle and movable vane export relative wind angle；
It is lost according to turbine calculation of design parameters；
According to costing bio disturbance dynamic efficiency of turbine.
Wherein, loss may include nozzle loss, moving blade loss, leaving loss, friction loss and reveal loss, and according to
Costing bio disturbance dynamic efficiency of turbine may include:
Nozzle loss is calculated according to the first formula, the first formula isWherein ξnIndicate nozzle
Loss,Indicate that nozzle velocity coefficient, Ω indicate degree of reaction；
Loss coefficient is calculated according to the second formula, the second formula isWherein ξrIndicate movable vane
Loss, w2Indicate that movable vane exports relative velocity, Δ hsIndicate that turbine isentropic enthalpy drop, ψ indicate movable vane velocity coeffficient；
Leaving loss is calculated according to third formula, third formula isWherein ξeIndicate leaving loss, c2Table
Show that movable vane exports absolute velocity；
Friction loss is calculated according to the 4th formula, the 4th formula isWherein, ξfIndicate friction loss, D1Indicate movable vane inlet diameter, u1Table
Show movable vane entrance rim velocity, v1Indicate movable vane import specific volume, mfIndicate working medium mass flow；
Leakage loss is calculated according to the 5th formula, the 5th formula is For 0.01~
Any real number between 0.20, wherein ξ1Indicate leakage loss, δ is tip clearance, D2For movable vane outlet diameter, l2Go out for movable vane
Mouth leaf is high.
Further, according to costing bio disturbance dynamic efficiency of turbine, may include:
Dynamic efficiency of turbine is calculated according to the 6th formula, the 6th formula is ηtur=1- ξn-ξr-ξe-ξf-ξ1, wherein ηturTable
Show dynamic efficiency of turbine.
In addition, comprehensively considering organic rankine cycle system thermal performance and economic performance in the present embodiment, wherein heating power
It may include net output work, the thermal efficiency, cost of investment, system product with economic indexUnit price.
In step 16, unit output work system investment cost and system product are selectedUnit price is objective function, evaporation
Temperature and condensation temperature are optimized variable, are optimized to the organic rankine cycle system of Coupled Dynamic efficiency of turbine.Multiple target
Optimized model is expressed as follows:
Wherein, SIC is unit output work system investment cost, cp,totalFor system productUnit price.
Constraint condition may include:
Evaporating temperature should be less than heat source temperature, and be less than working medium critical-temperature, and the pinch point temperature of evaporator is greater than design
Minimum heat transfer temperature difference (value is 5K in this example)；
Condensation temperature should be greater than environment temperature and be less than evaporating temperature, and condensation temperature takes 303.15K- in the present embodiment
323.15K。
Constraint condition can be expressed as follows:
Wherein, TheatIndicate heat source temperature, TcriticalIndicate working medium critical-temperature, Δ TheatIndicate the narrow point temperature of evaporator
Degree, TambIndicate heat source temperature.
It should be noted that the present embodiment is excellent to organic rankine cycle system progress multiple target using multiple target grey wolf algorithm
Change, grey wolf algorithm is a kind of meta-heuristic algorithm for simulating wolf pack collective and hunting behavior and swelling, convergence speed simple with mechanism
Spend the advantages that fast.
Social Grading, encirclement prey, the mathematical model of hunting behavior for establishing grey wolf respectively are as follows:
It is Social Grading first, during designing grey wolf algorithm, in order to simulate the Social Grading of grey wolf, according to adaptation
Grey wolf individual in wolf pack is divided into four kinds of different types by angle value: the optimal individual of fitness is defined as α wolf, fitness suboptimum
Excellent individual is respectively defined as β wolf and δ wolf with third, remaining individual is designated as ω wolf.In grey wolf algorithm, searching process is main
It is guided and is completed by α, β, δ wolf, ω wolf is responsible for following the head wolf of front three to find optimal solution.
Prey is followed by surrounded, grey wolf needs to surround during hunting prey, and when encirclement first has to determine oneself and hunt
The distance of object, formula areWherein, t indicates current iteration number；Indicate prey position to
Amount,Indicate the position vector of grey wolf,To swing the factor,It can be expressed asIt is thereinFor in [0,1]
Random vector.
Grey wolf carries out location updating according to the distance between itself and prey:Wherein
Indicate convergence coefficient vector, and For the random vector in [0,1],In an iterative process with band
The increase of number drops to 0 from 2, for example, when band number be 5 when, value 2,1.5,1,0.5,0.
It is finally hunting behavior, in grey wolf algorithm, grey wolf is not aware that the specific location of prey (optimal solution), for mould
Quasi- hunting behavior, preferable three wolf α, β, δ wolves of Jiading fitness are realized to hunting according to the location information of three near prey
The positioning of object.Therefore, three optimal wolf α, β, δ wolves of fitness are saved in each iterative process, and according to their position
The position of other grey wolves (ω wolf) of information update, mathematical model are as follows:
The detailed process of grey wolf algorithm is as follows:
It initializes grey wolf quantity, variables number, maximum number of iterations, Pareto and achieves number；It is random to generate initial population simultaneously
Storage, initiation parameter a, C, A, t；Calculate the fitness and their position vector of all grey wolf individuals；Determine non-domination solution
And it saves it in the Pareto archive that meets accident；The crowding distance that each Pareto achieves individual is calculated, selects three wolves: α, β,
δ wolf；According to formulaCalculate the position arrow of three wolves
Amount；According to formulaAnd formulaUpdate current grey wolf position vector；The fitness of all updated grey wolf individuals is calculated, really
Fixed new non-domination solution is stored in Pareto archive, is deleted except the solution dominated in Pareto archive；Calculate each Pareto
Achieve the crowding distance of individual；When Pareto, which achieves size, is greater than permission size, first Pareto is achieved according to crowding distance
Size is cut to permission size, then carries out non-dominated ranking further according to crowding distance, selects and update globally optimal solution；Work as pa
It is tired that non-dominated ranking is directly carried out according to crowding distance when depositing grade size no more than size is allowed, select and update it is global most
Excellent solution；Non-dominated ranking is being carried out according to crowding distance, after selecting and updating globally optimal solution, is judging whether iteration criterion is full
Foot, if so, output Pareto optimal solution, if it is not, then returning to above-mentioned determining non-domination solution and saving it in the Pareto that meets accident
Step cycle in archive.
Wherein, Pareto optimal solution refers to a series of unit output work system investment cost and system productUnit price
Combination.
Embodiment two
Referring to Fig. 2, Fig. 2 is a kind of organic rankine cycle system analysis optimization device that embodiments herein two provides
Structural schematic diagram.
As shown in Fig. 2, organic rankine cycle system analysis optimization device provided in this embodiment includes:
Module 21 is obtained, for obtaining system operational parameters and system design parameters；
First computing module 22 obtains systems thermodynamics parameter for carrying out calculation of thermodynamics according to initial efficiency of turbine；
Second computing module 23, it is pre- for inputting system operational parameters, system design parameters and systems thermodynamics parameter
The one-dimensional radial-inward-flow turbine efficiency calculation model first designed obtains dynamic efficiency of turbine；
Update module 24, it is pre- more than or equal to error for the relative error when dynamic efficiency of turbine and initial efficiency of turbine
If the value of initial efficiency of turbine to be updated to the value of dynamic efficiency of turbine when value；
Third computing module 25, it is default less than error for the relative error when dynamic efficiency of turbine and initial efficiency of turbine
When value, heating power and economic index are calculated according to system operational parameters, system design parameters, thermodynamic parameter；
Optimization module 26, for passing through Model for Multi-Objective Optimization according to heating power and economic index and system operational parameters
The parameter that optimizes the system operation and system design parameters.
Further, the second computing module includes:
Determination unit, for determining that turbine design parameter, turbine design parameter include speed ratio, degree of reaction, nozzle velocity system
Number, movable vane velocity coeffficient, impeller diameter ratio, movable vane entrance absolute air flow angle and movable vane export relative wind angle；
First computing unit, for being lost according to turbine calculation of design parameters；
Second computing unit, for according to costing bio disturbance dynamic efficiency of turbine.
Further, loss includes nozzle loss, moving blade loss, leaving loss, friction loss and leakage loss；
First computing unit includes:
First computation subunit, for calculating nozzle loss according to the first formula, the first formula isWherein ξnIndicate nozzle loss,Indicate that nozzle velocity coefficient, Ω indicate degree of reaction；
Second computation subunit, for calculating loss coefficient according to the second formula, the second formula isWherein ξrIndicate moving blade loss, w2Indicate that movable vane exports relative velocity, Δ hsIndicate that turbine is ideal
Enthalpy drop, ψ indicate movable vane velocity coeffficient；
Third computation subunit, for calculating leaving loss according to third formula, third formula isWherein
ξeIndicate leaving loss, c2Indicate that movable vane exports absolute velocity；
4th computation subunit, for calculating friction loss according to the 4th formula, the 4th formula isWherein, ξfIndicate friction loss, D1Indicate movable vane inlet diameter, u1Table
Show movable vane entrance rim velocity, v1Indicate movable vane import specific volume, mfIndicate working medium mass flow；
5th computation subunit, for calculating leakage loss according to the 5th formula, the 5th formula is For any real number between 0.01~0.20, wherein ξ1Indicate leakage loss, δ is tip
Gap, D2For movable vane outlet diameter, l2It is high that leaf is exported for movable vane.
Further, the second computing unit includes:
6th computation subunit, for calculating dynamic efficiency of turbine according to the 6th formula, the 6th formula is ηtur=1- ξn-
ξr-ξe-ξf-ξ1, wherein ηturIndicate dynamic efficiency of turbine.
Embodiment three
Referring to Fig. 3, Fig. 3 is a kind of knot for organic Rankine network analysis optimization equipment that embodiments herein three provides
Structure schematic diagram.
Organic Rankine network analysis provided in this embodiment optimizes equipment, comprising:
Processor 31, and the memory 32 being connected with processor；
For memory for storing computer program, computer program is at least used to execute the organic bright of the embodiment of the present application one
Agree network analysis optimization method；
Processor is for calling and executing the computer program in memory.
About the device in above-described embodiment, wherein modules execute the concrete mode of operation in related this method
Embodiment in be described in detail, no detailed explanation will be given here.
It is understood that same or similar part can mutually refer in the various embodiments described above, in some embodiments
Unspecified content may refer to the same or similar content in other embodiments.
It should be noted that term " first ", " second " etc. are used for description purposes only in the description of the present application, without
It can be interpreted as indication or suggestion relative importance.In addition, in the description of the present application, unless otherwise indicated, the meaning of " multiple "
Refer at least two.
Any process described otherwise above or method description are construed as in flow chart or herein, and expression includes
It is one or more for realizing specific logical function or process the step of executable instruction code module, segment or portion
Point, and the range of the preferred embodiment of the application includes other realization, wherein can not press shown or discussed suitable
Sequence, including according to related function by it is basic simultaneously in the way of or in the opposite order, to execute function, this should be by the application
Embodiment person of ordinary skill in the field understood.
It should be appreciated that each section of the application can be realized with hardware, software, firmware or their combination.Above-mentioned
In embodiment, software that multiple steps or method can be executed in memory and by suitable instruction execution system with storage
Or firmware is realized.It, and in another embodiment, can be under well known in the art for example, if realized with hardware
Any one of column technology or their combination are realized: having a logic gates for realizing logic function to data-signal
Discrete logic, with suitable combinational logic gate circuit specific integrated circuit, programmable gate array (PGA), scene
Programmable gate array (FPGA) etc..
Those skilled in the art are understood that realize all or part of step that above-described embodiment method carries
Suddenly be that relevant hardware can be instructed to complete by program, program can store in a kind of computer readable storage medium
In, which when being executed, includes the steps that one or a combination set of embodiment of the method.
It, can also be in addition, can integrate in a processing module in each functional unit in each embodiment of the application
It is that each unit physically exists alone, can also be integrated in two or more units in a module.Above-mentioned integrated mould
Block both can take the form of hardware realization, can also be realized in the form of software function module.If integrated module with
The form of software function module is realized and when sold or used as an independent product, also can store computer-readable at one
It takes in storage medium.
Storage medium mentioned above can be read-only memory, disk or CD etc..
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is contained at least one embodiment or example of the application.In the present specification, schematic expression of the above terms are not
Centainly refer to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be any
One or more embodiment or examples in can be combined in any suitable manner.
Although embodiments herein has been shown and described above, it is to be understood that above-described embodiment is example
Property, it should not be understood as the limitation to the application, those skilled in the art within the scope of application can be to above-mentioned
Embodiment is changed, modifies, replacement and variant.
Claims (10)
1. a kind of organic Rankine network analysis optimization method characterized by comprising
Obtain system operational parameters and system design parameters；
Calculation of thermodynamics is carried out according to initial efficiency of turbine, obtains systems thermodynamics parameter；
The system operational parameters, system design parameters and systems thermodynamics parameter are inputted to the one-dimensional radial-inward-flow turbine being pre-designed
Efficiency calculation model obtains dynamic efficiency of turbine；
When the relative error of the dynamic efficiency of turbine and the initial efficiency of turbine is greater than or equal to error preset value, by institute
The value for stating initial efficiency of turbine is updated to the value of the dynamic efficiency of turbine；
When the relative error of the dynamic efficiency of turbine and the initial efficiency of turbine is less than error preset value, according to the system
Operating parameter, system design parameters, the thermodynamic parameter of uniting calculate heating power and economic index；
It is transported with the system operational parameters by Model for Multi-Objective Optimization optimization system according to the heating power and economic index
Row parameter and system design parameters.
2. the method according to claim 1, wherein described by the system operational parameters, system design parameters
And the one-dimensional radial-inward-flow turbine efficiency calculation model that the input of systems thermodynamics parameter is pre-designed obtains dynamic efficiency of turbine, comprising:
Determine that turbine design parameter, the turbine design parameter include speed ratio, degree of reaction, nozzle velocity coefficient, movable vane speed system
Number, impeller diameter ratio, movable vane entrance absolute air flow angle and movable vane export relative wind angle；
It is lost according to the turbine calculation of design parameters；
According to dynamic efficiency of turbine described in the costing bio disturbance.
3. according to the method described in claim 2, it is characterized in that, the loss includes nozzle loss, moving blade loss, leaving velocity damage
It loses, friction loss and leakage are lost；
It is described to be lost according to the turbine calculation of design parameters, comprising:
Nozzle loss is calculated according to the first formula, first formula isWherein ξnIndicate nozzle damage
It loses,Indicate that nozzle velocity coefficient, Ω indicate degree of reaction；
Loss coefficient is calculated according to the second formula, second formula isWherein ξrIndicate movable vane
Loss, w2Indicate that movable vane exports relative velocity, Δ hsIndicate that turbine isentropic enthalpy drop, ψ indicate movable vane velocity coeffficient；
Leaving loss is calculated according to third formula, the third formula isWherein ξeIndicate leaving loss, c2Table
Show that movable vane exports absolute velocity；
Friction loss is calculated according to the 4th formula, the 4th formula isWherein, ξfIndicate friction loss, D1Indicate movable vane inlet diameter, u1Table
Show movable vane entrance rim velocity, v1Indicate movable vane import specific volume, mfIndicate working medium mass flow；
Leakage loss is calculated according to the 5th formula, the 5th formula is For 0.01~
Any real number between 0.20, wherein ξ1Indicate leakage loss, δ is tip clearance, D2For movable vane outlet diameter, l2Go out for movable vane
Mouth leaf is high.
4. according to the method described in claim 3, it is characterized in that, the dynamic turbine according to the costing bio disturbance is imitated
Rate, comprising:
The dynamic efficiency of turbine is calculated according to the 6th formula, the 6th formula is ηtur=1- ξn-ξr-ξe-ξf-ξ1, wherein
ηturIndicate dynamic efficiency of turbine.
5. method according to any one of claims 1 to 4, which is characterized in that the Model for Multi-Objective Optimization uses more mesh
Mark grey wolf algorithm.
6. a kind of organic Rankine network analysis optimizes device characterized by comprising
Module is obtained, for obtaining system operational parameters and system design parameters；
First computing module obtains systems thermodynamics parameter for carrying out calculation of thermodynamics according to initial efficiency of turbine；
Second computing module, it is preparatory for inputting the system operational parameters, system design parameters and systems thermodynamics parameter
The one-dimensional radial-inward-flow turbine efficiency calculation model of design obtains dynamic efficiency of turbine；
Update module is greater than or equal to error for the relative error when the dynamic efficiency of turbine and the initial efficiency of turbine
When preset value, the value of the initial efficiency of turbine is updated to the value of the dynamic efficiency of turbine；
Third computing module, it is pre- less than error for the relative error when the dynamic efficiency of turbine and the initial efficiency of turbine
If when value, calculating heating power and economic index according to the system operational parameters, system design parameters, thermodynamic parameter；
Optimization module, for passing through multiple-objection optimization mould according to the heating power and economic index and the system operational parameters
Type optimizes the system operation parameter and system design parameters.
7. device according to claim 6, which is characterized in that second computing module includes:
Determination unit, for determining that turbine design parameter, the turbine design parameter include speed ratio, degree of reaction, nozzle velocity system
Number, movable vane velocity coeffficient, impeller diameter ratio, movable vane entrance absolute air flow angle and movable vane export relative wind angle；
First computing unit, for being lost according to the turbine calculation of design parameters；
Second computing unit is used for the dynamic efficiency of turbine according to the costing bio disturbance.
8. device according to claim 7, which is characterized in that the loss includes nozzle loss, moving blade loss, leaving velocity damage
It loses, friction loss and leakage are lost；
First computing unit includes:
First computation subunit, for calculating nozzle loss according to the first formula, first formula isWherein ξnIndicate nozzle loss,Indicate that nozzle velocity coefficient, Ω indicate degree of reaction；
Second computation subunit, for calculating loss coefficient according to the second formula, second formula isWherein ξrIndicate moving blade loss, w2Indicate that movable vane exports relative velocity, Δ hsIndicate that turbine is ideal
Enthalpy drop, ψ indicate movable vane velocity coeffficient；
Third computation subunit, for calculating leaving loss according to third formula, the third formula isWherein
ξeIndicate leaving loss, c2Indicate that movable vane exports absolute velocity；
4th computation subunit, for calculating friction loss according to the 4th formula, the 4th formula isWherein, ξfIndicate friction loss, D1Indicate movable vane inlet diameter, u1Table
Show movable vane entrance rim velocity, v1Indicate movable vane import specific volume, mfIndicate working medium mass flow；
5th computation subunit, for calculating leakage loss according to the 5th formula, the 5th formula is For any real number between 0.01~0.20, wherein ξ1Indicate leakage loss, δ is tip
Gap, D2For movable vane outlet diameter, l2It is high that leaf is exported for movable vane.
9. device according to claim 8, which is characterized in that second computing unit includes:
6th computation subunit, for calculating the dynamic efficiency of turbine according to the 6th formula, the 6th formula is ηtur=1-
ξn-ξr-ξe-ξf-ξ1, wherein ηturIndicate dynamic efficiency of turbine.
10. a kind of organic Rankine network analysis optimizes equipment characterized by comprising
Processor, and the memory being connected with the processor；
The memory is at least used for perform claim and requires any one of 1-5 for storing computer program, the computer program
The organic Rankine network analysis optimization method；
The processor is for calling and executing the computer program in the memory.
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