CN112417773B - Multidisciplinary optimization design method, device and equipment of multistage axial flow expander - Google Patents
Multidisciplinary optimization design method, device and equipment of multistage axial flow expander Download PDFInfo
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Abstract
The application discloses multidisciplinary optimization design method, device and equipment of a multistage axial flow expander, and relates to the technical field of expander design, wherein the method comprises the following steps: firstly, forward modeling is carried out on the multistage axial flow expansion machine according to design parameters solved by reverse fitting, and a target model is obtained; then defining corresponding boundary conditions for the fluid region model and the solid region model after grid division, calculating the fluid region model by utilizing a CFD module, and performing multidisciplinary calculation on the solid region model by utilizing a multidisciplinary calculation module comprising strength, dynamic characteristics and the like; and finally, performing multi-objective optimization on two stages of blade grids in the multi-stage axial flow expander by using a genetic algorithm according to the CFD calculation result and the multiple objective functions of the multidisciplinary calculation result to obtain the blade forward bending and backward bending coupling rule and the optimization information of blade profile along the blade height distribution. The multi-disciplinary optimization design of the multistage axial flow expander can be effectively performed.
Description
Technical Field
The application relates to the technical field of expander design, in particular to a multidisciplinary optimization design method, device and equipment of a multistage axial flow expander.
Background
The expander is a core power component of the Rankine cycle, and researchers at home and abroad have conducted long-term and deep exploration and research. The axial flow expansion machine has wide application, and the applicable design parameters such as the type of working medium, pressure, temperature, rotating speed, power, blade series and the like are different.
At present, the multidisciplinary optimization design of the high-power multistage axial flow expansion machine is still blank, and the multidisciplinary optimization design of the multistage axial flow expansion machine cannot be effectively carried out, so that the multistage axial flow expansion machine cannot be well optimized, the running cost of the device can be increased, and good energy conservation and emission reduction effects cannot be achieved.
Disclosure of Invention
In view of this, the present application provides a multi-disciplinary optimization design method, apparatus and device for a multi-stage axial flow expander, and mainly aims to solve the technical problem that in the prior art, the multi-disciplinary optimization design of the multi-stage axial flow expander cannot be effectively performed, so that the multi-disciplinary optimization of the multi-stage axial flow expander cannot be achieved.
According to one aspect of the present application, there is provided a multidisciplinary optimization design method of a multistage axial flow expander, the method comprising:
forward modeling is carried out on the multistage axial flow expansion machine according to the design parameters of the reverse fitting solution, and a target model is obtained;
Respectively carrying out grid division on a fluid region model and a solid region model in the target model;
defining corresponding boundary conditions for the fluid region model and the solid region model after grid division, calculating the fluid region model by using a computational fluid dynamics (Computational Fluid Dynamics, CFD) module, and performing multidisciplinary calculation on the solid region model by using a multidisciplinary calculation module comprising strength and dynamic characteristics;
and performing multi-objective optimization on two stages of blade grids in the multistage axial flow expander by using a genetic algorithm according to the CFD calculation result and the multiple objective functions of the multidisciplinary calculation result to obtain blade forward and backward bending coupling rules and optimization information of blade profile distribution along the blade height.
According to another aspect of the present application, there is provided a multidisciplinary optimum design apparatus for a multistage axial flow expander, the apparatus comprising:
the modeling unit is used for carrying out forward modeling on the multistage axial flow expansion machine according to the design parameters of the reverse fitting solution to obtain a target model;
the grid dividing unit is used for respectively dividing the grids of the fluid region model and the solid region model in the target model;
The computing unit is used for defining corresponding boundary conditions for the fluid region model and the solid region model after grid division, computing the fluid region model by utilizing a computational fluid dynamics CFD module, and performing multidisciplinary computation on the solid region model by utilizing a multidisciplinary computation module comprising strength and dynamic characteristics;
and the optimization processing unit is used for performing multi-objective optimization on the two-stage blade cascade in the multistage axial flow expander by using a genetic algorithm according to the CFD calculation result and the multiple objective functions of the multidisciplinary calculation result to obtain the blade forward and backward bending coupling rule and the optimization information of the blade profile along the blade height distribution.
According to still another aspect of the present application, there is provided a storage device having stored thereon a computer program which, when executed by a processor, implements the multi-disciplinary optimization design method of the multistage axial flow expander described above.
According to still another aspect of the present application, there is provided a multi-disciplinary optimization design apparatus for a multistage axial flow expander, including a storage device, a processor, and a computer program stored on the storage device and executable on the processor, the processor implementing the multi-disciplinary optimization design method for a multistage axial flow expander as described above when executing the program.
By means of the technical scheme, the multi-disciplinary optimization design method, device and equipment for the multistage axial flow expansion machine, compared with the multi-disciplinary optimization design which cannot be effectively performed on the multistage axial flow expansion machine in the prior art, can perform forward modeling on the multistage axial flow expansion machine according to design parameters which are reversely fitted and solved, and obtain a target model; respectively carrying out grid division on a fluid region model and a solid region model in the target model, setting boundary conditions and the like; then performing multidisciplinary calculation by using the CFD module and a multidisciplinary calculation module comprising strength, dynamic characteristics and the like; and finally, performing multi-objective optimization on two stages of blade grids in the multi-stage axial flow expander by using a genetic algorithm according to the CFD calculation result and the multiple objective functions of the multidisciplinary calculation result to obtain the blade forward bending and backward bending coupling rule and the optimization information of blade profile along the blade height distribution. And then can effectively carry out multistage axial-flow expander's multidisciplinary optimal design for multistage axial-flow expander can obtain fine optimization, not only can improve unit efficiency and stability, can reduce the running cost of device moreover, accords with energy-conserving emission reduction's relevant requirement.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
fig. 1 shows a flow diagram of a multi-disciplinary optimization design method of a multi-stage axial flow expansion machine according to an embodiment of the present application;
FIG. 2 illustrates an example schematic view of a blade axial chord thickness distribution provided by an embodiment of the present application;
FIG. 3 illustrates an example schematic view of an axial chord angle distribution of a blade provided by an embodiment of the present application;
FIG. 4 illustrates an example schematic diagram of fluid zone extraction provided by an embodiment of the present application;
FIG. 5 illustrates an example schematic diagram of fluid region meshing provided by embodiments of the present application;
FIG. 6 shows an example schematic of solid region meshing provided by embodiments of the present application;
FIG. 7 illustrates an example schematic diagram of model boundary condition settings provided by embodiments of the present application;
FIG. 8 illustrates an example schematic diagram of loading a pressure field and a temperature field for a blade provided by an embodiment of the present application;
fig. 9 shows a schematic flow chart of the MOGA algorithm provided in the embodiment of the present application;
fig. 10 shows a schematic structural diagram of a multi-disciplinary optimization design device of a multi-stage axial flow expander according to an embodiment of the present application.
Detailed Description
The present application will be described in detail hereinafter with reference to the accompanying drawings in conjunction with embodiments. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other.
In order to solve the technical problems that the multi-disciplinary optimization design of the multi-stage axial flow expansion machine cannot be effectively performed in the prior art, and the multi-disciplinary optimization of the multi-stage axial flow expansion machine cannot be achieved. The embodiment provides a multidisciplinary optimization design method of a multistage axial flow expansion machine, as shown in fig. 1, the method comprises the following steps:
101. and (3) forward modeling is carried out on the multistage axial flow expander according to the design parameters of the reverse fitting solution, so as to obtain a target model.
According to the design parameters of the reverse fitting solution, forward modeling is carried out, a more standard target model of the multistage axial flow expander can be accurately obtained, and therefore the effect of optimal design is guaranteed when multidisciplinary optimal design is carried out on the basis of the model.
102. And respectively carrying out grid division on the fluid region model and the solid region model in the target model.
After forward modeling, extracting fluid and solid areas from the obtained target model, and then meshing the extracted fluid and solid areas.
103. Corresponding boundary conditions are defined for the fluid region model and the solid region model after grid division, the CFD module is utilized to calculate the fluid region model, and the multidisciplinary calculation module comprising strength and dynamic characteristics is utilized to calculate the solid region model.
In this embodiment, the multidisciplinary computing module may include intensity computation, dynamic characteristic computation, computation of various other disciplines, and the like, and may be specifically determined according to actual computing requirements.
104. And performing multi-objective optimization on two-stage blade grids in the multi-stage axial flow expander by using a genetic algorithm according to the CFD calculation result and the multiple objective functions of the multidisciplinary calculation result to obtain the blade forward bending and backward bending coupling rule and the optimization information of blade profile along the blade height distribution.
The multi-working condition multi-disciplinary optimization of the multistage axial flow expander is a multi-objective optimization problem, for example, for the embodiment, a genetic algorithm can be adopted to perform multi-objective optimization on two-stage blade cascades, the overall efficiency, the output power, the axial thrust, the maximum stress of the movable blades, the maximum radial displacement of the movable blades and the like obtained according to a CFD calculation result and an intensity calculation result are used as objective functions, mass, flow variable and the like are selected as constraint conditions, the space stacking rule of the two-dimensional blade cascades of the movable blades and the stationary blades and the two-dimensional blade cascades control parameters of a plurality of blade high sections (such as 0, 25%, 50%, 75%, 100% and the like) are used as design variables, and the optimal blade forward bending and backward bending coupling rule and blade cascades along the distribution of the blade heights are obtained, and the aerodynamic performance, the mechanical performance and the like of the expander can be analyzed and optimized afterwards.
Compared with the multi-disciplinary optimization design of the multi-stage axial flow expander which cannot be effectively performed in the prior art, the multi-disciplinary optimization design method of the multi-stage axial flow expander provided by the embodiment can be used for performing forward modeling on the multi-stage axial flow expander according to the design parameters of reverse fitting solution to obtain a target model; respectively carrying out grid division on a fluid region model and a solid region model in the target model, setting boundary conditions and the like; then performing multidisciplinary calculation by using the CFD module and a multidisciplinary calculation module comprising strength, dynamic characteristics and the like; and finally, performing multi-objective optimization on two stages of blade grids in the multi-stage axial flow expander by using a genetic algorithm according to the CFD calculation result and the multiple objective functions of the multidisciplinary calculation result to obtain the blade forward bending and backward bending coupling rule and the optimization information of blade profile along the blade height distribution. And then can effectively carry out multistage axial-flow expander's multidisciplinary optimal design for multistage axial-flow expander can obtain fine optimization, not only can improve unit efficiency and stability, can reduce the running cost of device moreover, accords with energy-conserving emission reduction's relevant requirement.
Further, as an extension and refinement of the present embodiment, in order to fully describe the implementation process of the present embodiment, in step 101, a reverse engineering process is required before forward modeling, and there may be various alternative manners, and as an alternative manner of one of the reverse engineering processes, if the blades of the multistage axial flow expander are subsonic turbine blades with a folding angle smaller than a preset angle threshold and a blade thickness variation smaller than a preset variation threshold, step 101 may specifically include: firstly, a parameterized modeling module is utilized to introduce a three-dimensional model of the multistage axial flow expander; then drawing a flow channel area in a three-dimensional model, setting the number of streamline sections, selecting a blade body for identification fitting, and parameterizing the fitted blade body into an angle and thickness distribution curve of a camber line along the chord length; finally, setting a multi-order Bezier curve according to fitting information so as to realize forward modeling and deformation of the blade through the change of control points of the multi-order Bezier curve; and taking the control point variable as a design parameter and adding the design parameter into a preset parameter pool.
For example, taking ANSYS WORKBENCH as an example, the embodiment may utilize a geometric modeling module of ANSYS, namely a unsigner, to import a three-dimensional model file in a general format, then draw a flow channel region, set the number of streamline sections, select a blade body for identification fitting, and the like, the parameterization mode after fitting may be an angle and thickness distribution curve of a mean camber line along the chord length, finally a multi-order bezier curve may be set according to the fitting condition, and forward modeling and deformation of the blade are realized through the change of curve control points, as shown in fig. 2 and 3, the control point variables are added into a parameter pool of ANSYS WORKBENCH. The alternative mode is suitable for the compressor blade and the subsonic turbine blade with small variation of the folding angle and the blade thickness based on the NACA series blade profile, and is characterized by less design parameter number and can improve the efficiency.
As another alternative way of reverse engineering treatment, if the blades of the multistage axial flow expander are turbine blades with a camber angle greater than a preset angle threshold and a blade thickness variation greater than a preset variation threshold, step 101 may specifically include: based on an eleven-parameter method, independently defining a pressure surface and a suction surface to carry out forward modeling of the multistage axial flow expander blade; and then, taking the pressure surface and suction surface curve control point variables as independent design parameters, and adding the independent design parameters into a preset parameter pool.
For example, modeling of individual definition of pressure and suction surfaces based on the eleven-parameter method is performed by using third party software such as CAESES, wherein pressure surface suction surface curve control points are used as independent design variables, and the design variables are added into a parameter pool of ANSYS workbend. The alternative mode is suitable for the turbine blade with large deflection angle and large thickness variation, and has the advantages of good adaptability, good universality and high fitting degree. Forward modeling can be performed by selecting design parameters solved by reverse fitting using DESIGNMODELEER or third party software according to a reverse engineering mode. When the shell is optimized, third party professional three-dimensional modeling software such as SOLIDWORKS, CREO, UG can be used for forward modeling, and identification prefixes (DS_, ANS_ by default) are added to the geometric size names required to be transferred as design variables, and extraction and skip can be performed in the DESIGNMODEER.
In the case of model mesh partitioning, step 102 may specifically include, for the purpose of mesh quality to improve accuracy of subsequent calculations: extracting a fluid region model in the target model, and outputting a data format required by grid division; when optimizing the blade grid, reading a fluid region model to carry out grid division, selecting a corresponding Y+ or estimating the size of a first layer of grid according to a turbulence model to be selected by CFD, adopting H-O-H grid topology on the B2B surface, adopting butterfly grids for blade top gaps, carrying out encryption processing on grids near the wall surface, and carrying out maximum expansion ratio of 1.3 so as to ensure that the maximum aspect ratio of the calculated region grid of the fluid region model is smaller than 1000, the orthogonality is 15-165 degrees, and no negative grid appears; automatically dividing grids according to the target quantity, or drawing multiple sets of grids to perform grid independence verification in the later stage, and determining that the fluid region model grid division is completed after verification is passed; when the shell is optimized, the fluid area model is read to divide structured and unstructured grids, the grid quality identical to the blade grid is used for judging, the grid thickness of the Y+ or first layer identical to the blade grid is selected from a solver, and the average grid quality is not more than 0.8; performing volume extraction on a solid region model in the target model, and grouping and naming boundaries to be defined in the CFD; and (3) reading the solid area model for grid division, setting a global size, carrying out local encryption on chamfer angles, rounding holes and open holes, and setting the grid division type so that the average grid quality is not more than 0.8.
For example, when optimizing the cascade, after forward modeling, the fluid region is extracted by using a blast eidtor module or third party software, as shown in fig. 4, and the data format required by the next GRID division by the TURBO-GRID module is output; the solid area is not required to be extracted, and data transmission is directly carried out on the blade molded line. The drainage basin meshing uses a TURBO-GRID module, a hub, shroud, blade profile is read, a proper Y+ or first layer mesh size is estimated according to a turbulence model to be selected by CFD, a B2B surface adopts H-O-H mesh topology, a blade top gap adopts butterfly meshes, as shown in fig. 5, a near wall mesh is subjected to encryption processing, the maximum expansion ratio is 1.3, the maximum length-width ratio of the calculated domain mesh is ensured to be smaller than 1000, the orthogonality degree is 15-165 ℃, negative meshes cannot appear, the mesh is automatically divided according to experience and the target quantity, or mesh independence verification is carried out after a plurality of sets of meshes are drawn, when multistage turbine optimization is carried out, coarse meshes can be adopted for iteration, and fine meshes are used for back calculation verification after optimization. When the shell is optimized, the river basin meshing can be automatically divided into structured and unstructured GRIDs by using an ICEM or MESH module, higher GRID quality can be obtained by manually creating GRID topology, dividing and mapping operation, and the GRID quality identical to that of the leaf GRID is used for judging, and the GRID thickness identical to that of the TURBO-GRID divided leaf GRID is Y+ or the first layer of GRID thickness; the MESH module needs to set physical parameters as CFD, and selects a solver to be used, wherein the average grid quality is not more than 0.8.
The solid area of the blade grid and the shell is subjected to grid division, an ICEM (information and communication technology) module and an MESH module are used for automatic division, a proper global size is set, local encryption is carried out on chamfering, rounding, perforating and the like, grid division types are set, good grid quality can be obtained by using SWEEP on a rotating surface, and the average grid quality is not more than 0.8, as shown in figure 6.
After meshing is completed, the present embodiment may perform a model CFD computational analysis and a multidisciplinary computational analysis. Illustratively, in step 103, defining corresponding boundary conditions for the fluid region model after grid division, and calculating the fluid region model by using a CFD module may specifically include: adopting a finite volume method to discretely solve a three-dimensional steady compressible Reynolds time-average N-S equation, wherein a turbulence model uses an SST model of two equations, a main flow area adopts a k-epsilon model, a near wall area replaces an epsilon equation by an omega equation, a mixing function is used for integrating the k-epsilon model and the k-omega model, a gas medium is set as R245fa, the specific heat capacity is defined by temperature interpolation and polynomial fitting; defining a calculation domain boundary condition of a fluid region model, wherein an inlet given total pressure, total temperature, outlet static pressure or outlet mass flow rate, a blade adopts a single-channel periodic boundary, a rotating region sets rotating speed, all solid wall surfaces in the calculation domain are smooth, adiabatic and slip-free, a dynamic-static interface is generally connected with the solid wall surfaces by using a planar mixing method, a blade tip clearance of the rotating region is in reverse rotation with the solid wall surfaces at two sides of the outlet region under a relative coordinate system, and the value of a global residual root mean square value RMS is smaller than 10 -4 Judging that the calculation converges when the difference of the mass flow of the inlet and the outlet of the calculation domain is less than 0.5%; and writing calculation formulas of efficiency, power, axial thrust, energy loss coefficient, total pressure loss coefficient and static pressure recovery coefficient by using a post-processing module, and adding a plurality of objective functions serving as CFD calculation results into a preset parameter pool.
For example, in CFD calculation analysis, an ANSYS CFX module is used for carrying out discrete solution on a three-dimensional steady compressible Reynolds time-average N-S equation in aerodynamic numerical calculation, a turbulence model suggests to use an SST model of two equations, a k-epsilon model with good convergence is adopted in a main flow area, an omega equation is used for replacing the epsilon equation in a near-wall area, reverse pressure gradient flow of a viscous bottom layer area can be well captured, a mixing function is used for integrating the two models, and the advantages of the k-epsilon model and the k-omega model are considered. The gas medium is R245fa, the physical property of the gas medium is checked by NIST software, the specific heat capacity is defined by temperature interpolation and polynomial fitting, and the control equation is as follows:
the continuous equation is shown in equation one:
the momentum equation is shown in formula two:
the total energy equation is shown in equation three:
where ρ is the density of the fluid medium, t is the time,is the velocity vector, P is the pressure, h 0 Is total enthalpy, lambda is heat conductivity, T is temperature, S M Is the power source item, S E As an energy source term, τ is the stress tensor.
Defining boundary conditions of a calculation domain, wherein the total pressure, total temperature, outlet static pressure or outlet mass flow are generally set by an inlet, the blades adopt a single-channel periodic boundary, the rotation region sets the rotating speed, all solid wall surfaces in the calculation domain are smooth, adiabatic and slip-free, as shown in figure 7, a dynamic-static interface is generally connected with the solid wall surfaces by a planar mixing method, the solid wall surfaces at the two sides of the outlet and the top wall surfaces of the rotating region are reversely rotated under a relative coordinate system, and the value of a global residual root mean square value RMS is smaller than 10 -4 The calculated domain ingress and egress mass flow difference of less than 0.5% is considered as the calculation convergence. Post-processing, writing calculation formulas such as efficiency, power, axial thrust, energy loss coefficient, total pressure loss coefficient, static pressure recovery coefficient and the like, and outputting the calculation formulas as an objective function to ANSYS WORKBAnd an ENCH parameter pool.
Illustratively, in step 103, using a multidisciplinary calculation module, multidisciplinary calculation is performed on the solid region model, which may specifically include: when a multistage axial flow expander machine runs, loading prestress, solving strength and dynamic characteristics by using a finite element method, dividing a continuum into a plurality of finite node units, and solving a global unknown field function by using an approximate function assumed in the units; introducing a solid calculation domain after grid division, introducing a pressure field and a temperature field, realizing unidirectional fluid-solid coupling, and solving the constraint related degrees of freedom and displacement; and adding the calculation results of the maximum equivalent stress, the maximum equivalent strain and the maximum displacement in the target direction into a preset parameter pool as a plurality of objective functions of the multidisciplinary calculation results.
For example, when the machine is in operation, the blades are subjected to various influences such as centrifugal load, thermal load and pneumatic load to generate stress, a ANSYS Static Structural module is adopted for calculating the strength of the loading prestress, the strength and the dynamic characteristics are solved by using an FEM method, a continuum is divided into a limited number of node units, and a global unknown field function is solved by using an approximate function assumed in the units. After the solid region model is led into to divide grids, CFD data of the last step is led into the module under the condition of WORKBENCH, a pressure field and a temperature field are led into the module, unidirectional fluid-solid coupling is realized, and as shown in figure 8, the constraint related degrees of freedom and displacement are solved. And outputting calculation results of maximum equivalent stress, strain, maximum displacement in a certain direction and the like to an ANSYS WORKBENCH parameter pool as an objective function.
To illustrate the multidisciplinary optimization process in this embodiment, step 104 may specifically include: setting design parameters added in a preset parameter pool as design variables, setting a plurality of objective functions of a CFD calculation result and an intensity calculation result as optimization variables, setting upper and lower boundaries for the design variables by using a MOGA algorithm, adding the most value targets and constraints for the optimization variables, and automatically setting initial population, maximum evolution generation, intersection rate and variation rate by a module according to the number of variables; and (3) sorting according to the PARETO optimal population by using an MOGA algorithm, dividing the MOGA algorithm into an dominant solution and a non-dominant solution, selecting an optimal variable which needs to carry out multi-objective optimization on two stages of blade grids in the multi-stage axial flow expander, and selecting an optimal solution set as an effective population according to one optimal variable under the condition that other optimal variables are not influenced, wherein if conflict exists among the optimal variables, combining the optimal variables into a scalar fitness function, and searching in a multidimensional space to determine the optimal solution to obtain the blade forward-bending and backward-bending coupling rule and the optimization information of blade profile distribution along the blade height.
For example, using a third party business software optisang module integrated under ANSYS workbecch, design parameters exported to a parameter pool in forward modeling are set as design variables, objective functions of CFD calculation and intensity calculation results are set as optimization variables, a multidisciplinary genetic algorithm (MOGA) algorithm is used, the design variables set suitable upper and lower boundaries, the optimization variables add the most objective and constraint, and according to the number of variables, the module automatically sets initial population, maximum evolution generation, crossover rate, mutation rate, etc., and a flow chart of the MOGA is shown in fig. 9, and mathematical expressions are as follows:
Objectives：F(X)＝[f 1 (X),f 2 (X),……f k (X)] n
g i (X)≤0,i＝{1,......m}
Subject to：h j (X)＝0,j＝{1,......p}
where k is the objective function dimension space, m and p are both the maximum constraint number, g i (X) is an unequal constraint, h i (X) is an equality constraint.
The MOGA technology is divided into dominant solutions and non-dominant solutions according to PARETO optimal population sequencing, and the optimal solution set is selected as an effective population according to one function under the condition that other objective functions are not affected. In the method, conflict exists among objective functions, the objective functions are combined into a scalar fitness function, and an optimal solution is found by searching in a multidimensional space.
It should be noted that the foregoing is an exemplary description of the MOGA algorithm, and in practical application, other algorithms may be used, such as RSM, simulated annealing, and other random algorithms, gradient optimization algorithms, and the like.
Taking ANSYS workberch as an example, the embodiment builds a workflow and multi-working condition trans-disciplinary multistage axial flow expander automatic optimization platform comprising model reverse engineering, forward modeling, gas, solid region extraction, gas, solid region meshing, solving a RANS equation to obtain an optimization variable, and obtaining a solid region finite element through gas-solid unidirectional coupling to obtain mechanical properties, namely MOGA, RSM, simulated annealing and other random and gradient optimization algorithms. The multi-disciplinary optimization design of the multi-stage axial flow expansion machine with different types, such as a 5MW two-stage axial flow organic Rankine cycle expansion machine, can be applied to multi-working-condition multi-disciplinary optimization design, not only can the efficiency and stability of a machine set be improved, but also the running cost of the device can be reduced, benefits are created for enterprises, and the related requirements of energy conservation and emission reduction are met.
Further, as a specific implementation of the method of fig. 1, the embodiment provides a multi-disciplinary optimization design device of a multi-stage axial flow expander, as shown in fig. 10, where the device includes: a modeling unit 21, a grid dividing unit 22, a calculating unit 23 and an optimizing processing unit 24.
The modeling unit 21 is configured to perform forward modeling on the multistage axial flow expander according to the design parameters obtained by inverse fitting to obtain a target model;
A mesh dividing unit 22, configured to divide the fluid region model and the solid region model in the target model into meshes respectively;
a calculating unit 23, configured to define corresponding boundary conditions for the fluid region model and the solid region model after grid division, calculate the fluid region model by using a computational fluid dynamics CFD module, and perform multidisciplinary calculation on the solid region model by using a multidisciplinary calculation module including strength and dynamic characteristics;
the optimization processing unit 24 may be configured to perform multi-objective optimization on the two-stage cascade in the multi-stage axial flow expander by using a genetic algorithm according to the CFD calculation result and the multiple objective functions of the multidisciplinary calculation result, so as to obtain the blade forward-bending and backward-bending coupling rule and the optimization information of the blade profile along the blade height distribution.
In a specific application scenario, the modeling unit 21 may be specifically configured to, if the blades of the multistage axial flow expander are subsonic turbine blades with a folding angle smaller than a preset angle threshold and a blade thickness variation smaller than a preset variation threshold, introduce a three-dimensional model of the multistage axial flow expander by using a parameterized modeling module; drawing a flow channel region in the three-dimensional model, setting the number of streamline sections, selecting a blade body for identification fitting, and parameterizing the fitted blade body into an angle and thickness distribution curve of a camber line along the chord length; setting a multi-order Bezier curve according to fitting information so as to realize forward modeling and deformation of the blade through the change of control points of the multi-order Bezier curve; and taking the control point variable as a design parameter and adding the design parameter into a preset parameter pool.
In a specific application scenario, the modeling unit 21 may be specifically configured to perform forward modeling of the multistage axial flow expander blade by separately defining a pressure surface and a suction surface based on an eleven-parameter method if the multistage axial flow expander blade is a turbine blade with a folding angle greater than a preset angle threshold and a blade thickness variation greater than a preset variation threshold; and taking the pressure surface and suction surface curve control point variables as independent design parameters, and adding the independent design parameters into a preset parameter pool.
In a specific application scenario, the grid dividing unit 22 may be specifically configured to extract a fluid region model in the target model, and output a data format required for dividing a grid; when optimizing the blade grid, reading the fluid region model to carry out grid division, selecting a corresponding Y+ or estimating the size of a first layer of grid according to a turbulence model to be selected by CFD, adopting H-O-H grid topology on the B2B surface, adopting butterfly-shaped grids for blade top gaps, carrying out encryption treatment on grids near the wall surface, and carrying out maximum expansion ratio of 1.3, so that the maximum aspect ratio of the calculated domain grid of the fluid region model is smaller than 1000, the orthogonality is 15-165 degrees, and no negative grid appears; automatically dividing grids according to the target quantity, or drawing multiple sets of grids to perform grid independence verification in the later stage, and determining that the fluid region model grid division is completed after verification is passed; when the shell is optimized, the fluid region model is read to divide structured and unstructured grids, the grid quality identical to that of the blade grid is used for judging, the grid thickness of the Y+ or first layer identical to that of the blade grid is selected, and the average grid quality is not more than 0.8; performing volume extraction on a solid region model in the target model, and grouping and naming boundaries to be defined in the CFD; and reading the solid region model to carry out grid division, setting a global size, carrying out local encryption on chamfer angles, rounding holes and open holes, and setting the grid division type so that the average grid quality is not more than 0.8.
In a specific application scenario, the computing unit 23 is specifically configured to use a finite volume method to discretely solve a three-dimensional steady compressible reynolds time-averaged N-S equation, wherein the turbulence model uses an SST model of two equations, a main flow region thereof uses a k-epsilon model, a near wall region uses an omega equation to replace the epsilon equation, and a mixing function is used to integrate the k-epsilon and k-omega models, a gas medium is set as R245fa, and a specific heat capacity is defined by temperature interpolation and polynomial fitting; defining the calculation domain boundary condition of the fluid region model, wherein the inlet is given with total pressure, total temperature, outlet static pressure or outlet mass flow, the blade adopts a single-channel periodic boundary, the rotating region sets rotating speed, all solid wall surfaces in the calculation domain are smooth, adiabatic and non-slip, the dynamic and static interfaces use a planar mixing method, the clearance between the blade tips of the moving blades is generally connected with the solid wall surfaces, the blade tip wall surfaces of the rotating region and the solid wall surfaces at two sides of the outlet region are reversely rotated under a relative coordinate system, and the value of the global residual root mean square value RMS is less than 10 -4 Judging that the calculation converges when the difference of the mass flow of the inlet and the outlet of the calculation domain is less than 0.5%; and writing calculation formulas of efficiency, power, axial thrust, energy loss coefficient, total pressure loss coefficient and static pressure recovery coefficient by using a post-processing module, and adding a plurality of objective functions serving as CFD calculation results into a preset parameter pool.
In a specific application scenario, the computing unit 23 is specifically further configured to load prestress when the multistage axial flow expander machine is running, solve strength and dynamic characteristics by using a finite element method, divide a continuum into a plurality of node units, and solve a global unknown field function by using an approximate function assumed in the units; importing the solid area model after grid division, importing the CFD data obtained by calculation into a solid calculation domain, importing a pressure field and a temperature field, realizing unidirectional fluid-solid coupling, and solving the constraint related degrees of freedom and displacement; and adding the calculation results of the maximum equivalent stress, the maximum equivalent strain and the maximum displacement in the target direction into a preset parameter pool as a plurality of objective functions of the multidisciplinary calculation results.
In a specific application scenario, the optimization processing unit 24 is specifically configured to set design parameters added in the preset parameter pool as design variables, set a plurality of objective functions of a CFD calculation result and an intensity calculation result as optimization variables, set upper and lower boundaries for the design variables by using a MOGA algorithm, and add the most significant targets and constraints for the optimization variables, and automatically set an initial population, a maximum evolution generation, a crossover rate, and a mutation rate according to the number of variables; and (3) sorting according to the PARETO optimal population by using a MOGA algorithm, dividing the MOGA algorithm into an dominant solution and a non-dominant solution, selecting an optimal variable which needs to carry out multi-objective optimization on two stages of blade grids in the multi-stage axial flow expander, and selecting an optimal solution set as an effective population according to one optimal variable under the condition that other optimal variables are not influenced, wherein if conflict exists among the optimal variables, combining the optimal variables into a scalar fitness function, and searching in a multidimensional space to determine the optimal solution to obtain the blade forward-bending and backward-bending coupling rule and the optimization information of blade profile distribution along the blade height.
It should be noted that, other corresponding descriptions of each functional unit related to the multi-disciplinary optimization design device of the multi-stage axial flow expansion machine provided in this embodiment may refer to corresponding descriptions in fig. 1, and are not repeated herein.
Based on the method shown in fig. 1, correspondingly, the present embodiment further provides a storage device, on which a computer program is stored, which when executed by the processor, implements the multi-disciplinary optimization design method of the multi-stage axial flow expander shown in fig. 1.
Based on the method shown in fig. 1 and the embodiment of the virtual device shown in fig. 10, the embodiment further provides a multidisciplinary optimization design device of the multistage axial flow expansion machine, where the device includes: a processor, a storage device, and a computer program stored on the storage device and executable on the processor, which when executed implements the method shown in fig. 1; the apparatus further comprises: a bus configured to couple the processor and the memory device.
By applying the technical scheme of the embodiment, the multidisciplinary optimization design of the multistage axial flow expansion machine can be effectively carried out, so that the multistage axial flow expansion machine can be well optimized, the efficiency and stability of the machine set can be improved, the running cost of the device can be reduced, and the related requirements of energy conservation and emission reduction are met.
From the above description of the embodiments, it will be apparent to those skilled in the art that the present application may be implemented in hardware, or may be implemented by means of software plus necessary general hardware platforms. Based on such understanding, the technical solution of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.), and includes several instructions for causing a computer device (may be a personal computer, a server, or a network device, etc.) to perform the methods described in various implementation scenarios of the present application.
Those skilled in the art will appreciate that the drawings are merely schematic illustrations of one preferred implementation scenario, and that the modules or flows in the drawings are not necessarily required to practice the present application.
Those skilled in the art will appreciate that modules in an apparatus in an implementation scenario may be distributed in an apparatus in an implementation scenario according to an implementation scenario description, or that corresponding changes may be located in one or more apparatuses different from the implementation scenario. The modules of the implementation scenario may be combined into one module, or may be further split into a plurality of sub-modules.
The foregoing application serial numbers are merely for description, and do not represent advantages or disadvantages of the implementation scenario.
The foregoing disclosure is merely a few specific implementations of the present application, but the present application is not limited thereto and any variations that can be considered by a person skilled in the art shall fall within the protection scope of the present application.
Claims (9)
1. The multidisciplinary optimization design method of the multistage axial flow expander is characterized by comprising the following steps of:
forward modeling is carried out on the multistage axial flow expansion machine according to the design parameters of the reverse fitting solution, and a target model is obtained; if the blades of the multistage axial flow expander are subsonic turbine blades with the folding angle smaller than a preset angle threshold and the blade thickness change smaller than a preset change threshold, the design parameters solved according to reverse fitting are used for carrying out forward modeling on the multistage axial flow expander to obtain a target model, and the method specifically comprises the following steps:
introducing a three-dimensional model of the multistage axial flow expander by using a parameterized modeling module;
drawing a flow channel region in the three-dimensional model, setting the number of streamline sections, selecting a blade body for identification fitting, and parameterizing the fitted blade body into an angle and thickness distribution curve of a camber line along the chord length;
setting a multi-order Bezier curve according to fitting information so as to realize forward modeling and deformation of the blade through the change of control points of the multi-order Bezier curve;
Taking the control point variable as a design parameter, and adding the control point variable into a preset parameter pool;
respectively carrying out grid division on a fluid region model and a solid region model in the target model;
defining corresponding boundary conditions for the fluid region model and the solid region model after grid division, calculating the fluid region model by using a computational fluid dynamics CFD module, and performing multidisciplinary calculation on the solid region model by using a multidisciplinary calculation module containing strength and dynamic characteristics;
and performing multi-objective optimization on two stages of blade grids in the multistage axial flow expander by using a genetic algorithm according to the CFD calculation result and the multiple objective functions of the multidisciplinary calculation result to obtain blade forward and backward bending coupling rules and optimization information of blade profile distribution along the blade height.
2. The method according to claim 1, wherein if the blades of the multistage axial flow expander are turbine blades with a camber angle greater than a preset angle threshold and a blade thickness variation greater than a preset variation threshold, the method further comprises the step of performing forward modeling on the multistage axial flow expander according to the design parameters solved by reverse fitting to obtain a target model, and the method specifically comprises:
Based on an eleven-parameter method, independently defining a pressure surface and a suction surface to perform forward modeling of the multistage axial flow expander blade;
and taking the pressure surface and suction surface curve control point variables as independent design parameters, and adding the independent design parameters into a preset parameter pool.
3. The method according to claim 1 or 2, wherein the meshing of the fluid region model and the solid region model within the target model, respectively, comprises:
extracting a fluid region model in the target model, and outputting a data format required by grid division;
when optimizing the blade grid, reading the fluid region model to carry out grid division, selecting a corresponding Y+ or estimating the size of a first layer of grid according to a turbulence model to be selected by CFD, adopting H-O-H grid topology on the B2B surface, adopting butterfly-shaped grids for blade top gaps, carrying out encryption treatment on grids near the wall surface, and carrying out maximum expansion ratio of 1.3, so that the maximum aspect ratio of the calculated domain grid of the fluid region model is smaller than 1000, the orthogonality is 15-165 degrees, and no negative grid appears; automatically dividing grids according to the target quantity, or drawing multiple sets of grids to perform grid independence verification in the later stage, and determining that the fluid region model grid division is completed after verification is passed;
When the shell is optimized, the fluid region model is read to divide structured and unstructured grids, the grid quality identical to that of the blade grid is used for judging, the grid thickness of the Y+ or first layer identical to that of the blade grid is selected, and the average grid quality is not more than 0.8;
performing volume extraction on a solid region model in the target model, and grouping and naming boundaries to be defined in the CFD;
and reading the solid region model to carry out grid division, setting a global size, carrying out local encryption on chamfer angles, rounding holes and open holes, and setting the grid division type so that the average grid quality is not more than 0.8.
4. A method according to claim 3, characterized in that corresponding boundary conditions are defined for the fluid region model after meshing and the fluid region model is calculated using a CFD module, comprising in particular:
adopting a finite volume method to discretely solve a three-dimensional steady compressible Reynolds time-average N-S equation, wherein a turbulence model uses an SST model of two equations, a main flow area adopts a k-epsilon model, a near wall area replaces an epsilon equation by an omega equation, a mixing function is used for integrating the k-epsilon model and the k-omega model, a gas medium is set as R245fa, the specific heat capacity is defined by temperature interpolation and polynomial fitting;
Defining the calculation domain boundary condition of the fluid region model, wherein the inlet is given with total pressure, total temperature, outlet static pressure or outlet mass flow, the blade adopts a single-channel periodic boundary, the rotating region sets rotating speed, all solid wall surfaces in the calculation domain are smooth, adiabatic and non-slip, the dynamic and static interfaces use a planar mixing method, the clearance between the blade tips of the moving blades is generally connected with the solid wall surfaces, the blade tip wall surfaces of the rotating region and the solid wall surfaces at two sides of the outlet region are reversely rotated under a relative coordinate system, and the value of the global residual root mean square value RMS is less than 10 -4 Judging that the calculation converges when the difference of the mass flow of the inlet and the outlet of the calculation domain is less than 0.5%;
and writing calculation formulas of efficiency, power, axial thrust, energy loss coefficient, total pressure loss coefficient and static pressure recovery coefficient by using a post-processing module, and adding a plurality of objective functions serving as CFD calculation results into a preset parameter pool.
5. The method of claim 4, wherein performing multidisciplinary calculations on the solid region model using a multidisciplinary calculation module, specifically comprises:
when a multistage axial flow expander machine runs, loading prestress, solving strength and dynamic characteristics by using a finite element method, dividing a continuum into a plurality of finite node units, and solving a global unknown field function by using an approximate function assumed in the units;
Importing the solid area model after grid division, importing the CFD data obtained by calculation into a solid calculation domain, importing a pressure field and a temperature field, realizing unidirectional fluid-solid coupling, and solving the constraint related degrees of freedom and displacement;
and adding the calculation results of the maximum equivalent stress, the maximum equivalent strain and the maximum displacement in the target direction into a preset parameter pool as a plurality of objective functions of the multidisciplinary calculation results.
6. The method according to claim 5, wherein the performing multi-objective optimization on the two-stage cascade in the multi-stage axial flow expansion machine by using a genetic algorithm according to the multiple objective functions of the CFD calculation result and the multidisciplinary calculation result to obtain the blade forward and backward bending coupling rule and the optimization information of the blade profile distributed along the blade height specifically comprises:
setting design parameters added in the preset parameter pool as design variables, setting a plurality of objective functions of a CFD calculation result and an intensity calculation result as optimization variables, setting upper and lower boundaries for the design variables by using a MOGA algorithm, adding the most value targets and constraints for the optimization variables, and automatically setting initial population, maximum evolution times, cross rates and variation rates by a module according to the number of variables;
And (3) sorting according to the PARETO optimal population by using a MOGA algorithm, dividing the MOGA algorithm into an dominant solution and a non-dominant solution, selecting an optimal variable which needs to carry out multi-objective optimization on two stages of blade grids in the multi-stage axial flow expander, and selecting an optimal solution set as an effective population according to one optimal variable under the condition that other optimal variables are not influenced, wherein if conflict exists among the optimal variables, combining the optimal variables into a scalar fitness function, and searching in a multidimensional space to determine the optimal solution to obtain the blade forward-bending and backward-bending coupling rule and the optimization information of blade profile distribution along the blade height.
7. A multidisciplinary optimum design device of a multistage axial flow expander, comprising:
the modeling unit is used for carrying out forward modeling on the multistage axial flow expansion machine according to the design parameters of the reverse fitting solution to obtain a target model; if the blades of the multistage axial flow expander are subsonic turbine blades with the folding angle smaller than a preset angle threshold and the blade thickness change smaller than a preset change threshold, the design parameters solved according to reverse fitting are used for carrying out forward modeling on the multistage axial flow expander to obtain a target model, and the method specifically comprises the following steps:
Introducing a three-dimensional model of the multistage axial flow expander by using a parameterized modeling module;
drawing a flow channel region in the three-dimensional model, setting the number of streamline sections, selecting a blade body for identification fitting, and parameterizing the fitted blade body into an angle and thickness distribution curve of a camber line along the chord length;
setting a multi-order Bezier curve according to fitting information so as to realize forward modeling and deformation of the blade through the change of control points of the multi-order Bezier curve;
taking the control point variable as a design parameter, and adding the control point variable into a preset parameter pool;
the grid dividing unit is used for respectively dividing the grids of the fluid region model and the solid region model in the target model;
the computing unit is used for defining corresponding boundary conditions for the fluid region model and the solid region model after grid division, computing the fluid region model by utilizing a computational fluid dynamics CFD module, and performing multidisciplinary computation on the solid region model by utilizing a multidisciplinary computation module comprising strength and dynamic characteristics;
and the optimization processing unit is used for performing multi-objective optimization on the two-stage blade cascade in the multistage axial flow expander by using a genetic algorithm according to the CFD calculation result and the multiple objective functions of the multidisciplinary calculation result to obtain the blade forward and backward bending coupling rule and the optimization information of the blade profile along the blade height distribution.
8. A storage device having stored thereon a computer program, wherein the program when executed by a processor implements the multidisciplinary optimization design method of the multistage axial flow expander of any one of claims 1 to 6.
9. A multidisciplinary optimization design device of a multistage axial flow expander, comprising a storage device, a processor, and a computer program stored on the storage device and executable on the processor, wherein the processor implements the multidisciplinary optimization design method of a multistage axial flow expander according to any one of claims 1 to 6 when executing the program.
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