CN112417773A - Multidisciplinary optimization design method, device and equipment for multistage axial flow expansion machine - Google Patents
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Abstract
The application discloses a multidisciplinary optimization design method, a multidisciplinary optimization design device and multidisciplinary optimization design equipment for a multistage axial flow expansion machine, and relates to the technical field of expansion machine design, wherein the method comprises the following steps: firstly, carrying out forward modeling on a multistage axial flow expander according to design parameters solved by inverse fitting to obtain a target model; then defining corresponding boundary conditions for the fluid region model and the solid region model after the grid division, calculating the fluid region model by utilizing a CFD (computational fluid dynamics) module, and performing multidisciplinary calculation on the solid region model by utilizing a multidisciplinary calculation module containing strength, dynamic characteristics and the like; and finally, performing multi-objective optimization on the two-stage blade cascade in the multi-stage axial flow expansion machine by using a genetic algorithm according to a plurality of objective functions of the CFD calculation result and the multidisciplinary calculation result to obtain the optimization information of the positive bending and negative bending coupling rule of the blade and the distribution of the blade profile along the blade height. The multi-disciplinary optimization design of the multistage axial flow expansion machine can be effectively carried out.
Description
Technical Field
The application relates to the technical field of expander design, in particular to a multidisciplinary optimization design method, device and equipment for a multistage axial flow expander.
Background
The expansion machine is a core power component of Rankine cycle, and researchers at home and abroad deeply explore and research for a long time. The axial flow expander is widely applied, and the applicable working medium types, pressure, temperature, rotating speed, power, blade stage number and other design parameters are different.
At present, multidisciplinary optimization design of a high-power multistage axial flow expander is still blank, and the multidisciplinary optimization design of the multistage axial flow expander cannot be effectively carried out, so that the multistage axial flow expander cannot be well optimized, the running cost of a device can be increased, and good energy-saving and emission-reducing effects cannot be achieved.
Disclosure of Invention
In view of this, the present application provides a multidisciplinary optimization design method, device and equipment for a multistage axial flow expander, and mainly aims to solve the technical problem that the multidisciplinary optimization design of the multistage axial flow expander cannot be effectively performed in the prior art, and further the multistage axial flow expander cannot be subjected to multidisciplinary optimization.
According to an aspect of the present application, there is provided a multidisciplinary optimization design method of a multistage axial flow expander, the method comprising:
carrying out forward modeling on the multistage axial flow expander according to the design parameters solved by the inverse fitting to obtain a target model;
respectively carrying out meshing 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 the mesh division, calculating the Fluid region model by utilizing a Computational Fluid Dynamics (CFD) module, and performing multidisciplinary calculation on the solid region model by utilizing a multidisciplinary calculation module containing strength and dynamic characteristics;
and performing multi-objective optimization on the two-stage blade cascade in the multi-stage axial flow expansion machine by using a genetic algorithm according to a plurality of objective functions of the CFD calculation result and the multidisciplinary calculation result to obtain the optimization information of the positive bending and negative bending coupling rule of the blade and the distribution of the blade profile along the blade height.
In accordance with another aspect of the present application, there is provided a multidisciplinary optimization 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 expander according to the design parameters solved by the inverse fitting to obtain a target model;
the meshing unit is used for respectively meshing the fluid region model and the solid region model in the target model;
a calculation unit, configured to define corresponding boundary conditions for the fluid region model and the solid region model after mesh 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;
and the optimization processing unit is used for performing multi-objective optimization on the two-stage blade cascade in the multi-stage axial flow expansion machine by using a genetic algorithm according to a plurality of objective functions of the CFD calculation result and the multidisciplinary calculation result to obtain the optimization information of the positive-bending and negative-bending coupling rule of the blade and the distribution of the blade profile along the blade height.
According to yet 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 multidisciplinary optimization design method of the above-described multistage axial flow expander.
According to still another aspect of the present application, there is provided a multidisciplinary 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, wherein the processor executes the program to implement the multidisciplinary optimization design method for the multistage axial flow expander.
By means of the technical scheme, compared with the prior art that multidisciplinary optimization design of the multistage axial flow expander cannot be effectively carried out, the multidisciplinary optimization design method, the device and the equipment of the multistage axial flow expander can carry out forward modeling on the multistage axial flow expander according to design parameters solved by inverse fitting to obtain a target model; respectively carrying out mesh division on the fluid region model and the solid region model in the target model, setting boundary conditions and the like; then, a CFD module and a multidisciplinary calculation module containing intensity, dynamic characteristics and the like are used for multidisciplinary calculation; and finally, performing multi-objective optimization on the two-stage blade cascade in the multi-stage axial flow expansion machine by using a genetic algorithm according to a plurality of objective functions of the CFD calculation result and the multidisciplinary calculation result to obtain optimization information of the forward-bending and backward-bending coupling rule of the blade and the distribution of the blade profile along the blade height. And furthermore, the multidisciplinary optimization design of the multistage axial flow expander can be effectively carried out, so that the multistage axial flow expander can be well optimized, the efficiency and the stability of a unit can be improved, the running cost of the device can be reduced, and the relevant requirements of energy conservation and emission reduction are met.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
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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 refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a flow chart of a multidisciplinary optimization design method of a multistage axial flow expander according to an embodiment of the present application;
FIG. 2 illustrates an example schematic view of a blade axial chord length 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 of fluid zone extraction provided by embodiments of the present application;
FIG. 5 illustrates an example schematic diagram of fluid region meshing provided by embodiments of the present application;
FIG. 6 illustrates an example schematic diagram of solid region meshing provided by embodiments of the present application;
FIG. 7 is a diagram illustrating an example of setting boundary conditions of a model provided by an embodiment of the present application;
FIG. 8 illustrates an example schematic view of loading a blade with a pressure field and a temperature field provided by an embodiment of the present application;
FIG. 9 shows a schematic flow chart of the MOGA algorithm provided by the embodiment of the present application;
fig. 10 shows a schematic structural diagram of a multidisciplinary optimization design device of a multistage axial flow expander provided by an embodiment of the application.
Detailed Description
The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The technical problem that the multi-disciplinary optimization design of the multistage axial flow expansion machine cannot be effectively carried out in the prior art, and then the multi-disciplinary optimization of the multistage axial flow expansion machine cannot be achieved is solved. The embodiment provides a multidisciplinary optimization design method of a multistage axial flow expander, as shown in fig. 1, the method includes:
101. and carrying out forward modeling on the multistage axial flow expander according to the design parameters solved by the inverse fitting to obtain a target model.
According to the method, forward modeling is carried out according to the design parameters solved by inverse fitting, and a more standard multistage axial flow expander target model can be accurately obtained, so that the effect of optimization design is ensured when multidisciplinary optimization design is carried out on the basis of the model.
102. And respectively meshing the fluid region model and the solid region model in the target model.
And after forward modeling, extracting fluid and solid areas of the obtained target model, and then meshing the extracted fluid and solid areas.
103. Defining corresponding boundary conditions for the fluid region model and the solid region model after the mesh division, calculating the fluid region model by utilizing a CFD (computational fluid dynamics) module, and performing multidisciplinary calculation on the solid region model by utilizing a multidisciplinary calculation module containing strength and dynamic characteristics.
In this embodiment, the multidisciplinary calculation module may include calculation of other various disciplines besides intensity calculation and dynamic characteristic calculation, and the calculation may be specifically determined according to actual calculation requirements.
104. And performing multi-objective optimization on the two-stage blade cascade in the multi-stage axial flow expansion machine by using a genetic algorithm according to a plurality of objective functions of the CFD calculation result and the multidisciplinary calculation result to obtain the optimization information of the positive bending and negative bending coupling rule of the blade and the distribution of the blade profile along the blade height.
For example, for this embodiment, a genetic algorithm may be used to perform multi-objective optimization on the two-stage blade cascade, the overall efficiency, the output power, the axial thrust, the maximum stress of the movable blade, the maximum radial displacement of the movable blade, and the like obtained according to the CFD calculation result and the strength calculation result are taken as objective functions, the mass, the flow variation, and the like are selected as constraint conditions, the two-dimensional stacking rule of the two-dimensional blade profiles of the movable and stationary blades and the two-dimensional blade profile control parameters of the multiple blade height sections (such as multiple height sections of 0, 25%, 50%, 75%, 100%) of the blades are taken as design variables, the optimal blade positive and negative bending coupling rule and the distribution of the blade profiles along the blade height are obtained, and the pneumatic performance and the mechanical performance of the expander after subsequent analysis and optimization are obtained.
Compared with the prior art that the multidisciplinary optimization design of the multistage axial flow expander cannot be effectively carried out, the multidisciplinary optimization design method of the multistage axial flow expander provided by the embodiment can carry out forward modeling on the multistage axial flow expander according to the design parameters solved by inverse fitting to obtain the target model; respectively carrying out mesh division on the fluid region model and the solid region model in the target model, setting boundary conditions and the like; then, a CFD module and a multidisciplinary calculation module containing intensity, dynamic characteristics and the like are used for multidisciplinary calculation; and finally, performing multi-objective optimization on the two-stage blade cascade in the multi-stage axial flow expansion machine by using a genetic algorithm according to a plurality of objective functions of the CFD calculation result and the multidisciplinary calculation result to obtain optimization information of the forward-bending and backward-bending coupling rule of the blade and the distribution of the blade profile along the blade height. And furthermore, the multidisciplinary optimization design of the multistage axial flow expander can be effectively carried out, so that the multistage axial flow expander can be well optimized, the efficiency and the stability of a unit can be improved, the running cost of the device can be reduced, and the relevant requirements of energy conservation and emission reduction are met.
Further, as an extension and a refinement of the present embodiment, in order to fully describe a specific implementation process of the present embodiment, a reverse engineering process is required in step 101 before the forward modeling, and there are various alternatives, as an alternative to the reverse engineering process, if the blade of the multistage axial flow expander is a subsonic turbine blade with a turning 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 a multistage axial flow expander; then drawing a flow channel region in the three-dimensional model, setting the number of streamline sections, selecting a blade body for recognition fitting, and carrying out parameterization after fitting on an angle and thickness distribution curve of a mean camber line along the chord length; finally, a multi-order Bezier curve is set according to the fitting information, so that forward modeling and deformation of the blade are achieved through the change of control points of the multi-order Bezier curve; and adding the control point variable serving as a design parameter into a preset parameter pool.
For example, taking ANSYS works bench as an example, in this embodiment, a three-dimensional model file in a general format may be imported by using a geometric modeling module, namely, a denorm model of ANSYS, then a flow channel region is drawn, the number of streamline sections is set, a blade body is selected for recognition fitting, and the like, a parameterization mode after fitting may be an angle and thickness distribution curve of a mean camber line along a chord length, and finally a multi-order bezier curve may be set according to a fitting condition, and forward modeling and deformation of the blade may be realized by changing a curve control point, as shown in fig. 2 and 3, a control point variable is added to a parameter pool of ANSYS works bench. The alternative mode is suitable for compressor blades developed based on NACA series blade profiles and subsonic turbine blades with small changes of the deflection angle and the blade thickness, and is characterized by less design parameters and capability of improving the efficiency.
As another alternative to the reverse engineering process, if the blade of the multistage axial flow expander is a turbine blade with a turning 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, a pressure surface and a suction surface are independently defined to carry out forward modeling on the multistage axial flow expander blade; and then adding the curve control point variables of the pressure surface and the suction surface into a preset parameter pool as independent design parameters.
For example, a third party software such as CAESES is used to perform modeling based on eleven-parameter method for separately defining the pressure surface and the suction surface, curve control points of the pressure surface and the suction surface are used as independent design variables, and the design variables are added into a parameter pool of ANSYS WORKBENCH. The alternative mode is suitable for the turbine blade with large folding angle and large thickness change, and has the advantages of good adaptability, good universality and high fitting degree. The design parameters solved by inverse fitting can be used for forward modeling by selectively using DESIGNMODELER or third-party software according to an inverse engineering mode. When the shell is optimized, a third-party professional three-dimensional modeling software such as SOLIDWORKS, CREO, UG and the like can be used for forward modeling, and for geometric dimension names needing to be transmitted as design variables, identification prefixes (DS _, ANS _bydefault) are added, and extraction and jumping can be carried out in the DESIGNMODELER.
In dividing the model mesh, in order to improve the mesh quality and the accuracy of subsequent calculation, optionally, step 102 may specifically include: extracting a fluid region model in the target model, and outputting a data format required by grid division; when optimizing the leaf grids, reading a fluid region model for grid division, selecting corresponding Y + or estimating the size of a first layer of grids according to a turbulence model to be selected by CFD (computational fluid dynamics), wherein the B2B surface adopts H-O-H grid topology, the leaf top gap adopts butterfly-shaped grids, and the near-wall surface grids are encrypted, and the maximum extension ratio is 1.3, so that the maximum length-width ratio of the computational domain grids of the fluid region model is less than 1000, the orthogonality is 15-165 degrees, and no negative grids appear; carrying out automatic grid division according to the target quantity, or carrying out grid independence verification at the later stage of drawing a plurality of sets of grids, and determining that the fluid region model is completed by grid division after the verification is passed; when the shell is optimized, reading a fluid region model to divide structured and unstructured grids, judging the grid quality which is the same as that of a blade grid, selecting a solver according to the Y + or the first layer of grid thickness which is the same as that of the blade grid, and enabling the average grid quality not to exceed 0.8; extracting the volume of the solid region model in the target model, and grouping and naming the boundaries to be defined in the CFD; and reading the solid region model for meshing, setting the global size, carrying out local encryption on chamfers, rounds and openings, and setting the meshing type so that the average mesh quality does not exceed 0.8.
For example, when optimizing the cascade, after forward modeling, a BLADEEIDTOR module or third-party software is used to extract the fluid region, as shown in fig. 4, and a data format required by the next TURBO-GRID module for GRID division is output; the solid area does not need to be extracted, and data transmission is directly carried out on the blade-shaped wire. The method comprises the steps of using a TURBO-GRID module for basin GRID division, reading hub, shade and blade type line files, selecting proper Y + or estimating the size of a first layer GRID according to a turbulence model to be selected by CFD, using H-O-H GRID topology for a B2B surface, using butterfly GRIDs for leaf top gaps, performing encryption processing on near-wall surface GRIDs as shown in figure 5, ensuring that the maximum extension ratio is 1.3, ensuring that the maximum length-width ratio of a computational domain GRID is less than 1000, the orthogonality is 15-165 degrees, and negative GRIDs cannot appear, automatically dividing the GRIDs according to the target number according to experience, or performing GRID independence verification at the later stage of drawing multiple sets of GRIDs, and performing iteration by using coarse GRIDs during multistage turbine optimization, and performing recalculation verification by using fine GRIDs after optimization. When the shell is optimized, the basin GRID division can use an ICEM or MESH module to automatically divide structured and unstructured GRIDs, the GRID topology is manually established, the GRID topology is divided, the mapping operation is carried out to obtain higher GRID quality, the GRID quality judgment which is the same as that of the blade GRIDs is used, and the Y + or first layer GRID thickness which is the same as that of the blade GRIDs divided by the TURBO-GRID is used; the MESH module needs to set physical parameters as CFD, selects a solver to be used, and the average grid quality does not exceed 0.8.
The method comprises the steps of meshing solid areas of a blade grid and a machine shell, automatically dividing by using ICEM and MESH modules, setting a proper global size, locally encrypting chamfers, rounds, holes and the like, setting a meshing type, obtaining good MESH quality by using SWEEP for a revolution surface, and enabling the average MESH quality to be not more than 0.8, wherein the average MESH quality is shown in figure 6.
After the meshing is completed, the present embodiment may perform model CFD computational analysis and multidisciplinary computational analysis. Exemplarily, the step 103 defines a corresponding boundary condition for the fluid region model after the mesh division, and calculates the fluid region model by using a CFD module, which may specifically include: the method comprises the steps of discretely solving a three-dimensional steady compressible Reynolds time-mean N-S equation by adopting a finite volume method, wherein a turbulence model uses an SST model with two equations, a mainstream region of the turbulence model adopts a k-epsilon model, a near-wall region uses an omega equation to replace an epsilon equation, a mixing function is used for integrating the k-epsilon model and the k-omega model, a gas medium is set to be R245fa, and the specific heat capacity is defined by temperature interpolation and polynomial fitting; defining calculation domain boundary conditions of a fluid region model, wherein total pressure, total temperature, outlet static pressure or outlet mass flow are given at an inlet, a single-channel periodic boundary is adopted by a blade, the rotating region is provided with rotating speed, all solid wall surfaces in the calculation domain are smooth, adiabatic and non-slip, a dynamic-static interface uses a plane mixing method, a blade top gap of a movable blade is generally connected with the solid wall surfaces, the blade top wall surfaces in the rotating region and the solid wall surfaces on two sides of an outlet region are provided with reverse rotation in a relative coordinate system, and the value of a global root mean square value RMS is less than 10-4Judging that the calculation is converged when the difference of the mass flow of the inlet and the outlet of the calculation domain is less than 0.5%; compiling efficiency, power, axial thrust, energy loss coefficient, total pressure loss coefficient and static pressure recovery coefficient by utilizing post-processing moduleAnd adding a plurality of objective functions serving as CFD calculation results into a preset parameter pool.
For example, in CFD computational analysis, the three-dimensional steady compressible Reynolds time mean N-S equation is discretely solved by using an ANSYS CFX module in pneumatic numerical computation through a finite volume method, an SST model with two equations is suggested to be used in a turbulence model, a k-epsilon model with good convergence is adopted in a main flow region, an epsilon equation is replaced by an omega equation in a near wall region, the adverse pressure gradient flow in a viscous bottom layer region can be well captured, the two models are integrated through a mixing function, and the advantages of the k-epsilon and k-omega models are taken into consideration. The gas medium is R245fa, the physical property of the gas medium is found 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 formula one:
the momentum equation is shown in equation two:
the total energy equation is shown in equation three:
where ρ is the density of the fluid medium, t is the time,
is a velocity vector, P is pressure, h0Is the total enthalpy, λ is the thermal conductivity, T is the temperature, SMAs a power source term, SEτ is the stress tensor, the energy source term.
Defining the boundary conditions of the calculation domain, generally setting total pressure, total temperature, static pressure at the outlet or mass flow at the outlet at the inlet, adopting a single-channel periodic boundary and rotating bladesSetting rotating speed in a rotating area, calculating all solid wall surfaces in the area to be smooth, adiabatic and non-slip, using a plane mixing method for a dynamic interface and a static interface as shown in figure 7, connecting a blade top gap of a movable blade with the solid wall surfaces generally, setting reverse rotation of the blade top wall surfaces in the rotating area and the solid wall surfaces on two sides of an outlet area under a relative coordinate system, and setting the root mean square value RMS (root mean square) value of global residual error to be less than 10-4And the calculation convergence is considered when the difference of the inlet mass flow and the outlet mass flow of the calculation domain is less than 0.5%. And (4) compiling calculation formulas such as efficiency, power, axial thrust, energy loss coefficient, total pressure loss coefficient, static pressure recovery coefficient and the like in the post-processing, taking the calculation formulas as target functions, and outputting the target functions to an ANSYS WORKBENCH parameter pool.
Illustratively, the step 103 of performing multidisciplinary calculation on the solid region model by using a multidisciplinary calculation module may specifically include: loading prestress when a multistage axial flow expander machine operates, solving the strength and dynamic characteristics by using a finite element method, dividing a continuum into a finite number of node units, and solving a global unknown field function by using an approximate function assumed in the units; leading in a solid calculation domain after grid division, leading in a pressure field and a temperature field, realizing unidirectional fluid-solid coupling, and constraining the relevant degree of freedom and displacement to solve; and adding the calculation results of the maximum equivalent stress, the maximum equivalent strain and the maximum displacement of the target direction into a preset parameter pool as a plurality of target functions of the multidisciplinary calculation result.
For example, when the machine runs, the blades are influenced by various aspects such as centrifugal load, thermal load, aerodynamic load and the like to generate stress, an ANSYS Static Structural module is adopted for intensity calculation of the loading prestress, an FEM method is used for solving intensity and dynamic characteristics, 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 introduced to divide the meshes, the CFD data of the previous step is transmitted into the module under the WORKBENCH, and a pressure field and a temperature field are introduced to realize unidirectional fluid-solid coupling, and as shown in FIG. 8, the related degree of freedom and displacement are constrained to solve. And outputting the calculation results of the maximum equivalent stress, the strain, the maximum displacement in a certain direction and the like serving as target functions to an ANSYS WORKBENCH parameter pool.
To illustrate the multidisciplinary optimization process in this embodiment, for example, the step 104 may specifically include: setting design parameters added in a preset parameter pool as design variables, setting a plurality of objective functions of CFD (computational fluid dynamics) calculation results and intensity calculation results as optimization variables, setting upper and lower boundaries for the design variables by using an MOGA (multi-domain genetic algorithm), adding a most significant target and a constraint for the optimization variables, and automatically setting an initial population, a maximum evolution generation, a cross rate and a variation rate by a module according to the number of the variables; the method comprises the steps of sorting according to PARETO optimal population by using an MOGA algorithm, dividing the sorted optimal population into dominant solution and non-dominant solution, selecting optimization variables needing multi-objective optimization on two stages of blade grids in a multistage axial flow expander, selecting an optimal solution set as an effective population according to one optimization variable under the condition of not influencing other optimization variables, combining the optimization variables into a scalar fitness function if conflicts exist among the optimization variables, and searching in a multi-dimensional space to determine the optimal solution so as to obtain optimization information of blade positive-bending and reverse-bending coupling rules and blade profile distribution along the blade height.
For example, using a third party commercial software OPTISLANG module integrated into ANSYS works bench, setting design parameters derived to a parameter pool during forward modeling as design variables, setting objective functions of CFD calculation and intensity calculation results as optimization variables, using a multidisciplinary genetic algorithm (MOGA) algorithm, setting appropriate upper and lower boundaries for the design variables, adding maximum targets and constraints to the optimization variables, automatically setting an initial population, a maximum evolutionary generation, a cross rate, a mutation rate, and the like according to the number of variables by the module, and mathematically expressing the following as shown in fig. 9:
Objectives：F(X)＝[f1(X),f2(X),……fk(X)]n
gi(X)≤0,i＝{1,......m}
Subject to：hj(X)＝0,j＝{1,......p}
where k is the objective function dimension space, m and p are both the maximum number of constraints, gi(X) is an unequal constraint, hi(X) is an equal constraint.
The MOGA technology is divided into dominant solutions and non-dominant solutions according to PARETO optimal population sequencing, and an optimal solution set is selected as an effective population according to a function under the condition that other objective functions are not influenced. The method has the advantages that conflicts exist among target functions, the target functions are combined into a scalar fitness function, and the optimal solution is found by searching in a multi-dimensional space.
It should be noted that, the above is exemplified by the MOGA algorithm, and in practical applications, other algorithms may also be used, such as various random-type and gradient-type optimization algorithms like RSM and simulated annealing.
In the embodiment, by taking ANSYS WORKBENCH as an example, a multi-stage axial flow expander automatic optimization platform which comprises a model reverse engineering, forward modeling, gas and solid region extraction, gas and solid region grid division, RANS equation solving, optimization variable obtaining, gas and solid one-way coupling, solid region finite element solving and mechanical property obtaining, MOGA, RSM, simulated annealing and other various random and gradient optimization algorithms, and multiple working conditions and interdisciplinary stages is established. The multi-working-condition multi-disciplinary optimization design method is applicable to multi-disciplinary optimization design of multi-stage axial flow expanders with different models, such as a 5MW two-stage axial flow organic Rankine cycle expander and the like, multi-working-condition multi-disciplinary optimization design can be carried out, the efficiency and the stability of a unit can be improved, the running cost of the device can be reduced, benefits are created for enterprises, and relevant requirements of energy conservation and emission reduction are met.
Further, as a specific implementation of the method in fig. 1, the present embodiment provides a multidisciplinary optimization design apparatus for a multistage axial flow expander, as shown in fig. 10, the apparatus includes: a modeling unit 21, a mesh dividing unit 22, a calculating unit 23, and an optimization processing unit 24.
The modeling unit 21 is used for carrying out forward modeling on the multistage axial flow expander according to the design parameters solved by the inverse fitting to obtain a target model;
a meshing unit 22 operable to mesh the fluid region model and the solid region model within the target model, respectively;
a calculating unit 23, configured to define corresponding boundary conditions for the fluid region model and the solid region model after the mesh division, calculate the fluid region model by using a computational fluid dynamics CFD module, and perform a multidisciplinary calculation on the solid region model by using a multidisciplinary calculating module including strength and dynamic characteristics;
and the optimization processing unit 24 is configured to perform multi-objective optimization on the two-stage blade cascade in the multistage axial flow expander by using a genetic algorithm according to a plurality of objective functions of the CFD calculation result and the multidisciplinary calculation result, so as to obtain optimization information of the positive-bending and negative-bending coupling rule of the blade and the distribution of the blade profile along the blade height.
In a specific application scenario, the modeling unit 21 is specifically configured to, if a blade of the multistage axial flow expander is a subsonic turbine blade with a turning angle smaller than a preset angle threshold and a blade thickness variation smaller than a preset variation threshold, import 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 recognition fitting, and performing parameterization after fitting to obtain an angle and thickness distribution curve of a mean camber line along the chord length; setting a multi-order Bezier curve according to the 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 adding the control point variable serving as a design parameter into a preset parameter pool.
In a specific application scenario, the modeling unit 21 may be further configured to perform forward modeling on the multistage axial flow expander blades by separately defining a pressure surface and a suction surface based on an eleven-parameter method if the blades of the multistage axial flow expander are turbine blades having a turning angle greater than a preset angle threshold and a blade thickness variation greater than a preset variation threshold; and adding curve control point variables of the pressure surface and the suction surface into a preset parameter pool as independent design parameters.
In a specific application scenario, the mesh dividing unit 22 may be specifically configured to extract a fluid region model in the target model and output a data format required for mesh division; when optimizing the leaf grids, reading the fluid region model for grid division, selecting corresponding Y + or estimating the size of a first layer of grids according to a turbulence model to be selected by CFD, wherein the B2B surface adopts H-O-H grid topology, the leaf top gap adopts butterfly-shaped grids, the near-wall surface grids are encrypted, and the maximum extension ratio is 1.3, so that the maximum length-width ratio of the computational domain grids of the fluid region model is less than 1000, the orthogonality is 15-165 degrees, and no negative grids appear; carrying out automatic grid division according to the target quantity, or carrying out grid independence verification at the later stage of drawing a plurality of sets of grids, and determining that the fluid region model is completed by grid division after the verification is passed; when the shell is optimized, reading the fluid region model to divide structured and unstructured grids, judging the grid quality which is the same as that of a blade grid, selecting a solver according to the Y + or the first layer of grid thickness which is the same as that of the blade grid, and enabling the average grid quality not to exceed 0.8; extracting the volume of the solid region model in the target model, and grouping and naming the boundaries to be defined in the CFD; and reading the solid region model for meshing, setting the global size, carrying out local encryption on chamfers, rounds and openings, and setting the meshing type so that the average mesh quality does not exceed 0.8.
In a specific application scenario, the calculating unit 23 is specifically configured to discretely solve a three-dimensional steady compressible reynolds time mean N-S equation by using a finite volume method, wherein a turbulence model uses a two-equation SST model, a main flow region of the turbulence model uses a k-epsilon model, a near-wall region uses an omega equation to replace the epsilon equation, a mixing function is used to integrate the k-epsilon and k-omega models, a gas medium is set to be R245fa, and a specific heat capacity is defined by temperature interpolation and polynomial fitting; defining the boundary conditions of a calculation domain of the fluid region model, wherein the inlet is given total pressure, total temperature, outlet static pressure or outlet mass flow, the blade adopts a single-channel periodic boundary, the rotation region is provided with the rotation speed, all solid wall surfaces in the calculation domain are smooth, adiabatic and non-slip, a dynamic-static interface uses a plane mixing method, the blade top gap of the movable blade is generally connected with the solid wall surfaces, the blade top wall surfaces in the rotation region and the solid wall surfaces on two sides of the outlet region are provided with reverse rotation in a relative coordinate system, and the value of the global root mean square value RMS is less than 10-4Judging that the calculation is converged when the difference of the mass flow of the inlet and the outlet of the calculation domain is less than 0.5%; using post-processing modulesAnd writing calculation formulas of efficiency, power, axial thrust, energy loss coefficient, total pressure loss coefficient and static pressure recovery coefficient, and adding a plurality of objective functions serving as CFD calculation results into a preset parameter pool.
In a specific application scenario, the calculating unit 23 may be further configured to load a prestress when the multistage axial flow expander machine is operated, solve the strength and the dynamic characteristics by using a finite element method, divide the continuum into a finite number of node units, and solve the global unknown field function by using an assumed approximate function in the unit; importing the solid region model after the grid division, transmitting 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 constraining the relevant degree of freedom and displacement to solve; and adding the calculation results of the maximum equivalent stress, the maximum equivalent strain and the maximum displacement of the target direction into a preset parameter pool as a plurality of target functions of the multidisciplinary calculation result.
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 multiple objective functions of the CFD calculation result and the intensity calculation result as optimization variables, set upper and lower boundaries for the design variables by using an MOGA algorithm, add a maximum objective and a constraint to the optimization variables, and automatically set an initial population, a maximum evolution generation number, a cross rate, and a variation rate according to the number of variables by using a module; and sorting according to the PARETO optimal population by using an MOGA algorithm, dividing the optimal population into an advantage solution and a non-advantage solution, selecting an optimization variable needing multi-objective optimization on two stages of blade grids in the multistage axial flow expander, selecting an optimal solution set as an effective population according to one optimization variable under the condition of not influencing other optimization variables, combining the optimization variables into a scalar fitness function if conflicts exist among the optimization variables, and searching in a multi-dimensional space to determine the optimal solution so as to obtain the blade positive-bending and negative-bending coupling rule and the optimization information of the blade profile along the blade height distribution.
It should be noted that other corresponding descriptions of the functional units related to the multidisciplinary optimization design device for a multistage axial flow expander provided in this embodiment may refer to the 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, and the program, when executed by a processor, implements the multidisciplinary optimization design method of the multistage axial flow expander shown in fig. 1.
Based on the above embodiments of the method shown in fig. 1 and the virtual device shown in fig. 10, the present embodiment further provides a multidisciplinary optimization design apparatus for a multistage axial flow expander, where the apparatus includes: a processor, a storage device, and a computer program stored on the storage device and executable on the processor, the processor implementing the method shown in fig. 1 when executing the program; the device also includes: a bus configured to couple the processor and the memory device.
By applying the technical scheme of the embodiment, the multi-disciplinary 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 the stability of a machine set can be improved, the running cost of the device can be reduced, and the relevant requirements of energy conservation and emission reduction are met.
Through the above description of the embodiments, those skilled in the art will clearly understand that the present application can be implemented by hardware, and also by software plus a necessary general hardware platform. 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 (which may be a CD-ROM, a usb disk, a removable hard disk, etc.), and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the implementation scenarios of the present application.
Those skilled in the art will appreciate that the figures are merely schematic representations of one preferred implementation scenario and that the blocks or flow diagrams in the figures are not necessarily required to practice the present application.
Those skilled in the art will appreciate that the modules in the devices in the implementation scenario may be distributed in the devices in the implementation scenario according to the description of the implementation scenario, or may be located in one or more devices different from the present implementation scenario with corresponding changes. The modules of the implementation scenario may be combined into one module, or may be further split into a plurality of sub-modules.
The above application serial numbers are for description purposes only and do not represent the superiority or inferiority of the implementation scenarios.
The above disclosure is only a few specific implementation scenarios of the present application, but the present application is not limited thereto, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present application.
Claims (10)
1. A multidisciplinary optimization design method of a multistage axial flow expander is characterized by comprising the following steps:
carrying out forward modeling on the multistage axial flow expander according to the design parameters solved by the inverse fitting to obtain a target model;
respectively carrying out meshing 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 meshing, 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 the two-stage blade cascade in the multi-stage axial flow expansion machine by using a genetic algorithm according to a plurality of objective functions of the CFD calculation result and the multidisciplinary calculation result to obtain the optimization information of the positive bending and negative bending coupling rule of the blade and the distribution of the blade profile along the blade height.
2. The method according to claim 1, wherein if the blade of the multistage axial flow expander is a subsonic turbine blade with a turning angle smaller than a preset angle threshold and a blade thickness variation smaller than a preset variation threshold, the forward modeling is performed on the multistage axial flow expander according to the design parameters solved by the inverse fitting to obtain the target model, and specifically includes:
importing 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 recognition fitting, and performing parameterization after fitting to obtain an angle and thickness distribution curve of a mean camber line along the chord length;
setting a multi-order Bezier curve according to the 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 adding the control point variable serving as a design parameter into a preset parameter pool.
3. The method according to claim 1, wherein if the blade of the multistage axial flow expander is a turbine blade with a turning angle greater than a preset angle threshold and a blade thickness variation greater than a preset variation threshold, the forward modeling of the multistage axial flow expander according to the design parameters solved by the inverse fitting to obtain the target model specifically comprises:
based on an eleven-parameter method, a pressure surface and a suction surface are independently defined to carry out forward modeling on the multistage axial flow expander blade;
and adding curve control point variables of the pressure surface and the suction surface into a preset parameter pool as independent design parameters.
4. The method according to claim 2 or 3, wherein the mesh partitioning of the fluid region model and the solid region model in 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 leaf grids, reading the fluid region model for grid division, selecting corresponding Y + or estimating the size of a first layer of grids according to a turbulence model to be selected by CFD, wherein the B2B surface adopts H-O-H grid topology, the leaf top gap adopts butterfly-shaped grids, the near-wall surface grids are encrypted, and the maximum extension ratio is 1.3, so that the maximum length-width ratio of the computational domain grids of the fluid region model is less than 1000, the orthogonality is 15-165 degrees, and no negative grids appear; carrying out automatic grid division according to the target quantity, or carrying out grid independence verification at the later stage of drawing a plurality of sets of grids, and determining that the fluid region model is completed by grid division after the verification is passed;
when the shell is optimized, reading the fluid region model to divide structured and unstructured grids, judging the grid quality which is the same as that of a blade grid, selecting a solver according to the Y + or the first layer of grid thickness which is the same as that of the blade grid, and enabling the average grid quality not to exceed 0.8;
extracting the volume of the solid region model in the target model, and grouping and naming the boundaries to be defined in the CFD;
and reading the solid region model for meshing, setting the global size, carrying out local encryption on chamfers, rounds and openings, and setting the meshing type so that the average mesh quality does not exceed 0.8.
5. The method according to claim 4, wherein the defining of the corresponding boundary conditions for the fluid region model after the meshing and the calculating of the fluid region model using the CFD module specifically include:
the method comprises the steps of discretely solving a three-dimensional steady compressible Reynolds time-mean N-S equation by adopting a finite volume method, wherein a turbulence model uses an SST model with two equations, a mainstream region of the turbulence model adopts a k-epsilon model, a near-wall region uses an omega equation to replace an epsilon equation, a mixing function is used for integrating the k-epsilon model and the k-omega model, a gas medium is set to be R245fa, and the specific heat capacity is defined by temperature interpolation and polynomial fitting;
defining the boundary conditions of a calculation domain of the fluid region model, wherein the inlet is given total pressure, total temperature, outlet static pressure or outlet mass flow, the blade adopts a single-channel periodic boundary, the rotation region is provided with the rotation speed, all solid wall surfaces in the calculation domain are smooth, adiabatic and non-slip, the dynamic and static interfaces use a plane mixing method, the blade top gap of the movable blade is generally connected with the solid wall surfaces, the blade top wall surfaces in the rotation region and the solid wall surfaces on two sides of the outlet regionSetting reverse rotation in a relative coordinate system, wherein the value of a global residual root mean square value RMS is less than 10-4Judging that the calculation is converged when the difference of the mass flow of the inlet and the outlet of the calculation domain is less than 0.5%;
and compiling calculation formulas of efficiency, power, axial thrust, energy loss coefficient, total pressure loss coefficient and static pressure recovery coefficient by using the post-processing module, and adding the calculation formulas into a preset parameter pool as a plurality of objective functions of CFD calculation results.
6. The method of claim 5, wherein performing multidisciplinary calculations on the solid zone model using a multidisciplinary calculation module, specifically comprising:
loading prestress when a multistage axial flow expander machine operates, solving the strength and dynamic characteristics by using a finite element method, dividing a continuum into a finite number of node units, and solving a global unknown field function by using an approximate function assumed in the units;
importing the solid region model after the grid division, transmitting 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 constraining the relevant degree of freedom and displacement to solve;
and adding the calculation results of the maximum equivalent stress, the maximum equivalent strain and the maximum displacement of the target direction into a preset parameter pool as a plurality of target functions of the multidisciplinary calculation result.
7. The method according to claim 6, wherein the multi-objective optimization of the two-stage blade cascade in the multi-stage axial flow expander is performed by using a genetic algorithm according to a plurality of objective functions of CFD calculation results and multidisciplinary calculation results to obtain optimization information of blade positive-bending and negative-bending coupling rules and blade profile distribution along the blade height, and the optimization information specifically comprises:
setting the design parameters added in the preset parameter pool as design variables, setting a plurality of objective functions of CFD (computational fluid dynamics) calculation results and intensity calculation results as optimization variables, setting upper and lower boundaries for the design variables by using an MOGA (multi-domain genetic algorithm), adding a most significant target and a constraint for the optimization variables, and automatically setting an initial population, a maximum evolution generation number, a cross rate and a variation rate by a module according to the number of the variables;
and sorting according to the PARETO optimal population by using an MOGA algorithm, dividing the optimal population into an advantage solution and a non-advantage solution, selecting an optimization variable needing multi-objective optimization on two stages of blade grids in the multistage axial flow expander, selecting an optimal solution set as an effective population according to one optimization variable under the condition of not influencing other optimization variables, combining the optimization variables into a scalar fitness function if conflicts exist among the optimization variables, and searching in a multi-dimensional space to determine the optimal solution so as to obtain the blade positive-bending and negative-bending coupling rule and the optimization information of the blade profile along the blade height distribution.
8. A multidisciplinary optimal design device for a multistage axial flow expander, comprising:
the modeling unit is used for carrying out forward modeling on the multistage axial flow expander according to the design parameters solved by the inverse fitting to obtain a target model;
the meshing unit is used for respectively meshing the fluid region model and the solid region model in the target model;
a calculation unit, configured to define corresponding boundary conditions for the fluid region model and the solid region model after mesh 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;
and the optimization processing unit is used for performing multi-objective optimization on the two-stage blade cascade in the multi-stage axial flow expansion machine by using a genetic algorithm according to a plurality of objective functions of the CFD calculation result and the multidisciplinary calculation result to obtain the optimization information of the positive-bending and negative-bending coupling rule of the blade and the distribution of the blade profile along the blade height.
9. A storage device having stored thereon a computer program, wherein the program, when executed by a processor, implements a multidisciplinary optimization design method for a multistage axial flow expander according to any one of claims 1 to 7.
10. A multidisciplinary optimization design apparatus for 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 executes the program to implement the multidisciplinary optimization design method for the multistage axial flow expander according to any one of claims 1 to 7.
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