Uniformity prediction method for nozzle arrays in pure hydrogen mild-mixed combustion chambers

Abstract

The embodiment of the present disclosure discloses a uniformity prediction method suitable for a pure hydrogen micro-mixing combustion chamber nozzle array. A specific implementation of the method includes: obtaining a micro-mixing tube structure information set and nozzle structure information; for each micro-mixing tube in the micro-mixing tube set, executing the following generation steps: gridding the micro-mixing tube to obtain micro-mixing tube grid information; performing grid size evaluation processing on the micro-mixing tube grid information to obtain a grid evaluation value; generating a micro-mixing tube uniformity coefficient group; determining a cold state thermal conductivity physical property parameter set; generating a cold state nozzle uniformity coefficient; generating a cold state nozzle uniformity coefficient; in response to determining that the cold state nozzle uniformity coefficient is greater than a preset nozzle uniformity coefficient threshold, adjusting the nozzle geometry. This implementation can improve the accuracy of nozzle array prediction, avoid the occurrence of flashback, improve the design rationality of micro-mixing tubes and nozzles, and avoid damage to pure hydrogen gas turbines.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of computer technology, and in particular to a uniformity prediction method applicable to a pure hydrogen micro-mixing combustion chamber nozzle array. Background Art

[0002] Micro-mixing combustion technology is a new combustion technology suitable for pure hydrogen fuel, and is used in pure hydrogen gas turbines and other fields. Micro-mixing combustion technology is composed of the mixing uniformity of the gas at the outlet of a single micro-mixing tube and the overall uniformity of the gas at the nozzle outlet, which affects the equivalence ratio, anti-flashback and oscillation of the micro-mixing combustion chamber. Therefore, the evaluation of the outlet gas mixing uniformity of the micro-mixing tube and the nozzle array is a key link in the design of a pure hydrogen combustion chamber. For the uniformity test of the nozzle array, the usual method is to arrange a large number of sensors at the micro-mixing tube and the nozzle to detect the gas concentration at different positions. Then, use statistical methods or programming algorithms to predict the gas mixing uniformity at the outlet of the micro-mixing tube and the nozzle.

[0003] However, it is found in practice that when the above-mentioned method is used to predict the uniformity of the nozzle array, the following technical problems often occur: Since a large number of tests are required when predicting the uniformity under each operating condition using statistical methods or programming algorithms, each test requires a large amount of test data, the combustion chamber test cycle is long, the test frequency is low and the test cost is high, resulting in a large amount of storage resources and computing resources being required when predicting the uniformity of the same structure. When predicting the uniformity of the same structure with the same storage resources and computing resources, the accuracy of the predicted uniformity of the micro-mixing tube and the nozzle is low, which increases the number of flashbacks and the damage rate of the pure hydrogen gas turbine.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0005] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0006] Some embodiments of the present disclosure propose a uniformity prediction method applicable to a pure hydrogen mild-mix combustion chamber nozzle array to solve one or more of the technical problems mentioned in the above background technology section.

[0007] In a first aspect, some embodiments of the present disclosure provide a uniformity prediction method for a pure hydrogen micro-mixing combustion chamber nozzle array, comprising: obtaining a micro-mixing tube structure information set and nozzle structure information of a micro-mixing tube set and a nozzle in a pure hydrogen micro-mixing combustion chamber, wherein the nozzle is a structure composed of a micro-mixing tube set; for each micro-mixing tube in the micro-mixing tube set, performing the following generation steps: gridding the micro-mixing tube according to the micro-mixing tube structure information corresponding to the micro-mixing tube to obtain micro-mixing tube grid information; performing grid size evaluation processing on the micro-mixing tube grid information according to the micro-mixing tube structure information corresponding to the micro-mixing tube to obtain a grid evaluation value; in response to determining that the grid evaluation value is greater than or equal to a preset grid evaluation value, A threshold is estimated, and a micro-mixing tube uniformity coefficient group is generated according to the micro-mixing tube grid information; in response to determining that each micro-mixing tube uniformity coefficient in the obtained micro-mixing tube uniformity coefficient group is greater than or equal to a preset uniformity threshold, a thermal conductivity physical property parameter set of the nozzle under a cold condition is determined as a cold thermal conductivity physical property parameter set; a cold nozzle uniformity coefficient of the nozzle under a cold condition is generated according to the cold thermal conductivity physical property parameter set and the nozzle grid information, wherein the nozzle grid information is information of a grid obtained by gridding the nozzle according to the nozzle structure information; in response to determining that the cold nozzle uniformity coefficient is greater than the preset nozzle uniformity coefficient threshold, a geometric structure adjustment is performed on the nozzle.

[0008] In a second aspect, some embodiments of the present disclosure provide a uniformity prediction device suitable for a pure hydrogen micro-mixing combustion chamber nozzle array, comprising: an acquisition unit, configured to acquire a micro-mixing tube structure information set and nozzle structure information of a micro-mixing tube set and a nozzle in a pure hydrogen micro-mixing combustion chamber, wherein the nozzle is a structure composed of a micro-mixing tube set; an execution unit, configured to execute the following generation steps for each micro-mixing tube in the micro-mixing tube set: gridding the micro-mixing tube according to the micro-mixing tube structure information corresponding to the micro-mixing tube to obtain micro-mixing tube grid information; performing grid size evaluation processing on the micro-mixing tube grid information according to the micro-mixing tube structure information corresponding to the micro-mixing tube to obtain a grid evaluation value; in response to determining that the grid evaluation value is greater than or equal to a preset grid evaluation threshold, according to the above The micro-mixing tube grid information is used to generate a micro-mixing tube uniformity coefficient group; a determining unit is configured to determine a thermal conductivity physical property parameter set of the above-mentioned nozzle under a cold condition as a cold thermal conductivity physical property parameter set in response to determining that each micro-mixing tube uniformity coefficient in the obtained micro-mixing tube uniformity coefficient group is greater than or equal to a preset uniformity threshold; a generating unit is configured to generate a cold nozzle uniformity coefficient of the above-mentioned nozzle under a cold condition according to the above-mentioned cold thermal conductivity physical property parameter set and the nozzle grid information, wherein the above-mentioned nozzle grid information is information of a grid obtained by gridding the above-mentioned nozzle according to the above-mentioned nozzle structure information; a geometric structure adjusting unit is configured to adjust the geometric structure of the above-mentioned nozzle in response to determining that the above-mentioned cold nozzle uniformity coefficient is greater than the preset nozzle uniformity coefficient threshold.

[0009] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner in the first aspect.

[0010] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method described in any implementation manner in the first aspect is implemented.

[0011] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: the uniformity prediction method applicable to the pure hydrogen micro-mixing combustion chamber nozzle array of some embodiments of the present disclosure can improve the accuracy of nozzle array prediction by predicting the uniformity of micro-mixing tubes and nozzles in cold and hot states, avoid the occurrence of flashback, improve the design rationality of micro-mixing tubes and nozzles, and avoid damage to gas turbines. Specifically, the reasons for the need for a large amount of storage resources and computing resources when predicting the same structure, and the low accuracy of the predicted uniformity of the micro-mixing tube and the nozzle when predicting the uniformity of the same structure under the same storage resources and computing resources, and the increase of the occurrence of flashback and the damage rate of the pure hydrogen gas turbine are: due to the use of statistical methods or programming algorithms, a large number of tests are required when predicting the uniformity under each working condition, and each test requires a large amount of test data, the combustion chamber test cycle is long, the test frequency is low, and the test cost is high, resulting in the need for a large amount of storage resources and computing resources when predicting the uniformity of the same structure, and the low accuracy of the predicted uniformity of the micro-mixing tube and the nozzle when predicting the uniformity of the same structure under the same storage resources and computing resources, and the increase of the occurrence of flashback and the damage rate of the pure hydrogen gas turbine. Based on this, the uniformity prediction method for the nozzle array of the pure hydrogen micro-mixing combustion chamber in some embodiments of the present disclosure can first obtain the micro-mixing tube structure information set and nozzle structure information of the micro-mixing tube set and the nozzle in the pure hydrogen micro-mixing combustion chamber, wherein the above-mentioned nozzle is a structure composed of the micro-mixing tube set. Here, the micro-mixing tube structure information and the nozzle structure information are used for the subsequent meshing of the micro-mixing tube and the nozzle. Secondly, for each micro-mixing tube in the above-mentioned micro-mixing tube set, the following generation steps are performed: the first step is to mesh the above-mentioned micro-mixing tube according to the micro-mixing tube structure information corresponding to the above-mentioned micro-mixing tube to obtain the micro-mixing tube mesh information. Here, the fluid domain of the micro-mixing tube is discretized into a series of micro-element areas to facilitate the subsequent evaluation of the mesh size. The second step is to perform mesh size evaluation processing on the above-mentioned micro-mixing tube mesh information according to the micro-mixing tube structure information corresponding to the above-mentioned micro-mixing tube to obtain a mesh evaluation value. Here, the turbulent flow of the mixed gas in the micro-mixing tube can be described more accurately and the computing resources can be optimized. The third step is to generate a micro-mixing tube uniformity coefficient group according to the above-mentioned micro-mixing tube mesh information in response to determining that the above-mentioned grid evaluation value is greater than or equal to the preset grid evaluation threshold. Here, the accuracy of the subsequent micro-mixing tube uniformity coefficient can be improved, which is convenient for judging whether the micro-mixing tube design is reasonable. Again, in response to determining that each micro-mixing tube uniformity coefficient in the obtained micro-mixing tube uniformity coefficient set is greater than or equal to the preset uniformity threshold, the thermal conductivity physical property parameter set of the above nozzle under the cold working condition is determined as the cold thermal conductivity physical property parameter set. Here, due to the large temperature difference between air and hydrogen, determining the cold thermal conductivity physical property parameter set can be more in line with the actual situation, improving the accuracy of the cold thermal conductivity physical property parameter set and the accuracy of subsequent uniformity.Subsequently, according to the above-mentioned cold state thermal conductivity physical property parameter set and nozzle grid information, the cold state nozzle uniformity coefficient of the above-mentioned nozzle under cold state conditions is generated, wherein the above-mentioned nozzle grid information is the information of the grid obtained by gridding the above-mentioned nozzle according to the above-mentioned nozzle structure information. Here, the accuracy of the uniformity of the nozzle under cold state conditions can be improved. Finally, in response to determining that the above-mentioned cold state nozzle uniformity coefficient is greater than the preset nozzle uniformity coefficient threshold, the geometric structure of the above-mentioned nozzle is adjusted. Here, it can be determined whether the geometric structure design of the nozzle is reasonable, and the occurrence of flashback can be avoided to a certain extent, reducing the damage of the pure hydrogen gas turbine. It can be obtained that the uniformity prediction method of the nozzle array of the pure hydrogen micro-mixing combustion chamber can improve the accuracy of the nozzle array prediction, avoid the occurrence of flashback, improve the design rationality of the micro-mixing tube and the nozzle, and avoid damage to the gas turbine by predicting the uniformity of the micro-mixing tube, the nozzle in the cold state and the hot state. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0013] Figure 1 is a flow chart of some embodiments of a uniformity prediction method applicable to a pure hydrogen mild-mix combustion chamber nozzle array according to the present disclosure;

[0014] Figure 2 It is a schematic structural diagram of some embodiments of a uniformity prediction device applicable to a pure hydrogen mild-mix combustion chamber nozzle array according to the present disclosure;

[0015] Figure 3 is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure;

[0016] Figure 4 is a schematic structural diagram of a micro-mixing tube according to some embodiments of a uniformity prediction device applicable to a pure hydrogen micro-mixing combustion chamber nozzle array according to the present disclosure;

[0017] Figure 5 It is a schematic diagram of the structure of the nozzles of some embodiments of the uniformity prediction device applicable to the pure hydrogen micro-mixing combustion chamber nozzle array according to the present disclosure. DETAILED DESCRIPTION

[0018] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0019] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0020] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0021] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0022] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0023] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0024] Figure 1 The process 100 of some embodiments of the nozzle array uniformity prediction method according to the present disclosure is shown. The nozzle array uniformity prediction method comprises the following steps:

[0025] Step 101, obtaining a micro-mixing tube structure information set and nozzle structure information of a micro-mixing tube set and a nozzle in a pure hydrogen micro-mixing combustion chamber.

[0026] In some embodiments, the execution subject (e.g., electronic device) of the nozzle array uniformity prediction method can obtain the micro-mixing tube structure information set and nozzle structure information of the micro-mixing tube set and the nozzle in the pure hydrogen micro-mixing combustion chamber through a wired connection or a wireless connection, wherein the nozzle is a structure composed of a micro-mixing tube set. The pure hydrogen micro-mixing combustion chamber can be a device for promoting full mixing of hydrogen and air. The micro-mixing tubes in the micro-mixing tube set can be pipes for mixing air and hydrogen. The structural schematic diagram of the micro-mixing tube is shown in FIG. Figure 4 shown. Figure 4The circle in the figure may be a fuel hole, a hole for fuel to flow in. The nozzle may be a device for injecting hydrogen and air into the combustion chamber at a certain pressure and speed. The schematic diagram of the nozzle structure is shown in FIG. Figure 5 As shown. The micro-mixing tube structure information may be information related to the geometric structure of the micro-mixing tube. For example, the micro-mixing tube structure information may include but is not limited to at least one of the following: the three-dimensional structure, length, diameter, and material of the micro-mixing tube. The nozzle structure information may be information related to the geometric structure of the nozzle. For example, the nozzle structure information may include but is not limited to at least one of the following: the three-dimensional structure and material of the nozzle.

[0027] Step 102: for each micro-mixing tube in the micro-mixing tube set, perform the following generation steps:

[0028] Step 1021 , according to the micro-mixing tube structure information corresponding to the micro-mixing tube, the micro-mixing tube is grid-divided to obtain micro-mixing tube grid information.

[0029] In some embodiments, the execution subject may perform grid division on the micro-mixing tube according to the micro-mixing tube structure information corresponding to the micro-mixing tube to obtain micro-mixing tube grid information. The micro-mixing tube grid information may be information of a grid obtained by grid division of a fluid domain in the micro-mixing tube. The micro-mixing tube grid information may include the length of a single micro-mixing tube grid and the area of ​​the micro-mixing tube grid.

[0030] As an example, the execution subject may use a hexahedral meshing method to mesh the micro-mixing tube according to the micro-mixing tube structure information corresponding to the micro-mixing tube to obtain micro-mixing tube mesh information.

[0031] In the process of using technical solutions to solve the above technical problem 1, the following technical problem 2 is often accompanied: how to generate a micro-mixing tube grid with high precision and better characterization of the geometric characteristics of the micro-mixing tube to improve the prediction uniformity of the nozzle array, reduce the occurrence of backfire and the damage of the pure hydrogen gas turbine. For the above technical problem 2, the conventional solution is generally: using a unilateral discrete algorithm and the Advancing Front Technique, according to the micro-mixing tube structure information corresponding to the micro-mixing tube, the micro-mixing tube is meshed to obtain the micro-mixing tube grid information. However, the above conventional solution still has the following problems: since the unilateral discrete algorithm only targets a single boundary curve in the fluid domain, it does not take into account the complex situation of the fluid domain as a whole and complex areas, and the frontier advancement method is a greedy algorithm with global convergence problems, and it is necessary to manually specify the size field of the micro-mixing tube geometric area, there is a certain subjectivity, resulting in the generated micro-mixing tube grid density and accuracy are low, the generation efficiency is low, and then the prediction uniformity is low and the damage rate of the pure hydrogen gas turbine is increased. The inventors considered the shortcomings and combined with the advantages / technical status of the fluid domain meshing technology owned by the inventor's company, we decided to adopt the following solution:

[0032] In some optional implementations of some embodiments, the above-mentioned grid division of the micro-mixing tube according to the micro-mixing tube structure information corresponding to the micro-mixing tube to obtain the micro-mixing tube grid information may include the following steps:

[0033] In the first step, the geometric structure corresponding to the above-mentioned micro-mixing tube structure information is represented by the surface boundary using B-Rep (Boundary Representation) to obtain a set of micro-mixing tube surface representation parameter groups. Among them, the micro-mixing tube surface representation parameter group may include: a first tangent vector measurement, a tangent vector association measurement, and a second tangent vector measurement. The above-mentioned first tangent vector measurement can characterize the tangent vector length measurement of the surface in the u direction of the parameter domain. The above-mentioned tangent vector association measurement can characterize the inner product of the tangent vectors of the surface in the u direction of the parameter domain and the v direction of the parameter domain, that is, the correlation relationship between the moving speeds of points in the two directions. The above-mentioned second tangent vector measurement can characterize the tangent vector length measurement of the surface in the v direction of the parameter domain.

[0034] The second step is to determine the micro-mixing tube size field parameter set for the above-mentioned micro-mixing tube fluid domain. Among them, the above-mentioned micro-mixing tube size field parameter set may include: global size, target size, adaptive angle, growth rate and fusion tolerance. The above-mentioned global size may be the maximum value of the mesh size for dividing the fluid domain. The above-mentioned target size may be the minimum value of the mesh size for dividing the fluid domain. The above-mentioned adaptive angle may characterize the sensitivity of the divided mesh at the change of the fluid domain, and is a parameter for adjusting the mesh size after division. The larger the above-mentioned adaptive angle, the smoother the adjustment of the mesh. The above-mentioned adaptive angle may be 10 degrees. The above-mentioned growth rate may characterize the amplitude of adjusting the mesh size. The smaller the above-mentioned growth rate, the smoother the mesh size change filtering. The above-mentioned growth rate may be 1.2. The above-mentioned fusion tolerance may characterize the degree of aggregation of the vertices of the fluid domain.

[0035] As an example, the execution subject may first determine the diagonal length of the micro-mixing tube fluid domain using the AABB bounding box algorithm (Axis-Aligned Bounding Box). Then, the product of 0.05 and the diagonal length is determined as the global size. Then, the product of 0.05 and the global size is determined as the target size. Finally, the product of 0.05 and the target size is used as the fusion tolerance.

[0036] The third step is to perform virtual fusion clustering on the micro-mixing tube boundary vertex set included in the fluid domain of the micro-mixing tube according to the fusion tolerance in the micro-mixing tube size field parameter set to obtain a boundary vertex cluster set. The boundary vertex cluster in the boundary vertex cluster set can be a vertex cluster obtained by placing any two points whose distance between the micro-mixing tube boundary vertices is less than the fusion tolerance into the same cluster.

[0037] Step 4: For each boundary vertex in the above boundary vertex cluster, perform the following screening steps:

[0038] Sub-step 1, according to the above-mentioned micro-mixing tube surface representation parameter set, determine the curve curvature and surface curvature group of the above-mentioned boundary vertex at the corresponding fluid domain curved edge and fluid domain surface. Among them, the above-mentioned curve curvature can characterize the degree of curvature of the boundary vertex at the curve. The surface curvature in the above-mentioned surface curvature group can characterize the degree of curvature of the boundary vertex on the fluid domain surface. In practice, the above-mentioned execution subject substitutes the above-mentioned micro-mixing tube surface representation parameter set into the three-dimensional curve curvature formula and the three-dimensional surface curvature formula to obtain the curve curvature and the surface curvature group.

[0039] Sub-step 2: Screening the above curve curvature and the above surface curvature group to obtain a screened curvature, wherein the screened curvature may be a curvature with the largest value.

[0040] Sub-step 3, converting the size value of the above-screened curvature to obtain the curvature characteristic size value. In practice, the above-mentioned execution subject can first determine the ratio of 1 and the above-screened curvature as the curvature radius. Then, determine the intersection of the tangent vector measured by the above-mentioned first tangent vector and the circle corresponding to the above-mentioned curvature radius as the target point. Finally, determine the distance between the above-mentioned target point and the above-mentioned boundary vertex as the curvature characteristic size value.

[0041] Sub-step 4, determining the neighboring characteristic size value of the above-mentioned boundary vertex according to the vertex curve edge distance set, wherein the vertex curve distance in the above-mentioned vertex curve edge distance set is the distance from the above-mentioned boundary vertex to the curved edge of the opposite fluid domain. The above-mentioned neighboring characteristic size value can characterize the degree of adjacency of the boundary vertex on the adjacent edge.

[0042] As an example, the execution subject may first select the vertex curve-edge distance with the smallest value from the vertex curve-edge distance set as the target vertex curve-edge distance, and then determine the product of 0.5 and the target vertex curve-edge distance as the neighboring feature size value.

[0043] Sub-step 5, screening the curvature feature size value, the adjacent feature size value, the target size and the global size to obtain the screened size value as the endpoint size value. In practice, the execution subject may first screen the size value with the smallest value from the curvature feature size value, the adjacent feature size value and the global size as the screened size value. Then, screen the size value with the largest value from the screened size value and the target size to obtain the screened size value as the endpoint size value.

[0044] In the fifth step, according to the above growth rate, each fluid domain boundary curve in the fluid domain boundary curve set is subjected to adaptive boundary discretization processing to obtain a boundary curve discrete point set, wherein the above fluid domain boundary curve can be a fluid domain surface curve whose corresponding endpoint size value is greater than the length of the curved edge where the boundary vertex is located. The boundary curve discrete points in the above boundary curve discrete point set can represent the size of the subsequently generated grid.

[0045] As an example, the execution subject can perform the following discrete steps based on the boundary starting endpoint of the fluid domain boundary curve: first, according to the arc length distance between the boundary starting endpoint and the adjacent boundary vertex, the ratio of the endpoint size value of the boundary starting endpoint and the above-mentioned growth rate, the endpoint size value of the boundary starting endpoint is updated to obtain the updated endpoint size value. Among them, the updated endpoint size value can be: when the ratio is greater than the growth rate, it is the product of the growth rate and the arc length distance, when the ratio is less than or equal to the growth rate and greater than or equal to the difference between 2 and the growth rate, it is the original endpoint size, when the ratio is less than 2 and the difference between the growth rate, it is the product of the difference between 2 and the growth rate and the arc length distance. Secondly, iterate backward with the minimum value as the step size to obtain the iterative sampling point. Then, based on the iterative sampling point, perform the following sampling steps: the first step is to sample and determine the endpoint size value of the iterative sampling point as the sampling size value. The second step is to determine the sampling arc length distance between the iterative sampling point and the boundary starting endpoint in response to the sampling size value not being equal to the endpoint size value of the boundary termination endpoint. In the third step, in response to determining that the sampling arc length is less than the target size, and the updated endpoint size value is the minimum value of the endpoint size values ​​of all sampling points within the boundary starting endpoint and the iterative sampling point, the minimum value between the sampling size value and the global size is determined as the sampling size value. In the fourth step, in response to determining that the sampling arc length distance is less than the sampling size value, the iterative sampling point is determined as a boundary discrete point, and the iterative sampling point is used as the boundary starting endpoint to perform the above discrete steps again. Finally, otherwise, iterative sampling is continued with the minimum value to obtain a new sampling point as the iterative sampling point to perform the sampling step again.

[0046] Step 6: Smoothing the discrete points of the boundary curve to obtain a smoothed discrete point set. The smoothed discrete point set may be a discrete point set obtained by taking the edge with more discrete points as the criterion for a curve that is a plurality of surfaces at the same time.

[0047] In the seventh step, based on the discrete point set after the smoothing process, adaptively generate and optimize the micro-mixing tube fluid domain to obtain micro-mixing tube grid information. The optimization process may be an optimization process using a Laplace smoothing algorithm.

[0048] As an example, the above-mentioned execution subject can use a two-dimensional Delaunay triangulation algorithm to generate a two-dimensional initial grid for the fluid domain. Then, the three-dimensional initial grid in the fluid domain is obtained through the corresponding relationship with the three-dimensional fluid domain, wherein the in-plane interpolation algorithm of the three-dimensional initial grid adopts the frontier advancement algorithm. Finally, the BW interpolation algorithm of the Delaunay algorithm is used to triangulate according to the in-plane interpolation point set and the discrete point set after smoothing to obtain the micro-mixing tube grid and micro-mixing tube grid information.

[0049] The above technical solution and its related contents, as an inventive point of an embodiment of the present disclosure, solve the second technical problem mentioned in the background technology: "Since the unilateral discrete algorithm only targets a single boundary curve in the fluid domain, it does not take into account the complex situation of the fluid domain as a whole and complex areas, and the frontier advancing method is a greedy algorithm with global convergence problems, and it is necessary to manually specify the size field of the micro-mixing tube geometric area, which has a certain subjectivity, resulting in the generated micro-mixing tube grid density and accuracy are low, the generation efficiency is low, and then the prediction uniformity is low and the damage rate of pure hydrogen gas turbines is increased." The factors that lead to the generated micro-mixing tube grid density and accuracy are low, the generation efficiency is low, and then the prediction uniformity is low and the damage rate of pure hydrogen gas turbines is increased are often as follows: Since the unilateral discrete algorithm only targets a single boundary curve in the fluid domain, it does not take into account the complex situation of the fluid domain as a whole and complex areas, and the frontier advancing method is a greedy algorithm with global convergence problems, and it is necessary to manually specify the size field of the micro-mixing tube geometric area, which has a certain subjectivity. If the above factors are solved, it is possible to achieve the effect of improving the density control and accuracy of the generated micro-mixing tube grid, improving the generation efficiency, and then improving the prediction uniformity and reducing the damage of the pure hydrogen gas turbine. In order to achieve this effect, the present disclosure first determines the micro-mixing tube size field parameter set, curvature characteristic size value, adjacent characteristic size value and endpoint size value in the micro-mixing tube fluid domain, which can control the density of grid generation and realize the setting of reasonable grid density at the complex boundary of multilateral association for density transition. Secondly, the adaptive boundary discretization processing of the fluid domain boundary curve set can maintain a good endpoint size value, avoid conflicts with the growth rate and characteristic size, and improve the boundary curve discrete point set to better characterize the geometric characteristics of the fluid domain, as well as the density of the grid in the complex area. Subsequently, the boundary discrete point smoothing processing of the boundary curve discrete point set can improve the transition smoothness between discrete points and the coordination of regional boundaries, so as to improve the connection integrity of the subsequently generated grid. Then, by combining the frontier advancing algorithm and the Delaunay algorithm, adaptive grid generation and optimization processing are performed on the micro-mixing tube fluid domain according to the discrete point set after smoothing. This can improve the conformity and grid accuracy of the generated grid and the micro-mixing tube, reduce a large number of intersection calculations, reduce the waste of computing resources and improve the efficiency of grid generation.

[0050] Step 1022: Perform grid size evaluation processing on the micro-mixing tube grid information according to the micro-mixing tube structure information corresponding to the micro-mixing tube to obtain a grid evaluation value.

[0051] In some embodiments, the execution subject may perform a grid size evaluation process on the micro-mixing tube grid information according to the micro-mixing tube structure information corresponding to the micro-mixing tube to obtain a grid evaluation value. The grid evaluation value may characterize the ability to capture vortex structures of different scales that form turbulence in the mixed gas of air and hydrogen. The larger the grid evaluation value, the more it can capture small-scale vortex structures, thereby improving the accuracy of the uniformity coefficient of the micro-mixing tube.

[0052] In some optional implementations of some embodiments, performing grid size evaluation processing on the micro-mixing tube grid information according to the micro-mixing tube structure information corresponding to the micro-mixing tube to obtain a grid evaluation value may include the following steps:

[0053] In the first step, for each micro-mixing pipe division grid in each micro-mixing pipe division grid included in the micro-mixing pipe grid corresponding to the above micro-mixing pipe grid information, the following grid evaluation steps are performed:

[0054] Sub-step 1, using the Reynolds average calculation method, according to the division grid length of the above-mentioned micro-mixing tube division grid and the calculation domain length in the above-mentioned micro-mixing tube structure information, generate the turbulent kinetic energy value, turbulent dissipation rate and calculation domain velocity for the micro-mixing tube. Among them, the above-mentioned division grid length can be the length of a single grid element surface in the micro-mixing tube grid corresponding to the micro-mixing tube grid information. The above-mentioned calculation domain length can refer to the length of the micro-mixing tube. The above-mentioned turbulent kinetic energy value can characterize the kinetic energy of a unit mass of hydrogen and air mixed gas due to turbulent pulsation. The above-mentioned turbulent dissipation rate can characterize the energy dissipated by a unit mass of mixed gas due to turbulent pulsation per unit time. The above-mentioned calculation domain velocity can characterize the motion state of the mixed gas in the micro-mixing tube, that is, the direction and magnitude of the flow velocity of the mixed gas.

[0055] Sub-step 2, determining the ratio of the second third of the turbulent kinetic energy value to the turbulent dissipation rate as the integral length scale.

[0056] Sub-step 3, determining the ratio of the above integral length scale to the above divided grid length as the grid evaluation value.

[0057] Step 1023 , in response to determining that the grid evaluation value is greater than or equal to a preset grid evaluation threshold, generating a micro-mixing tube uniformity coefficient group according to the micro-mixing tube grid information.

[0058] In some embodiments, the execution subject may generate a micro-mixing tube uniformity coefficient group according to the micro-mixing tube grid information in response to determining that the grid evaluation value is greater than or equal to a preset grid evaluation threshold. The preset grid evaluation threshold may be a pre-set critical value for determining the division of the micro-mixing tube grid. For example, the preset grid evaluation threshold may be 5. The micro-mixing tube uniformity coefficient in the micro-mixing tube uniformity coefficient group may characterize the degree of mixing distribution of hydrogen and air at various positions at the micro-mixing tube outlet.

[0059] In some optional implementations of some embodiments, generating a micro-mixing tube uniformity coefficient group according to the micro-mixing tube grid information may include the following steps:

[0060] In the first step, the ratio of the above-mentioned calculation domain length and the above-mentioned calculation domain velocity is determined as the flow time.

[0061] The second step is to determine the ratio of each of the divided grid lengths corresponding to the above-mentioned divided grids of each micro-mixing pipe and the above-mentioned computational domain speed as the grid scale time, and obtain the grid scale time set.

[0062] The third step is to determine the ratio of the above flow time to each grid scale time in the above grid scale time set as the time step number to obtain the time step number set.

[0063] The fourth step is to use the large eddy simulation calculation method to generate the hydrogen mass fraction, velocity value, temperature and oxygen mass fraction of the microelement surface area of ​​each micro-mixing tube grid at the outlet of the micro-mixing tube according to the above-mentioned time step set and the above-mentioned flow time, and obtain the hydrogen mass fraction set, velocity value set, temperature set and oxygen mass fraction set at the outlet of the micro-mixing tube. Among them, the above-mentioned hydrogen mass fraction can be the proportion of the hydrogen mass in the total mass of the mixed gas at the outlet of the micro-mixing tube. The above-mentioned velocity value can be the velocity of the mixed gas at the outlet of the micro-mixing tube. The above-mentioned temperature can be the temperature of the mixed gas during the flow process. The above-mentioned oxygen mass fraction can be the proportion of the oxygen mass in the total mass of the mixed gas at the outlet of the micro-mixing tube.

[0064] The fifth step is to generate a mixed gas equivalence ratio according to each hydrogen mass fraction and the corresponding oxygen mass fraction in the above hydrogen mass fraction set to obtain a mixed gas equivalence ratio set. The above mixed gas equivalence ratio can characterize the combustion state of the mixed gas.

[0065] As an example, the execution subject may perform the following determination steps for each hydrogen mass fraction in the hydrogen mass fraction set: first, determine the product of 0.23 and the hydrogen mass fraction as the hydrogen mass fraction product. Then, determine the ratio of the hydrogen mass fraction product and the corresponding hydrogen mass fraction as the mixed gas mass fraction ratio. Finally, determine the product of 34.7826 and the mixed gas mass fraction ratio as the mixed gas equivalence ratio.

[0066] The sixth step is to generate the hydrogen mass fraction distribution uniformity coefficient, velocity distribution uniformity coefficient, temperature distribution uniformity coefficient and mixed gas equivalence ratio distribution uniformity coefficient as the micro-mixing tube uniformity coefficient group according to the above hydrogen mass fraction set, the above velocity value set, the above temperature set and the above mixed gas equivalence ratio set. Among them, the above hydrogen mass fraction distribution uniformity coefficient can characterize the distribution of hydrogen at various positions at the outlet of the micro-mixing tube. The above velocity distribution uniformity coefficient can characterize the distribution of the velocity of the mixed gas at various positions at the outlet of the micro-mixing tube. The above temperature distribution uniformity coefficient can characterize the distribution of the temperature of the mixed gas at various positions at the outlet of the micro-mixing tube. The above mixed gas equivalence ratio distribution uniformity coefficient can characterize the distribution of the mixed gas equivalence ratio at various positions at the outlet of the micro-mixing tube.

[0067] Optionally, the generating of the hydrogen mass fraction distribution uniformity coefficient, the velocity distribution uniformity coefficient, the temperature distribution uniformity coefficient and the mixed gas equivalence ratio distribution uniformity coefficient as the micro-mixing tube uniformity coefficient group according to the hydrogen mass fraction set, the velocity value set, the temperature set and the mixed gas equivalence ratio set may include the following steps:

[0068] The first step is to determine the average value of the above hydrogen mass fraction set to obtain the average value of the hydrogen mass fraction in the micro-mixing tube.

[0069] The second step is to determine the product of the standard deviation of each hydrogen mass fraction in the hydrogen mass fraction set and the average value of the hydrogen mass fraction surface of the micro-mixing tube, and the grid microelement surface area corresponding to the hydrogen mass fraction, as the micro-mixing tube hydrogen mass fraction standard deviation microelement surface value, and obtain the micro-mixing tube hydrogen mass fraction standard deviation microelement surface value set. The above-mentioned grid microelement surface area can be the area of ​​the micro-mixing tube divided grid.

[0070] The third step is to determine the sum of the above-mentioned micro-mixing tube hydrogen mass fraction standard deviation infinitesimal face value set as the hydrogen mass fraction infinitesimal face value.

[0071] The fourth step is to determine the product of the surface average value of the hydrogen mass fraction of the micro-mixing tube and the integral of the surface area of ​​the grid microelement as the surface score of the hydrogen mass fraction of the micro-mixing tube. The integral of the surface area of ​​the grid microelement may be the grid area of ​​the entire grid of the micro-mixing tube, that is, the sum of the surface areas of each grid divided by each micro-mixing tube.

[0072] The fifth step is to determine the ratio of the above-mentioned hydrogen mass fraction microelement face value to the above-mentioned micro-mixing tube hydrogen mass fraction area integral value as the relative deviation of the micro-mixing tube hydrogen mass fraction.

[0073] The sixth step is to determine the difference between the first preset value and the relative deviation of the hydrogen mass fraction of the micro-mixing tube as the hydrogen mass fraction distribution uniformity coefficient. The first preset value may be a pre-set value. The first preset value may be 1.

[0074] The seventh step is to generate the velocity distribution uniformity coefficient, the temperature distribution uniformity coefficient and the mixed gas equivalence ratio distribution uniformity coefficient based on the velocity value set, the temperature set and the mixed gas equivalence ratio set.

[0075] As an example, the above-mentioned execution entity can replace the above-mentioned hydrogen mass fraction set with the above-mentioned velocity value set, the above-mentioned temperature set and the above-mentioned mixed gas equivalence ratio set, so as to execute the first step to the sixth step again to obtain the above-mentioned velocity distribution uniformity coefficient, the above-mentioned temperature distribution uniformity coefficient and the above-mentioned mixed gas equivalence ratio distribution uniformity coefficient.

[0076] Optionally, in response to determining that the grid evaluation value is greater than or equal to a preset grid evaluation threshold, after generating a micro-mixing tube uniformity coefficient group according to the micro-mixing tube grid information, the method may further include the following steps:

[0077] In response to determining that the grid evaluation value is less than the preset grid evaluation threshold, the micro-mixing pipe grid corresponding to the micro-mixing pipe grid information is mesh encrypted to obtain the encrypted micro-mixing pipe grid information. The mesh encryption process may be divided into encrypted mesh encryption processes.

[0078] Step 103, in response to determining that each micro-mixing tube uniformity coefficient in the obtained micro-mixing tube uniformity coefficient set is greater than or equal to a preset uniformity threshold, determine a thermal conductivity physical property parameter set of the nozzle under a cold working condition as a cold thermal conductivity physical property parameter set.

[0079] In some embodiments, the execution subject may determine the thermal conductivity physical property parameter set of the nozzle under cold working conditions in response to determining that each micro-mixing tube uniformity coefficient in the obtained micro-mixing tube uniformity coefficient set is greater than or equal to a preset uniformity threshold, as a cold thermal conductivity physical property parameter set. The preset uniformity threshold may be a pre-set critical value for judging the uniformity of the mixed gas in the micro-mixing tube. For example, the preset uniformity threshold may be 0.95. The cold thermal conductivity physical property parameter in the cold thermal conductivity physical property parameter set may be a physical factor related to thermal conductivity that affects the prediction of nozzle uniformity due to the large temperature difference between air and hydrogen in the nozzle under cold working conditions and the influence of thermal conductivity on the distribution of the mixed gas. The cold thermal conductivity physical property parameter set may include: thermal conductivity, specific heat capacity, and thermal diffusivity. The thermal conductivity, specific heat capacity, and thermal diffusivity may be obtained by the following fitting formula: the product of 0.02362 and the temperature of the nozzle under cold working conditions and the sum of 12.5317 are determined as the thermal conductivity under cold conditions. The product of 0.23989 and the temperature of the nozzle under the cold condition and 338.62951 were determined as the specific heat capacity under the cold condition. The product of 0.00264 and the temperature of the nozzle under the cold condition and 2.77498 were determined as the thermal conductivity under the cold condition.

[0080] As an example, the above-mentioned execution entity can take the nickel-based high-temperature alloy GH3536 as the nozzle material as an example. In response to determining that each micro-mixing tube uniformity coefficient group in the obtained micro-mixing tube uniformity coefficient group set is greater than or equal to a preset uniformity threshold, by collecting a large amount of data and using a linear fitting algorithm, the thermal conductivity physical property parameter set of the above-mentioned nozzle under cold conditions is determined as the cold thermal conductivity physical property parameter set.

[0081] Optionally, in response to determining that each micro-mixing tube uniformity coefficient group in the obtained micro-mixing tube uniformity coefficient group set is greater than or equal to a preset uniformity threshold, after determining the thermal conductivity physical property parameter set of the nozzle under the cold working condition as the cold thermal conductivity physical property parameter set, the method may further include the following steps:

[0082] In response to determining that each micro-mixing tube uniformity coefficient group in the obtained micro-mixing tube uniformity coefficient group set is less than a preset uniformity threshold, the micro-mixing tube is geometrically adjusted. In practice, the geometric adjustment may be an adjustment to increase the length of the micro-mixing tube.

[0083] Step 104 , generating a cold nozzle uniformity coefficient of the nozzle under a cold working condition according to the cold thermal conductivity physical property parameter set and the nozzle grid information.

[0084] In some embodiments, the execution entity may generate a cold nozzle uniformity coefficient of the nozzle under cold conditions based on the cold thermal conductivity physical property parameter set and the nozzle grid information, wherein the nozzle grid information is information of a grid obtained by gridding the nozzle according to the nozzle structure information. The cold nozzle uniformity coefficient may characterize the degree of mixing and distribution of hydrogen and air at various positions at the nozzle outlet under cold conditions. The nozzle grid information may be information of a grid obtained by gridding the solid and fluid of the nozzle. The nozzle grid information may include: the length and area of ​​a single solid grid microelement surface of the nozzle, and the length and area of ​​a single fluid grid microelement surface.

[0085] In some optional implementations of some embodiments, the generating of the cold nozzle uniformity coefficient of the nozzle under the cold working condition according to the cold thermal conductivity physical property parameter set and the nozzle grid information may include the following steps:

[0086] In the first step, the Reynolds time-averaged method and the component transport calculation model are used to generate a cold nozzle hydrogen mass fraction set for each nozzle microelement surface area in the nozzle grid corresponding to the above nozzle grid information according to the above cold thermal conductivity physical property parameter set and the above nozzle grid information. Among them, the cold nozzle hydrogen mass fraction in the above cold nozzle hydrogen mass fraction set can represent the proportion of the hydrogen mass at the nozzle outlet in the total mass of the mixed gas. The above nozzle microelement surface area can be the area of ​​each small grid of the nozzle grid.

[0087] The second step is to determine the average value of the above cold nozzle hydrogen mass fraction set to obtain the surface average value of the cold nozzle hydrogen mass fraction.

[0088] The third step is to determine the difference between each cold nozzle hydrogen mass fraction in the cold nozzle hydrogen mass fraction set and the surface average of the cold nozzle hydrogen mass fraction as the cold nozzle hydrogen mass fraction error, and obtain the cold nozzle hydrogen mass fraction error set.

[0089] The fourth step is to determine the square of the ratio of each cold nozzle hydrogen mass difference in the above cold nozzle hydrogen mass difference set to the surface average of the above cold nozzle hydrogen mass fraction as the cold nozzle hydrogen mass fraction discrete rate, and obtain the cold nozzle hydrogen mass fraction discrete rate set.

[0090] The fifth step is to determine the sum of the above cold nozzle hydrogen mass fraction discrete rate set as the cold nozzle uniformity coefficient.

[0091] Step 105 , in response to determining that the cold nozzle uniformity coefficient is greater than a preset nozzle uniformity coefficient threshold, adjusting the geometry of the nozzle.

[0092] In some embodiments, the execution subject may adjust the geometry of the nozzle in response to determining that the cold nozzle uniformity coefficient is greater than a preset nozzle uniformity coefficient threshold. The preset nozzle uniformity coefficient threshold may be a pre-set value for determining whether the geometry of the nozzle is reasonable under cold conditions. For example, the preset nozzle uniformity coefficient threshold may be 0.04. The geometry adjustment may be an adjustment of the shape of the nozzle.

[0093] Optionally, after step 105, the execution subject may further perform the following steps:

[0094] The first step is to determine the nozzle temperature rise value of the nozzle temperature increase caused by heat radiation during the simulated combustion process.

[0095] The second step is to determine the thermal conductivity physical property parameter set when the nozzle reaches the hot state during the simulated combustion process according to the nozzle temperature rise value, as the hot state thermal conductivity physical property parameter set. The hot state thermal conductivity physical property parameter set may be the hot state thermal conductivity, hot state specific heat capacity and hot state thermal diffusivity of the nozzle in the hot state. The above determination may be performed by collecting a large number of values ​​for data fitting.

[0096] As an example, the above-mentioned execution subject can first determine the hot-state operating temperature corresponding to the hot-state operating condition, and determine the thermal conductivity temperature rise value of the nozzle temperature increase caused by convective heat transfer and heat conduction through fluid-heat coupling simulation. Secondly, determine the sum of the above-mentioned hot-state operating temperature, the above-mentioned thermal conductivity temperature rise value and the above-mentioned nozzle temperature rise value as the hot-state temperature value. Again, determine the product of 0.02326 and the above-mentioned hot-state temperature value and 12.53176 as the hot-state thermal conductivity. Then, determine the product of 0.23989 and the above-mentioned hot-state temperature value and 338.62951 as the hot-state specific heat capacity. Finally, determine the product of 0.00264 and the above-mentioned hot-state temperature value and 2.77498 as the hot-state thermal diffusivity.

[0097] The third step is to determine the hot nozzle uniformity coefficient of the nozzle under hot working conditions according to the thermal conductivity parameter set and the nozzle grid information. The hot nozzle uniformity coefficient can characterize the distribution of gas at the nozzle outlet during simulated combustion.

[0098] As an example, the execution subject can use the RANS algorithm and the partially premixed combustion model to determine the hot nozzle hydrogen mass fraction set at the nozzle outlet under hot conditions according to the thermal conductivity parameter set and the nozzle grid information. The hot nozzle hydrogen mass fraction set is input into the uniformity calculation formula corresponding to the first to fifth steps to obtain the hot uniformity coefficient.

[0099] The fourth step is to determine whether to adjust the geometric structure of the nozzle according to the hot nozzle uniformity coefficient and the preset nozzle uniformity coefficient threshold.

[0100] As an example, the execution subject may determine that the geometric structure design of the nozzle is reasonable and no geometric structure adjustment is required in response to determining that the hot nozzle uniformity coefficient is less than or equal to a preset nozzle uniformity coefficient threshold. In response to determining that the hot nozzle uniformity coefficient is greater than a preset nozzle uniformity coefficient threshold, the nozzle is geometrically adjusted.

[0101] It should be noted that by simulating and predicting the uniformity of the nozzle at the outlet under both cold and hot conditions, the adaptability of the nozzle array under different working conditions can be improved, the universality of the nozzle and the rationality of the geometric structure design can be improved, the occurrence of flashback can be reduced, and damage to the pure hydrogen gas turbine can be avoided.

[0102] In some optional implementations of some embodiments, determining the nozzle temperature rise value of the nozzle temperature rise caused by the heat radiation of the nozzle during the simulated combustion process may include the following steps:

[0103] The first step is to determine the product of the nozzle diameter in the nozzle structure information and the second preset value as the average jet line length of the nozzle under the heat radiation of the high-temperature gas. The second preset value may be a pre-set value. The second preset value may be 0.6. The average jet line length may be the average straight-line distance of energy transmitted in the nozzle under the heat radiation caused by the high-temperature gas.

[0104] The second step is to obtain the water vapor component ratio of the nozzle under the thermal radiation of the high-temperature gas and the nozzle working pressure of the nozzle. Among them, due to the heat conduction of thermal radiation, the main components of the high-temperature gas are superheated water vapor and nitrogen. The water vapor component ratio can represent the proportion of superheated water vapor in the high-temperature gas. The nozzle working pressure can represent the pressure value of the nozzle in the high-temperature gas.

[0105] The third step is to determine the product of the water vapor component ratio and the nozzle working pressure as the water vapor partial pressure. The water vapor partial pressure can represent the pressure of superheated water vapor in the high-temperature fuel gas.

[0106] The fourth step is to determine the product of the water vapor pressure correction coefficient and the water vapor emissivity according to the water vapor partial pressure as the water vapor thermal emissivity. The water vapor pressure correction coefficient and the water vapor emissivity may be values ​​obtained by looking up the water vapor partial pressure table. The water vapor thermal emissivity may characterize the ratio of the outward radiated energy of water vapor at the temperature and wavelength corresponding to the high-temperature fuel gas to the radiated energy of the black body radiation source under the same conditions. The water vapor pressure correction coefficient may be the difference between the temperature corresponding to the high-temperature fuel gas and the ideal condition. The water vapor emissivity may be the emissivity of water vapor under the reference parameter.

[0107] The fifth step is to generate the radiation force of the high-temperature gas on the nozzle under the hot working condition according to the above-mentioned water vapor hot emissivity and the high-temperature gas temperature corresponding to the hot working condition as the high-temperature gas radiation force. The above-mentioned high-temperature gas radiation force can represent the force exerted by the high-temperature gas on the nozzle through thermal radiation.

[0108] As an example, the execution subject may first determine the fourth power of the ratio of the high-temperature gas temperature to 100 to obtain a first temperature value. Then, the product of the first temperature value, the water vapor thermal emissivity, and the blackbody radiation coefficient is determined as the high-temperature gas radiation force. The blackbody radiation coefficient may be a constant, which is .

[0109] The sixth step is to determine the product of the water vapor pressure correction coefficient and the water vapor reference absorption rate, and obtain the water vapor absorption rate under the hot working condition as the water vapor hot absorption rate. The water vapor reference absorption rate is a value obtained by looking up the table according to the water vapor partial pressure. The water vapor hot absorption rate can characterize the absorption rate of superheated water vapor in high-temperature fuel gas. The water vapor reference absorption rate can be the absorption rate of water vapor under the reference parameter.

[0110] In the seventh step, based on the above-mentioned water vapor thermal absorption rate and the nozzle wall temperature corresponding to the hot working condition, the radiation force of the wall gas on the nozzle in the hot state is generated as the wall gas radiation force. The above-mentioned wall gas radiation force can represent the force exerted by the nozzle wall gas on the nozzle through thermal radiation.

[0111] As an example, the execution subject may first determine the gas temperature of the gas near the nozzle wall. Then, determine the fourth power of the ratio of the gas temperature to 100 as the second temperature value. Finally, determine the product of the water vapor thermal absorption rate, the second temperature value, and the black body radiation coefficient as the wall gas radiation force.

[0112] Step 8: Determine the difference between the product of the water vapor thermal emissivity and the high-temperature gas radiation force and the product of the water vapor thermal absorptivity and the wall gas radiation force as the heat flux density. The heat flux density may be the heat energy of the high-temperature gas passing through the nozzle per unit area per unit time.

[0113] The ninth step is to determine the ratio of the product of the heat flux density and the nozzle wall thickness to the thermal conductivity of the nozzle material as the nozzle temperature rise value, wherein the nozzle wall thickness and the thermal conductivity of the nozzle material are values ​​obtained from the nozzle structure information. The temperature difference value may be a temperature value of the temperature rise caused by thermal radiation.

[0114] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: the uniformity prediction method applicable to the pure hydrogen micro-mixing combustion chamber nozzle array of some embodiments of the present disclosure can improve the accuracy of nozzle array prediction by predicting the uniformity of micro-mixing tubes and nozzles in cold and hot states, avoid the occurrence of flashback, improve the design rationality of micro-mixing tubes and nozzles, and avoid damage to gas turbines. Specifically, the reasons for the need for a large amount of storage resources and computing resources when predicting the same structure, and the low accuracy of the predicted uniformity of the micro-mixing tube and the nozzle when predicting the uniformity of the same structure under the same storage resources and computing resources, and the increase of the occurrence of flashback and the damage rate of the pure hydrogen gas turbine are: due to the use of statistical methods or programming algorithms, a large number of tests are required when predicting the uniformity under each working condition, and each test requires a large amount of test data, the combustion chamber test cycle is long, the test frequency is low, and the test cost is high, resulting in the need for a large amount of storage resources and computing resources when predicting the uniformity of the same structure, and the low accuracy of the predicted uniformity of the micro-mixing tube and the nozzle when predicting the uniformity of the same structure under the same storage resources and computing resources, and the increase of the occurrence of flashback and the damage rate of the pure hydrogen gas turbine. Based on this, the uniformity prediction method for the nozzle array of the pure hydrogen micro-mixing combustion chamber in some embodiments of the present disclosure can first obtain the micro-mixing tube structure information set and nozzle structure information of the micro-mixing tube set and the nozzle in the pure hydrogen micro-mixing combustion chamber, wherein the above-mentioned nozzle is a structure composed of the micro-mixing tube set. Here, the micro-mixing tube structure information and the nozzle structure information are used for the subsequent meshing of the micro-mixing tube and the nozzle. Secondly, for each micro-mixing tube in the above-mentioned micro-mixing tube set, the following generation steps are performed: the first step is to mesh the above-mentioned micro-mixing tube according to the micro-mixing tube structure information corresponding to the above-mentioned micro-mixing tube to obtain the micro-mixing tube mesh information. Here, the fluid domain of the micro-mixing tube is discretized into a series of micro-element areas to facilitate the subsequent evaluation of the mesh size. The second step is to perform mesh size evaluation processing on the above-mentioned micro-mixing tube mesh information according to the micro-mixing tube structure information corresponding to the above-mentioned micro-mixing tube to obtain a mesh evaluation value. Here, the turbulent flow of the mixed gas in the micro-mixing tube can be described more accurately and the computing resources can be optimized. The third step is to generate a micro-mixing tube uniformity coefficient group according to the above-mentioned micro-mixing tube mesh information in response to determining that the above-mentioned grid evaluation value is greater than or equal to the preset grid evaluation threshold. Here, the accuracy of the subsequent micro-mixing tube uniformity coefficient can be improved, which is convenient for judging whether the micro-mixing tube design is reasonable. Again, in response to determining that each micro-mixing tube uniformity coefficient in the obtained micro-mixing tube uniformity coefficient set is greater than or equal to the preset uniformity threshold, the thermal conductivity physical property parameter set of the above nozzle under the cold working condition is determined as the cold thermal conductivity physical property parameter set. Here, due to the large temperature difference between air and hydrogen, determining the cold thermal conductivity physical property parameter set can be more in line with the actual situation, improving the accuracy of the cold thermal conductivity physical property parameter set and the accuracy of subsequent uniformity.Subsequently, according to the above-mentioned cold state thermal conductivity physical property parameter set and nozzle grid information, the cold state nozzle uniformity coefficient of the above-mentioned nozzle under cold state conditions is generated, wherein the above-mentioned nozzle grid information is the information of the grid obtained by gridding the above-mentioned nozzle according to the above-mentioned nozzle structure information. Here, the accuracy of the uniformity of the nozzle under cold state conditions can be improved. Finally, in response to determining that the above-mentioned cold state nozzle uniformity coefficient is greater than the preset nozzle uniformity coefficient threshold, the geometric structure of the above-mentioned nozzle is adjusted. Here, it can be determined whether the geometric structure design of the nozzle is reasonable, and the occurrence of flashback can be avoided to a certain extent, reducing the damage of the pure hydrogen gas turbine. It can be obtained that the uniformity prediction method of the nozzle array of the pure hydrogen micro-mixing combustion chamber can improve the accuracy of the nozzle array prediction, avoid the occurrence of flashback, improve the design rationality of the micro-mixing tube and the nozzle, and avoid damage to the gas turbine by predicting the uniformity of the micro-mixing tube, the nozzle in the cold state and the hot state.

[0115] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a uniformity prediction device for a pure hydrogen micro-mixed combustion chamber nozzle array. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the uniformity prediction device suitable for a pure hydrogen mild-mix combustion chamber nozzle array can be specifically applied to various electronic devices.

[0116] like Figure 2As shown, a uniformity prediction device 200 suitable for a pure hydrogen micro-mixing combustion chamber nozzle array includes: an acquisition unit 201, an execution unit 202, a determination unit 203, a generation unit 204 and a geometric structure adjustment unit 205. The acquisition unit 201 is configured to: acquire a micro-mixing tube structure information set and nozzle structure information of a micro-mixing tube set and a nozzle in a pure hydrogen micro-mixing combustion chamber, wherein the nozzle is a structure composed of a micro-mixing tube set. The execution unit 202 is configured to: for each micro-mixing tube in the micro-mixing tube set, perform the following generation steps: according to the micro-mixing tube structure information corresponding to the micro-mixing tube, mesh the micro-mixing tube to obtain micro-mixing tube mesh information; according to the micro-mixing tube structure information corresponding to the micro-mixing tube, perform mesh size evaluation processing on the micro-mixing tube mesh information to obtain a mesh evaluation value; in response to determining that the mesh evaluation value is greater than or equal to a preset mesh evaluation threshold, generate a micro-mixing tube uniformity coefficient group according to the micro-mixing tube mesh information. The determination unit 203 is configured to: in response to determining that each micro-mixing tube uniformity coefficient in the obtained micro-mixing tube uniformity coefficient set is greater than or equal to a preset uniformity threshold, determine the thermal conductivity physical property parameter set of the above-mentioned nozzle under cold working conditions as a cold thermal conductivity physical property parameter set. The generation unit 204 is configured to: generate the cold nozzle uniformity coefficient of the above-mentioned nozzle under cold working conditions according to the above-mentioned cold thermal conductivity physical property parameter set and the nozzle grid information, wherein the above-mentioned nozzle grid information is the information of the grid obtained by gridding the above-mentioned nozzle according to the above-mentioned nozzle structure information. The geometric structure adjustment unit 205 is configured to: in response to determining that the above-mentioned cold nozzle uniformity coefficient is greater than the preset nozzle uniformity coefficient threshold, perform geometric structure adjustment on the above-mentioned nozzle.

[0117] It can be understood that the units described in the uniformity prediction device 200 for the pure hydrogen mild-mixed combustion chamber nozzle array are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the uniformity prediction device 200 for the pure hydrogen mild-mixed combustion chamber nozzle array and the units contained therein, and will not be described in detail here.

[0118] Reference below Figure 3 , which shows a structural schematic diagram of an electronic device (eg, an electronic device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0119] like Figure 3As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 to a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0120] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0121] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0122] It should be noted that the computer-readable medium mentioned above in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0123] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (Hyper Text Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0124] The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist independently without being assembled into the electronic device. The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: obtains the micro-mixing tube structure information set and nozzle structure information of the micro-mixing tube set and the nozzle in the pure hydrogen micro-mixing combustion chamber, wherein the above-mentioned nozzle is a structure composed of the micro-mixing tube set; for each micro-mixing tube in the above-mentioned micro-mixing tube set, performs the following generation steps: according to the micro-mixing tube structure information corresponding to the above-mentioned micro-mixing tube, meshes the above-mentioned micro-mixing tube to obtain micro-mixing tube grid information; according to the micro-mixing tube structure information corresponding to the above-mentioned micro-mixing tube, meshes the above-mentioned micro-mixing tube grid information to obtain a grid evaluation value; in response to determining that the above-mentioned grid evaluation value is greater than or equal to a preset grid size, the grid size evaluation process is performed on ... a grid evaluation threshold, and generating a micro-mixing tube uniformity coefficient group according to the micro-mixing tube grid information; in response to determining that each micro-mixing tube uniformity coefficient in the obtained micro-mixing tube uniformity coefficient group is greater than or equal to a preset uniformity threshold, determining a thermal conductivity physical property parameter set of the nozzle under a cold condition as a cold thermal conductivity physical property parameter set; generating a cold nozzle uniformity coefficient of the nozzle under a cold condition according to the cold thermal conductivity physical property parameter set and the nozzle grid information, wherein the nozzle grid information is information of a grid obtained by gridding the nozzle according to the nozzle structure information; in response to determining that the cold nozzle uniformity coefficient is greater than the preset nozzle uniformity coefficient threshold, adjusting the geometric structure of the nozzle.

[0125] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0126] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0127] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be provided in a processor, for example, may be described as: a processor comprising an acquisition unit, an execution unit, a first determination unit, a generation unit, a second determination unit, and a geometry adjustment unit. Among them, the names of these units do not constitute a limitation on the unit itself in certain circumstances, for example, the acquisition unit may also be described as "a unit for acquiring a micro-mixing tube structure information set and a nozzle structure information of a micro-mixing tube set and a nozzle in a pure hydrogen micro-mixing combustion chamber".

[0128] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0129] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A uniformity prediction method for a pure hydrogen mild-mix combustion chamber nozzle array, comprising: Acquire a micro-mixing tube structure information set and nozzle structure information of a micro-mixing tube set and a nozzle in a pure hydrogen micro-mixing combustion chamber, wherein the nozzle is a structure composed of a micro-mixing tube set; For each micro-mixing tube in the micro-mixing tube set, the following generation steps are performed: According to the micro-mixing tube structure information corresponding to the micro-mixing tube, the micro-mixing tube is grid-divided to obtain micro-mixing tube grid information; According to the micro-mixing tube structure information corresponding to the micro-mixing tube, a grid size evaluation process is performed on the grid information of the micro-mixing tube to obtain a grid evaluation value; In response to determining that the grid evaluation value is greater than or equal to a preset grid evaluation threshold, a micro-mixing tube uniformity coefficient group is generated according to the micro-mixing tube grid information, wherein the micro-mixing tube uniformity coefficient group is a turbulent kinetic energy value set, a turbulent dissipation rate set, and a computational domain velocity set of each micro-mixing tube division grid for determining the micro-mixing tube grid information, and is input into a large eddy simulation calculation method to obtain a hydrogen mass fraction set, a velocity value set, a temperature set, an oxygen mass fraction set, and a mixed gas equivalence ratio set, and then input into a uniformity calculation formula to obtain a hydrogen mass fraction distribution uniformity coefficient, a velocity distribution uniformity coefficient, a temperature distribution uniformity coefficient, and a mixed gas equivalence ratio distribution uniformity coefficient, as the micro-mixing tube uniformity coefficient group, which characterizes the degree of mixing distribution of hydrogen and air in each division grid at the micro-mixing tube outlet; In response to determining that each micro-mixing tube uniformity coefficient in the obtained micro-mixing tube uniformity coefficient set is greater than or equal to a preset uniformity threshold, determining a thermal conductivity physical property parameter set of the nozzle under a cold working condition as a cold thermal conductivity physical property parameter set; According to the cold state thermal conductivity physical property parameter set and the nozzle grid information, a cold state nozzle uniformity coefficient of the nozzle under the cold state working condition is generated, wherein the nozzle grid information is information of a grid obtained by gridding the nozzle according to the nozzle structure information, and the cold state nozzle uniformity coefficient is the sum of a cold state nozzle hydrogen mass fraction set of each nozzle micro-element surface area at the nozzle grid information outlet and a discrete rate set of a cold state nozzle hydrogen mass fraction surface average value, which characterizes the mixing distribution of hydrogen and air on each micro-element surface at the nozzle outlet; In response to determining that the cold nozzle uniformity coefficient is greater than a preset nozzle uniformity coefficient threshold, a geometric structure adjustment is performed on the nozzle.

2. The method according to claim 1, wherein: The method further comprises: Determining a nozzle temperature rise value of a nozzle temperature increase caused by heat radiation of the nozzle during a simulated combustion process; According to the nozzle temperature rise value, determining a thermal conductivity physical property parameter set when the nozzle reaches a hot state during the simulated combustion process as a hot state thermal conductivity physical property parameter set; Determining a hot nozzle uniformity coefficient of the nozzle under hot working conditions according to the hot thermal conductivity physical property parameter set and the nozzle grid information; Whether to perform geometric structure adjustment on the nozzle is determined according to the hot nozzle uniformity coefficient and the preset nozzle uniformity coefficient threshold.

3. The method according to claim 1, wherein: The step of performing grid size evaluation processing on the grid information of the micro-mixing tube according to the micro-mixing tube structure information corresponding to the micro-mixing tube to obtain a grid evaluation value includes: For each micro-mixing pipe division grid in each micro-mixing pipe division grid included in the micro-mixing pipe grid corresponding to the micro-mixing pipe grid information, the following grid evaluation step is performed: Using the Reynolds average calculation method, according to the grid length of the micro-mixing tube grid and the calculation domain length in the micro-mixing tube structure information, a turbulent kinetic energy value, a turbulent dissipation rate and a calculation domain velocity for the micro-mixing tube are generated; Determine a ratio of the second third power of the turbulent kinetic energy value to the turbulent dissipation rate as an integral length scale; The ratio of the integral length scale to the divided grid length is determined as a grid evaluation value.

4. The method according to claim 3, wherein: Generating a micro-mixing tube uniformity coefficient group according to the micro-mixing tube grid information includes: determining a ratio of the computational domain length to the computational domain velocity as flow time; Determine a ratio of each of the divided grid lengths corresponding to each of the divided grids of the micro-mixing tube and the computational domain speed as a grid scale time, and obtain a grid scale time set; Determine a ratio of the flow time to each grid-scale time in the grid-scale time set as a time step, and obtain a time step set; Using a large eddy simulation calculation method, according to the time step set and the flow time, the hydrogen mass fraction, velocity value, temperature and oxygen mass fraction of each micro-mixing tube grid at the micro-mixing tube outlet are generated to obtain a hydrogen mass fraction set, a velocity value set, a temperature set and an oxygen mass fraction set at the micro-mixing tube outlet; generating a mixed gas equivalence ratio according to each hydrogen mass fraction and the corresponding oxygen mass fraction in the hydrogen mass fraction set, to obtain a mixed gas equivalence ratio set; According to the hydrogen mass fraction set, the velocity value set, the temperature set and the mixed gas equivalence ratio set, a hydrogen mass fraction distribution uniformity coefficient, a velocity distribution uniformity coefficient, a temperature distribution uniformity coefficient and a mixed gas equivalence ratio distribution uniformity coefficient are generated as a micro-mixing tube uniformity coefficient group.

5. The method according to claim 4, wherein: The method of generating a hydrogen mass fraction distribution uniformity coefficient, a velocity distribution uniformity coefficient, a temperature distribution uniformity coefficient and a mixed gas equivalence ratio distribution uniformity coefficient as a micro-mixing tube uniformity coefficient group according to the hydrogen mass fraction set, the velocity value set, the temperature set and the mixed gas equivalence ratio set includes: Determine the average value of the hydrogen mass fraction set to obtain the average value of the hydrogen mass fraction of the micro-mixing tube; Determine the product of the standard deviation of each hydrogen mass fraction in the hydrogen mass fraction set and the surface average of the hydrogen mass fraction of the micro-mixing tube and the area of ​​the grid microelement corresponding to the hydrogen mass fraction as the micro-mixing tube hydrogen mass fraction standard deviation microelement face value, and obtain the micro-mixing tube hydrogen mass fraction standard deviation microelement face value set; Determine the sum of the micro-mixing tube hydrogen mass fraction standard deviation micro-element face value set as the hydrogen mass fraction micro-element face value; Determine the product of the surface average value of the hydrogen mass fraction in the micro-mixing tube and the integral of the surface area of ​​the grid microelement as the area score of the hydrogen mass fraction in the micro-mixing tube; Determine the ratio of the microelement face value of the hydrogen mass fraction to the area integral value of the hydrogen mass fraction in the micro-mixing tube as the relative deviation of the hydrogen mass fraction in the micro-mixing tube; Determine a difference between a first preset value and a relative deviation of the hydrogen mass fraction in the micro-mixing tube as a hydrogen mass fraction distribution uniformity coefficient; The velocity distribution uniformity coefficient, the temperature distribution uniformity coefficient and the mixed gas equivalence ratio distribution uniformity coefficient are generated according to the velocity value set, the temperature set and the mixed gas equivalence ratio set.

6. The method according to claim 1, wherein: Generating a cold nozzle uniformity coefficient of the nozzle under a cold working condition according to the cold thermal conductivity physical property parameter set and the nozzle grid information includes: Using the Reynolds time-averaged method and the component transport calculation model, according to the cold state thermal conductivity physical property parameter set and the nozzle grid information, a cold state nozzle hydrogen mass fraction set for each nozzle microelement surface area in the nozzle grid corresponding to the nozzle grid information is generated; Determine the average value of the cold nozzle hydrogen mass fraction set to obtain a surface average value of the cold nozzle hydrogen mass fraction; Determine the difference between each cold nozzle hydrogen mass fraction in the cold nozzle hydrogen mass fraction set and the surface average value of the hydrogen mass fraction as a cold nozzle hydrogen mass fraction error, and obtain a cold nozzle hydrogen mass fraction error set; Determine the square of the ratio of each cold nozzle hydrogen mass fraction error in the cold nozzle hydrogen mass fraction error set to the cold nozzle hydrogen mass fraction surface average value as the cold nozzle hydrogen mass fraction discrete rate, and obtain a cold nozzle hydrogen mass fraction discrete rate set; The sum of the cold nozzle hydrogen mass fraction discrete rate set is determined as the cold nozzle uniformity coefficient.

7. The method according to claim 2, wherein: The step of determining a nozzle temperature rise value of a nozzle temperature rise caused by heat radiation during the simulated combustion process includes: Determine the product of the nozzle diameter in the nozzle structure information and a second preset value as the average jet length of the nozzle under the condition of radiation heat exchange of high-temperature fuel gas; Obtaining the water vapor component ratio of the nozzle and the nozzle working pressure of the nozzle under the condition of radiation heat exchange of high-temperature fuel gas; Determine the product of the water vapor component ratio and the nozzle working pressure as the water vapor partial pressure; According to the water vapor partial pressure, determining the product of the water vapor pressure correction coefficient and the water vapor emissivity as the water vapor thermal emissivity; According to the water vapor hot state emissivity and the high temperature gas temperature corresponding to the hot state working condition, a radiation force of the high temperature gas on the nozzle under the hot state working condition is generated as the high temperature gas radiation force; Determine the product of the water vapor pressure correction coefficient and the water vapor reference absorption rate to obtain the water vapor absorption rate under hot working conditions as the water vapor hot absorption rate; According to the water vapor thermal absorption rate and the nozzle wall temperature corresponding to the thermal working condition, generating the radiation force of the wall gas on the nozzle in the thermal state as the wall gas radiation force; Determine the difference between the product of the water vapor thermal emissivity and the high-temperature gas radiation force and the product of the water vapor thermal absorptivity and the wall gas radiation force as the heat flux density; The ratio of the product of the heat flux density and the nozzle wall thickness to the thermal conductivity of the nozzle material is determined as the nozzle temperature rise value, wherein the nozzle wall thickness and the thermal conductivity of the nozzle material are values ​​obtained from the nozzle structure information.

8. A uniformity prediction device for a pure hydrogen mild-mix combustion chamber nozzle array, comprising: An acquisition unit is configured to acquire a micro-mixing tube structure information set and nozzle structure information of a micro-mixing tube set and a nozzle in a pure hydrogen micro-mixing combustion chamber, wherein the nozzle is a structure composed of a micro-mixing tube set; The execution unit is configured to execute the following generation steps for each micro-mixing tube in the micro-mixing tube set: grid-dividing the micro-mixing tube according to the micro-mixing tube structure information corresponding to the micro-mixing tube to obtain micro-mixing tube grid information; performing grid size evaluation processing on the micro-mixing tube grid information according to the micro-mixing tube structure information corresponding to the micro-mixing tube to obtain a grid evaluation value; in response to determining that the grid evaluation value is greater than or equal to a preset grid evaluation threshold, generating a micro-mixing tube uniformity coefficient group according to the micro-mixing tube grid information, wherein the micro-mixing tube uniformity coefficient group is determined The turbulent kinetic energy value set, turbulent dissipation rate set and computational domain velocity set of each micro-mixing tube grid division of the micro-mixing tube grid information are input into the large eddy simulation calculation method to obtain the hydrogen mass fraction set, velocity value set, temperature set, oxygen mass fraction set and mixed gas equivalence ratio set, which are then input into the uniformity calculation formula to obtain the hydrogen mass fraction distribution uniformity coefficient, velocity distribution uniformity coefficient, temperature distribution uniformity coefficient and mixed gas equivalence ratio distribution uniformity coefficient as the micro-mixing tube uniformity coefficient group, which characterizes the degree of mixing distribution of hydrogen and air in each grid division at the outlet of the micro-mixing tube; a determination unit configured to determine a thermal conductivity physical property parameter set of the nozzle under a cold working condition as a cold thermal conductivity physical property parameter set in response to determining that each micro-mixing tube uniformity coefficient in the obtained micro-mixing tube uniformity coefficient set is greater than or equal to a preset uniformity threshold; A generating unit is configured to generate a cold nozzle uniformity coefficient of the nozzle under a cold working condition according to the cold thermal conductivity physical property parameter set and the nozzle grid information, wherein the nozzle grid information is information of a grid obtained by gridding the nozzle according to the nozzle structure information, and the cold nozzle uniformity coefficient is the sum of a cold nozzle hydrogen mass fraction set of each nozzle micro-element surface area at the nozzle grid information outlet and a discrete rate set of a cold nozzle hydrogen mass fraction surface average value, which characterizes the mixing distribution of hydrogen and air at each micro-element surface at the nozzle outlet; The geometry adjustment unit is configured to adjust the geometry of the nozzle in response to determining that the cold nozzle uniformity coefficient is greater than a preset nozzle uniformity coefficient threshold.

9. An electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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