Flow field simulation precision standardization verification method and device, storage medium and program product

Abstract

The embodiment of the invention provides a flow field simulation precision standardization verification method and device, a storage medium and a program product, and relates to the technical field of computational fluid mechanics simulation. The method comprises the following steps: acquiring simulation data of a target flow field; determining at least one feature sampling path based on geometric features of the physical model corresponding to the target flow field; extracting a numerical sequence of the flow field parameters from the feature sampling path, and performing normalization processing on the numerical sequence to obtain normalized simulation data; and obtaining experimental reference data corresponding to the target flow field, comparing the normalized simulation data with the experimental reference data, and outputting a verification result for evaluating the simulation precision by calculating at least one quantitative overlap ratio index between the normalized simulation data and the experimental reference data. According to the method provided by the invention, the consistency of verification results among different cases is improved, and the efficiency and reliability of flow field simulation precision evaluation are improved.

Description

Technical Field

[0001] This application relates to the field of computational fluid dynamics (CFD) simulation technology, and in particular to a method, device, storage medium and program product for standardizing the verification of flow field simulation accuracy. Background Technology

[0002] In fluid machinery and engineering design, CFD simulation software is widely used to predict the distribution characteristics of internal flow fields. The accuracy of simulation results directly determines the feasibility of engineering solutions; therefore, comparing simulation data with experimental benchmarks is a necessary step to ensure its reliability.

[0003] Currently, flow field verification mainly relies on engineers' manual operation and experience-based judgment in simulation post-processing software. The typical process includes: loading simulation results, qualitatively identifying flow field characteristics by observing velocity contour maps; then, manually creating sampling paths in suspected critical areas based on personal experience, extracting physical quantity data, and exporting it to external tools for plotting and comparison.

[0004] However, the above implementation method cannot guarantee the consistency of verification results between different cases. Summary of the Invention

[0005] This application provides a standardized verification method, device, storage medium, and program product for flow field simulation accuracy, in order to solve the technical problem that verification results may be inconsistent between different cases.

[0006] In a first aspect, embodiments of this application provide a method for standardized verification of flow field simulation accuracy, the method comprising:

[0007] Obtain simulation data of the target flow field;

[0008] Based on the geometric features of the physical model corresponding to the target flow location, at least one feature sampling path is determined;

[0009] Numerical sequences of flow field parameters are extracted from the feature sampling path, and the numerical sequences are normalized to obtain normalized simulation data.

[0010] Obtain experimental baseline data corresponding to the target flow field, compare the normalized simulation data with the experimental baseline data, calculate at least one quantitative overlap index between the two, and output the verification results for evaluating the simulation accuracy.

[0011] In this embodiment, by establishing objective rules for determining sampling paths based on geometric features, the subjective selection method relying on personal experience is replaced, thereby ensuring systematic coverage of key flow field regions (such as recirculation zones and high gradient zones). Simultaneously, this method employs a unified normalization criterion, eliminating data incomparability issues caused by differences in operating parameters, enabling objective and quantitative horizontal comparisons of simulation accuracy between different cases. Finally, by calculating and outputting a quantitative overlap index, the verification conclusion is elevated from traditional qualitative visual judgment to quantitative objective evaluation, forming a complete, standardized, and repeatable verification process, improving the consistency and efficiency of flow field simulation accuracy verification.

[0012] In one possible implementation, the physical model corresponding to the target flow location is a triangular cavity laminar flow model or a waveform channel turbulent flow model.

[0013] In this implementation, the standardized verification of flow field simulation accuracy provided in this application is validated through specific applications on a triangular cavity laminar flow model and a waveform channel turbulent flow model. The results show that this method can generate quantitative verification results that highly match experimental benchmarks for the aforementioned specific physical models. This demonstrates that the method can effectively achieve standardized evaluation of the simulation accuracy of practical classic cases, and its output results possess objectivity and reliability, providing a direct basis for judging the accuracy of simulation calculations for this type of model.

[0014] In one possible implementation, based on the geometric features of the physical model corresponding to the target flow location, at least one feature sampling path is determined, including:

[0015] The feature sampling path is determined according to the preset sampling rules; for the geometric features of the axis of symmetry, the sampling rule is to set the sampling path along the axis; for the geometric features of the extreme points of wall curvature, the sampling rule is to set the sampling path along the wall normal starting from the extreme point.

[0016] In this implementation, by applying the aforementioned preset rules to the laminar flow model of the triangular cavity and the turbulent flow model of the waveform channel, the objectification and standardization of the sampling path positioning are achieved. This solves the problem that the sampling position depends on human experience and is prone to missing key areas, and provides a reliable input benchmark for subsequent data extraction and comparison.

[0017] In one possible implementation, the numerical sequence is normalized using a max-min normalization method, with the normalization formula as follows:

[0018]

[0019] in, For each data point in the numerical sequence, the result is calculated using the normalization formula. The set of values ​​constitutes the normalized simulation data; and These are normalized parameters determined based on inlet boundary conditions or geometric feature dimensions.

[0020] In this implementation, by using parameters and The determination rules are specified and associated with physical properties, establishing reasonable and consistent comparison benchmarks for flow fields with different physical natures. This solves the problem of data incomparability caused by inconsistent normalization standards, thereby transforming data processing from subjective, empirical scaling to standardized technical steps, providing an objective data foundation for subsequent quantitative accuracy assessment.

[0021] In one possible implementation, and The target flow field is determined by inputting the operating parameters and / or flow field characteristic parameters of the target flow field into a preset mapping relationship;

[0022] The operating parameters include inlet velocity and geometric dimensions, while the flow field characteristic parameters include backflow intensity or velocity gradient. The mapping relationship defines the operating parameters and / or flow field characteristic parameters and normalized parameters for different ranges. and The correspondence rules between the values.

[0023] In this implementation, the determination of normalization parameters is upgraded from a static, globally set approach to a dynamic decision-making process driven by multiple parameters and possessing state-aware capabilities. This enables data processing to adapt to the physical characteristics of different flows, providing a more targeted data benchmark for the quantitative comparison of core flow field features.

[0024] In one possible implementation, the quantification of overlap includes the correlation coefficient and the root mean square error;

[0025] The output is used to evaluate the simulation accuracy, including: when the correlation coefficient is lower than the first preset threshold and the root mean square error is higher than the second preset threshold, the output is a verification result indicating that the simulation model of the region corresponding to the feature sampling path is inaccurate.

[0026] This approach combines quantitative indicators such as correlation coefficient and root mean square error with explicit logical judgment rules to achieve automated and quantitative verification of higher-order physical quantities such as velocity gradient. This not only expands the verification dimension from basic flow fields to derived flow field characteristics, but also replaces subjective visual judgment with objective mathematical criteria, making the evaluation of the simulation model's ability to capture physical characteristics more scientific.

[0027] In one possible implementation, the flow field parameters include a first physical quantity and a second physical quantity, wherein the first physical quantity and the second physical quantity are any two different of velocity, pressure, and turbulent kinetic energy; the method further includes:

[0028] Calculate the first overlap index between the first physical quantity and the corresponding physical quantity in the experimental baseline data, and calculate the second overlap index between the second physical quantity and the corresponding physical quantity in the experimental baseline data.

[0029] Output verification results for evaluating simulation accuracy, including a comprehensive verification result containing the first overlap index and the second overlap index.

[0030] In this implementation method, the verification results not only provide multi-point data, but also reveal the comprehensive performance of the model in reproducing complex physical coupling phenomena by juxtaposing and comparing the accuracy indicators of the first and second physical quantities, thereby achieving a more comprehensive evaluation and avoiding the limitations of a single quantity.

[0031] In a second aspect, this application provides an electronic device, including: a processor and a memory communicatively connected to the processor;

[0032] The memory stores instructions that the computer executes;

[0033] The processor executes computer-executable instructions stored in memory to implement any of the methods of the first aspect.

[0034] Thirdly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method of any one of the first aspects.

[0035] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method of any one of the first aspects. Attached Figure Description

[0036] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0037] Figure 1 A flowchart illustrating a standardized verification method for flow field simulation accuracy provided in this application embodiment;

[0038] Figure 2 A schematic diagram of a triangular cavity physical model provided in an embodiment of this application;

[0039] Figure 3 A schematic diagram of a waveform channel physical model provided in this application embodiment;

[0040] Figure 4 A comparative schematic diagram of the normalized X-direction velocity distribution along the central axis of a triangular cavity, provided in an embodiment of this application;

[0041] Figure 5 A comparative schematic diagram of the normalized X-direction velocity distribution of the waveform channel peak position provided in an embodiment of this application;

[0042] Figure 6 A schematic diagram of the velocity gradient distribution in the X direction along the central axis of a triangular cavity, provided in an embodiment of this application;

[0043] Figure 7 This is a schematic diagram of the velocity gradient distribution along the X direction of the flow direction of a waveform channel peak position, provided in an embodiment of this application.

[0044] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0045] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application.

[0046] It should be noted that the flow field simulation accuracy standardization verification method, equipment, storage medium and program products provided in this application can be used in the field of computational fluid dynamics simulation technology, and can also be used in any field other than computational fluid dynamics simulation technology. This application does not limit the application field of the flow field simulation accuracy standardization verification method, equipment, storage medium and program products.

[0047] The specific application scenarios of this application include various engineering scenarios that rely on computational fluid dynamics simulation for design and analysis. The following are some common application scenarios as examples.

[0048] In the aerospace field, this solution is applicable to the internal flow field verification of complex configurations such as engine combustors and turbine blade channels. The system can identify key flow characteristics such as the swirl zone at the combustor head and the recirculation zone inside the flame tube, and extract temperature, velocity, and component concentration data along the characteristic paths. By quantitatively comparing with experimental benchmarks, the simulation prediction accuracy of key performance indicators such as combustion efficiency and cooling effect can be evaluated.

[0049] In the energy and chemical industry, this solution can be used for flow and heat transfer analysis of equipment such as plate heat exchangers and reactor pipes. When dealing with complex geometries such as corrugated plates and finned tubes, this method can automatically locate characteristic positions such as peaks and troughs, and arrange sampling paths along the normal or flow direction. The system verifies the simulation's ability to capture secondary flow, boundary layer separation, and local heat transfer coefficients, providing a reliable basis for equipment optimization.

[0050] In building and environmental engineering, this solution is applicable to the simulation and verification of ventilation and air conditioning systems in spaces such as data centers and large venues. Based on the symmetry axis of the building structure and the positional relationship between the supply and return air vents, a sampling network covering key areas can be generated to objectively and comprehensively quantify the simulation results of indoor velocity fields, temperature fields, and pollutant concentration fields.

[0051] In the aerodynamic design of transportation vehicles, this scheme can effectively verify the simulation accuracy of external flow fields for automobiles, high-speed trains, and aircraft. The system identifies key areas such as flow separation lines and wake vortex regions based on the curvature of the vehicle body surface and the geometric features of the wing. It also performs standardized comparisons of simulation results for surface pressure distribution and aerodynamic noise sources, providing solid data support for drag reduction and noise reduction design.

[0052] For the above application scenarios, existing technical solutions typically employ a manual post-processing workflow that relies on the individual experience of engineers. The specific implementation steps are as follows:

[0053] First, qualitative observation and empirical judgment of the flow field are conducted. Engineers load the calculation results in CFD post-processing software (such as ANSYS CFX, Fluent), and by viewing the two-dimensional or three-dimensional contour plots of physical quantities such as velocity and pressure, combined with their own experience, they subjectively identify and preliminarily judge the approximate location of key flow features (such as vortex core regions and flow separation regions).

[0054] Secondly, experience-based manual data sampling is performed. Based on the aforementioned qualitative judgments, engineers manually define one-dimensional sampling lines or two-dimensional observation planes for quantitative analysis in the software interface. For example, in the simulation verification of an aero-engine combustion chamber, sampling lines need to be manually laid out at the center of the predicted recirculation zone; in the verification of a heat exchanger corrugated channel, normal sampling lines need to be manually created at geometric abrupt changes such as peaks and troughs based on experience to obtain the velocity gradient near the wall.

[0055] Finally, the distributed data processing and subjective accuracy assessment are completed. The raw simulation data with specific physical dimensions (e.g., velocity in m / s) obtained from each sampling line are exported to external tools (e.g., Excel, MATLAB). Since the entry conditions and geometric dimensions of different simulation cases may vary significantly, engineers need to perform individualized normalization processing on the data (e.g., manually setting a reference velocity). The processed simulation curves and experimental curves are then plotted on the same graph. Finally, by visually observing the overlap of the trends of the two curves, an empirical judgment is made on the accuracy of the simulation model.

[0056] However, the solution described above has the following technical problems:

[0057] First, the above-mentioned approach lacks a unified data normalization standard. Due to differences in inlet boundary conditions (e.g., flow velocity) and the geometric dimensions of physical models in different simulation cases, the extracted raw simulation data have different dimensions and numerical ranges. Direct comparison makes it impossible to establish an objective and comparable quantitative evaluation standard between different cases and different simulation software, thus limiting the verification conclusions to specific working conditions and resulting in poor universality.

[0058] Secondly, the selection of sampling paths in the above schemes is subjective and arbitrary. The sampling location relies entirely on the engineer's personal experience, lacking objective and unified positioning rules. When faced with physical models with complex geometric features (such as triangular cavities and wave channels), empirically selected sampling paths are prone to missing key feature regions in the flow field (such as vortex cores, flow separation zones, and high gradient regions on the walls), thus preventing the verification process from systematically evaluating the simulation model's ability to realistically simulate these core flow states.

[0059] Finally, the entire verification process of the above scheme is fragmented. From visual recognition and manual sampling in CFD post-processing software to exporting data to external spreadsheet tools for individualized data processing and chart creation, each step is disconnected and relies on manual coordination and experience-based judgment. This is not only cumbersome and inefficient, but more importantly, it fails to form a standardized, automatically reusable, and complete set of operating procedures, severely restricting the standardization, consistency, and large-scale application of flow field verification work.

[0060] The standardized verification method for flow field simulation accuracy provided in this application aims to solve the aforementioned technical problems of existing technologies. Firstly, to overcome the subjectivity and arbitrariness in sampling path selection, the determination of the verification path is transformed from relying on human experience to being directly driven by the geometric features of the physical model. By identifying and utilizing key geometric features such as the axis of symmetry and extreme points of wall curvature, characteristic sampling paths covering key regions of the flow field (e.g., recirculation zones, high-speed gradient zones) can be objectively determined.

[0061] Secondly, to address the lack of unified criteria for data normalization, a unified normalization benchmark based on operating conditions is proposed. By setting normalization parameters by associating inlet boundary conditions or geometric feature dimensions, simulation data under different operating conditions are transformed to a unified dimensional benchmark, thereby eliminating the data incomparability caused by differences in inlet flow velocity, model scale, etc., and providing a basis for objective quantitative comparison across cases and software.

[0062] Ultimately, to address the fragmentation and reliance on manual intervention in the entire verification process, this application constructs a closed-loop, automated standard operating procedure. It integrates the discrete steps of feature-driven sampling, unified normalization processing, and subsequent quantization comparison and result output into a coherent, programmable sequence of methods. This concept aims to replace manual intervention with a systematic process, ultimately forming a set of automatically reusable, highly consistent, and standardized verification procedures, thereby improving the efficiency and standardization of the verification process.

[0063] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0064] First, combine Figures 1 to 3 This application introduces a standardized verification method for flow field simulation accuracy provided in its embodiments. Figure 1 This is a flowchart illustrating a standardized verification method for flow field simulation accuracy provided in an embodiment of this application. Figure 2 This is a schematic diagram of a triangular cavity physical model provided in an embodiment of this application. Figure 3 This is a schematic diagram of a waveform channel physical model provided in an embodiment of this application.

[0065] It should be noted that the standardized verification method for flow field simulation accuracy provided in this application can be executed by software modules programmed into a computing device or logic circuits embedded in dedicated hardware. Alternatively, it can be completed by an operator using general-purpose or dedicated computing equipment, guided by the standardized steps defined in this method. The following embodiments illustrate this method as an example, and this description should not be construed as limiting the scope of protection of this application. Figure 1 As shown, the method includes the following steps:

[0066] S101. Obtain simulation data of the target flow field.

[0067] Specifically, the target flow field in this embodiment can be, for example, two classic CFD verification cases: a triangular cavity laminar flow model and a waveform channel turbulent flow model. The simulation data is calculated by a commercial CFD solver (such as ANSYS CFX) and contains complete distribution information of physical fields such as velocity and pressure within the computational domain.

[0068] To visually demonstrate the physical model and preliminary flow patterns, Figure 2 A schematic diagram of the triangular cavity model is provided. Figure 3 A schematic diagram of the waveform channel model is provided. Figure 2 The triangular cavity shown has parallel horizontal lines drawn inside. The variation in the thickness of the lines represents the distribution trend of the flow velocity: the lines near the top are thicker, indicating that the flow velocity in that area is higher; the lines near the bottom are thinner, indicating that the flow velocity in that area is lower. This is consistent with the typical laminar flow characteristics of this type of cavity, where there is a backflow zone at the top and the bottom is close to stagnation.

[0069] Figure 3 The waveform channel shown has a clearly visible wavy wall structure, which is used to characterize the complex turbulent field formed when fluid flows through such a periodic geometry.

[0070] S102. Based on the geometric features of the physical model corresponding to the target flow location, determine at least one feature sampling path.

[0071] The system determines the location of key sampling paths based on the inherent geometric features of the physical model, thereby replacing subjective selection that relies on human experience.

[0072] For the triangular cavity model, this model has a clear axis of symmetry, namely Figure 2 A dashed line runs vertically from the vertex to the midpoint of the base. The system identifies this axis of symmetry and, based on this, determines a straight line coinciding with the axis of symmetry as the feature sampling path, such as... Figure 2 As shown, this path runs through the entire critical flow regime change region from the high-speed recirculation zone (top) to the low-speed stagnation zone (bottom), ensuring that sampling can systematically capture the changes in the recirculation zone.

[0073] For the waveform channel model, the wall surface has a periodic peak and trough structure. The system identifies the two extreme points of wall curvature, the peaks and troughs, and determines the straight lines perpendicular to the wall direction originating from these extreme points as feature sampling paths. For example... Figure 3 As shown, the path (normal at the crest) passes vertically through the channel cross section, and its position is set to ensure that the flow acceleration effect caused by the crest geometry and the significant velocity gradient near the wall can be effectively captured.

[0074] The above rules enable the sampling path to objectively and comprehensively cover the key feature regions in the flow field.

[0075] S103. Extract the numerical sequence of flow field parameters from the feature sampling path, and normalize the numerical sequence to obtain normalized simulation data.

[0076] The system extracts a numerical sequence of target physical quantities (such as velocity in the X direction) from the simulation data along the characteristic sampling path determined by S102, which is a series of data points arranged according to the path position.

[0077] To eliminate the dimensional effects caused by differences in inlet flow velocity and geometric dimensions between different cases, the system performs max-min normalization on the extracted numerical sequences. Specifically, the following formula is used for calculation:

[0078]

[0079] in, These are the original data points in the numerical sequence. These are the normalized values. Normalization parameters. and This is based on the pre-defined inlet boundary conditions or geometric feature dimensions of the physical model. For example, in the case of a triangular cavity laminar flow model, the following can be set: , After calculating based on this formula for all data points, the result is... The set of values ​​constitutes the normalized simulation data.

[0080] S104. Obtain experimental baseline data corresponding to the target flow field, compare the normalized simulation data with the experimental baseline data, calculate at least one quantitative overlap index between the two, and output the verification results for evaluating the simulation accuracy.

[0081] The system acquires experimental baseline data corresponding to the target flow field obtained by experimental methods such as particle image velocimetry (PIV) or laser doppler velocimetry (LDV), and performs the same coordinate alignment and data preprocessing on them.

[0082] Subsequently, the normalized simulation data obtained in S103 is quantitatively compared with the experimental baseline data. This comparison is achieved by calculating the quantitative overlap index between the two; in this embodiment, the correlation coefficient and root mean square error (RMSE) can be selected as the indexes. Finally, the verification results are automatically output based on preset judgment rules. These judgment rules are based on the physical meaning of the quantitative overlap index; for example, when the correlation coefficient used to evaluate trend consistency is higher than a first threshold, and the RMSE used to evaluate the absolute deviation level is lower than a second threshold, the simulation accuracy is deemed acceptable. This result can be output in the form of reports, charts, or signals, providing information for the simulation model's performance. Figure 2 , Figure 3 The simulation accuracy of the key feature regions shown provides an objective and quantitative evaluation basis.

[0083] The standardized verification method for flow field simulation accuracy provided in this embodiment establishes objective rules for determining sampling paths based on geometric features, replacing the subjective selection method that relies on personal experience. This ensures systematic coverage of key areas of the flow field (such as the recirculation zone and high gradient zone). Simultaneously, the method employs a unified normalization criterion, eliminating data incomparability issues caused by differences in operating parameters, enabling objective and quantitative horizontal comparisons of simulation accuracy between different cases. Finally, by calculating and outputting a quantitative overlap index, the verification conclusion is elevated from traditional qualitative visual judgment to quantitative objective evaluation, forming a complete, standardized, and repeatable verification process, improving the consistency and efficiency of flow field simulation accuracy verification.

[0084] In one possible embodiment, for example, it can be combined with Figure 4 and Figure 5 This paper further demonstrates the application and verification results of the flow field simulation accuracy standardization verification method provided in this application embodiment on two classical physical models: a triangular cavity laminar flow model and a waveform channel turbulent flow model. Figure 4 This is a comparative schematic diagram showing the normalized X-direction velocity distribution along the central axis of a triangular cavity, provided in an embodiment of this application. Figure 5 This is a comparative schematic diagram of the normalized X-direction velocity distribution of the waveform channel peak position provided in an embodiment of this application.

[0085] For the triangular cavity laminar flow model, the system determines the feature sampling path based on the model's axis of symmetry (as in the above embodiment). Figure 2 (The described content), and then perform subsequent data extraction, normalization, and comparison processes. To verify the effectiveness of this method on this model, Figure 4 A comparison graph of the normalized X-direction velocity distribution along the central axis of the triangular cavity is shown. The horizontal axis (Y-Coordinate) represents the position along the central axis, and the vertical axis (x_vel_norm) represents the normalized X-direction velocity. The solid black line in the graph represents the simulated velocity distribution curve obtained using this method (corresponding to normalized simulation data), and the black dots represent experimental baseline data points.

[0086] By observing the curves, we can see that: in the section from Y=-4.0 m to -2.0 m, the velocity is close to 0, which represents the stagnant flow zone; in the section from Y=-2.0 m to -1.0 m, the velocity drops to a negative value and reaches a negative peak, clearly capturing the typical recirculation zone characteristics of laminar flow in a triangular cavity; in the section from Y=-1.0 m to 0 m, the velocity changes from negative to positive and recovers to 1.0, reflecting the process of fluid flowing out of the recirculation zone. Figure 4 The high degree of agreement between the simulated velocity distribution curve and the baseline data points intuitively demonstrates that when applied to the laminar flow model of the triangular cavity, this method can effectively extract key flow characteristics. Furthermore, the output results (normalized simulation data) show good consistency with the experimental baseline data, verifying the effectiveness of the method.

[0087] For the waveform channel turbulence model, the system determines the feature sampling path based on the geometric feature of the wave crest (as in the above embodiment). Figure 3 The content described. Figure 5 A comparison diagram of the normalized X-direction velocity distribution at the peak position of the waveform channel is shown to verify the effectiveness of this method on the turbulence model.

[0088] Figure 5 The horizontal axis (y2-norm) represents the normalized coordinates from the wall to the center of the channel, and the vertical axis (x-vel-norm) represents the normalized velocity in the X direction. The solid black line in the figure represents the simulated velocity distribution curve obtained by this method, and the black squares represent the experimental baseline data points.

[0089] By observing the curves, we can see that in the y2-norm=0.0 to 0.5 range, the velocity rises rapidly from 0 to a peak value (about 1.3), which represents the main acceleration zone of the channel; in the y2-norm=0.5 to 0.9 range, the velocity drops from the peak value to 0, which reflects the low-velocity boundary layer near the wall. Figure 5 The close match between the simulated velocity distribution curve and the experimental baseline data points indicates that when this method is applied to the waveform channel turbulence model, it can accurately capture the complex velocity distribution (including the high-speed main flow and the low-speed region at the wall) caused by geometric effects at the wave crest position. The output results are consistent with the experimental measurement results, verifying the applicability of this method in turbulent scenarios.

[0090] The standardized verification of flow field simulation accuracy provided in this application was validated by applying it to a triangular cavity laminar flow model and a waveform channel turbulent flow model. The results show that this method can generate quantitative verification results that closely match experimental benchmarks for the aforementioned specific physical models. This demonstrates that the method can effectively achieve standardized evaluation of the simulation accuracy of real-world classic cases, and its output results possess objectivity and reliability, providing a direct basis for judging the accuracy of simulation calculations for this type of model.

[0091] In one possible embodiment, a feature sampling path is determined based on the geometric features of the physical model corresponding to the target flow location. This can be achieved, for example, through a preset sampling rule. This rule clearly specifies the standardized sampling path generation method corresponding to different geometric features. The following explanation uses two geometric features, namely the axis of symmetry and the extreme point of wall curvature, as examples.

[0092] For the geometric features of the axis of symmetry, the sampling rule is to set a sampling path along this axis. This means that when the system identifies a physical model (e.g., a triangular cavity laminar flow model) with an axis of symmetry, a straight line coinciding with this axis of symmetry will be generated as the feature sampling path. This rule ensures that for symmetrical flow fields, the verification rules are constrained to the centerline that best reflects the overall flow characteristics, avoiding incomplete feature capture due to human bias.

[0093] For the geometric characteristics of wall curvature extrema, the sampling rule is to set a sampling path along the wall normal starting from the extrema point. This means that when the system identifies a curvature extrema point on the wall (such as a peak or trough in a waveform channel), a straight line perpendicular to the wall starting from that point will be generated as the feature sampling path. This rule ensures that the sampling path can accurately penetrate the most critical flow change zones induced by geometric abrupt changes (such as flow separation zones and high-speed gradient zones in the boundary layer), geometrically locking the verification location where the flow field characteristics are most obvious.

[0094] By applying the aforementioned preset rules to the laminar flow model of the triangular cavity and the turbulent flow model of the waveform channel, the objectification and standardization of sampling path positioning are achieved, thereby solving the problem that the sampling position depends on human experience and is prone to missing key areas, and providing a reliable input benchmark for subsequent data extraction and comparison.

[0095] In one possible implementation, the max-min normalization process is implemented in the following specific manner to ensure the consistency and comparability of the data transformation. Normalization parameters and The value of is directly related to the essential properties of the physical model, rather than being arbitrarily assigned by humans. The following two examples illustrate this.

[0096] Example 1: Laminar Flow Model of a Triangular Cavity

[0097] For laminar flow in a triangular cavity driven by a top cover, the flow is entirely dominated by the moving top cover. In this example, the normalization parameters are set directly based on the explicit inlet boundary conditions:

[0098] parameter The value is taken as the known velocity of the moving top cover, for example, 2.0 m / s.

[0099] parameter The value is taken as the velocity corresponding to the initial state of the stationary wall and the cavity, i.e., 0 m / s.

[0100] A series of velocity data points extracted along the central axis Substitute into the formula =( The calculation is performed using (-0) / (2.0-0). After this processing, all simulation and experimental data are converted to the dimensionless interval [0, 1], so that the velocity distribution curves of the recirculation zone obtained under different inlet flow velocities can be directly compared on the same reference in terms of shape and peak position. This allows us to focus on evaluating the simulation's ability to capture the flow structure and eliminates the interference caused by differences in absolute velocity values.

[0101] Example 2: Waveform Channel Turbulence Model

[0102] For pressure-driven waveform channel turbulence, the internal flow is closely related to the channel geometry. In this example, the normalization parameters are derived based on the geometric feature dimensions of the model:

[0103] First, obtain the geometric dimensions of the channel, such as its hydraulic diameter and length. Then, combining known fluid properties (density, viscosity) with flow condition parameters (e.g., average velocity or target pressure drop), calculate the characteristic velocity that characterizes the flow intensity under this condition using the fundamental relationship described by the Darcy-Weisbach equation in fluid mechanics.

[0104] Specifically, if the average flow velocity is known, it can be directly used as the characteristic velocity; if the target pressure drop is known, the corresponding characteristic velocity can be obtained through standard engineering fluid dynamics calculations based on the pressure drop, geometric dimensions, and fluid properties. The characteristic velocity determined in this way is set as... The reference value is set, and 0 m / s corresponding to the no-slip condition on the wall is set as... .

[0105] Subsequently, the velocity data points extracted along the wave crest normal will be... Substitute into the normalization formula Perform the calculation.

[0106] Therefore, after the above-mentioned normalization process based on clear physical meaning, the dimensionless velocities on the cross sections of the simulation results of waveform channels with different flow conditions or different sizes are directly comparable, so as to objectively judge the accuracy of the turbulence model in predicting key physical processes such as wall acceleration effect, flow separation and boundary layer development.

[0107] By parameters and The determination rules are specified and associated with physical properties, establishing reasonable and consistent comparison benchmarks for flow fields with different physical natures. This solves the problem of data incomparability caused by inconsistent normalization standards, thereby transforming data processing from subjective, empirical scaling to standardized technical steps, providing an objective data foundation for subsequent quantitative accuracy assessment.

[0108] In one possible embodiment, and The determination follows the following dynamic adaptive logic, the core of which lies in dynamically adjusting the normalization benchmark based on the real-time physical characteristics of the flow to optimize the ability to characterize different flow field structures.

[0109] The predefined mapping relationship is specifically embodied in a queryable rule base or a computable function. This mapping relationship receives real-time parameters of the target flow field as input, and maps and outputs normalized parameters that match the physical type and specific value of the input parameters. and The value of .

[0110] If the input includes geometric dimensions (such as hydraulic diameter), the mapping relationship can be derived from the dimensions to determine the characteristic velocity, and then set accordingly. .

[0111] When further combining flow field characteristic parameters for finer adjustments, a high value for the recirculation intensity among the input flow field characteristic parameters indicates the presence of a significant recirculation zone in the current flow field. In this case, the flow field should be appropriately expanded according to the preset mapping relationship. and The range of values ​​(e.g., increasing the upper and lower limits proportionally) allows for dynamic adjustment, ensuring that the high-speed characteristics in the recirculation region and the characteristics of the adjacent low-speed region are clearly displayed within a unified numerical range after subsequent normalization processing. This avoids the compression or distortion of flow field details caused by using a fixed, narrow normalization benchmark.

[0112] If the local velocity gradient is large in the input flow field characteristic parameters, it indicates that the flow in that region is changing drastically. In this case, a set of parameters more focused on that local velocity range can be determined based on a preset mapping relationship. and Value selection. By applying this set of more targeted normalization parameters, the contrast details of this key region can be effectively amplified in the normalization results, thereby improving the sensitivity of verification of drastic flow change characteristics.

[0113] By upgrading the determination of normalization parameters from a static, globally set approach to a dynamic decision-making process driven by multiple parameters and possessing state-aware capabilities, data processing can adapt to the physical characteristics of different flows, providing a more targeted data benchmark for the quantitative comparison of core flow field features.

[0114] In one possible embodiment, to illustrate how to automatically output verification results based on the quantified overlap index, the following is combined with... Figure 6 and Figure 7 Taking the velocity gradient, a key flow field characteristic parameter, as an example, we will introduce the application of the judgment rule. Figure 6 This is a schematic diagram of the velocity gradient distribution in the X direction along the central axis of a triangular cavity, provided in an embodiment of this application. Figure 7 This is a schematic diagram of the velocity gradient distribution along the X direction of the flow direction of a waveform channel peak position, provided in an embodiment of this application.

[0115] Example 1, Combination Figure 6 Verification of the development process of the triangular cavity reflux region

[0116] Figure 6 This is a multi-parameter comparative line graph along the central axis of the triangular cavity, primarily used to verify the velocity gradient in the X direction. The horizontal axis represents the normalized position along the central axis, and the vertical axis contains the velocity and velocity gradient values ​​(unit: ...). The figure clearly shows the location of the sampling path through multiple curves (the X and Z coordinate values ​​are stable near the zero point, confirming that the path is along the central axis), and highlights the velocity gradient curve in the X direction (as shown by the black solid line in the figure).

[0117] This velocity gradient curve fully depicts the development process of the recirculation zone: in the section of the horizontal axis 1-10 (corresponding to the inlet of the recirculation zone), the gradient value is positive, indicating that the fluid accelerates in the X direction; in the section of the horizontal axis 10-15 (corresponding to the center of the recirculation zone), the gradient value approaches zero, indicating that the flow velocity reaches its peak and tends to stabilize; in the section of the horizontal axis 15-21 (corresponding to the outlet of the recirculation zone), the gradient value sharply turns negative, indicating that the fluid decelerates sharply or even reverses flow. This gradient curve is a key basis for quantitatively assessing the physical characteristics of the recirculation zone.

[0118] In this embodiment, the system compares the gradient curve obtained from simulation with the baseline gradient curve measured experimentally. First, the correlation coefficient between the two curves is calculated across all data points to assess their consistency in gradient change trends. Simultaneously, the root mean square error of the two curves across all data points is calculated to quantify the overall deviation between the simulation and the experiment in the absolute value of the gradient.

[0119] Subsequently, a preset judgment rule is applied: if the calculated correlation coefficient is lower than the first preset threshold (e.g., 0.95) and the root mean square error is higher than the second preset threshold, then the judgment condition is met simultaneously. At this time, the system will automatically output the verification result, clearly indicating that the simulation model's prediction of the key flow characteristic of "the development process of the triangular cavity reflux zone" is unreliable.

[0120] Example 2, Combination Figure 7 Verification of the flow acceleration effect at the peak position of the waveform channel

[0121] Figure 7 This figure illustrates the velocity gradient distribution along the X-direction at the peak of the waveform channel. The core purpose of this figure is to validate the X-direction velocity gradient to evaluate the model's ability to capture the downstream flow acceleration effect of the peak. The horizontal axis represents the normalized position along the flow direction, and the vertical axis represents the X-direction velocity gradient value (unit: ...). The gradient curve in the figure (corresponding to the black solid line in the figure) shows that in a specific region downstream of the peak (for example, the section corresponding to the horizontal axis 80 to 91), the velocity gradient value increases sharply, which quantitatively indicates the local acceleration effect of the flow.

[0122] In this embodiment, the system focuses on the high-gradient region and extracts gradient data from simulation and experiments for comparison. The correlation coefficient and root mean square error (RMSE) of the data in this region are calculated respectively. If, in this core feature region, the correlation coefficient is lower than a first preset threshold and the RMSE is higher than a second preset threshold, the same judgment rule as in Example 1 is applied. The system will output the verification result, indicating that the simulation model's ability to capture the important turbulence feature of "flow acceleration effect downstream of the waveform channel peak" is insufficient.

[0123] By combining the correlation coefficient and root mean square error (RMSE) as quantitative indicators with explicit logical judgment rules, automated and quantitative verification of higher-order physical quantities such as velocity gradients has been achieved. This not only expands the verification dimension from basic flow fields to derived flow field characteristics, but also replaces subjective visual judgment with objective mathematical criteria, making the evaluation of the simulation model's ability to capture physical characteristics more scientific.

[0124] In one possible implementation, relying solely on comparisons of a single physical quantity (e.g., velocity) to evaluate the accuracy of a flow field simulation may be insufficient in practice. Complex flow phenomena often involve coupling and coordinated changes among multiple physical quantities. If the model matches the experiment in one physical quantity (e.g., velocity distribution) but deviates in another related physical quantity (e.g., pressure gradient), this deviation may be masked by a single comparison, leading to a biased assessment of the model's overall simulation capability.

[0125] Therefore, this embodiment provides an implementation method for jointly verifying multiple physical quantities. In this method, at least two different, physically related parameters selected from the flow field are used as the analysis objects. For ease of description, they can be referred to as the first physical quantity and the second physical quantity. For example, in one specific implementation, the first physical quantity can be the velocity component of the fluid, while the second physical quantity can be the pressure of the fluid. The selection of the first and second physical quantities is based on their ability to jointly characterize key flow features; for example, the combination of velocity and pressure can effectively assess the momentum characteristics of the flow and the wall shear stress.

[0126] In practical implementation, let's take evaluating a waveform channel turbulence model as an example. First, a key feature sampling path is determined based on the channel's geometric characteristics (such as the crest line). Along this path, two sets of data sequences are extracted simultaneously: the first set of data sequences corresponds to a first physical quantity (e.g., the X-direction velocity values ​​at each point along the path), and the second set of data sequences corresponds to a second physical quantity (e.g., the static pressure values ​​at the same points). These two sets of data are strictly one-to-one corresponding in spatial location.

[0127] Subsequently, the data sequences of the first physical quantity (velocity) and the second physical quantity (pressure) are independently normalized to eliminate the influence of their respective dimensions. Then, for the first physical quantity, a first overlap index (e.g., correlation coefficient) is calculated between its normalized data and the corresponding velocity data in the experimental baseline. Simultaneously, for the second physical quantity, a second overlap index (e.g., root mean square error) is calculated between its normalized data and the corresponding pressure data in the experimental baseline. These two indices are calculated independently and reflect the simulation accuracy of the model in different physical dimensions.

[0128] Ultimately, the output verification results are comprehensive. The system performs automatic diagnosis based on pre-defined comprehensive judgment rules for multi-physical quantity verification. For example, the rule might define: if the correlation coefficient of velocity is higher than a passing threshold (e.g., 0.95), but the root mean square error of pressure is simultaneously higher than a tolerance threshold, then the system automatically generates a conclusion that the mainstream structure is accurate but the pressure field has a deviation. The report will simultaneously present the indicators, the comparison status with the thresholds, and the diagnostic conclusion. One possible presentation format is as follows:

[0129] Indicator status: Speed ​​distribution fit: R=0.98 (pass); Pressure distribution deviation: RMSE=0.12 (out of tolerance).

[0130] Diagnostic conclusion: The mainstream structure is accurate, but the pressure field has deviations.

[0131] In this way, the verification results not only provide multi-point data, but also reveal the comprehensive performance of the model in reproducing complex physical coupling phenomena by juxtaposing and comparing the accuracy indicators of the first and second physical quantities, thereby achieving a more comprehensive evaluation and avoiding the limitations of a single quantity.

[0132] The electronic device provided in this application embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0133] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the methods in any of the above method embodiments.

[0134] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the methods in any of the above method embodiments.

[0135] All or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable memory. When the program is executed, it performs the steps of the above method embodiments; and the aforementioned memory (storage medium) includes: read-only memory (ROM), RAM, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof.

[0136] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processing unit of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0137] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0138] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0139] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

[0140] In this application, the term "comprising" and its variations can refer to non-limiting inclusion; the term "or" and its variations can refer to "and / or". The terms "first", "second", etc., in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. In this application, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0141] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0142] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0143] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0144] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0145] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0146] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A flow field simulation accuracy standardization verification method, characterized in that, The method comprises: acquiring simulation data of a target flow field; determining at least one characteristic sampling path based on geometric characteristics of a physical model corresponding to the target flow field; extracting a numerical sequence of flow field parameters from the characteristic sampling path, and performing normalization processing on the numerical sequence to obtain normalized simulation data; acquiring experimental reference data corresponding to the target flow field, comparing the normalized simulation data with the experimental reference data, calculating at least one quantitative coincidence degree index between the two, and outputting a verification result for evaluating simulation accuracy.

2. The method of claim 1, wherein, The physical model corresponding to the target flow field is a triangular cavity laminar flow model or a wave-shaped channel turbulent flow model.

3. The method of claim 2, wherein, The determination of the at least one characteristic sampling path based on the geometric characteristics of the physical model corresponding to the target flow field comprises: determining the characteristic sampling path according to a preset sampling rule; for a symmetric axis geometric feature, the sampling rule is to set a sampling path along the axis; for a wall curvature extreme point geometric feature, the sampling rule is to set a sampling path along the wall normal from the extreme point.

4. The method according to any one of claims 1-3, characterized in that, The normalization processing on the numerical sequence adopts a maximum-minimum normalization method, and the normalization formula is as follows: wherein, for each data point in the numerical sequence, a set of values calculated from the normalization formula for each data point in the numerical sequence, constitute the normalized simulation data; and is a normalization parameter determined based on an inlet boundary condition or a geometric characteristic dimension.

5. The method of claim 4, wherein, The With , by inputting the working condition parameters and / or flow field characteristic parameters of the target flow field into a preset mapping relationship to determine; The working condition parameters include an inlet flow rate and a geometric size, and the flow field characteristic parameters include a backflow intensity or a velocity gradient; the mapping relationship defines a corresponding rule between values of different ranges of the working condition parameters and / or the flow field characteristic parameters and the normalized parameters With the values of the normalized parameters.

6. The method of claim 1, wherein, The quantitative coincidence degree index includes a correlation coefficient and a root mean square error; The output of the verification result for evaluating simulation accuracy comprises: when the correlation coefficient is lower than a first preset threshold and the root mean square error is higher than a second preset threshold, outputting a verification result indicating that the simulation model in the region corresponding to the characteristic sampling path is inaccurate.

7. The method of claim 1, wherein, The flow field parameters include a first physical quantity and a second physical quantity, and the first physical quantity and the second physical quantity are any two different ones of velocity, pressure, and turbulent kinetic energy; the method further comprises: calculating a first coincidence degree index of the first physical quantity and a corresponding physical quantity in the experimental reference data, and calculating a second coincidence degree index of the second physical quantity and a corresponding physical quantity in the experimental reference data; The output of the verification result for evaluating simulation accuracy comprises outputting a comprehensive verification result containing the first coincidence degree index and the second coincidence degree index.

8. An electronic device, comprising: It comprises: a processor and a memory connected in communication with the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to realize the method of any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to realize the method of any one of claims 1 to 7.

10. A computer program product, characterised in that, It comprises a computer program, which is executed by the processor to realize the method of any one of claims 1 to 7.

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