Flow field optimization element performance quantification evaluation method and system

Abstract

The application discloses a kind of flow field optimization element performance quantification evaluation method and system, it is related to fluid measurement and flow field optimization technical field, comprising: obtaining the configuration information of flow field optimization element and corresponding flow condition;Based on configuration information and flow condition, the flow field optimization effect of flow field optimization element to target measurement section is analyzed;Mapping relationship between configuration information and flow field optimization effect is established;Based on mapping relationship, the contribution degree of flow field optimization element to the improvement of flow rate measuring instrument measurement accuracy is quantified.The application establishes the cross-level mapping relationship from component configuration to flow field optimization effect to measurement accuracy contribution degree, realizes the systematic quantitative evaluation of the performance of optimization component, provides scientific basis for component selection and configuration under complex working conditions, effectively improves the accuracy of fluid flow measurement.

Description

Technical Field

[0001] This invention belongs to the field of fluid measurement and flow field optimization technology, specifically relating to a method and system for quantitative evaluation of the performance of flow field optimization components. Background Technology

[0002] In the field of industrial fluid flow measurement, especially when using flow velocity measuring instruments such as Pitot tubes, the uniformity of velocity and temperature distribution in the flow field is crucial to the accuracy of the measurement results. The measurement principle of flow velocity measuring instruments relies on the uniformity and stability of the incoming flow velocity distribution. When the incoming flow velocity distribution is asymmetrical or vortex regions exist, the local velocity sensed by the instrument will deviate from the average velocity of the cross-section, thus introducing systematic measurement bias. However, in practical engineering applications, due to limited pipe layout and the presence of flow obstructions such as elbows and valves, the measurement cross-section is often under non-ideal flow conditions, leading to problems such as velocity distribution distortion, secondary flow, and vortices, which seriously affect measurement accuracy. In typical industrial piping systems, the velocity distribution at the measurement cross-section behind an elbow may exhibit a significant skewed distribution with higher velocities on the outer side and lower velocities on the inner side, with the velocity distribution coefficient deviating from the ideal value. This results in a significant increase in the measurement bias of the Pitot tube, which is unacceptable for industrial process control requiring high-precision flow measurement.

[0003] To improve flow field quality, engineering engineers typically install flow field optimization components such as rectifiers, guide vane assemblies, or static mixing units upstream of the measurement section. However, different types of flow field optimization components differ fundamentally in their working mechanisms, applicable conditions, and performance. Tube-type rectifiers achieve rectification through boundary layer development within narrow tubes, performing well in high Reynolds number turbulent conditions, but with significant pressure loss. Blade-type guide vanes correct the flow field by changing the flow direction, significantly improving skewed flow fields after bends, but are sensitive to installation angles. Static mixing units promote temperature homogenization by generating radial mixing, excelling in heat exchange scenarios, but have limited ability to improve velocity distribution. This difference in component performance means that the selection of different components under the same operating conditions will directly affect the final measurement accuracy. Incorrect selection not only fails to improve measurement accuracy but may also exacerbate measurement deviations due to additional pressure loss or flow field disturbances.

[0004] In existing technologies, the selection of these flow field optimization components often remains at the stage of empirical selection or verification based on a single test. This approach has several significant drawbacks. On the one hand, the lack of a systematic quantitative evaluation method makes it difficult to accurately assess the specific improvement effect of components with different structural parameters and arrangements on the uniformity of flow field distribution. Engineers can only rely on manufacturer recommendations or personal experience when selecting components, and cannot make scientific decisions for specific operating conditions. On the other hand, existing technologies fail to establish a quantitative correlation between component characteristics and final measurement accuracy. That is, they cannot establish a predictable correspondence between the structural parameters of the components (such as orifice ratio, blade angle, tube bundle length) and the arrangement (such as the distance of the installation position from the bend) and the degree of improvement in velocity distribution uniformity, the degree of improvement in temperature distribution uniformity, and the final reduction in measurement error. This makes it impossible to predict the performance of a certain type of component under specific operating conditions before actual installation and testing, and each selection requires a lot of time and cost for on-site testing and verification. On the other hand, due to the lack of a unified quantitative evaluation benchmark, it is impossible to objectively compare the performance of different components, making it difficult to form a replicable and scalable selection basis, resulting in the inability to effectively transfer and reuse selection experience under similar working conditions.

[0005] Therefore, there is an urgent need for a method that can systematically and quantitatively evaluate the performance of flow field optimization components. This method needs to be able to construct a complete evaluation chain from the physical characteristics of the components to the intermediate state of the flow field and then to the final measurement effect, establish the mapping relationship between the component characteristics and the flow field optimization effect, and further quantify the contribution of the components to the improvement of measurement accuracy, so as to solve the problems of lack of scientific basis for component selection and difficulty in ensuring measurement accuracy in the existing technology. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a method and system for quantitative evaluation of the performance of flow field optimization components, which addresses the shortcomings of the prior art. The method establishes a mapping relationship between component configuration information and flow field optimization effect and measurement accuracy, thereby realizing a scientific quantitative evaluation of the performance of flow field optimization components. This solves the technical problems of lack of quantitative evaluation basis for the selection of flow field optimization components and difficulty in ensuring measurement accuracy in the prior art.

[0007] The present invention adopts the following technical solution: A method for quantitatively evaluating the performance of flow field optimization components includes the following steps: Obtain the configuration information of the flow field optimization components and the corresponding flow conditions; Based on the configuration information and the flow conditions, the flow field optimization effect of the flow field optimization element on the target measurement section is analyzed; Establish a mapping relationship between the configuration information and the flow field optimization effect; Based on the mapping relationship, the contribution of the flow field optimization element to improving the measurement accuracy of the flow velocity measuring instrument is quantified.

[0008] Preferably, analyzing the flow field optimization effect of the flow field optimization element on the target measurement section includes: The effects of the flow field optimization element on the uniformity of velocity distribution at the target measurement section are analyzed; and the effects of the flow field optimization element on the uniformity of temperature distribution at the target measurement section are also analyzed.

[0009] Preferably, establishing a mapping relationship between the configuration information and the flow field optimization effect includes: Based on computational fluid dynamics simulation results and physical experiment results, a mapping relationship is established between the configuration information and the changes in velocity distribution uniformity and temperature distribution uniformity.

[0010] Preferably, the mapping relationship includes at least one of a functional relationship, a data lookup table, or a prediction model.

[0011] Preferably, quantifying the contribution of the flow field optimization element to improving the measurement accuracy of the flow velocity measuring instrument includes: Based on the mapping relationship, the degree to which the flow field optimization element improves the measurement accuracy of the flow velocity measuring instrument is determined.

[0012] Preferably, the improvement contribution is characterized by at least one of the following indicators: measurement error reduction rate, measurement uncertainty reduction value, or flow velocity distribution coefficient improvement value.

[0013] Preferably, the flow conditions include at least one of the following: upstream straight pipe length, downstream straight pipe length, medium type, velocity distribution, temperature distribution, and humidity variation.

[0014] Secondly, embodiments of the present invention provide a quantitative evaluation system for the performance of flow field optimization components, comprising: The information input unit is configured to acquire the configuration information of the flow field optimization elements and the corresponding flow conditions; The simulation analysis unit is configured to analyze the flow field optimization effect of the flow field optimization element on the target measurement section based on the configuration information and the flow conditions. The experimental comparison unit is configured to establish a mapping relationship between the configuration information and the flow field optimization effect; The evaluation output unit is configured to quantify the contribution of the flow field optimization element to improving the measurement accuracy of the flow velocity measuring instrument based on the mapping relationship.

[0015] Preferably, the data flow between the simulation analysis unit and the experimental comparison unit is directed to the evaluation output unit, so that the evaluation output unit can output the performance quantification evaluation results.

[0016] Preferably, in the simulation analysis unit, the analysis of the flow field optimization effect of the flow field optimization element on the target measurement section includes: The effects of the flow field optimization element on the uniformity of velocity distribution at the target measurement cross section are analyzed; and the effects of the flow field optimization element on the uniformity of temperature distribution at the target measurement cross section are also analyzed. Establishing a mapping relationship between the configuration information and the flow field optimization effect includes: Based on computational fluid dynamics simulation results and physical experiment results, a mapping relationship is established between the configuration information and the changes in velocity distribution uniformity and temperature distribution uniformity. This mapping relationship includes at least one of a functional relationship, a data lookup table, or a prediction model. The quantification of the contribution of the flow field optimization element to improving the accuracy of the flow velocity measurement instrument includes: Based on the mapping relationship, the degree to which the flow field optimization element improves the measurement accuracy of the flow velocity measuring instrument is determined; The improvement contribution is characterized by at least one of the following indicators: measurement error reduction rate, measurement uncertainty reduction value, or flow velocity distribution coefficient improvement value.

[0017] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for quantitatively evaluating the performance of flow field optimization elements.

[0018] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described method for quantitatively evaluating the performance of flow field optimization elements.

[0019] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for quantitatively evaluating the performance of flow field optimization elements.

[0020] In a sixth aspect, embodiments of the present invention provide an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-described method for quantitatively evaluating the performance of flow field optimization elements.

[0021] Compared with the prior art, the present invention has at least the following beneficial effects: A method for quantitatively evaluating the performance of flow field optimization components is proposed. This method acquires the configuration information and corresponding flow conditions of the flow field optimization components, analyzes the flow field optimization effect of the components on the target measurement section based on the configuration information and flow conditions, establishes a mapping relationship between the configuration information and the flow field optimization effect, and quantifies the contribution of the flow field optimization components to improving the measurement accuracy of flow velocity instruments based on the mapping relationship. This constructs a complete evaluation chain from the physical characteristics of the components to the intermediate state of the flow field and finally to the measurement effect, breaking through the limitations of existing technologies that rely on experience-based selection or single-point test comparisons. It makes the performance evaluation of different components comparable and quantitative, providing scientific decision-making support for component selection under complex working conditions.

[0022] Furthermore, by analyzing the changes in velocity distribution uniformity and temperature distribution uniformity of the target measurement section caused by the flow field optimization element, it can be seen that velocity distribution uniformity directly affects the sampling representativeness of flow velocity measurement, while temperature distribution uniformity affects the measurement accuracy of instruments such as thermal flow meters. The dual-dimensional analysis can comprehensively reflect the ability of the flow field optimization element to improve non-ideal flow fields, avoiding the problem of missing key flow field distortions that may occur with single-dimensional evaluation.

[0023] Furthermore, a mapping relationship is established between the configuration information and the changes in velocity distribution uniformity and temperature distribution uniformity based on computational fluid dynamics simulation results and physical experiment results. Simulation analysis provides low-cost, high-coverage parameter scanning capabilities, enabling rapid acquisition of flow field optimization effect predictions under different configurations. Physical experiments provide a verification benchmark under real working conditions, ensuring consistency between predicted results and actual performance. The complementarity of the two makes the mapping relationship both broad and accurate, improving the credibility of the evaluation results.

[0024] Furthermore, by setting the mapping relationship as at least one of a functional relation, a data lookup table, or a prediction model; the functional relation is suitable for scenarios with clear parameter patterns, the data lookup table is suitable for fast queries of discrete configurations, and the prediction model is suitable for fitting complex nonlinear relationships. Multiple forms of expression adapt to different data processing needs and application scenarios, enhancing the flexibility and applicability of the method.

[0025] Furthermore, by determining the degree to which the flow field optimization element improves the measurement accuracy of the flow velocity measuring instrument based on the mapping relationship, the intermediate effects of flow field improvement are further correlated with the final improvement in measurement accuracy. This directly quantifies the contribution of the element to the measurement results, transforming the evaluation results from a qualitative description of flow field improvement into a quantitative indicator of measurement accuracy improvement, which has direct engineering guiding significance.

[0026] Furthermore, the contribution of the improvement is characterized by at least one of the following indicators: measurement error reduction rate, measurement uncertainty reduction value, or flow velocity distribution coefficient improvement value. The measurement error reduction rate directly reflects the improvement in measurement accuracy, the measurement uncertainty reduction value reflects the improvement in the reliability of the measurement results, and the flow velocity distribution coefficient improvement value reflects the quantitative improvement in flow field quality. These indicators make the evaluation results more intuitive and comparable, facilitating horizontal comparison of the performance of different components.

[0027] Furthermore, by setting the flow conditions to at least one of the following: upstream straight pipe length, downstream straight pipe length, medium type, velocity distribution, temperature distribution, and humidity variation, the main operating conditions affecting the flow field distribution are covered, ensuring the integrity of the operating condition input during the evaluation process. This enables the evaluation model to adapt to performance evaluation under different installation and medium conditions, thus improving the universality of the evaluation model.

[0028] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0029] In summary, this invention constructs a complete quantitative evaluation chain for flow field optimization components, breaking through the limitations of experience-based selection. By combining two-dimensional flow field analysis with simulation experiments, it accurately quantifies the effect of components on improving measurement accuracy, shortens the test cycle by more than 85%, significantly reduces engineering costs, and forms a closed-loop evaluation system, providing scientific support for high-precision flow measurement in industry.

[0030] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0031] Figure 1 This is a flowchart of the flow field optimization element performance quantification evaluation method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the performance quantification evaluation system for flow field optimization elements according to an embodiment of the present invention; Figure 3 A schematic diagram of a computer device provided in an embodiment of the present invention; Figure 4 This is a block diagram of a chip provided according to an embodiment of the present invention.

[0032] Among them, 60. Computer equipment; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0035] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0036] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0037] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0038] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0039] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0040] In existing technologies, the selection of flow field optimization components lacks a systematic evaluation chain from component physical characteristics to flow field state and finally to measurement results. Engineers can only rely on experience or single test verification for selection, making it impossible to predict the component's performance under specific operating conditions before installation. This results in a lack of scientific basis for selection decisions and high costs for on-site testing. This invention provides a method for quantitatively evaluating the performance of flow field optimization components. By constructing a complete evaluation chain from component physical characteristics to flow field state and finally to measurement results, it achieves a systematic quantitative evaluation of the performance of optimization components, transforming selection decisions from experience-driven to data-driven, and significantly reducing the time and cost of on-site testing verification.

[0041] Example 1 Please see Figure 1 The present invention provides a method for quantitatively evaluating the performance of flow field optimization components, comprising the following steps: Step S1: Obtain the configuration information of the flow field optimization elements and the corresponding flow conditions. Specifically, flow field optimization elements refer to devices used to improve the fluid flow state within pipelines, such as rectifiers, guide vane assemblies, and static mixing units. Configuration information refers to a set of data that uniquely identifies the geometric characteristics and installation status of the element. For standardized rectifiers, configuration information may include their type (e.g., tube bundle type, blade type), structural parameters (e.g., tube bundle length, diameter, blade angle), and arrangement (e.g., distance from the installation location to bends). For non-standard elements, configuration information may also include detailed 3D model data or design drawings. Flow conditions refer to the parameters of the operating environment in which the element operates. These can be obtained from pipeline layout parameters in design drawings or from real-time data such as medium temperature, pressure, and flow velocity collected by field sensors. For example, in industrial field applications, configuration information can be input by engineers through a human-machine interface, while flow conditions can be automatically read through a distributed control system (DCS) interface.

[0042] Step S2: Based on the configuration information and flow conditions, analyze the flow field optimization effect of the flow field optimization element on the target measurement section. The physical meaning of flow field optimization effect lies in characterizing the degree of improvement in the flow state of fluid after passing through the optimization element. In this embodiment, the target measurement section usually refers to the cross-sectional location where the velocity measuring instrument (such as a Pitot hydrostatic tube) is installed. Analyzing the flow field optimization effect essentially involves comparing the changes in fluid dynamic parameters at this cross-section before and after the installation of the element. For example, without the optimization element installed, due to the influence of upstream bends or valves, the velocity distribution at the target cross-section may exhibit significant asymmetry or the presence of vortex regions, resulting in uneven velocity distribution. After the optimization element is installed, the fluid is rectified or guided, the velocity distribution tends to be uniform, and vortices weaken or disappear. This degree of improvement in velocity distribution is what is referred to as the flow field optimization effect in this embodiment. It should be understood that the flow field optimization effect is not limited to the improvement of velocity distribution, but may also include improvements in temperature distribution uniformity, reductions in turbulence intensity, etc., depending on the factors affecting measurement accuracy in the application scenario.

[0043] Step S3: Establish the mapping relationship between configuration information and flow field optimization effect. Mapping relationships serve as a bridge connecting component characteristics and performance. Since different configuration information (such as different rectifier orifice ratios and different blade installation angles) will produce significantly different flow field optimization effects under different flow conditions (such as different Reynolds numbers and different upstream disturbance intensities), it is necessary to establish a correspondence between the two. This mapping relationship can be understood as a function model or data association, with the component configuration information and flow conditions as inputs, and the predicted flow field optimization effect as the output. By establishing mapping relationships, discrete experimental data or simulation results can be transformed into a queryable and predictable knowledge base, thereby allowing the performance of a certain type of component under specific operating conditions to be predicted before actual installation and testing.

[0044] Step S4: Based on the mapping relationship, quantify the contribution of the flow field optimization element to the improvement of the measurement accuracy of the flow velocity measuring instrument. The contribution of improvement is the ultimate quantitative indicator for evaluating component performance. The measurement accuracy of flow velocity instruments is highly dependent on the uniformity and stability of the velocity distribution of the incoming flow. Through the mapping relationship established in step S3, the degree to which a specific component improves the flow field uniformity can be determined, and thus the specific contribution of this improvement to measurement accuracy can be derived. For example, if the mapping relationship shows that a rectifier can improve the velocity distribution coefficient by 20%, based on fluid mechanics principles and instrument measurement characteristics, the specific value or percentage reduction in the measurement error of the Pitot hydrostatic tube due to this improvement can be calculated. Quantifying the contribution of improvement has significant guiding value for engineering selection. Engineers can use this quantitative indicator to scientifically weigh cost, installation space, and performance improvement, selecting the flow field optimization component scheme with the best cost-effectiveness, avoiding the drawbacks of relying on experience or blind trial and error in traditional methods.

[0045] In summary, this embodiment addresses the problem of the lack of a systematic quantitative evaluation chain in the selection of flow field optimization components in the prior art. By constructing a complete evaluation chain from component configuration information to flow field optimization effect and then to the contribution of measurement accuracy improvement, it achieves the objective comparison of the performance of different flow field optimization components under a unified quantitative benchmark, providing a scientific basis for engineering selection and significantly reducing the time and cost of on-site test verification.

[0046] Example 2: This embodiment, based on Embodiment 1, provides a detailed explanation of the specific analytical dimensions of the flow field optimization effect and the construction method of the mapping relationship. In existing technologies, the evaluation of the flow field optimization effect often focuses only on a single dimension (such as examining only velocity distribution), and the data source is singular (relying solely on simulation or experiment), leading to one-sided and unreliable evaluation results. To address this issue, this embodiment comprehensively analyzes the flow field optimization effect from two dimensions: velocity distribution and temperature distribution, and establishes a mapping relationship using a combination of simulation and experiment to ensure the comprehensiveness and accuracy of the evaluation results.

[0047] In step S2, the analysis of the flow field optimization effect of the flow field optimization element on the target measurement section specifically includes the analysis of the change in the velocity distribution uniformity of the target measurement section by the flow field optimization element, and the analysis of the change in the temperature distribution uniformity of the target measurement section by the flow field optimization element.

[0048] The change in velocity distribution uniformity can be characterized by comparing the velocity profile coefficient or velocity distribution distortion index before and after the installation of the optimization element. For example, without the optimization element installed, due to the centrifugal force of the upstream bend, the velocity distribution at the measurement section may exhibit a significant skewed distribution with higher velocity on the outer side and lower velocity on the inner side, resulting in poor velocity distribution uniformity. After installing the rectifier, the fluid flow is cut and guided by the rectifier unit, making the velocity at each point of the section tend to be uniform, with the velocity profile coefficient approaching 1.0. This degree of improvement is the result of the change in velocity distribution uniformity. The change in temperature distribution uniformity, especially in pipelines involving heat exchange or mixing processes, can be evaluated by calculating the variance or range of the temperature field at the cross section. Optimization elements, such as static mixing units, can promote the mixing of hot and cold fluids by generating radial flow, thereby reducing the cross section temperature gradient, decreasing the temperature field variance, and improving the uniformity of temperature distribution. Through analysis in these two dimensions, the ability of flow field optimization elements to improve the fluid dynamics state can be comprehensively captured.

[0049] In step S3, the establishment of the mapping relationship between configuration information and flow field optimization effect is specifically based on computational fluid dynamics simulation results and physical experiment results, establishing a mapping relationship between configuration information and changes in velocity distribution uniformity and temperature distribution uniformity.

[0050] This embodiment employs a dual verification mechanism of "simulation as the primary method and experimental correction." First, a pipe model incorporating flow field optimization elements is constructed using computational fluid dynamics (CFD) software. Different configuration information (such as rectifier orifice ratio and blade installation angle) and flow conditions (such as different Reynolds numbers) are set, and numerical simulations are performed to obtain a large amount of simulation data. However, simple simulations may contain deviations due to the selection of turbulence models or simplification of boundary conditions. Therefore, this embodiment introduces physical experimental results for correction. Specifically, a corresponding test loop is built in a fluid dynamics laboratory, and the velocity distribution of the target cross-section is measured using a Pitot hydrostatic tube array or a hot-wire anemometer, while the temperature distribution is measured using a thermocouple array. The experimental data is compared with the simulation data, and the boundary conditions or turbulence parameters of the simulation model are corrected using the experimental data, thereby improving the accuracy of the mapping relationship. This combined approach leverages the low-cost advantage of obtaining large amounts of data through simulation while ensuring consistency between the data and physical reality.

[0051] Regarding the specific manifestation of the mapping relationship, this embodiment provides a variety of optional implementation methods to adapt to different data processing needs and application scenarios.

[0052] As an alternative implementation, the mapping relationship can be a functional relationship. For example, by regression analysis of simulation and experimental data, a quadratic polynomial function relationship of "rectifier aperture ratio - velocity uniformity index" or an exponential function relationship of "guide vane angle - temperature field variance" can be fitted. Using this functional relationship, the predicted optimization effect under a specific configuration can be directly calculated.

[0053] As an alternative implementation, the mapping relationship can be a data lookup table. Discrete configuration information (such as the diameter, number of tubes, and length of a tube bundle rectifier) ​​and the corresponding flow field optimization effect data are created into a two-dimensional or multi-dimensional table and stored in a database. When specific configuration parameters are input, the system quickly obtains the corresponding optimization effect by looking up the table and using an interpolation algorithm.

[0054] As another alternative implementation, the mapping relationship can also be a predictive model. Especially when there is a complex nonlinear relationship between configuration parameters and optimization results, a predictive model can be constructed based on machine learning algorithms such as neural networks or support vector machines. Simulation and experimental data are used as training samples to train the model, and the trained model can accurately predict the optimization results of unknown configuration schemes. It should be understood that the above three forms can be used individually or in combination; for example, combining function interpolation with a lookup table, or embedding empirical formulas in the predictive model, all fall within the scope of this invention.

[0055] In summary, this embodiment addresses the problems of limited evaluation dimensions and single data sources in existing technologies, which lead to biased and unreliable evaluation results. By comprehensively analyzing the flow field optimization effect from two dimensions—velocity distribution and temperature distribution—and establishing a mapping relationship through a combination of simulation and experiment, it achieves comprehensiveness and accuracy in the evaluation results, providing a reliable data foundation for subsequent quantification of contribution.

[0056] Example 3: This embodiment, based on the mapping relationship established in Embodiment 2, provides a detailed explanation of how to quantify the contribution of flow field optimization components to improving the measurement accuracy of flow velocity measuring instruments. In existing technologies, even if the improvement effect of flow field optimization components on flow field uniformity can be evaluated, this improvement cannot be translated into directly perceptible and comparable measurement accuracy indicators in engineering. This results in evaluation results remaining at the level of flow field physical parameters, failing to guide actual measurement accuracy improvement decisions. To address this issue, this embodiment further links the flow field improvement effect to the final improvement in measurement accuracy, directly characterizing the contribution of the components to the measurement results through quantitative indicators such as the reduction rate of measurement error, the reduction value of measurement uncertainty, or the improvement value of the flow velocity distribution coefficient.

[0057] In step S4, the contribution of the quantified flow field optimization element to the improvement of the measurement accuracy of the flow velocity measuring instrument specifically includes determining the degree of improvement of the flow field optimization element to the measurement accuracy of the flow velocity measuring instrument based on the mapping relationship.

[0058] The core of this process lies in translating improvements in the physical parameters of the flow field into directly perceptible measurement accuracy indicators in engineering. Since the measurement principle of velocity measuring instruments (such as Pitot hydrostatic tubes) relies on the uniformity and stability of the flow field distribution, the improvement in velocity distribution uniformity brought about by flow field optimization components will inevitably cause a systematic change in the measurement results. This embodiment uses a mapping relationship to link the aforementioned changes in velocity distribution uniformity and temperature distribution uniformity with the final measurement accuracy indicators, thereby achieving the final evaluation of component performance.

[0059] Furthermore, in order to comprehensively and objectively characterize the contribution of the improvement, this embodiment uses at least one of the following indicators: measurement error reduction rate, measurement uncertainty reduction value, or flow velocity distribution coefficient improvement value.

[0060] The calculation logic for the measurement error reduction rate focuses on reflecting the degree of elimination of systematic errors. In practice, under the same flow conditions, the measured values ​​of the flow velocity measuring instrument can be recorded before and after the installation of the flow field optimization element, and compared with the standard flow meter or standard flow velocity value. For example, without the optimization element installed, due to flow field distortion, the measured value of the Pitot hydrostatic tube may deviate from the standard value by 5%; after the optimization element is installed, the flow field is rectified, and the deviation between the measured value and the standard value is reduced to 1%. At this time, the measurement error reduction rate can be determined by calculating the reduction ratio of the absolute value of the deviation. This indicator directly reflects the element's ability to correct measurement accuracy.

[0061] The calculation logic for the reduction in measurement uncertainty focuses on reflecting the degree of suppression of random errors. In actual measurements, measurement data often fluctuates due to factors such as turbulent fluctuations. By analyzing the changes in the standard deviation or variance of the measurement data before and after the installation of the component, the reduction in measurement uncertainty can be quantified. For example, flow field optimization components may significantly narrow the fluctuation range of measurement data by suppressing eddies, thereby reducing the uncertainty range of the measurement results. This is of great significance for industrial scenarios requiring high-confidence measurements.

[0062] The calculation logic for the improvement value of the velocity distribution coefficient focuses on reflecting the degree of optimization of the physical nature of the flow field. The velocity distribution coefficient (such as the velocity profile coefficient) is a key parameter describing the velocity distribution pattern of a cross-section. By comparing the changes in the velocity distribution coefficient on the target measurement cross-section before and after the installation of the component, the improvement in flow field quality can be directly quantified. For example, if the mapping relationship shows that a rectifier can improve the velocity distribution coefficient from 0.85 to 0.95, then this improvement value directly characterizes the component's contribution to the uniformity of the flow field, and the contribution to measurement accuracy can be derived from the instrument characteristic curve. It should be understood that the above three indicators can be used individually or in combination to form a comprehensive evaluation index to adapt to the differentiated requirements for measurement accuracy in different application scenarios.

[0063] In summary, this embodiment addresses the problem in existing technologies where the improvement effect of flow field cannot be translated into measurement accuracy indicators that can be directly perceived in engineering. By further linking the improvement effect of flow field to the improvement of measurement accuracy and characterizing it with quantitative indicators such as the reduction rate of measurement error, the reduction value of measurement uncertainty, or the improvement value of flow velocity distribution coefficient, this embodiment achieves the transformation of evaluation results from the level of flow field physical parameters to the level of measurement accuracy that can be perceived in engineering. This allows the performance of different components to be objectively compared under unified quantitative indicators, directly guiding engineering selection decisions.

[0064] Example 4: This embodiment, based on the above embodiment, provides a detailed explanation of the specific parameters of the flow conditions obtained in step S1 and their impact on the evaluation process. In actual engineering, flow conditions vary greatly. Parameters such as the upstream straight pipe section length, medium type, and temperature distribution directly determine the inlet boundary conditions of the flow field optimization element. If the evaluation method does not consider these differences in operating conditions, the applicability of the established mapping relationship and quantification results under different operating conditions will be greatly reduced, and may even lead to misleading conclusions. To address this issue, this embodiment includes key parameters such as the upstream straight pipe section length, downstream straight pipe section length, medium type, velocity distribution, temperature distribution, and humidity changes in the scope of flow condition acquisition, ensuring the adaptability of the evaluation model to different pipeline layout conditions.

[0065] Specifically, flow conditions include at least one of the following: upstream straight pipe length, downstream straight pipe length, medium type, velocity distribution, temperature distribution, and humidity variation. These parameters directly determine the inlet boundary conditions of the flow field optimization element, thus affecting the accuracy of establishing the mapping relationship.

[0066] The physical significance of upstream and downstream straight pipe lengths lies in characterizing the stability of the fluid flow before it enters the measurement section after passing through flow obstructions (such as elbows and valves). According to fluid mechanics principles, secondary flow or eddies are generated after the fluid flows through flow obstructions, requiring sufficient straight pipe length to return to a stable, fully developed flow state. If the upstream straight pipe length is insufficient, the inlet velocity distribution will exhibit significant distortion. In this case, the rectification burden on the flow field optimization elements increases, and the mapping relationship between their configuration information and optimization effect will change significantly. For example, under conditions where the upstream straight pipe length is short (e.g., less than 5 times the pipe diameter), a rectifier with a specific structure may exhibit excellent rectification performance; however, under conditions where the straight pipe length is long (e.g., greater than 20 times the pipe diameter), the performance improvement contribution of this rectifier may not be significant. Therefore, incorporating the straight pipe length as a key parameter of flow conditions into the input variable of the mapping relationship can significantly improve the adaptability of the evaluation model to different pipeline layout conditions.

[0067] The influence of media type is mainly reflected in the decisive role of fluid physical parameters on flow patterns. Different media types, such as gases, liquids, or steam, have different physical properties such as density and viscosity, which directly determine the Reynolds number of the flow. The Reynolds number is a key dimensionless number for determining the fluid flow state (laminar or turbulent) and its turbulence intensity. The flow resistance characteristics and rectification mechanisms of the same flow field optimization element may differ fundamentally at different Reynolds numbers. For example, some microporous rectification elements may generate excessive pressure loss in high-viscosity liquid (low Reynolds number) environments, while they can well balance rectification effect and pressure loss in gas (high Reynolds number) environments. Therefore, clearly defining the media type helps to construct a mapping relationship library for different operating conditions and improve the accuracy of evaluation results.

[0068] Furthermore, temperature distribution and humidity variations are also environmental parameters that cannot be ignored. Temperature changes alter the viscosity and density of the medium, thus affecting the Reynolds number and velocity profile. In multiphase flow or humid gas measurement scenarios, humidity variations may lead to droplet formation or condensation in the fluid. This not only changes the assumption of single-phase homogeneity of the flow field but may also cause physical blockage or measurement deviations in the sensing orifices of measuring instruments such as Pitot tubes. By acquiring these parameters, the method in this embodiment can fully consider the systematic errors caused by environmental factors when establishing mapping relationships, thereby quantifying the actual contribution of flow field optimization elements to improving measurement accuracy under complex operating conditions.

[0069] In summary, this embodiment addresses the problem that existing evaluation methods do not consider differences in operating conditions, leading to insufficient applicability of mapping relationships and quantification results. By incorporating key parameters such as upstream straight pipe length, downstream straight pipe length, medium type, velocity distribution, temperature distribution, and humidity changes into the flow conditions, this embodiment achieves the effect of broad adaptability of the evaluation model to different pipeline layouts and operating conditions, thereby improving the reliability and applicability of mapping relationships and quantification results under complex operating conditions.

[0070] Example 5: Please see Figure 2 This embodiment provides a quantitative evaluation system for the performance of flow field optimization components. If the aforementioned quantitative evaluation method is performed manually, the data acquisition, simulation calculation, experimental comparison, and result output lack automated connections, resulting in low evaluation efficiency and susceptibility to human error. To address this issue, this embodiment, through a modular architecture design, materializes the evaluation method into an automatically running hardware system, achieving fully automated processing from data acquisition and simulation analysis to result output, significantly improving evaluation efficiency and result reliability. The system specifically includes an information input unit, a simulation analysis unit, an experimental comparison unit, and an evaluation output unit.

[0071] The information input unit is configured to acquire configuration information of flow field optimization components and corresponding flow conditions. This unit serves as the entry point for human-machine interaction, and its physical form can be a keyboard, mouse, scanner, or standard data interface. Specifically, engineers can manually input configuration parameters such as the rectifier model and orifice ratio via the keyboard, or quickly input information by directly reading barcodes on design drawings using a scanner. For flow conditions, such as real-time temperature and pressure data within the pipeline, the information input unit can directly connect to the field distributed control system (DCS) via communication interfaces such as RS485, Modbus, or Ethernet to achieve automatic data acquisition and synchronization. This multi-source data acquisition capability ensures that the evaluation system can adapt to the different needs of offline laboratory analysis and online industrial field evaluation.

[0072] The simulation analysis unit is configured to analyze the flow field optimization effect of flow field optimization elements on the target measurement section based on configuration information and flow conditions. This unit is the core computing engine of the system, and its hardware typically uses workstations or server clusters equipped with high-performance processors (such as multi-core CPUs) and graphics accelerator cards (GPUs). The software environment includes a pre-built computational fluid dynamics (CFD) solver, which can automatically generate or call the corresponding fluid calculation model based on the input configuration information. For example, when the parameters of a certain type of guide vane are input, the simulation analysis unit automatically generates the mesh, sets the boundary conditions, and runs the numerical simulation, outputting preliminary analysis results such as velocity distribution contour maps and pressure loss data of the target section. It should be understood that the simulation analysis unit is not limited to numerical calculation; it may also include a data preprocessing module to normalize the input flow conditions to improve computational efficiency.

[0073] The experimental comparison unit is configured to establish a mapping relationship between configuration information and flow field optimization effects. This unit primarily processes physical experimental data and compares and fuses it with the output of the simulation analysis unit. Hardware-wise, the experimental comparison unit can be a high-performance computer shared with the simulation analysis unit, or it can be a separate dedicated data processing terminal. In practical applications, the experimental comparison unit receives measured data from physical experimental platforms (such as wind tunnels or water circulation loops), such as velocity profile data measured by a Pitot hydrostatic tube array. Through a built-in data correction algorithm, the experimental comparison unit uses the measured data to correct deviations in the simulation model, thereby establishing a more accurate mapping relationship. This mapping relationship can be stored in a local database or uploaded to a cloud server for subsequent querying and retrieval.

[0074] The evaluation output unit is configured to quantify the contribution of flow field optimization components to improving the accuracy of flow velocity measurement instruments based on mapping relationships. This unit is responsible for transforming complex calculation results into intuitive evaluation metrics. The hardware can be a high-resolution display, a printer, or a mobile terminal device. The evaluation output unit integrates an evaluation algorithm that, upon receiving mapping relationship data, automatically calculates metrics such as the reduction rate of measurement error and the reduction in uncertainty, and generates a visual evaluation report. The report may include a comparison of the flow field before and after optimization, a contribution bar chart, and selection recommendations. Engineers can view the evaluation results instantly via a display or print a paper report for archiving.

[0075] Furthermore, this embodiment clarifies the data interaction logic between each unit to ensure efficient flow in the evaluation process. Specifically, data flows from the simulation analysis unit and the experimental comparison unit to the evaluation output unit, allowing the evaluation output unit to output performance quantification evaluation results. This data flow design avoids data transmission redundancy and blockage. The predicted flow field data generated by the simulation analysis unit and the correction coefficient data generated by the experimental comparison unit are uniformly converged to the evaluation output unit. As the data aggregation point, the evaluation output unit can comprehensively call these two types of data for final fusion calculation. For example, the evaluation output unit first reads the velocity uniformity index predicted by the simulation analysis unit, then calls the correction factor provided by the experimental comparison unit to calibrate the index, and finally calculates the contribution to measurement accuracy based on the calibrated data. This architecture design not only ensures the independence and specialized processing capabilities of each functional unit, but also achieves overall system collaboration through a clear data flow, effectively improving the operating efficiency and result reliability of the evaluation system.

[0076] In summary, this embodiment addresses the issues of low efficiency and susceptibility to human error when quantitative evaluation methods are executed manually. By adopting a modular architecture design, the evaluation method is materialized into an automatically running hardware system. This achieves full-process automation from data acquisition and simulation analysis to result output, significantly improving evaluation efficiency and result reliability.

[0077] This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or corresponding function. The processor described in this embodiment can be used for the operation of a flow field optimization element performance quantitative evaluation method, including: Obtain the configuration information and corresponding flow conditions of the flow field optimization element; based on the configuration information and the flow conditions, analyze the flow field optimization effect of the flow field optimization element on the target measurement section; establish a mapping relationship between the configuration information and the flow field optimization effect; based on the mapping relationship, quantify the contribution of the flow field optimization element to improving the measurement accuracy of the velocity measuring instrument.

[0078] Please see Figure 3 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the flow field optimization element performance quantification evaluation method in this embodiment. To avoid repetition, details are omitted here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the flow field optimization element performance quantification evaluation system of this embodiment. To avoid repetition, details are omitted here.

[0079] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 3 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0080] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0081] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.

[0082] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0083] Please see Figure 4 The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0084] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1 The steps are shown in the figure.

[0085] Storage unit 620 may include readable media in the form of volatile storage units, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include read-only memory (ROM) 6203.

[0086] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0087] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0088] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem). This communication can be performed via input / output interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0089] Example 6 This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). More specific examples of the computer-readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0090] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0091] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0092] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the flow field optimization element performance quantification evaluation method in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps: Obtain the configuration information and corresponding flow conditions of the flow field optimization element; based on the configuration information and the flow conditions, analyze the flow field optimization effect of the flow field optimization element on the target measurement section; establish a mapping relationship between the configuration information and the flow field optimization effect; based on the mapping relationship, quantify the contribution of the flow field optimization element to improving the measurement accuracy of the velocity measuring instrument.

[0093] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0094] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0095] Verification was conducted using a typical industrial DN200 carbon steel pipeline: the medium was ambient temperature air, and the Reynolds number Re = 5 × 10⁻⁶. 5 The measurement section was located at the 3D (600mm) outlet of the 90° bend. The performance of different flow field optimization elements was compared, and the experimental data are as follows:

[0096] The main air flow measurement system of a petrochemical plant's catalytic cracking unit originally had its measurement point located 2D after two 90° bends. Without optimization components, the measurement error reached -7.5%, causing an energy consumption calculation deviation of over 8%. Using the method of this invention, 12 rectifier schemes with different parameters were scanned through CFD simulation, combined with 3 sets of physical experiments for verification. Finally, a tube-bundle rectifier with an opening ratio of 65% and a length of 2D was selected. After installation, the measurement error decreased to -0.7%, and the measurement uncertainty decreased from 3.2% to 0.9%, reducing annual energy losses by approximately 1.2 million yuan. The on-site testing and verification time was shortened from the traditional 14 days to 2 days.

[0097] In summary, this invention provides a quantitative evaluation method and system for the performance of flow field optimization components. It constructs a complete quantitative evaluation chain, from component physical characteristics to intermediate flow field states and finally to measurement results, overcoming the limitations of existing technologies that rely on experience-based selection or single-point testing. Through dual-dimensional flow field analysis of velocity and temperature, the establishment of a mapping relationship between simulation and experimental verification, and a quantifiable index system such as the measurement error reduction rate, objective comparison of different flow field optimization components under a unified benchmark is achieved. Applying this method can transform component selection decisions from experience-driven to data-driven, shortening on-site testing and verification time by more than 85%, improving measurement accuracy by 85%-90%, and significantly reducing engineering implementation costs and timelines. Furthermore, the closed-loop evaluation system formed by this method can support flow field diagnosis and active optimization under complex operating conditions, providing scientific and technological support for high precision and high reliability in industrial fluid flow measurement, and possessing significant engineering application value and industry promotion significance.

[0098] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0099] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0100] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0101] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0102] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0103] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0104] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random-access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0105] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this application. It will 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 processor 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 processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0106] 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 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0107] 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.

[0108] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for quantitatively evaluating the performance of flow field optimization components, characterized in that, Includes the following steps: Obtain the configuration information of the flow field optimization components and the corresponding flow conditions; Based on the configuration information and the flow conditions, the flow field optimization effect of the flow field optimization element on the target measurement section is analyzed; Establish a mapping relationship between the configuration information and the flow field optimization effect; Based on the mapping relationship, the contribution of the flow field optimization element to improving the measurement accuracy of the flow velocity measuring instrument is quantified.

2. The method for quantitatively evaluating the performance of flow field optimization elements according to claim 1, characterized in that, The analysis of the flow field optimization effect of the aforementioned flow field optimization element on the target measurement section includes: The effects of the flow field optimization element on the uniformity of velocity distribution at the target measurement section are analyzed; and the effects of the flow field optimization element on the uniformity of temperature distribution at the target measurement section are also analyzed.

3. The method for quantitatively evaluating the performance of flow field optimization elements according to claim 2, characterized in that, Establishing a mapping relationship between the configuration information and the flow field optimization effect includes: Based on computational fluid dynamics simulation results and physical experiment results, a mapping relationship is established between the configuration information and the changes in velocity distribution uniformity and temperature distribution uniformity.

4. The method for quantitatively evaluating the performance of flow field optimization elements according to claim 3, characterized in that, The mapping relationship includes at least one of a functional relationship, a data lookup table, or a prediction model.

5. The method for quantitatively evaluating the performance of flow field optimization elements according to claim 3, characterized in that, The quantification of the contribution of the flow field optimization element to the improvement of the measurement accuracy of the flow velocity measuring instrument includes: Based on the mapping relationship, the degree to which the flow field optimization element improves the measurement accuracy of the flow velocity measuring instrument is determined.

6. The method for quantitatively evaluating the performance of flow field optimization elements according to claim 5, characterized in that, The improvement contribution is characterized by at least one of the following indicators: measurement error reduction rate, measurement uncertainty reduction value, or flow velocity distribution coefficient improvement value.

7. The method for quantitatively evaluating the performance of flow field optimization elements according to claim 1, characterized in that, The flow conditions include at least one of the following: upstream straight pipe length, downstream straight pipe length, medium type, velocity distribution, temperature distribution, and humidity variation.

8. A quantitative evaluation system for the performance of flow field optimization components, characterized in that, include: The information input unit is configured to acquire the configuration information of the flow field optimization elements and the corresponding flow conditions; The simulation analysis unit is configured to analyze the flow field optimization effect of the flow field optimization element on the target measurement section based on the configuration information and the flow conditions. The experimental comparison unit is configured to establish a mapping relationship between the configuration information and the flow field optimization effect; The evaluation output unit is configured to quantify the contribution of the flow field optimization element to improving the measurement accuracy of the flow velocity measuring instrument based on the mapping relationship.

9. The performance quantification evaluation system for flow field optimization elements according to claim 8, characterized in that, The data flow between the simulation analysis unit and the experimental comparison unit is directed to the evaluation output unit, so that the evaluation output unit can output the performance quantitative evaluation results.

10. The performance quantification evaluation system for flow field optimization elements according to claim 8, characterized in that, In the simulation analysis unit, the flow field optimization effect of the flow field optimization element on the target measurement section is analyzed, including: The effects of the flow field optimization element on the uniformity of velocity distribution at the target measurement cross section are analyzed; and the effects of the flow field optimization element on the uniformity of temperature distribution at the target measurement cross section are also analyzed. Establishing a mapping relationship between the configuration information and the flow field optimization effect includes: Based on computational fluid dynamics simulation results and physical experiment results, a mapping relationship is established between the configuration information and the changes in velocity distribution uniformity and temperature distribution uniformity. This mapping relationship includes at least one of a functional relationship, a data lookup table, or a prediction model. The quantification of the contribution of the flow field optimization element to improving the accuracy of the flow velocity measurement instrument includes: Based on the mapping relationship, the degree to which the flow field optimization element improves the measurement accuracy of the flow velocity measuring instrument is determined; The improvement contribution is characterized by at least one of the following indicators: measurement error reduction rate, measurement uncertainty reduction value, or flow velocity distribution coefficient improvement value.

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