A Parametric Design and Evaluation Method and System for Fine-Diameter Threaded Pipes Based on Digital Twins

Abstract

This invention relates to the field of threaded pipe technology, and more particularly to a parameter design and evaluation method and system for fine-diameter threaded pipes based on digital twins. The method includes: acquiring design parameter information of the fine-diameter threaded pipe and assembling this information into a parameter design scheme; collecting information on the manufacturing materials of the fine-diameter threaded pipe and obtaining multi-dimensional material performance information of these materials; constructing a physical model of the threaded pipe according to the parameter design scheme, selecting the manufacturing materials, and holographically mapping the corresponding multi-dimensional material performance information to the physical model; importing test fluid parameters, running a simulation application, obtaining heat transfer efficiency and pressure drop efficiency, and obtaining parameter design evaluation results based on these efficiency values. This invention effectively solves the problems of low efficiency and difficulty in optimizing multi-objective performance contradictions caused by traditional fine-diameter threaded pipe design relying on experience-based trial and error.

Description

Technical Field

[0001] This invention relates to the field of threaded pipe technology, and in particular to a parameter design and evaluation method and system for fine-diameter threaded pipes based on digital twins. Background Technology

[0002] As a core component of high-efficiency heat exchangers, microreactors, and other equipment, the structural parameters of fine-diameter threaded pipes (such as pitch, thread depth, and pipe diameter) directly affect fluid heat transfer and flow performance. Digital twin technology, by constructing high-fidelity virtual models of physical entities, enables dynamic simulation and optimization throughout the entire lifecycle, and has become a core tool for the design of complex equipment.

[0003] Existing design technologies for fine-diameter threaded pipes are fragmented. The trial-and-error method relies on manual iteration, which can optimize the performance of a single parameter (such as heat exchange efficiency) locally, but cannot cope with the overall optimization of multiple parameter combinations. Although single-objective simulation technology can quantify the flow or heat transfer characteristics under specific working conditions, it severs the coupling effect of multi-physics fields between flow heat transfer and structure, resulting in the design results being unbalanced under actual multi-objective requirements.

[0004] The aforementioned decentralized technologies lack systematic integration. How to overcome the limitations of traditional methods and achieve efficient collaborative design of parameters for fine-diameter threaded pipes and accurate prediction of multi-physics fields has become an urgent technical problem to be solved.

[0005] The information disclosed in this background section is intended only to enhance the understanding of the general background of this disclosure and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0006] This invention provides a parameter design and evaluation method and system for fine-diameter threaded pipes based on digital twins, which can effectively solve the problems in the background art.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A parameter design and evaluation method for small-diameter threaded pipes based on digital twins, the method comprising:

[0009] Obtain the design parameter information of the fine-diameter threaded pipe, and combine the design parameter information to form a parameter design scheme;

[0010] The material used in the processing and manufacturing of the fine-diameter threaded pipe is collected, and multi-dimensional material property information of the material is obtained.

[0011] A physical model of the threaded pipe is constructed based on the parameter design scheme, and the processing and manufacturing material is selected. The corresponding multi-dimensional material performance information is holographically mapped to the physical model of the threaded pipe.

[0012] Import the test fluid parameters, run the simulation application, obtain the heat exchange efficiency and pressure drop efficiency, and obtain the parameter design evaluation results based on the heat exchange efficiency and pressure drop efficiency.

[0013] Furthermore, run simulation applications, including:

[0014] The test fluid is discretized into a swarm of fluid particles. A lattice region is defined based on the geometric parameters of the physical model of the threaded pipe, and the distribution of the swarm of fluid particles in the lattice region is initialized.

[0015] A preset velocity set is defined, and the collision rules and migration directions of the fluid particle swarm are defined, wherein the velocity set includes axial direction, diagonal direction, and symmetric velocity weights;

[0016] The fluid particle swarm is driven to collide and migrate in the regional lattice, and the vorticity distribution of the secondary rotating flow is calculated simultaneously. The secondary rotating flow is induced and generated by the threaded pipe wall structure.

[0017] Based on the eddy current distribution, the near-wall flow separation region is identified, and the threaded pipe wall structure in the parameter design scheme is associated with it to analyze the conditions for flow separation.

[0018] Based on the coupling relationship between the distribution characteristics of the near-wall flow separation region and the heat transfer efficiency and the pressure drop efficiency, the parameter design evaluation results are generated.

[0019] Furthermore, obtaining heat exchange efficiency and pressure drop efficiency includes:

[0020] Based on the eddy current distribution, the temperature gradient change rate between the fluid particle swarm and the threaded pipe wall structure is obtained, and the heat transfer efficiency is generated by combining the thermal conductivity in the multidimensional material performance information.

[0021] Extract the pressure gradient distribution of the near-wall flow separation region, calculate the kinetic energy loss rate along the axial direction based on the velocity set of the fluid particle swarm, and generate the pressure drop efficiency according to the pressure gradient distribution and the kinetic energy loss rate.

[0022] Further, the vorticity distribution of the secondary rotating flow is calculated, including:

[0023] Based on the aforementioned geometric parameters, the wall curvature gradient and axial pitch change rate are extracted to generate the initial velocity field of the secondary rotating flow.

[0024] Based on the symmetry velocity weights of the fluid particle swarm during the collision and migration process in the initial velocity field, calculate the velocity curl components of each node within the region grid.

[0025] The velocity curl component is coupled with the surface roughness parameter in the multidimensional material property information, and the eddy current distribution is output by iteratively solving the eddy current transport equation.

[0026] By correlating the vorticity distribution with the thread groove depth ratio in the parameter design scheme, the matching degree between the intensity of the secondary rotating flow and the design parameters is verified.

[0027] Furthermore, the parameter design evaluation results are obtained based on the heat exchange efficiency and pressure drop efficiency, including:

[0028] Construct a multi-objective optimization function for the heat transfer efficiency and the pressure drop efficiency, wherein the heat transfer efficiency characterizes the improvement of heat transfer performance and the pressure drop efficiency characterizes the degree of loss of flow resistance.

[0029] Using the geometric parameters as optimization variables, the range of values ​​for the geometric parameters is adjusted through an iterative algorithm to solve the non-dominated solution set of the multi-objective optimization function;

[0030] Based on preset operating condition constraints, optimized parameter schemes that simultaneously meet the requirements of heat transfer performance and flow resistance balance are selected from the set of non-dominated solutions.

[0031] By comparing the performance differences between the optimized parameter scheme and the parameter design scheme, an evaluation result of the parameter design, including recommended structural parameters and a comprehensive performance rating, is generated.

[0032] Furthermore, a parameter design scheme is generated, including:

[0033] Extract the core structural parameters of the fine-diameter threaded pipe, including pitch, thread depth, pipe diameter, helix angle, and wall thickness;

[0034] A collaborative optimization relationship is constructed among the core structural parameters, wherein the pitch and thread depth are generated nonlinear combination rules through an empirical model, and the pipe diameter and wall thickness are generated proportional constraints based on the thin-walled pipe strength equation.

[0035] Multiple sets of candidate parameter combinations are generated based on the aforementioned collaborative optimization relationship;

[0036] The candidate parameter combinations are matched with the multidimensional material performance information, and the candidate parameter combinations that meet the performance conditions are selected to generate the parameter design scheme.

[0037] Furthermore, based on the parameter design evaluation results, fluid flow separation prediction is performed, including:

[0038] Based on the distribution characteristics of the near-wall flow separation region in the parameter design evaluation results, the flow separation intensity is obtained and the correlation weight with the threaded pipe wall structure is established.

[0039] By combining the Reynolds number range of the test fluid parameters with the threshold of the surface roughness parameter in the multidimensional material performance information, a flow separation risk prediction model is constructed.

[0040] The critical triggering conditions for flow separation are calculated based on the flow separation risk prediction model. The critical triggering conditions include the adverse pressure gradient threshold and the geometric change sensitivity coefficient.

[0041] The critical triggering conditions are mapped to the physical model of the threaded pipe to generate a flow separation risk heat map.

[0042] Furthermore, a multi-level materials database is constructed, including:

[0043] The basic physical layer stores the static performance parameters of the materials being processed and manufactured;

[0044] A dynamic response layer records the dynamic characteristic data of the processed and manufactured materials under periodic thermo-mechanical coupling loads;

[0045] An environmental coupling layer is used to associate performance degradation data of the processed and manufactured materials in corrosive media, high-temperature oxidation, and irradiation environments.

[0046] Based on accelerated aging tests and real-time monitoring data, the stress relaxation rate prediction results and critical conditions for microstructure phase transformation of the long-term aged layer are constructed.

[0047] A parameter design evaluation system based on digital twin fine-diameter threaded pipes, the system comprising:

[0048] The scheme generation module obtains the design parameter information of the fine-diameter threaded pipe and combines the design parameter information to form a parameter-generated design scheme.

[0049] The performance acquisition module collects the materials used in the processing and manufacturing of fine-diameter threaded pipes and obtains multi-dimensional material performance information of these materials.

[0050] The twin construction module constructs a physical model of the threaded pipe based on the parameter design scheme, selects the processing and manufacturing materials, and holographically maps the corresponding multi-dimensional material performance information to the physical model of the threaded pipe.

[0051] The results evaluation module imports test fluid parameters, runs the simulation application, obtains heat exchange efficiency and pressure drop efficiency, and obtains parameter design evaluation results based on heat exchange efficiency and pressure drop efficiency.

[0052] Furthermore, the result evaluation module includes:

[0053] The region partitioning unit discretizes the test fluid into a fluid particle swarm, delineates the region grid based on the geometric parameters of the threaded pipe physical model, and initializes the distribution of the fluid particle swarm in the region grid.

[0054] The rule definition unit presets a velocity set and defines the collision rules and migration direction of the fluid particle swarm. The velocity set includes axial direction, diagonal direction and symmetric velocity weights.

[0055] The vorticity calculation unit drives the fluid particle swarm to collide and migrate in the regional grid, and simultaneously calculates the vorticity distribution of the secondary rotating flow, which is induced by the spiral pipe wall structure.

[0056] The separation analysis unit identifies near-wall flow separation regions based on vorticity distribution and correlates the threaded pipe wall structure in the parameter design scheme to analyze the conditions for flow separation.

[0057] The design generation unit generates parameter design evaluation results based on the coupling relationship between the distribution characteristics of the near-wall flow separation region and heat transfer efficiency and pressure drop efficiency.

[0058] The technical solution of this invention can achieve the following technical effects:

[0059] This invention integrates multi-source data and multi-objective collaborative optimization through digital twin technology to achieve efficient parameter design, accurate prediction of flow separation risks, and full life cycle performance assurance, significantly improving design reliability and adaptability to complex operating conditions.

[0060] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 This is a flowchart illustrating a parameter design and evaluation method for a small-diameter threaded pipe based on digital twins.

[0063] Figure 2 A flowchart illustrating the process of running a simulation application;

[0064] Figure 3 A structural diagram of the parameter design scheme;

[0065] Figure 4 This is a schematic diagram of the process for predicting fluid flow separation. Detailed Implementation

[0066] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0067] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0068] Example 1;

[0069] like Figure 1 As shown, this application provides a parameter design and evaluation method for small-diameter threaded pipes based on digital twins. The method includes:

[0070] S10: Obtain the design parameter information of the fine-diameter threaded pipe and combine the design parameter information into a parameter design scheme;

[0071] S20: Collect the materials used in the processing and manufacturing of fine-diameter threaded pipes, and obtain multi-dimensional material performance information of the materials used in the processing and manufacturing;

[0072] S30: Construct a physical model of the threaded pipe based on the parameter design scheme, select the processing and manufacturing materials, and holographically map the corresponding multi-dimensional material performance information to the physical model of the threaded pipe.

[0073] S40: Import test fluid parameters, run simulation application, obtain heat transfer efficiency and pressure drop efficiency, and obtain parameter design evaluation results based on heat transfer efficiency and pressure drop efficiency.

[0074] Specifically, the first step is to obtain design parameters for the fine-diameter threaded pipe using engineering design or CAD (computer-aided design) software. These parameters include thread specifications (such as pitch and thread depth), pipe inner diameter, outer diameter, wall thickness, and length. The above design parameters are then compiled and integrated into a complete parameter design scheme. This scheme should include performance requirements and material selection needs under different operating conditions, serving as the basis for subsequent analysis. Next, materials for manufacturing the fine-diameter threaded pipe, such as stainless steel, copper alloys, and aluminum alloys, are selected, ensuring that these materials meet the design requirements. Multidimensional material performance information for the selected materials is obtained through experiments, databases, or literature, including but not limited to thermal conductivity, compressive strength, corrosion resistance, elastic modulus, and density. Based on the parameter design scheme, a physical model of the fine-diameter threaded pipe is constructed using simulation software (such as ANSYS or COMSOL). This physical model should accurately reflect the pipe's geometry, material properties, and other design details. When constructing the physical model, materials consistent with the design scheme are selected, and it is ensured that the material possesses suitable multidimensional material properties. Material performance information: The material performance data should match the actual process and operating conditions. The multi-dimensional material performance information of the selected material is holographically mapped into the physical model of the threaded pipe, so that the physical model of the threaded pipe can accurately simulate the material behavior under actual working conditions (such as heat conduction, fluid flow, and pressure drop characteristics). Input test fluid parameters, such as fluid type, flow rate, temperature, and pressure, etc. These test fluid parameters are usually measured experimentally or set based on specific application scenarios. Run fluid flow and heat exchange simulation in the digital twin model. Predict the effects of heat exchange and flow loss between the fluid and the surface of the threaded pipe through numerical simulation (such as CFD analysis). Through simulation, calculate the system's heat exchange efficiency (the ratio of heat exchange per unit time to the maximum theoretical exchange rate) and pressure drop efficiency (the pressure loss during fluid flow). These efficiency values ​​will reflect the performance of the small-diameter threaded pipe under different working conditions. Based on the heat exchange efficiency and pressure drop efficiency, evaluate the effectiveness of the design scheme. If the efficiency values ​​meet the expected requirements, the design scheme is the optimized scheme; otherwise, further adjustments to the design parameters or material selection are required.

[0075] The technical solution of this invention enables efficient parameter design, accurate prediction of flow separation risks, and full life cycle performance assurance, significantly improving design reliability and adaptability to complex working conditions.

[0076] Furthermore, such as Figure 2 As shown, running the simulation application includes:

[0077] The test fluid is discretized into a swarm of fluid particles. The region grid is defined based on the geometric parameters of the threaded pipe physical model, and the distribution of the swarm of fluid particles in the region grid is initialized.

[0078] A preset velocity set is defined, and the collision rules and migration directions of the fluid particle swarm are defined. The velocity set includes axial direction, diagonal direction, and symmetry velocity weights.

[0079] The driving fluid particle swarm collides and migrates in the regional lattice, and the vorticity distribution of the secondary rotating flow is calculated simultaneously. The secondary rotating flow is induced by the spiral pipe wall structure.

[0080] Based on the eddy current distribution, the near-wall flow separation region was identified, and the threaded pipe wall structure in the parameter design scheme was correlated to analyze the conditions for flow separation.

[0081] Based on the coupling relationship between the distribution characteristics of the near-wall flow separation region and the heat transfer efficiency and pressure drop efficiency, parameter design evaluation results are generated.

[0082] As a preferred embodiment of the above, the continuous medium model of the fluid is discretized into a swarm of fluid particles. These fluid particles represent tiny mass units at different locations in the fluid, and each particle carries information about the fluid state (such as velocity, pressure, temperature, etc.). Based on the physical model of the threaded pipe, spatial grids of the simulation region are defined. These grids can cover the entire fluid flow region, ensuring that the position of each fluid particle can be accurately tracked during the simulation. Within the grid, based on the fluid flow conditions (such as velocity distribution, temperature gradient, etc.), the swarm of fluid particles is distributed into different grid cells. During particle initialization, the distribution needs to be appropriately adjusted according to the actual fluid state to ensure that the actual flow conditions are reflected. A particle is defined with a velocity set, comprising velocity components in multiple directions. These components include axial velocity (parallel to the central axis of the threaded pipe, representing fluid flow along the pipe direction), diagonal velocity (at an angle to the pipe axis, representing possible oblique flow), and symmetric velocity weights. Appropriate velocity weights are set based on the structural characteristics of the threaded pipe's inner wall to ensure that the motion of the fluid particle swarm accurately reflects the influence of the wall structure on fluid flow during the simulation. Collision rules are established between fluid particles and between fluid particles and the threaded pipe wall. Specifically, when fluid particles collide, their velocity and direction are calculated according to certain rules. The changes in vortex distribution simulate energy transfer and momentum exchange in actual flow. Based on collision rules and migration direction, the migration and collision of fluid particles in lattice cells are calculated using a discretized fluid dynamics model (such as the lattice Boltzmann method or particle swarm optimization algorithm) to simulate the fluid flow process. Simultaneously, the vortex distribution of the secondary rotating flow is calculated. Vortex is a physical quantity describing the degree of fluid rotation; its distribution reflects the changes in rotational and shear forces during flow. The wall structure of a threaded pipe (such as the thread shape) induces rotating flow in the fluid, affecting the vortex distribution. By analyzing the vortex distribution, regions of separation in the flow are identified. These separation regions typically appear near the contact surface between the fluid and the pipe wall, where the fluid flow is affected by the wall's shear force. The influence of surface structure can lead to flow instability or streamline deviation from the wall. Based on the design scheme of the threaded pipe, its wall structure (such as thread shape and angle) is combined with vorticity distribution to evaluate the impact of the threaded pipe design on flow separation. The wall structure affects the shear force and rotational flow of the fluid, thus affecting the occurrence of flow separation. By analyzing the flow separation region and combining the physical properties of the threaded pipe and the flow characteristics of the fluid, the conditions for the occurrence of flow separation are evaluated. Flow separation is usually affected by factors such as flow velocity, pressure gradient, and wall structure. Through simulation and numerical calculation, it is determined which design parameters (such as thread depth and pipe diameter) may lead to flow separation and predict their impact on system performance (such as heat exchange efficiency and pressure drop).By analyzing the distribution characteristics of the flow separation region and its relationship with heat transfer efficiency and pressure drop efficiency, the overall effectiveness of the design scheme is evaluated. The flow separation region affects the degree of fluid turbulence, which in turn affects heat transfer efficiency and pressure drop efficiency. Based on the coupling relationship between the flow separation analysis results and efficiency, the final parameter design evaluation results are calculated and generated. These results will help designers determine whether the threaded pipe design meets the actual application requirements and provide optimization solutions.

[0083] Furthermore, obtaining heat exchange efficiency and pressure drop efficiency includes:

[0084] Based on the vorticity distribution, the rate of change of temperature gradient between the fluid particle swarm and the threaded pipe wall structure is obtained, and the heat transfer efficiency is generated by combining the thermal conductivity in the multidimensional material performance information.

[0085] The pressure gradient distribution in the near-wall flow separation region is extracted. Based on the velocity set of the fluid particle swarm, the kinetic energy loss rate along the axial direction is calculated. The pressure drop efficiency is generated based on the pressure gradient distribution and the kinetic energy loss rate.

[0086] As a preferred embodiment of the above, firstly, the vorticity distribution of the fluid inside the threaded pipe is obtained through the aforementioned simulation method (such as collision migration based on fluid particle swarms). Vorticity describes the intensity of the rotating flow in the fluid and reflects the degree of fluid turbulence. The vorticity distribution inside the threaded pipe directly affects the heat exchange process between the fluid and the pipe wall. Based on the vorticity distribution and the position of the fluid particle swarm, the rate of change of the temperature gradient near the threaded pipe wall can be calculated. The rate of change of the temperature gradient reflects the intensity of heat exchange between the fluid and the wall. A larger temperature gradient usually indicates a stronger heat exchange process. Based on the thermal conductivity of the selected threaded pipe material, the thermal conductivity is combined with the rate of change of the temperature gradient to obtain the heat transfer effect between the threaded pipe wall and the fluid. Thermal conductivity describes the thermal conductivity of the material. A higher thermal conductivity helps to improve the heat transfer efficiency. Using the information of temperature gradient and thermal conductivity, an appropriate heat transfer model (such as a thermal resistance model or numerical simulation) is used to calculate the heat transfer efficiency. The heat transfer efficiency is usually the ratio of heat transfer per unit time to the maximum theoretical heat transfer, reflecting the heat exchange performance of the threaded pipe system. The pressure gradient distribution in the near-wall flow separation region is analyzed. Flow separation regions typically appear near the wall of threaded pipes, where fluid flow may become unstable or separate, leading to drastic changes in local pressure. The pressure gradient is a key factor describing pressure changes during fluid flow. Based on the velocity set of the fluid particle swarm (including axial velocity and velocities in other directions), the kinetic energy loss rate along the axial direction is calculated. The kinetic energy loss rate reflects the energy loss of the fluid during flow due to friction, turbulence, and other factors. In practical applications, kinetic energy loss is one of the main factors affecting pressure drop. By combining the pressure gradient distribution with the kinetic energy loss rate, the relationship between pressure change and kinetic energy loss is analyzed. Generally, a larger pressure gradient means that the fluid has experienced greater energy loss during flow, which may also lead to a larger pressure drop. Based on the calculation results of the pressure gradient and kinetic energy loss rate, the pressure drop efficiency is generated. The pressure drop efficiency is usually defined as the ratio of the pressure drop (energy loss) when the fluid passes through the pipe to the pressure drop under ideal conditions with theoretically no energy loss. The pressure drop efficiency reflects the degree of energy loss in the system during fluid transport.

[0087] Furthermore, calculating the vorticity distribution of the secondary rotating flow includes:

[0088] Based on geometric parameters, the wall curvature gradient and axial pitch change rate are extracted to generate the initial velocity field of the secondary rotating flow.

[0089] Based on the symmetry velocity weights of the fluid particle swarm during the collision and migration process in the initial velocity field, the velocity curl components of each node in the regional grid are calculated.

[0090] The velocity curl component is coupled with the surface roughness parameter in the multidimensional material property information, and the vorticity distribution is output by iteratively solving the vorticity transport equation.

[0091] By correlating the vorticity distribution with the thread groove depth ratio in the parameter design scheme, the matching degree between the intensity of the secondary rotating flow and the design parameters is verified.

[0092] As a preferred embodiment of the above, the curvature of the inner wall of the threaded pipe is calculated based on its geometric parameters (such as thread depth, angle, and pipe inner diameter). The wall curvature gradient describes the degree of curve change of the threaded pipe wall; a larger curvature change usually leads to a stronger rotational flow of the fluid. The axial pitch is the spacing between the threads inside the threaded pipe, affecting the flow direction and velocity distribution of the fluid. The rate of change of axial pitch represents the influence of the thread spacing change on the flow, and is usually related to the flow disturbance and rotational effect. Calculating the rate of change of axial pitch helps to establish more accurate initial flow conditions. Based on the wall curvature gradient and the rate of change of axial pitch, combined with the physical properties of the fluid (such as density and viscosity), an initial velocity field is generated inside the threaded pipe. The initial velocity field defines the initial flow state of each fluid particle, particularly its rotational velocity and direction. During the collision and migration of the fluid particle swarm, symmetry weights are considered when defining the velocity set. Specifically, the flow of fluid particles involves not only axial velocity components but also interactions between the particles and the pipe wall, as well as the formation of local vortices within the fluid. Symmetry-based velocity weights can influence the migration direction and velocity of fluid particles, especially in rotational flow near the wall. Velocity curl is a physical quantity describing the degree of rotation in a fluid, reflecting the rotational rate of fluid particles per unit time. Based on the initial velocity field and the collision and migration process of the fluid particle swarm, the velocity curl component at each lattice node is calculated. The curl component can be calculated using finite difference equations or numerical integration methods, and is usually directly related to the vorticity distribution of the fluid. During fluid flow, the roughness of the pipe wall has a significant impact on flow and vortex generation. Surface roughness is typically represented by the microstructure of the material (such as surface irregularities, protrusions, etc.). Coupled with the velocity curl component, it can more accurately reflect the influence of the pipe wall on fluid flow and vortex generation. The vortex transport equation describes the propagation and variation of vortex in the fluid. By combining the velocity curl component with surface roughness, the vortex transport equation can be constructed and solved iteratively to obtain the vortex distribution over the entire region. The vortex distribution reveals the intensity and location of the rotating flow in the fluid, reflecting... The behavior of secondary rotating flow; the thread groove depth ratio (i.e., the ratio of thread depth to pipe inner diameter) is a key parameter in the design, affecting the intensity of fluid flow, the degree of rotating flow, and heat transfer and pressure drop performance. By comparing the vorticity distribution with the thread groove depth ratio, the relationship between the intensity of secondary rotating flow and design parameters can be analyzed. Deeper thread grooves may lead to stronger rotating flow and turbulence, thus affecting heat transfer and pressure drop. Based on the relationship between vorticity distribution and thread groove depth ratio, it can be verified whether the design parameters can produce the ideal rotating flow intensity. If the vorticity distribution matches the design requirements, it indicates that the design parameters are reasonable and the flow state meets expectations; otherwise, the design may need to be adjusted to optimize the flow effect.

[0093] Furthermore, the parameter design evaluation results are obtained based on heat exchange efficiency and pressure drop efficiency, including:

[0094] A multi-objective optimization function for heat transfer efficiency and pressure drop efficiency is constructed, where heat transfer efficiency characterizes the improvement of heat transfer performance and pressure drop efficiency characterizes the degree of loss of flow resistance.

[0095] Using geometric parameters as optimization variables, the range of values ​​for the geometric parameters is adjusted through an iterative algorithm to solve the non-dominated solution set of the multi-objective optimization function;

[0096] Based on the preset operating conditions and constraints, the optimal parameter schemes that simultaneously meet the requirements of heat transfer performance and flow resistance balance are selected from the set of non-dominated solutions.

[0097] By comparing the performance differences between the optimized parameter scheme and the parameter design scheme, a parameter design evaluation result including recommended structural parameters and comprehensive performance rating is generated.

[0098] As a preferred embodiment of the above, in order to simultaneously consider heat transfer efficiency and pressure drop efficiency, a multi-objective optimization function needs to be constructed first. This multi-objective optimization function includes two main objectives: first, heat transfer efficiency, which represents the improvement in heat transfer performance. Heat transfer efficiency is typically defined as the ratio of actual heat exchange capacity to the theoretical maximum heat exchange capacity. This objective aims to optimize heat transfer performance to improve the system's thermal efficiency; second, pressure drop efficiency, which represents the degree of flow resistance loss. Pressure drop efficiency is typically the ratio of the pressure drop experienced by the fluid passing through a threaded pipe to the pressure drop under theoretical ideal conditions. This objective aims to minimize flow resistance to improve flow efficiency and reduce energy loss. The multi-objective optimization function can be constructed based on the mathematical relationship between heat transfer efficiency and pressure drop efficiency. For example, the importance of heat transfer efficiency and pressure drop efficiency can be adjusted using weighting factors, ensuring that the optimization objective balances the trade-off between heat transfer performance and flow resistance. During optimization, geometric parameters considered include the inner diameter, outer diameter, wall thickness, thread depth, thread angle, and pitch of the threaded pipe, which directly affect heat transfer and pressure drop performance. Appropriate iterative optimization algorithms (such as genetic algorithms, particle swarm optimization, and simulated annealing) are used to adjust the range of geometric parameters; by continuously adjusting these parameters, the iterative algorithm gradually approaches the optimal solution. In multi-objective optimization, the non-dominated solution set (Pareto front) represents the best compromise between heat transfer efficiency and pressure drop efficiency. If further optimization is not possible without sacrificing the performance of one objective, iterative solutions can be used to find all non-dominated solutions. Operating constraints may include fluid temperature range, pressure range, flow velocity limits, and equipment pressure resistance. These constraints further confine the optimal solution set to a practically feasible design space. Based on actual needs (such as finding the optimal balance between heat transfer performance and flow resistance), optimized parameter schemes that meet the constraints and are most suitable for practical applications are selected from the non-dominated solution set. These optimized schemes provide optimal heat transfer performance and minimum flow resistance while satisfying specific operating conditions. A comparison is made between the optimized parameter schemes and the initial design scheme (i.e., the parameter design scheme) in terms of heat transfer efficiency and pressure resistance. The difference in efficiency can be assessed by calculating the performance gap between the two solutions, allowing us to evaluate the performance improvement of the optimized scheme compared to the original design. Based on the comparison results, a comprehensive performance rating is generated. This rating can be a comprehensive indicator that considers the balance between heat exchange efficiency and pressure drop efficiency, or it can provide scores for each efficiency value separately. Ultimately, this comprehensive rating provides designers with a complete parametric design evaluation result. Based on the parametric design evaluation result, optimized structural parameters are recommended. For example, it may be suggested to use a specific thread depth or pitch parameter to improve the system's thermal efficiency or reduce energy loss. Recommended structural parameters can help designers optimize product design to meet the performance requirements of specific applications.

[0099] Furthermore, such as Figure 3 As shown, the parameter design scheme includes:

[0100] Extract the core structural parameters of the fine-diameter threaded pipe, including pitch, thread depth, pipe diameter, helix angle, and wall thickness.

[0101] A collaborative optimization relationship is constructed among the core structural parameters, where the pitch and thread depth are generated nonlinear combination rules through an empirical model, and the pipe diameter and wall thickness are generated proportional constraints based on the thin-walled pipe strength equation.

[0102] Multiple sets of candidate parameter combinations are generated based on the collaborative optimization relationship;

[0103] Candidate parameter combinations are matched with multidimensional material performance information, and candidate parameter combinations that meet the performance conditions are selected to generate parameter design schemes.

[0104] As a preferred embodiment of the above, the pitch refers to the axial length of each turn of the thread in the threaded pipe, affecting the path and disturbance intensity of fluid flow; the thread depth is the vertical depth of the thread, directly affecting the heat transfer performance and flow resistance of the pipe; the pipe diameter is the outer or inner diameter of the threaded pipe, determining the flow capacity and space for fluid flow; the helix angle is the helix angle of the threaded pipe, affecting the rotational effect of the fluid and the degree of turbulence; the wall thickness determines the strength and pressure resistance of the pipe; too thin a wall thickness may lead to pipe deformation or rupture, while too thick a wall thickness increases weight and material cost; there is usually a non-linear relationship between the pitch and the thread depth, and empirical models (such as polynomial models) can be used based on experimental data or engineering experience. The nonlinear combination rules between these parameters are determined using regression models or curve fitting methods. These rules describe the impact of pitch and thread depth variations on pipe performance, particularly flow and heat exchange performance. The strength of thin-walled pipes can typically be estimated using classical thin-walled pipe strength equations (e.g., equations based on the load-bearing capacity of thin-walled pipes under internal and external pressure). These equations usually consider factors such as the pipe's inner diameter, outer diameter, wall thickness, and fluid pressure. By using these equations, the proportional relationship between pipe diameter and wall thickness can be generated, ensuring that the designed pipe meets strength requirements without wasting excessive material. The relationships between pitch, thread depth, pipe diameter, and wall thickness are then expressed using mathematical models or rules. This is illustrated to allow for simultaneous consideration of the interrelationships between parameters during the design process. The co-optimization relationship ensures that the selection of each parameter achieves optimal design performance while meeting strength and flow performance requirements. Based on the co-optimization relationship, optimization algorithms (such as Latin hypercube sampling, genetic algorithms, or Monte Carlo simulations) are used to generate multiple sets of candidate parameter combinations. Each combination includes different values ​​for pitch, thread depth, pipe diameter, helix angle, and wall thickness to cover a wide design space. These candidate combinations involve different parameter ranges, thus providing diverse options for subsequent performance screening. Based on the multidimensional performance information of the selected material (such as thermal conductivity, compressive strength, corrosion resistance, and elastic modulus), [further details are provided]. The process involves matching candidate parameter combinations with material properties. Each candidate combination must ensure that the material properties match the parameter design in practical applications, meeting requirements such as heat exchange and pressure resistance. Based on preset performance conditions (e.g., heat exchange efficiency, pressure drop efficiency, pressure resistance), candidate parameter combinations that meet the performance requirements are selected. These combinations can meet the design goals and provide optimal performance under predetermined operating conditions. Finally, one or more candidate parameter combinations are selected and formed into parameter design schemes. These design schemes include optimal structural parameters (pitch, thread depth, pipe diameter, helix angle, and wall thickness) and can achieve optimized heat exchange and flow performance, as well as meet strength and pressure resistance requirements.

[0105] Furthermore, such as Figure 4 As shown, fluid flow separation prediction is performed based on the parameter design evaluation results, including:

[0106] Based on the distribution characteristics of the near-wall flow separation region in the parametric design evaluation results, the flow separation intensity is obtained and the correlation weight with the threaded pipe wall structure is established.

[0107] By combining the Reynolds number range of the test fluid parameters with the threshold of the surface roughness parameter in the multidimensional material performance information, a flow separation risk prediction model is constructed.

[0108] The critical triggering conditions for flow separation are calculated based on the flow separation risk prediction model. The critical triggering conditions include the adverse pressure gradient threshold and the geometric change sensitivity coefficient.

[0109] The critical triggering conditions are mapped to the physical model of the threaded pipe to generate a heat map of flow separation risk.

[0110] As a preferred embodiment of the above, a near-wall flow separation region is obtained within the threaded pipe. This near-wall flow separation region is where instability occurs in the fluid flow, typically at the contact surface between the fluid and the pipe wall. Based on the location, size, and distribution of the flow separation region, its distribution characteristics are extracted, such as the frequency of flow separation and the depth of the separated area. Flow separation intensity refers to the degree or severity of flow separation. By analyzing the characteristics of the near-wall flow separation region, the intensity of flow separation can be calculated. This intensity is usually related to factors such as fluid velocity, flow direction, and pressure. The greater the intensity, the greater the degree of flow instability. Severe flow separation can lead to greater pressure drop and poor heat exchange. By analyzing the relationship between wall structure (such as thread shape, angle, and roughness) and flow separation intensity, correlation weights can be established. Different wall structures will have different degrees of influence on flow separation; the rougher the wall and the deeper the threads, the stronger the flow separation effect may be. By establishing these correlation weights, the flow separation intensity can be accurately correlated with specific wall design, thereby predicting and controlling flow separation during the design phase. The Reynolds number is an important dimensionless parameter describing fluid flow characteristics, usually used to distinguish between laminar and turbulent flow states. The Reynolds number is closely related to the stability of the flow. When the Reynolds number is too high, the flow is prone to instability, leading to turbulence and flow separation. Therefore, the Reynolds number range of the test fluid needs to be considered in the model. Surface roughness is an important factor affecting fluid flow and separation, especially in the design of small-diameter threaded pipes. Surface roughness has a significant impact on flow separation. The surface roughness parameter is obtained from the multidimensional performance information of the material, and its critical value is determined. Surfaces below this value may lead to relatively stable flow, while those above this value are prone to flow separation. Combining the Reynolds number and the surface roughness threshold, numerical simulation or machine learning can be used to determine the critical value. This study describes a method for constructing a flow separation risk prediction model. This model can predict the risk of flow separation under different operating conditions, taking into account the combined effects of fluid velocity, flow pattern, and wall structure, thereby predicting the likelihood and severity of flow separation. The reverse pressure gradient refers to the pressure gradient that changes in the opposite direction to the flow direction when fluid flows in a pipe. An excessively large reverse pressure gradient can lead to fluid flow instability, thus triggering flow separation. The flow separation risk prediction model calculates the reverse pressure gradient threshold that leads to flow separation. When the pressure changes too rapidly, it may trigger flow separation.Geometric mutations refer to sudden changes in the shape of a pipe, such as abrupt changes in thread depth, sudden increases or decreases in pipe diameter, etc. Geometric mutation sensitivity coefficients are used to assess the impact of these structural changes on flow. Especially in complex geometries like threaded pipes, mutations can lead to flow instability and increase the risk of flow separation. By calculating the geometric mutation sensitivity coefficient, the sensitivity of structural changes to flow separation can be quantified. By combining the adverse pressure gradient threshold and the geometric mutation sensitivity coefficient, the critical triggering conditions for flow separation can be calculated. These conditions determine the critical state in which flow separation occurs and provide key information for avoiding flow separation in the design. Mapping the calculated critical triggering conditions (including the adverse pressure gradient threshold and the geometric mutation sensitivity coefficient) onto the threaded pipe physical model yields regions where flow separation may occur under actual operating conditions. These regions are typically associated with pressure distribution, velocity field, and vorticity distribution. Based on the mapping results, a flow separation risk heatmap is generated. The flow separation risk heatmap displays the flow separation risk in different regions through color or intensity changes; red areas represent high-risk areas, and green areas represent low-risk areas. The heatmap helps designers visually identify areas where flow separation may occur and optimize or adjust the design according to the risk level.

[0111] Furthermore, constructing a multi-level materials database includes:

[0112] The basic physical layer stores the static performance parameters of the materials being processed and manufactured.

[0113] The dynamic response layer records the dynamic characteristic data of the processed and manufactured materials under periodic thermo-mechanical coupling loads;

[0114] The environmental coupling layer correlates the performance degradation data of materials in corrosive media, high-temperature oxidation, and irradiation environments.

[0115] Based on accelerated aging tests and real-time monitoring data, the stress relaxation rate prediction results and critical conditions for microstructure phase transformation of the long-term aged layer are constructed.

[0116] As a preferred embodiment of the above, the basic physical layer is used to store the static performance parameters of the material, specifically including the material's conventional mechanical properties, such as tensile strength, yield strength, and hardness, which are important parameters for evaluating the material's resistance to deformation and fracture under static load; elastic modulus, which describes the material's rigidity within its elastic deformation range and affects the material's deformation under load; density, thermal conductivity, and specific heat capacity, which affect heat exchange performance and are the basis of thermodynamic analysis; and the coefficient of thermal expansion, which describes the material's volume change under temperature changes and affects its adaptability to different temperature environments. All these static performance parameters will be provided through experimental data or literature and stored in the basic physical layer of the database. A relational database management system can be used to manage these data, providing material performance references for subsequent simulations and designs. The dynamic characteristic data, the dynamic response layer records the dynamic characteristics of the material under periodic thermo-mechanical coupled loads. Periodic thermo-mechanical coupled loads refer to the material's response characteristics under constantly changing temperature and load conditions, such as thermal fatigue performance, the material's resistance to fatigue under repeated temperature changes. A common experiment is the high-temperature cycling test, which measures the potential for cracking, fatigue, and other damage to the material after multiple thermal cycles. Thermal shock resistance refers to a material's impact resistance under sudden thermal loads, which is crucial for applications subject to rapid temperature changes. Vibration response refers to the material's response characteristics under external periodic loads (such as mechanical vibration), including stiffness, damping, and vibration amplitude. Dynamic response experiments obtain data through experiments (such as high-temperature vibration tests and thermal fatigue tests), recording information such as deformation, fracture, and vibration of the material under different periodic thermal loads. This dynamic characteristic data is stored in the dynamic response layer, typically using a time-series database for storage and management, enabling dynamic analysis and real-time monitoring. Environmental coupling data... The environmental coupling layer records data on the performance degradation of materials under harsh environmental conditions, specifically including corrosive media, such as the corrosion behavior of materials in corrosive media such as acids, alkalis, and salt water, for example, using salt spray tests or immersion tests to test the corrosion resistance of materials; high-temperature oxidation, where at high temperatures, the material surface reacts with oxygen to form an oxide layer, affecting its mechanical properties, and this type of data is usually obtained through high-temperature oxidation experiments to assess the oxidation degradation rate of materials in high-temperature environments; and irradiation environment, which records the performance degradation of materials under irradiation conditions (such as radioactive radiation or high-energy particle radiation), mainly involving radiation damage and structural changes of materials.Radiation experiments are used to test the changes in strength and reliability of materials under long-term radiation. Environmental degradation tests are conducted to obtain the degradation characteristics of materials under different corrosive media, high-temperature oxidizing environments, and irradiation conditions. This data is crucial for assessing the service life and reliability of materials in extreme environments. This degradation data is stored in an environmental coupling layer and correlated with the material's environmental conditions (such as temperature, humidity, and radiation dose). Accelerated aging tests simulate the aging process of materials during long-term service. During these tests, greater stress, temperature, or radiation are applied to accelerate the degradation process and simulate the impact of long-term use on material performance. For example, high-temperature aging tests are conducted to test the durability of materials and obtain real-time monitoring data. Sensors monitor the real-time performance data of materials, especially changes in stress, temperature, and microstructure. This data helps to understand the aging process of materials under actual operating conditions over time. Stress relaxation refers to the change of internal stress of materials under long-term load. Based on accelerated aging tests and real-time monitoring data, the stress relaxation behavior of materials over service time is predicted, helping to estimate the long-term stability of materials. During long-term use, materials may undergo microstructural phase transformations (such as grain coarsening and phase separation). Based on the aging behavior of materials and real-time monitoring data, the critical conditions for microstructure phase transformation are predicted, and the reliability of materials after long-term service is assessed. This aging-related data will be stored in the long-term aging layer, and combined with accelerated aging tests and real-time monitoring data, it will provide support for predicting the long-term performance of materials.

[0117] Example 2;

[0118] Based on the same inventive concept as the parameter design evaluation method for a digital twin-based fine-diameter threaded tube in the foregoing embodiments, this invention also provides a parameter design evaluation system for a digital twin-based fine-diameter threaded tube, the system comprising:

[0119] The scheme generation module obtains the design parameter information of the fine-diameter threaded pipe and combines the design parameter information to form a parameter-generated design scheme.

[0120] The performance acquisition module collects the materials used in the processing and manufacturing of fine-diameter threaded pipes and obtains multi-dimensional material performance information of these materials.

[0121] The twin construction module constructs a physical model of the threaded pipe based on the parameter design scheme, selects the processing and manufacturing materials, and holographically maps the corresponding multi-dimensional material performance information to the physical model of the threaded pipe.

[0122] The results evaluation module imports test fluid parameters, runs the simulation application, obtains heat exchange efficiency and pressure drop efficiency, and obtains parameter design evaluation results based on heat exchange efficiency and pressure drop efficiency.

[0123] The adjustment system described above in this invention can be effectively implemented, and the technical effects it can achieve are as described in the above embodiments, and will not be repeated here.

[0124] Furthermore, the results evaluation module includes:

[0125] The region partitioning unit discretizes the test fluid into a fluid particle swarm, delineates the region grid based on the geometric parameters of the threaded pipe physical model, and initializes the distribution of the fluid particle swarm in the region grid.

[0126] The rule definition unit presets a velocity set and defines the collision rules and migration direction of the fluid particle swarm. The velocity set includes axial direction, diagonal direction and symmetric velocity weights.

[0127] The vorticity calculation unit drives the fluid particle swarm to collide and migrate in the regional grid, and simultaneously calculates the vorticity distribution of the secondary rotating flow, which is induced by the spiral pipe wall structure.

[0128] The separation analysis unit identifies near-wall flow separation regions based on vorticity distribution and correlates the threaded pipe wall structure in the parameter design scheme to analyze the conditions for flow separation.

[0129] The design generation unit generates parameter design evaluation results based on the coupling relationship between the distribution characteristics of the near-wall flow separation region and heat transfer efficiency and pressure drop efficiency.

[0130] Similarly, the above-mentioned optimization schemes for the system can also achieve the optimization effects corresponding to the methods in Embodiment 1, which will not be repeated here.

[0131] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A parameter design and evaluation method for small-diameter threaded pipes based on digital twins, characterized in that, The method comprises: Obtaining the design parameter information of the small-diameter threaded pipe, and composing the parameter design scheme based on the design parameter information; Collecting the processing and manufacturing materials of the small-diameter threaded pipe, and obtaining the multi-dimensional material performance information of the processing and manufacturing materials; Constructing a threaded pipe physical model according to the parameter design scheme, selecting the processing and manufacturing materials, and holographically mapping the corresponding multi-dimensional material performance information to the threaded pipe physical model; Importing the test fluid parameters, running the simulation application, obtaining the heat exchange efficiency and pressure drop efficiency, and obtaining the parameter design evaluation result according to the heat exchange efficiency and pressure drop efficiency; Running the simulation application comprises: Discretizing the test fluid into a fluid particle group, dividing the region lattice based on the geometric parameters of the threaded pipe physical model, and initializing the distribution of the fluid particle group in the region lattice; Predefining a set of velocities and defining the collision rules and migration directions of the fluid particle group, wherein the set of velocities includes axial direction, diagonal direction and symmetry velocity weight; Driving the fluid particle group to collide and migrate in the region lattice, synchronously calculating the vorticity distribution of the secondary rotational flow induced by the threaded pipe wall surface structure; Identifying the near-wall flow separation area based on the vorticity distribution, and associating the threaded pipe wall surface structure in the parameter design scheme to analyze the occurrence conditions of flow separation; According to the coupling relationship between the distribution characteristics of the near-wall flow separation area and the heat exchange efficiency and pressure drop efficiency, the parameter design evaluation result is generated.

2. The method of claim 1, wherein, Obtaining the heat exchange efficiency and pressure drop efficiency comprises: According to the vorticity distribution, obtaining the temperature gradient change rate between the fluid particle group and the threaded pipe wall surface structure, combining the thermal conductivity in the multi-dimensional material performance information, and generating the heat exchange efficiency; Extracting the pressure gradient distribution of the near-wall flow separation area, calculating the kinetic energy loss rate along the axial direction based on the set of velocities of the fluid particle group, and generating the pressure drop efficiency according to the pressure gradient distribution and the kinetic energy loss rate.

3. The method of claim 1, wherein, Calculating the vorticity distribution of the secondary rotational flow comprises: Based on the geometric parameters, extracting the wall surface curvature gradient and the axial pitch change rate to generate the initial velocity field of the secondary rotational flow; According to the symmetry velocity weight of the fluid particle group in the collision and migration process in the initial velocity field, calculating the velocity vorticity component of each node in the region lattice; Coupling the velocity vorticity component with the surface roughness parameter in the multi-dimensional material performance information, iteratively solving by the vorticity transport equation, and outputting the vorticity distribution; Associating the vorticity distribution with the thread groove depth ratio in the parameter design scheme, verifying the matching degree of the strength of the secondary rotational flow and the design parameters.

4. The method for parametric design evaluation of digitally twinned fine pitch thread tube according to claim 2, wherein, Obtaining the parameter design evaluation result according to the heat exchange efficiency and pressure drop efficiency comprises: Constructing a multi-objective optimization function of the heat exchange efficiency and the pressure drop efficiency, wherein the heat exchange efficiency represents the improvement amplitude of heat transfer performance, and the pressure drop efficiency represents the loss degree of flow resistance; Taking the geometric parameters as optimization variables, adjusting the value range of the geometric parameters by an iterative algorithm, and solving the non-inferior solution set of the multi-objective optimization function. Filtering an optimization parameter scheme satisfying the balance of heat transfer performance and flow resistance demand from the non-inferior solution set based on preset working condition constraints; Generating the parameter design evaluation result including recommended structure parameters and comprehensive performance rating by comparing the performance difference between the optimization parameter scheme and the parameter design scheme.

5. The digital twin based parametric design evaluation method for small diameter threaded tubing of claim 1, wherein, Generating a parameter design scheme, including: Extracting the core structure parameters of the small-diameter threaded pipe, including pitch, thread depth, pipe diameter, helix angle, and wall thickness; Building a synergistic optimization relationship between the core structure parameters, wherein the pitch and thread depth generate a nonlinear combination rule through an empirical model, and the pipe diameter and wall thickness generate a proportional constraint based on a thin-walled pipe strength equation; Generating multiple sets of candidate parameter combinations according to the synergistic optimization relationship; Matching the candidate parameter combinations with the multi-dimensional material performance information to filter the candidate parameter combinations that meet the performance conditions and generate the parameter design scheme.

6. The digital twin based parametric design evaluation method for small diameter threaded tubulars as claimed in claim 1, wherein, Performing fluid flow separation prediction according to the parameter design evaluation result, including: Based on the near-wall flow separation region distribution characteristics in the parameter design evaluation result, obtaining the flow separation intensity and establishing the associated weight of the threaded pipe wall structure; Combining the Reynolds number range of the test fluid parameters with the threshold value of the surface roughness parameter in the multi-dimensional material performance information to build a flow separation risk prediction model; According to the flow separation risk prediction model, calculating the critical trigger condition of flow separation, including the inverse pressure gradient threshold and the geometric mutation sensitivity coefficient; Mapping the critical trigger condition to the threaded pipe physical model to generate a flow separation risk thermal map.

7. The digital twin based parametric design evaluation method for small diameter threaded tubulars as claimed in claim 1 wherein, Building a multi-level material database, including: A basic physical layer storing static performance parameters of the manufacturing material; A dynamic response layer recording dynamic characteristic data of the manufacturing material under periodic thermal coupling load; An environmental coupling layer associating performance degradation data of the manufacturing material in corrosive media, high temperature oxidation, and irradiation environment; A long-term aging layer based on accelerated aging test and real-time monitoring data to build stress relaxation rate prediction results and microstructure phase transition critical conditions of material performance over time.

8. A parametric design evaluation system for digitally twinned small diameter threaded tubing, characterized by, The system includes: A scheme generation module that obtains design parameter information of a small-diameter threaded pipe and groups the design parameter information to generate a parameter design scheme; A performance acquisition module that acquires manufacturing materials of a small-diameter threaded pipe and obtains multi-dimensional material performance information of the manufacturing materials; A twin construction module that constructs a threaded pipe physical model according to a parameter design scheme and selects manufacturing materials, and holographically maps corresponding multi-dimensional material performance information to the threaded pipe physical model; A result evaluation module that imports test fluid parameters, runs simulation applications, obtains heat exchange efficiency and pressure drop efficiency, and obtains parameter design evaluation results according to the heat exchange efficiency and the pressure drop efficiency; The result evaluation module includes: A region division unit that disperses test fluid into a fluid particle group, divides regions based on geometric parameters of a threaded pipe physical model, and initializes distribution of the fluid particle group in the region grid; A rule defining unit is configured to define a set of velocities and collision rules and migration directions of the fluid particle group, wherein the set of velocities includes axial direction, diagonal direction and symmetry velocity weight; A vortex calculation unit is configured to drive the fluid particle group to collide and migrate in the region lattice, and synchronously calculate a vortex distribution of a secondary rotational flow induced by the thread pipe wall surface structure; A separation analysis unit is configured to identify a near-wall flow separation region based on the vortex distribution, and analyze occurrence conditions of the flow separation by associating the thread pipe wall surface structure in the parameter design scheme; A design generation unit is configured to generate a parameter design evaluation result according to a coupling relationship between a distribution characteristic of the near-wall flow separation region and heat exchange efficiency and pressure drop efficiency.

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