Transcritical flow self-adaptive multi-stage nested structure drag reduction channel

Abstract

The invention discloses a transcritical flow self-adaptive multi-stage nested structure drag reduction channel, and relates to the technical field of heat conduction and heat management. In order to overcome the technical defects that in the prior art, in an existing drag reduction and heat exchange strengthening technology, drastic changes of fluid physical properties caused by transcritical phase change in an LNG gasifier are difficult to deal with, and the efficient drag reduction effect is difficult to maintain, the technical scheme is that the drag reduction and heat exchange strengthening device comprises a flowing channel, a multi-stage nested groove structure is arranged on the inner wall of the flowing channel, the groove structure comprises a plurality of gradually-changed grooves formed in the flowing direction, and the width and the depth of the grooves are gradually changed in the flowing direction; the first-stage groove is a macroscopic groove which is processed along the inner wall of the flowing channel; the second-stage groove is formed in the first-stage groove and is a micro-scale groove; and the third-level micro-nano rough structure is arranged on the inner surface of the second-level groove and comprises micro-nano-level bulges and pits. The device is suitable for the work of drag reduction and heat exchange enhancement.

Description

Technical Field

[0001] The invention relates to the field of heat conduction and heat management technology, specifically to enhanced heat exchange and drag reduction of transcritical flow in a channel. Background Art

[0002] In the process of liquefied natural gas (LNG) gasification, enhanced heat exchange and drag reduction technology is one of the core research topics. With the increasing global demand for clean energy, LNG, as a low-pollution, high-calorific value energy source, is playing an increasingly important role. In particular, my country has a high degree of dependence on natural gas imports, and the efficiency and safety of LNG gasification are of great significance to the stability of energy supply. However, inside the LNG vaporizer, the fluid undergoes a complex phase change from liquid to supercritical state, involving transcritical flow with high temperature difference (close to 200°C) and high pressure (greater than 6MPa), which greatly challenges the design and operation of the vaporizer.

[0003] At present, one of the types of heat exchangers widely used in LNG gasifiers is the printed circuit board heat exchanger (PCHE). PCHE has significant advantages such as wide temperature adaptability, strong pressure resistance, compact structure, and high heat exchange efficiency, making it an ideal choice for gasification equipment. In order to improve the performance of PCHE in the LNG gasification process, domestic and foreign scholars have conducted a lot of research, mainly focusing on how to effectively reduce the flow resistance in the channel while enhancing heat exchange.

[0004] In terms of drag reduction, the groove structure has received widespread attention due to its outstanding performance in reducing turbulent flow resistance. For example, experimental studies have shown that when the dimensionless depth h+≤25 and the dimensionless width s+≤30, the V-groove structure can significantly reduce drag, with a maximum drag reduction rate of 8%. In addition, laser Doppler velocimetry (LDV) technology shows that the groove structure can expand the laminar boundary layer area, increase the transition Reynolds number from laminar to turbulent flow, and improve the stability of the boundary layer. Through these studies, the groove structure has been proven to be able to suppress the generation of turbulence, thereby achieving a drag reduction effect.

[0005] In terms of numerical simulation, the researchers used the direct numerical simulation (DNS) method to study the effect of the groove structure on the flow at different Reynolds numbers and found that when the aspect ratio of the groove is reasonable, the main vortex in the groove can effectively fill the groove space, allowing the free flow to "slide" through the groove area, thereby reducing the total resistance of the channel. This series of experimental and numerical simulation studies provides theoretical support for groove drag reduction technology and has been partially verified in actual engineering applications. Further studies have shown that by adding microstructures to the groove, such as spherical cap microstructures, the drag reduction effect can be further improved, with a maximum drag reduction rate of 24.8%.

[0006] Although these studies have achieved certain results in drag reduction and heat exchange enhancement, there are still many shortcomings. First, the best drag reduction effect of most groove drag reduction technologies can often only be achieved under specific flow conditions and structural dimensions, and it is difficult to cope with the drastic changes in fluid properties caused by transcritical phase change in the LNG vaporizer. Secondly, most of the existing technologies are optimized for groove structures of fixed sizes, and fail to fully consider the dynamic changes in fluid properties during the flow process, which makes it difficult to maintain efficient drag reduction effects throughout the entire process. In addition, since LNG changes from liquid to supercritical state in the vaporizer, its Reynolds number range changes significantly (from 2000 to 20000), and the existing fixed groove structure cannot adapt to this complex flow state change, resulting in unstable drag reduction efficiency and may even increase resistance under certain conditions.

[0007] Therefore, how to realize an adaptive multi-level nested structure in the transcritical flow process to ensure effective drag reduction and enhanced heat transfer under different flow conditions is a difficult problem that needs to be solved urgently by existing technologies. Summary of the invention

[0008] In order to solve the technical defects existing in the prior art, the existing drag reduction and heat exchange enhancement technologies can often only be implemented under specific flow conditions and structural dimensions, but are difficult to cope with the drastic changes in fluid properties caused by transcritical phase change in the LNG gasifier, and most of them are optimized for the groove structure of fixed size, but fail to fully consider the dynamic changes in fluid properties during the flow process, resulting in it being difficult to maintain an efficient drag reduction effect in the whole process. The technical solution provided by the present invention is:

[0009] A transcritical flow adaptive multi-stage nested structure drag reduction channel, the channel comprising:

[0010] A flow channel, wherein the inner wall of the flow channel is provided with a multi-level nested groove structure, wherein the groove structure comprises a plurality of gradually changing grooves arranged along the flow direction, and the width and depth of the grooves gradually change along the flow direction;

[0011] A primary groove, wherein the primary groove is a macro groove processed along the inner wall of the flow channel;

[0012] A secondary groove is provided inside the primary groove and is a micro-scale groove;

[0013] The third-level micro-nano rough structure is arranged on the inner surface of the second-level groove, and includes protrusions and depressions at the micro-nano level.

[0014] Furthermore, a preferred embodiment is provided, wherein the cross-section of the primary groove is V-shaped or U-shaped.

[0015] Furthermore, a preferred embodiment is provided, wherein the width and depth of the secondary groove vary periodically along the length direction of the groove.

[0016] Furthermore, a preferred embodiment is provided, wherein the heights and depths of the protrusions and depressions at the micro-nano level are inconsistent.

[0017] Furthermore, a preferred embodiment is provided, wherein the three-level micro-nano rough structure is prepared by laser processing.

[0018] Based on the same inventive concept, the present invention also provides a method for generating parameters of a transcritical flow adaptive multi-stage nested structure drag reduction channel, which is used to design the drag reduction channel, comprising:

[0019] Steps for establishing a flow channel model for an LNG gasification process;

[0020] The steps of collecting data on the flow characteristics of LNG under different temperature and pressure conditions by numerical simulation method;

[0021] Based on the collected data, the steps of constructing and training a preset artificial neural network model;

[0022] According to the prediction results of the artificial neural network model, the width and depth of the groove structure are dynamically adjusted along the direction of the flow channel to establish a step of adaptive multi-level nested groove structure.

[0023] Based on the same inventive concept, the present invention also provides a transcritical flow adaptive multi-stage nested structure drag reduction channel parameter generation device, which is used to design the drag reduction channel, comprising:

[0024] Module for building flow channel models for LNG gasification processes;

[0025] A module for collecting data on the flow characteristics of LNG under different temperature and pressure conditions through numerical simulation methods;

[0026] Build and train a module of a preset artificial neural network model based on the collected data;

[0027] According to the prediction results of the artificial neural network model, the width and depth of the groove structure are dynamically adjusted along the direction of the flow channel, and a module of an adaptive multi-level nested groove structure is established.

[0028] Based on the same inventive concept, the present invention also provides a computer storage medium for storing a computer program. When the computer program is read by a computer, the computer executes the method described.

[0029] Based on the same inventive concept, the present invention also provides a computer, including a processor and a storage medium. When the processor reads a computer program stored in the storage medium, the computer executes the method described.

[0030] Based on the same inventive concept, the present invention also provides a computer program product, which is a computer program. When the computer program is read, the method described above is implemented.

[0031] Compared with the prior art, the technical solution provided by the present invention is beneficial in that:

[0032] By adopting an adaptive multi-level nested groove design, the groove size can be automatically adjusted according to the changes in fluid properties during the flow process, ensuring that the groove structure at different positions always matches the turbulent vortex structure, thereby significantly reducing the flow resistance in the channel. Compared with a fixed-size groove structure, the adaptive design can maintain a stable drag reduction effect under different flow conditions, avoiding the problem of unstable drag reduction effect in the existing technology.

[0033] By combining machine learning models to optimize the groove structure in real time, it is possible to accurately predict the changes in the physical properties of the fluid at different temperatures and pressures, thereby determining the optimal groove size. This real-time optimization method enables the drag reduction channel to adapt to complex transcritical flows and ensure efficient drag reduction performance throughout the gasification process. Compared with traditional groove structures designed using empirical formulas or fixed parameters, machine learning optimization significantly improves the accuracy and adaptability of the drag reduction effect.

[0034] By adding microstructures, such as spherical cap microstructures, to the groove structure, not only can the flow resistance be further reduced, but the heat transfer efficiency can also be effectively improved. The presence of the microstructure increases the wall heat transfer area, thereby enhancing the heat transfer effect. This multi-stage nested structure design shows significant enhanced heat transfer effects under different flow conditions, and has a higher heat transfer coefficient and lower flow resistance than a single groove structure or a smooth wall structure.

[0035] The feasibility and reliability of the design of the adaptive multi-level nested structure in practical engineering applications are ensured by combining numerical simulation with experimental verification. Compared with the existing drag reduction technology, the present invention not only proposes an effective solution in theory, but also verifies its drag reduction and heat exchange effects under actual working conditions through high-precision numerical simulation, thereby enhancing the engineering practicality and promotion value of the technical solution.

[0036] It is suitable for use in drag reduction and heat exchange enhancement. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a schematic diagram of the LNG gasification process;

[0038] Figure 2 This is a schematic diagram of LNG physical properties changing with temperature;

[0039] Figure 3 Schematic diagram of channel cross-sectional shape and size;

[0040] Figure 4 It is a three-dimensional schematic diagram of the adaptive multi-level nested structure channel segment;

[0041] Figure 5 For machine learning model architecture;

[0042] Figure 6 It is a schematic diagram of the structured grid of the multi-level nested drag reduction channel;

[0043] Figure 7 It is a schematic diagram of the flow process in the adaptive multi-level nested structure channel;

[0044] Figure 8 It is a schematic diagram of the method for extracting data by micro-element segmentation;

[0045] Where T is temperature, P is pressure, qw is wall heat flux, x is coordinate position, and subscripts i, in and out are the numbers of microelement segments;

[0046] Fig. 9 Schematic diagram of the laser processing process of adaptive multi-level nested structure channel;

[0047] Fig.10 This is a schematic diagram of the drag reduction effect of the adaptive multi-level nested structure;

[0048] Fig.11 This is a schematic diagram of the enhanced heat transfer effect of the adaptive multi-level nested structure channel;

[0049] Fig.12 This is a schematic diagram for comparing flow velocity pulsation;

[0050] Fig.13 Schematic diagram of comparison of spanwise velocity pulsation. DETAILED DESCRIPTION

[0051] In order to make the advantages and benefits of the technical solution provided by the present invention more clearly reflected, the technical solution provided by the present invention is now further described in detail with reference to the accompanying drawings, specifically:

[0052] Embodiment 1: This embodiment provides a transcritical flow adaptive multi-stage nested structure drag reduction channel, the channel comprising:

[0053] A flow channel, wherein the inner wall of the flow channel is provided with a multi-level nested groove structure, wherein the groove structure comprises a plurality of gradually changing grooves arranged along the flow direction, and the width and depth of the grooves gradually change along the flow direction;

[0054] A primary groove, wherein the primary groove is a macro groove processed along the inner wall of the flow channel;

[0055] A secondary groove is provided inside the primary groove and is a micro-scale groove;

[0056] The third-level micro-nano rough structure is arranged on the inner surface of the second-level groove, and includes protrusions and depressions at the micro-nano level.

[0057] Specifically, the above channel is established as follows:

[0058] Step 1: Create a flow channel model

[0059] First, a flow channel model for the LNG gasification process is established, which includes a groove design with an adaptive multi-level nested structure. The geometry and size of the flow channel are defined by a printed circuit board heat exchanger (PCHE). Considering the gradual change of the flow state of the fluid from the inlet to the outlet, the groove structure needs to have the ability to adaptively adjust along the flow direction.

[0060] Step 2: Data collection and analysis

[0061] In the established flow channel model, numerical simulation methods are used to collect data on the flow characteristics of LNG at different temperatures and pressures. Large eddy simulation (LES) or direct numerical simulation (DNS) methods are used to extract fluid parameters at various locations in the channel, including mainstream velocity, temperature, pressure, kinematic viscosity, and friction coefficient. This data will serve as the basis for machine learning model training and real-time optimization in subsequent steps.

[0062] Step 3: Machine Learning Model Training

[0063] According to the data collection results, a machine learning model is constructed, and the model uses an artificial neural network structure. The temperature, pressure, mass flow rate, wall heat flux density and other parameters at each section in the channel are used as inputs to output the required groove width and depth. Through the training of a large amount of data, the model can predict the optimal groove structure size with high accuracy in real-time operation.

[0064] Step 4: Adaptive multi-level nested structure design

[0065] According to the prediction results of the machine learning model, the groove structure is dynamically adjusted to establish an adaptive multi-level nested structure. By adjusting the groove width and depth, the groove structure is ensured to always match the turbulent vortex structure of the flow. The design of the groove takes into account the constancy of the groove spacing and the gradual change of the groove width and depth to cope with the changes in physical properties along the flow direction.

[0066] Step 5: Multi-level nested structure processing

[0067] Ultrafast laser processing technology is used to process multi-level nested structural grooves in the channel. Secondary micro grooves are processed on the primary macro surface, and tertiary micro-nano rough structures are generated by laser processing. After processing, the groove channel is connected into a whole by vacuum diffusion welding to form a complete adaptive drag reduction channel structure.

[0068] Step 6: Experimental verification and optimization

[0069] The processed flow channel was applied to the LNG gasification process, and the drag reduction and heat exchange enhancement effects of the multi-stage nested structure were verified through an experimental device. The flow pressure drop and heat exchange efficiency under different flow, pressure and temperature conditions were compared in the experiment to verify the drag reduction and heat exchange effects of the groove structure. At the same time, the design parameters of the machine learning model and the groove structure were further optimized based on the experimental data.

[0070] Embodiment 2: This embodiment further defines a transcritical flow adaptive multi-stage nested structure drag reduction channel provided in embodiment 1, wherein the cross-section of the primary groove is V-shaped or U-shaped.

[0071] Embodiment 3: This embodiment further limits the transcritical flow adaptive multi-stage nested structure drag reduction channel provided in embodiment 1, and the width and depth of the secondary grooves vary periodically along the length direction of the grooves.

[0072] Embodiment 4: This embodiment further defines a transcritical flow adaptive multi-stage nested structure drag reduction channel provided in embodiment 1, wherein the heights and depths of the micro-nano-level protrusions and depressions are inconsistent.

[0073] Embodiment 5: This embodiment further limits the transcritical flow adaptive multi-level nested structure drag reduction channel provided in embodiment 1, and the three-level micro-nano rough structure is prepared by laser processing.

[0074] Embodiment 6: This embodiment provides a method for generating parameters of a multi-stage nested structure drag reduction channel for transcritical flow adaptation, which is used to design the drag reduction channel provided in embodiment 1, including:

[0075] Steps for establishing a flow channel model for an LNG gasification process;

[0076] The steps of collecting data on the flow characteristics of LNG under different temperature and pressure conditions by numerical simulation method;

[0077] Based on the collected data, the steps of constructing and training a preset artificial neural network model;

[0078] According to the prediction results of the artificial neural network model, the width and depth of the groove structure are dynamically adjusted along the direction of the flow channel to establish a step of adaptive multi-level nested groove structure.

[0079] Specifically, this implementation method proposes an adaptive multi-level nested structure drag reduction channel for the problem of drag reduction and enhanced heat exchange of transcritical flow in the gasification process of liquefied natural gas (LNG). The solution dynamically optimizes the groove structure by combining machine learning technology to ensure that the groove structure matches the turbulence in the flow to achieve efficient drag reduction and heat exchange. The main steps include establishing a flow channel model, data collection and analysis, machine learning model training, adaptive groove design, multi-level nested structure processing, and experimental verification and optimization.

[0080] Step 1: Create a flow channel model

[0081] Brief description: Design of adaptive multi-stage nested structure flow channel for LNG gasification process.

[0082] First, a flow channel model for the LNG gasification process needs to be established. The model uses a printed circuit board heat exchanger (PCHE) as the basic framework, which has a good temperature adaptability range and efficient heat exchange capacity. The geometric design of the flow channel takes into account the gradual change of the fluid from the inlet to the outlet, so the groove structure needs to be able to adjust adaptively along the flow direction. This model will provide an experimental and computational basic framework for the entire drag reduction channel design, and at the same time clarify the basic parameters of the groove structure in multi-level nesting.

[0083] Output: Flow channel model, which provides geometric and physical basis for subsequent data collection and machine learning model training.

[0084] Step 2: Data collection and analysis

[0085] Brief description: Use numerical simulation methods to collect data on the characteristics of fluid in the flow channel.

[0086] In the established flow channel model, advanced numerical simulation techniques such as large eddy simulation (LES) or direct numerical simulation (DNS) are used to simulate and analyze the flow characteristics of LNG at different temperatures and pressures. In this process, the focus is on collecting fluid parameters such as mainstream velocity, temperature, pressure, kinematic viscosity, and friction coefficient. Through detailed analysis of these data, we can further understand the fluid characteristics at different locations in the channel and provide reliable training data for the construction of machine learning models.

[0087] Output: Fluid property data for subsequent machine learning training was obtained to provide data support for predicting optimal groove parameters.

[0088] Step 3: Machine Learning Model Training

[0089] Brief description: Build and train a machine learning model based on the collected data to predict the optimal groove parameters.

[0090] Using the data from step 2, an artificial neural network (ANN) model is constructed. The model uses the fluid temperature, pressure, mass flow rate, wall heat flux density, etc. at each cross section as input variables and outputs the required groove width and depth. Through a large number of training samples, the prediction ability of the model is optimized so that it can predict the groove structure parameters with high accuracy in real-time operation. The training process of the model adopts a supervised learning method, with continuous iteration and verification until the model has good generalization ability.

[0091] Output: A trained machine learning model that can be used to predict groove parameters in real time to adapt to changes in fluid properties.

[0092] Step 4: Adaptive multi-level nested structure design

[0093] Brief description: According to the prediction results of the machine learning model, the groove structure is dynamically adjusted to establish an adaptive multi-level nested groove channel.

[0094] According to the trained machine learning model, the width and depth of the grooves are dynamically adjusted along the direction of the flow channel to ensure that the groove structure matches the turbulent vortex structure. The groove design includes multiple levels of nested structures, where the parameters of each level are optimized in real time by the machine learning model. The groove spacing remains constant, while the width and depth gradually change according to the turbulent characteristics of the flow to cope with the complex changes in the physical properties of the fluid, ensuring that each position is in the best drag reduction state.

[0095] Output: The adaptive multi-level nested structure design is completed, which has the ability to adapt to changes in flow conditions and provides a design basis for channel processing.

[0096] Step 5: Multi-level nested structure processing

[0097] Brief description: Ultrafast laser processing technology is used to process the flow channel to form a multi-level nested groove structure.

[0098] Ultrafast laser processing technology is used to micro-process the inner wall of the flow channel to form a multi-level nested groove structure. Secondary micro-grooves are processed on the primary macro surface, and a tertiary micro-nano rough structure is generated by laser processing to further enhance the drag reduction effect. After processing, the various parts of the groove channel are connected into a whole through vacuum diffusion welding and other technologies to ensure that the entire channel has the designed adaptive drag reduction function.

[0099] Output: The processed multi-level nested structure channel provides physical samples for experimental verification and engineering applications.

[0100] Step 6: Experimental verification and optimization

[0101] Brief description: Experimental verification is carried out on the processed channel to evaluate its drag reduction and heat transfer effects, and necessary optimization is carried out.

[0102] The processed flow channel was applied to the LNG gasification process, and its drag reduction and heat transfer enhancement effects were verified through an experimental device. Under different flow, temperature and pressure conditions, key parameters such as the flow pressure drop and heat transfer coefficient of the channel were measured to evaluate the actual performance of the channel. Based on the experimental results, the groove structure parameters and machine learning model were further optimized to improve the drag reduction and heat transfer performance of the channel. The experimental verification not only provides support for theoretical design, but also lays the foundation for future practical applications.

[0103] Output: Experimental verification results and optimized channel design provide a reliable basis for further promotion and industrial application of technology.

[0104] Embodiment 7: This embodiment provides a transcritical flow adaptive multi-stage nested structure drag reduction channel parameter generation device, which is used to design the drag reduction channel provided in embodiment 1, including:

[0105] Module for building flow channel models for LNG gasification processes;

[0106] A module for collecting data on the flow characteristics of LNG under different temperature and pressure conditions through numerical simulation methods;

[0107] Build and train a module of a preset artificial neural network model based on the collected data;

[0108] According to the prediction results of the artificial neural network model, the width and depth of the groove structure are dynamically adjusted along the direction of the flow channel, and a module of an adaptive multi-level nested groove structure is established.

[0109] Embodiment 8: This embodiment provides a computer storage medium for storing a computer program. When the computer program is read by a computer, the computer executes the method provided in embodiment 6.

[0110] Embodiment 9: This embodiment provides a computer, including a processor and a storage medium. When the processor reads a computer program stored in the storage medium, the computer executes the method provided in embodiment 6.

[0111] Embodiment 10: This embodiment provides a computer program product, which is a computer program. When the computer program is read, the method provided in embodiment 6 is implemented.

[0112] Implementation Method XI: Combination Figure 1-13 This embodiment is described in detail by using specific examples to further describe the technical solution provided above. Specifically:

[0113] According to the transcritical flow of LNG gasification process, Figure 1 shown.

[0114] Based on the change of fluid properties with temperature, such as Figure 2 As shown, the groove structure size with the best drag reduction effect at each position in the channel is calculated.

[0115] The optimal dimensionless groove width s+ is taken as 15, and the required groove width s is calculated by the formula, such as Figure 3 shown.

[0116]

[0117] Where U is the mainstream velocity, ν is the kinematic viscosity, C f is the friction coefficient.

[0118] According to the calculated groove width s at different positions, an adaptive multi-level nested structure channel is established while keeping the groove spacing l unchanged, such as Figure 4 As shown. The groove width and depth change gradually along the channel, and the change law is complex and nonlinear, which needs to be related to the change of the physical properties of the fluid flowing therein. Under different flowing working fluids or different working fluid temperatures, the corresponding groove structure size and change law need to be calculated according to the physical properties of the fluid.

[0119] However, since the physical properties of the working fluid change nonlinearly during the transcritical flow process, it is necessary to achieve a dimensionless channel width s at any position. + is 15, and needs to be calculated based on the local mainstream velocity U, kinematic viscosity ν and friction coefficient C f However, the above three parameters can only be obtained through specific numerical simulation results. If you want to predict the above three parameters with high accuracy in advance, you need to use advanced machine learning models. Figure 5 The figure shows the artificial neural network machine learning model architecture used. By taking the temperature, pressure, mass flow rate, wall heat flux density and other parameters at each section of the channel as input parameters, the required groove width s for each section is finally output.

[0120] The machine learning model needs to be trained with a large amount of existing data to achieve high-precision prediction. The ordinary Reynolds average numerical simulation method is difficult to simulate the fine flow in this complex multi-level nested drag reduction channel structure, so a more accurate large eddy numerical simulation method is needed. For this complex multi-level nested drag reduction channel, a unique structured grid needs to be established, such as Figure 6 shown.

[0121] From the numerical simulation results, we can see that when the fluid is heated in the channel, the fluid temperature gradually increases along the channel, such as Figure 7As shown. As the temperature increases, the turbulence coverage gradually increases, and the vortex structure scale also gradually increases. At this time, the groove structure size needs to gradually increase along the flow direction, such as Figure 7 As shown, the groove structure size and the turbulent vortex structure size are always in a matching state, so that each stage in the entire channel is in a state of efficient drag reduction.

[0122] The microelement segmentation method is used to extract data from a large number of numerical simulation results, such as Figure 8 As shown. The channel is divided into a large number of micro-segments with a micro-segment length of 1 mm. The fluid temperature, pressure, mass flow rate, wall heat flux, mainstream velocity U, kinematic viscosity ν, friction coefficient C in each micro-segment are extracted. f , channel width s and dimensionless channel width s + Parameters such as , etc. are used to establish a database. The machine learning model is trained using the data in the database to obtain a machine learning model that can achieve high-precision prediction of the groove width s.

[0123] Through ultrafast laser processing, secondary micro-grooves and tertiary micro-nano rough structures produced by splashing are processed on the primary macro surface to form multi-level nested structural channels, such as Fig. 9 The processed channels are stacked layer by layer and then connected into a whole by welding methods such as vacuum diffusion welding, thereby forming a heat exchanger with adaptive multi-level nested structure channels.

[0124] After specific implementation,

[0125] First, the grooves in the adaptive multi-level nested structure channel will break up the vortex structure, thereby weakening the turbulence intensity in the channel and producing a drag reduction effect, such as Fig.10 As shown in Figure 2, under different flow rates, the adaptive multi-level nested structure channel can significantly reduce the flow pressure drop and produce a significant drag reduction effect.

[0126] The groove structure on the channel surface and the three-level micro-nano rough structure produced by splashing during laser processing will increase the heat transfer area and the heat transfer amount. Fig.11 As shown in Figure 2, under different flow rates, the adaptive multi-level nested structure channels can improve the heat transfer coefficient and produce a significant enhanced heat transfer effect.

[0127] Due to the effect of the grooves on the vortex structure in the channel, the root mean square gradient of the pulsating velocity near the wall in the adaptive multi-stage nested structure channel is significantly reduced. The existence of the groove structure pushes the highly turbulent fluid away from the wall, so the spanwise disturbed flow will have less contact with the wall, thereby reducing the flow resistance. Since the velocity in the flow direction is large and the relative pulsation value is small, the turbulent pulsation in the flow direction is weakened to a certain extent, such as Fig.12As shown in the figure, in the span direction, since the lateral velocity is small and its direction is perpendicular to the direction of the groove, the velocity pulsation in the span direction is greatly weakened, as shown in the figure. Fig.13 It is this destruction of the spanwise vortex structure that greatly weakens the interaction between adjacent fluid micro-groups during the flow in the channel, thereby significantly reducing the flow resistance.

[0128] The technical solution provided by the present invention is further described in detail above through several specific implementation modes in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the several specific implementation modes described above are not intended to be used as limitations on the present invention. Any reasonable modification and improvement of the present invention, combination of implementation modes and equivalent substitution within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A transcritical flow adaptive multi-stage nested structure drag reduction channel, characterized in that: The channel comprises: A flow channel, wherein the inner wall of the flow channel is provided with a multi-level nested groove structure, wherein the groove structure comprises a plurality of gradually changing grooves arranged along the flow direction, and the width and depth of the grooves gradually change along the flow direction; A primary groove, wherein the primary groove is a macro groove processed along the inner wall of the flow channel; A secondary groove is provided inside the primary groove and is a micro-scale groove; The third-level micro-nano rough structure is arranged on the inner surface of the second-level groove, and includes protrusions and depressions at the micro-nano level.

2. The multi-stage nested structure drag reduction channel for transcritical flow adaptation according to claim 1, characterized in that: The cross section of the primary groove is V-shaped or U-shaped.

3. The multi-stage nested structure drag reduction channel with transcritical flow adaptation according to claim 1, characterized in that: The width and depth of the secondary groove vary periodically along the length direction of the groove.

4. The multi-stage nested structure drag reduction channel for transcritical flow adaptation according to claim 1, characterized in that: The heights and depths of the micro-nano-scale protrusions and depressions are inconsistent.

5. The transcritical flow adaptive multi-stage nested structure drag reduction channel according to claim 1, characterized in that: The three-level micro-nano rough structure is prepared by laser processing.

6. A method for generating parameters of a multi-level nested structure drag reduction channel for transcritical flow adaptation, characterized in that: The drag reduction channel according to claim 1 is designed, comprising: Steps for establishing a flow channel model for an LNG gasification process; The steps of collecting data on the flow characteristics of LNG under different temperature and pressure conditions by numerical simulation method; Based on the collected data, the steps of constructing and training a preset artificial neural network model; According to the prediction results of the artificial neural network model, the width and depth of the groove structure are dynamically adjusted along the direction of the flow channel to establish a step of adaptive multi-level nested groove structure.

7. A transcritical flow adaptive multi-stage nested structure drag reduction channel parameter generation device, characterized in that: The drag reduction channel according to claim 1 is designed, comprising: Module for building flow channel models for LNG gasification processes; A module for collecting data on the flow characteristics of LNG under different temperature and pressure conditions through numerical simulation methods; Build and train a module of a preset artificial neural network model based on the collected data; According to the prediction results of the artificial neural network model, the width and depth of the groove structure are dynamically adjusted along the direction of the flow channel, and a module of an adaptive multi-level nested groove structure is established.

8. A computer storage medium for storing a computer program, characterized in that: When the computer program is read by a computer, the computer executes the method according to claim 6.

9. A computer, comprising a processor and a storage medium, characterized in that: When the processor reads the computer program stored in the storage medium, the computer executes the method of claim 6 .

10. A computer program product, being a computer program, characterized in that When the computer program is read, the method according to claim 6 is implemented.

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