Simulation optimization method and device for heat dissipation of fracturing truck group, fracturing truck and medium

Abstract

The present application relates to heat dissipation simulation technical field, disclose a simulation optimization method, device, fracturing truck and medium for fracturing truck heat dissipation, the method comprises: obtaining the experimental boundary condition and experimental data of single fracturing truck;The corresponding porous medium model of radiator in fracturing truck is constructed;The porous medium model is parameterized using experimental boundary condition and experimental data, and based on the porous medium model after parameterization, the truck array model containing multiple fracturing trucks is constructed;The working condition simulation is carried out using truck array model, to reproduce the overtemperature phenomenon caused by cross thermal interference of truck, the porous medium model parameters are calibrated reversely through real single truck experimental boundary condition and experimental data, make the simulation model more fit actual radiator characteristics, and on the basis of parameter calibration, the truck array model of multiple fractivating trucks is constructed, to simulate the cross thermal reflux phenomenon caused by high temperature exhaust gas of front truck being sucked into the intake system of rear truck, so as to provide reliable simulation basis for subsequent heat dissipation optimization.

Description

Technical Field

[0001] This invention relates to the field of heat dissipation simulation technology, specifically to a simulation optimization method, device, fracturing truck, and medium for heat dissipation of fracturing trucks. Background Technology

[0002] Fracturing trucks are specialized vehicles used for fracturing operations in oil, gas, and water wells. Their function is to inject high-pressure, high-volume fracturing fluid into the well, breaking up the formation and forcing proppant into the fractures to improve the permeability of the oil layer, thereby increasing water injection or oil production. They can operate as single units or in multiple units. In oil and gas fields in western China, such as Xinjiang, fracturing trucks face harsh natural conditions characterized by extreme high temperatures (ambient temperatures can reach above 45°C) and calm / slight winds. In actual operations, multiple fracturing trucks are typically concentrated at the well site in a high-density array (i.e., fracturing truck groups). Under these conditions, the high-temperature exhaust gas discharged from one fracturing truck is easily re-inhaled by the air intake grilles of adjacent trucks, creating severe cross-heat backflow within the group. This causes the radiator intake temperature to far exceed the ambient temperature, potentially triggering widespread equipment overheating alarms or even system shutdowns.

[0003] Furthermore, existing technologies typically employ CFD (Computational Fluid Dynamics) software (such as Fluent) to simplify the radiator into a porous medium model for simulation analysis. However, this type of simulation method has many drawbacks. For example, it is rare to use real single-vehicle experimental data to reverse-calibrate and verify the drag coefficient of the porous medium before simulation, leading to severe distortion in subsequent vehicle-wide thermal recirculation simulations. The simulation model lacks experimental benchmark verification, making it difficult to accurately predict overheating phenomena under actual operating conditions. Moreover, existing technologies lack scientific comprehensive system performance evaluation indicators. After discovering overheating problems in the vehicle-wide system, the fan speed is often blindly increased. While this can suppress thermal recirculation to some extent, it also leads to a sharp increase in fluid pressure drop and power consumption. Currently, there is a lack of a means to quantitatively and scientifically evaluate "heat transfer capacity" and "flow resistance / power consumption cost" within a specific speed range. Consequently, it is impossible to effectively reproduce and analyze large-area overheating faults caused by cross-intake of exhaust gases when multiple fracturing trucks are operating intensively.

[0004] In summary, there is an urgent need for a simulation scheme that can ensure high fidelity and reproduce the cross-thermal interference problem of the fracturing tanker, so as to improve the simulation efficiency of heat dissipation of the fracturing tanker. Summary of the Invention

[0005] This invention provides a simulation optimization method, device, fracturing truck, and medium for heat dissipation of fracturing trucks, in order to solve the problem mentioned in the above-mentioned technical background that the existing technology is unable to guarantee the authenticity of the simulation and cannot solve the over-temperature problem caused by cross-thermal interference of the trucks, thus resulting in poor simulation efficiency of heat dissipation of fracturing trucks.

[0006] In a first aspect, the present invention provides a simulation optimization method for heat dissipation of fracturing vehicle crews, the method comprising: Obtain the experimental boundary conditions and experimental data for a single fracturing truck; Construct a porous media model corresponding to the radiator in the fracturing truck; Using experimental boundary conditions and experimental data, the parameters of the porous medium model were calibrated to obtain the parameter-calibrated porous medium model. Based on the porous media model after parameter calibration, a vehicle group array model containing multiple fracturing trucks is constructed. The working conditions were simulated using a train array model to reproduce the overheating phenomenon caused by cross thermal interference between train sets.

[0007] This invention calibrates the core parameters of a porous medium model of a radiator using experimental boundary conditions and data from a single fracturing truck. This not only corrects the discrepancy between purely theoretical simulations and actual physical characteristics, ensuring that the airflow and heat transfer characteristics of a single radiator closely match those of the actual equipment, but also provides a reliable foundation model for subsequent multi-truck array simulations. Furthermore, based on the calibrated model, a multi-truck array model is constructed, successfully simulating the cross-heat recirculation caused by the intake system of the rear truck drawing in high-temperature exhaust gas from the front truck under extreme high-temperature, calm wind conditions. This accurately reproduces large-area over-temperature alarm faults. It also provides a high-fidelity simulation basis for finding the optimal fan speed and optimizing heat dissipation performance, avoiding blindly adjusting parameters or over-reliance on field tests. This reduces R&D costs and time while improving the simulation efficiency of fracturing truck heat dissipation.

[0008] In one alternative implementation, after performing operational condition simulation using a train array model, the method further includes: Within the preset fan speed range, multiple speed operating points are selected at set intervals; Simulations were performed at each speed operating point, and the heat transfer performance parameters and flow resistance parameters of the porous medium region of the radiator were extracted accordingly. The comprehensive performance evaluation factor of the radiator is calculated based on the heat transfer performance parameters and flow resistance parameters at each operating point of rotational speed. The optimal fan speed is determined based on the comprehensive performance evaluation factor of the radiator at each operating speed point.

[0009] This invention, based on the reproduction of the overheating phenomenon caused by cross-heating interference between fracturing units, conducts quantitative optimization of the core control parameter of fan speed to alleviate the heat dissipation bottleneck caused by the thermal interference of the fracturing units at the lowest cost. Specifically, it conducts multi-speed operation simulation based on a real fracturing unit array model. The calculation process fully considers the thermal interference effects of actual operations such as hot air recirculation and cross-heat intake of multiple vehicles. The final optimal speed is perfectly adapted to the actual heat dissipation requirements of fracturing unit team operations, avoiding common problems such as "insufficient heat dissipation of a single unit at its rated speed under team operation" or "blindly increasing the speed and causing energy consumption redundancy". The above simulation process can improve the effective heat exchange capacity of the heat dissipation system by precisely optimizing the fan speed, offsetting the intake air temperature rise caused by cross-heating interference between the fracturing units, and alleviating the overheating fault on site with extremely low modification cost. This not only extends the duration of high-power continuous operation of the fracturing unit, but also greatly improves the stability of equipment operation and ensures the simulation efficiency of heat dissipation of the fracturing unit.

[0010] In one optional implementation, the comprehensive performance evaluation factor of the radiator is calculated based on the heat transfer performance parameters and flow resistance parameters at each operating speed, including: For each speed operating point, the preset index of the corresponding flow resistance parameter is calculated to obtain the corresponding resistance value. The ratio of the corresponding heat transfer performance parameter to the resistance value is then calculated to obtain the comprehensive performance evaluation factor of the radiator at the speed operating point.

[0011] This invention constructs a quantifiable and weighted single-dimensional evaluation index by using the calculation logic of "the ratio of the exponential power of the heat transfer performance parameter to the flow resistance". This solves the problem of the difficulty in directly comparing the merits of different parameters under multiple operating conditions. In other words, the two evaluation dimensions of heat transfer capacity and flow resistance, which are opposite in direction, are transformed into a single comprehensive evaluation value through mathematical formulas. This establishes a clear standard for subsequent judgment of the merits of operating conditions, avoids the subjective bias of human experience judgment, and to a certain extent ensures the simulation efficiency of heat dissipation of fracturing trucks.

[0012] In one optional implementation, the optimal fan speed is determined based on the comprehensive performance evaluation factor of the radiator at each speed operating point, including: From all operating speed points, select all target speed points that meet the preset temperature requirements; Based on the comprehensive performance evaluation factor of the radiator at each target speed point, the fan speed corresponding to the maximum value of the comprehensive performance evaluation factor of the radiator is determined as the optimal fan speed.

[0013] This invention uses meeting preset temperature requirements as the first screening criterion, eliminating all operating speeds with insufficient heat dissipation capacity. This fundamentally prevents the problem of "optimal energy efficiency but overheating equipment," ensuring that the final selected speed can completely offset the intake air temperature rise caused by cross-thermal interference between the fracturing units, thus guaranteeing the temperature safety of the fracturing unit during high-power continuous operation. At the same time, among all feasible solutions that meet the heat dissipation requirements, the speed with the highest comprehensive characteristic evaluation factor is selected, corresponding to the strongest heat exchange capacity per unit fan power consumption. This approach not only maintains the safety baseline of not exceeding the temperature limit but also avoids the side effects of blindly increasing the speed, such as fuel waste, excessive noise, and accelerated wear of fan components, finding a precise and optimal balance between heat dissipation reliability and operating costs.

[0014] In one optional implementation, the porous media model is calibrated using experimental boundary conditions and experimental data, including: Set the initial model parameters for the porous medium model; The heat dissipation process under single-vehicle operating conditions was simulated based on experimental boundary conditions, and simulation results were obtained. The consistency between the simulation results and the experimental data is verified to obtain the verification results. If the verification results are inconsistent, adjust the initial model parameters and return to the step of simulating the heat dissipation process of a single vehicle under working conditions according to the experimental boundary conditions until the simulation results are consistent with the experimental data, thus completing the parameter calibration of the porous medium model.

[0015] This invention designs a closed-loop verification process of "initial parameter assignment, single-vehicle simulation, experimental data comparison, and parameter iterative correction," which solves the problem of large deviation and insufficient accuracy between the pure theoretical porous medium model and the actual radiator characteristics. It is the cornerstone of the accuracy of the entire vehicle heat dissipation simulation system, so that the calibrated model can accurately reflect the actual heat dissipation characteristics of the fracturing truck under the on-site operating conditions, thereby making the subsequent vehicle simulation working condition reproduction closer to the actual operating scenario.

[0016] In one optional implementation, based on the parameter-calibrated porous media model, a vehicle array model containing multiple fracturing trucks is constructed, including: Based on the vehicle layout of the on-site operation, a three-dimensional array model of multiple fracturing trucks was established. Based on the porous media model after parameter calibration, determine the current model parameters; Import the current model parameters into the 3D array model to obtain the train array model.

[0017] This invention constructs a three-dimensional array model based strictly on the actual vehicle layout on site to accurately reproduce the on-site layout features such as vehicle spacing, arrangement, and vehicle orientation. This provides a simulation platform that fits the engineering reality for subsequent reproduction of the overheating phenomenon caused by cross-train thermal interference. At the same time, the porous medium model parameters calibrated by experimental data are directly imported into the three-dimensional array to completely transfer the calibration results of the single-vehicle model. This ensures that the radiator flow resistance and heat transfer performance of each vehicle in the array are consistent with the actual measured characteristics of the real vehicle. This avoids the problem of "complete distortion of multi-vehicle simulation results due to inaccurate basic model" from the source, thus ensuring that the subsequent working condition simulation and speed optimization conclusions have engineering reference value.

[0018] In one optional implementation, a train array model is used to simulate operating conditions to reproduce the overheating phenomenon caused by cross-heating interference between train sets, including: After setting the ambient temperature of the vehicle array model to a preset high temperature, a multi-vehicle flow thermal coupling simulation was performed to reproduce the over-temperature phenomenon caused by cross-heating interference of the vehicle group. The over-temperature phenomenon refers to the phenomenon that the high-temperature exhaust gas discharged by the front vehicle is re-inhaled by the air intake of the adjacent or rear vehicle, forming a heat backflow, which causes the radiator intake temperature to be much higher than the ambient temperature.

[0019] This invention fully simulates the entire process of high-temperature exhaust gas diffusion from the front vehicle and its absorption into the air intake of adjacent / rear vehicles to form a heat recirculation. It clarifies that the core cause of "radiator intake temperature far exceeding ambient temperature" is cross-heating interference between vehicles, rather than insufficient heat dissipation capacity of a single vehicle. This provides a clear direction for subsequent targeted optimization and avoids ineffective modification costs such as blindly increasing radiator capacity or replacing hardware.

[0020] Secondly, the present invention provides a simulation optimization device for heat dissipation of fracturing vehicle crews, the device comprising: The data acquisition module is used to acquire the experimental boundary conditions and experimental data of a single fracturing truck. The first building module is used to build a porous medium model corresponding to the radiator in the fracturing truck. The parameter calibration module is used to calibrate the parameters of the porous medium model using experimental boundary conditions and experimental data, and obtain the parameter-calibrated porous medium model. The second construction module is used to construct a vehicle array model containing multiple fracturing trucks based on the porous media model after parameter calibration. The over-temperature reproduction module is used to simulate operating conditions using a train array model in order to reproduce the over-temperature phenomenon caused by cross-thermal interference between trains.

[0021] The simulation optimization device for heat dissipation of fracturing trucks of the present invention uses real single-vehicle experimental boundary conditions and experimental data to reverse-calibrate the parameters of the porous medium model, making the simulation model more consistent with the actual radiator characteristics. Based on the parameter calibration, a truck array model of multiple fracturing trucks is constructed to simulate the cross-heat recirculation phenomenon caused by the high-temperature exhaust gas of the front truck being sucked into the intake system of the rear truck. This provides a reliable simulation basis for subsequent heat dissipation optimization and ensures the simulation efficiency of heat dissipation of fracturing trucks to a certain extent.

[0022] Thirdly, the present invention provides a fracturing truck, which includes a controller, a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the simulation optimization method for heat dissipation of the fracturing truck group described in the first aspect or any corresponding embodiment.

[0023] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the simulation optimization method for heat dissipation of fracturing trucks described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0024] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the first process of a simulation optimization method for heat dissipation of fracturing vehicle groups according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the second process of the simulation optimization method for heat dissipation of fracturing vehicle groups according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a CFD-based simulation optimization method. Figure 4 This is a structural block diagram of a simulation optimization device for heat dissipation of fracturing vehicle groups according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the hardware structure of the fracturing truck according to an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] According to an embodiment of the present invention, a simulation optimization method for heat dissipation of fracturing vehicle is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0028] This embodiment provides a simulation optimization method for heat dissipation of fracturing trucks. Figure 1 This is a schematic diagram of the first step in the simulation optimization method for heat dissipation of fracturing vehicle groups according to an embodiment of the present invention, as shown below. Figure 1 As shown, the process includes the following steps: Step S101: Obtain the experimental boundary conditions and experimental data for a single fracturing truck.

[0029] In this embodiment, the experimental boundary conditions are the input benchmarks for the heat dissipation experiment of the fracturing truck, used to ensure that the operating environment of the simulation model is completely consistent with the real experiment, and are a prerequisite for simulation calculation; the experimental data are the measured results in the experiment, used to be benchmarked with the simulation output, to verify and correct the simulation parameters of porous media, and are the reference standard for model calibration. Note that the specific acquisition methods and corresponding content of both can be adaptively adjusted according to actual needs. For example, through actual vehicle bench testing or on-site working condition testing, data acquisition equipment such as temperature sensors, differential pressure sensors, anemometers, and infrared thermal imagers can be used to simultaneously collect boundary conditions and result data. Among them, experimental boundary conditions may include environmental boundaries (such as ambient temperature, atmospheric pressure, ambient wind speed, etc.), working condition boundaries (such as engine rated heat output, coolant flow rate and inlet / outlet temperature, radiator fan operating speed, etc.), and structural boundaries (such as radiator core dimensions, fracturing vehicle exhaust port location and dimensions, etc.). Experimental data may include radiator air-side inlet / outlet temperature, air pressure drop, measured heat transfer, radiator core area temperature distribution, specific location and temperature value of overheating points, resistance characteristics under different wind speeds, airflow data, etc.; this is only an example for illustration.

[0030] Step S102: Construct a porous media model corresponding to the radiator in the fracturing truck.

[0031] It should be noted that the porous medium model is an engineering simplification method for CFD simulation of fracturing truck heat dissipation. The radiator core is composed of a large number of fine fins and flat tubes. If the microstructure is directly modeled, a massive mesh will be generated, and the computational cost of the vehicle-level simulation will be extremely high. Therefore, the radiator core is equivalent to a homogeneous porous fluid region with equivalent flow resistance and heat transfer capacity. The macroscopic resistance parameters and heat transfer parameters replace the role of the microscopic fin structure. While ensuring the accuracy of the flow field and temperature field calculations, the computational load of multi-vehicle array simulation is significantly reduced.

[0032] In this embodiment, the simulation steps of the above-mentioned porous medium model mainly include geometric equivalent replacement (i.e., in the three-dimensional simulation model, a cuboid fluid domain of the same size as the radiator core is used to replace the original fine fins and flat tube structure, and this area is defined as the porous medium calculation domain, retaining only the external installation dimensions and ventilation direction) and initial parameter configuration (i.e., setting the core properties of the porous medium, such as resistance properties, including viscous resistance coefficient (corresponding to low-speed friction resistance) and inertial resistance coefficient (corresponding to high-speed eddy current resistance), the initial values ​​can be taken from radiator manufacturer samples, empirical formulas, or obtained through small-scale fine simulation fitting; heat transfer properties, such as configuring the equivalent volume heat transfer coefficient to simulate the heat transfer between the air side and the coolant side).

[0033] Step S103: Using experimental boundary conditions and experimental data, the parameters of the porous medium model are calibrated to obtain the parameter-calibrated porous medium model.

[0034] In this embodiment, this step aims to use the experimental data of a single vehicle as a benchmark to reverse-calibrate the core parameters of the porous medium model, solve the problem of deviation between the initial empirical parameters and the actual radiator characteristics, and thus make the pressure drop, temperature, and over-temperature characteristics of the simulation output accurately match the experimental results, so as to obtain a high-fidelity equivalent model of the radiator, and fundamentally ensure the credibility of the subsequent vehicle group thermal reflow simulation.

[0035] Step S104: Based on the porous media model after parameter calibration, construct a vehicle array model containing multiple fracturing trucks.

[0036] In this embodiment, this step is an extension and reuse of the high-fidelity single-vehicle model to a multi-vehicle operation scenario. That is, the porous medium parameters of the radiator, which have been experimentally calibrated, are directly reused for each fracturing truck in the array to ensure that the heat dissipation simulation accuracy of all vehicles is consistent with the single-vehicle calibration level. At the same time, it accurately restores the real vehicle layout and extreme environment, and builds a coupled simulation model that can simulate the mutual interference of airflow and heat between vehicles, providing a computing platform for the subsequent reproduction of the cross-heat recirculation overheating problem.

[0037] In one specific embodiment, firstly, the calibrated single-vehicle model array is replicated according to the actual on-site operational layout (such as the number of vehicles, arrangement, vehicle orientation, and spacing). The radiators of all vehicles directly use the calibrated porous media resistance and heat transfer parameters, eliminating the need for repeated calibration. Subsequently, a large-scale external flow field computational domain encompassing all vehicles is established, with boundary conditions consistent with those on-site, such as extreme high-temperature environments, calm / light wind speeds, and ground and far-field pressure conditions, to recreate the real operational meteorological environment. Simultaneously, a full-load operating condition is uniformly set for each fracturing truck; for example, the computational domain is meshed, with mesh densification in areas with strong airflow interference, such as vehicle gaps and air intake / exhaust ports, to ensure the accuracy of the fluid-thermal coupling calculation. Finally, a fluid-thermal coupling solver and turbulence model are set up, confirming that the porous media parameters are globally effective, completing the construction of the vehicle array simulation model, which can be directly used for subsequent operating condition calculations.

[0038] Step S105: Use the train array model to simulate the working conditions in order to reproduce the over-temperature phenomenon caused by cross thermal interference of the train sets.

[0039] In this embodiment, this step is the core link from "single vehicle heat dissipation verification" to "vehicle group fault reproduction". That is, by using the calibrated vehicle group array model, the actual extreme working conditions are simulated to reproduce the cross heat recirculation phenomenon when multiple vehicles are working side by side, where "the high-temperature airflow discharged by the front vehicle is re-inhaled by the air intake of the adjacent / rear vehicle". This verifies that the root cause of the overheating on site is the mutual interference of hot airflow in multiple vehicles, rather than the insufficient heat dissipation capacity of a single vehicle. This provides a reliable simulation basis for subsequent targeted optimization of fan speed.

[0040] In one specific embodiment, the simulation model is first input with operating conditions consistent with the actual field conditions (such as full-load heat generation and initial fan speed) and extreme environmental conditions (high temperature environment, calm / light wind) to ensure that the simulation input is completely aligned with the actual fault conditions. Then, a steady-state fluid-thermal coupling simulation is initiated to solve the airflow and heat transfer processes in the entire vehicle group area. This captures the diffusion trajectory of the exhaust gas from the front vehicle's cooling system and high-temperature exhaust gas at low wind speeds, as well as the flow field characteristics of airflow being drawn in secondary by the air intakes of surrounding vehicles. Simultaneously, the radiator intake temperature and core component temperature data of each vehicle are extracted, and the patterns of overheating on-site (e.g., more severe overheating in the middle and rear vehicles) are compared to verify the consistency between the overheating point location, temperature amplitude, and actual field measurements. Finally, when the simulation results accurately reproduce the characteristics of "multi-vehicle cross-thermal interference causing excessive intake temperature and equipment overheating alarms," ​​the fault reproduction is complete, verifying the vehicle group model's simulation capability for thermal interference problems, and it can be used for subsequent optimization calculations.

[0041] The simulation optimization method for heat dissipation of fracturing trucks provided in this embodiment calibrates the core parameters of the porous medium model of the radiator using the measured boundary conditions and experimental data of a single fracturing truck. This not only corrects the deviation between the pure theoretical simulation and the actual characteristics, making the airflow and heat transfer characteristics of a single radiator highly consistent with the actual equipment, but also provides a reliable basic model for subsequent multi-truck array simulation. Furthermore, based on the parameter-calibrated model, a truck array model of multiple fracturing trucks is constructed, which can successfully simulate the cross-heat recirculation caused by the high-temperature exhaust gas of the preceding truck being drawn into the intake system of the following truck under extreme high-temperature and calm wind conditions, thus accurately reproducing large-area over-temperature alarm faults. It also provides a high-fidelity simulation basis for subsequently finding the optimal fan speed and optimizing heat dissipation performance, avoiding blindly adjusting parameters or over-reliance on field tests, reducing R&D costs and time, and helping to improve the simulation efficiency of fracturing truck heat dissipation.

[0042] This embodiment provides a simulation optimization method for heat dissipation of fracturing vehicle units. Figure 2 This is a schematic diagram of the second process of the simulation optimization method for heat dissipation of fracturing vehicle groups according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps: Step S201: Obtain the experimental boundary conditions and experimental data for a single fracturing truck. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0043] Step S202: Construct a porous media model corresponding to the radiator in the fracturing truck. For details, please refer to [link / reference]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0044] Step S203: Using experimental boundary conditions and experimental data, the parameters of the porous medium model are calibrated to obtain the parameter-calibrated porous medium model.

[0045] Specifically, step S203 includes: Step a1: Set the initial model parameters for the porous medium model.

[0046] In this embodiment, this step involves assigning a set of initial model parameters to the equivalent porous medium region of the radiator, such as resistance parameters (viscous drag coefficient corresponding to low-speed frictional resistance and inertial drag coefficient corresponding to high-speed eddy current resistance) and heat transfer parameters. This enables the simulation model to have basic flow and heat transfer calculation capabilities, serving as the starting point for subsequent back-iteration calibration. Note that the aforementioned initial model parameters can be obtained based on engineering experience, theoretical formulas, or manufacturer data, such as ① using wind resistance and heat transfer performance sample data provided by the radiator manufacturer; ② based on the radiator fin structure parameters and obtained through industry experience correlation calculations; ③ determined by referring to mature simulation parameters of similar radiators. This is only an example for illustration.

[0047] Step a2: Simulate the heat dissipation process under single-vehicle operating conditions based on experimental boundary conditions to obtain simulation results.

[0048] In this embodiment, this step aims to ensure that the input conditions are completely consistent with the experiment, and to reproduce the heat flow coupling process of a single vehicle's heat dissipation through simulation, so as to eliminate the interference caused by the difference in boundary conditions, and ensure that the deviation between the subsequent simulation and experimental data comes only from the accuracy of the porous medium parameters. This provides a benchmark simulation result that can be compared and calibrated for parameter calibration, such as simulating the complete heat dissipation process of the covering air flowing through the cabin, penetrating the radiator, exchanging heat with the coolant side, and hot air being discharged, and outputting two core results: flow and temperature.

[0049] In one specific embodiment, the measured single-vehicle experimental boundary data is first input into the simulation model at a 1:1 scale, including ambient temperature and pressure, inlet wind speed / fan speed, radiator coolant side heat generation, and wall heat transfer conditions, ensuring that the simulation input is completely consistent with the experimental conditions. Then, the energy equation and turbulence model are enabled, and the resistance and heat transfer settings for the porous media region are activated. A fluid-thermal coupling solution algorithm is used for steady-state calculations, monitoring residuals and key physical quantities until convergence. Finally, post-processing extracts the core indicators required for calibration, including radiator air-side inlet and outlet pressure drop, inlet and outlet temperatures, core region temperature distribution, overheating point location and temperature value, yielding complete single-vehicle heat dissipation simulation results.

[0050] Step a3: Verify the consistency between the simulation results and the experimental data to obtain the verification results.

[0051] In this embodiment, this step aims to compare the flow and temperature results output from the simulation with the experimentally measured data from multiple dimensions, provided that the boundary conditions are completely aligned, in order to determine whether the accuracy of the current porous media parameters meets the standards. The verification result serves as both a basis for determining whether parameter calibration is complete and a guide for the direction of deviation in parameter correction. Specifically, consistency verification is performed based on the engineering-allowed error range (e.g., pressure drop error ≤ 5%, temperature error ≤ 2℃).

[0052] In step a4, if the verification results are inconsistent, adjust the initial model parameters and return to the step of simulating the heat dissipation process of a single vehicle under the working conditions according to the experimental boundary conditions until the simulation results are consistent with the experimental data. Then, complete the parameter calibration of the porous medium model and obtain the porous medium model after parameter calibration.

[0053] In this embodiment, this step aims to distinguish the type of deviation based on the consistency verification results. For example, if the pressure drop is inconsistent, it belongs to the drag parameter deviation; if the temperature / heat transfer is inconsistent, it belongs to the heat transfer parameter deviation. The direction of the deviation is also clearly defined (simulated value is too large or too small). Then, targeted parameter adjustments are made. For example, for pressure drop deviations, the viscous drag coefficient and inertial drag coefficient are adjusted (increase the drag coefficient if the simulated pressure drop is too small, and decrease it if it is too large); for heat transfer deviations, the equivalent volume heat transfer coefficient is adjusted (increase the heat transfer coefficient if the simulated heat transfer is too small or the temperature is too high, and decrease it if it is too high). Simultaneously, the corrected parameters are updated in the simulation model, the experimental boundary conditions are reloaded, and the single-vehicle heat dissipation simulation is run again. The consistency verification between the simulation results and experimental data is performed again until the errors of all core indicators (such as pressure drop, temperature, and the location and value of over-temperature points) meet the engineering accuracy requirements. At this point, the parameter calibration is complete, and the final usable calibrated porous media model is output.

[0054] In this embodiment of the invention, a closed-loop verification process of "initial parameter assignment, single-vehicle simulation, experimental data comparison, and parameter iterative correction" is designed to solve the problem of large deviation and insufficient accuracy between the pure theoretical porous medium model and the actual radiator characteristics. It is the cornerstone of the accuracy of the entire vehicle heat dissipation simulation system, so that the calibrated model can accurately reflect the actual heat dissipation characteristics of the fracturing truck under the on-site operating conditions, thereby making the subsequent vehicle simulation working condition reproduction closer to the actual operating scenario.

[0055] Step S204: Based on the porous media model after parameter calibration, construct a vehicle array model containing multiple fracturing trucks.

[0056] Specifically, step S204 includes: Step b1: Based on the vehicle layout for on-site operations, establish a three-dimensional array model of multiple fracturing trucks.

[0057] In this embodiment, a three-dimensional array model is built strictly according to the actual vehicle layout on site (such as the number of fracturing trucks, the arrangement of rows and columns, the orientation of the truck heads, the lateral / longitudinal spacing between adjacent vehicles, and the relative position of vehicles to the ground, etc.). This model accurately reproduces the on-site layout features such as vehicle spacing, arrangement, and vehicle orientation, rather than using an idealized symmetrical arrangement. This ensures that the effects of hot air recirculation, cross-heat absorption, and regional heat accumulation in the model are highly matched with the actual operating scenario, providing a simulation platform that fits the engineering reality for the subsequent reproduction of the cross-heat interference and overheating phenomenon of the vehicle group.

[0058] Step b2: Determine the current model parameters based on the porous media model after parameter calibration.

[0059] In this embodiment, this step aims to extract and solidify the precise porous medium parameters obtained through experimental iterative calibration in the single-vehicle stage, and use them as unified standard parameters for the radiators of all fracturing trucks in the truck group array. The purpose is to ensure that the equivalent characteristics of the radiator of each truck in the truck group are completely consistent with the experimentally verified single-vehicle model, eliminate the accuracy deviation of the truck group simulation from the source of parameters, and make the simulation results of subsequent truck group thermal interference credible.

[0060] Step b3: Import the current model parameters into the 3D array model to obtain the train array model.

[0061] It should be noted that the three-dimensional array model built in the previous steps only replicated the vehicle's geometric layout, and the radiator area has not yet been configured with accurate physical parameters. The purpose of this step is to batch assign the porous medium parameters, which were experimentally calibrated in the single-vehicle stage, to the radiator area of ​​each fracturing truck in the array, so as to obtain a complete and computable vehicle group model with "geometric layout that fits the site and physical parameters that have been calibrated". This model inherits the high precision of the single-vehicle calibration and enables rapid expansion of multi-vehicle scenarios, providing a reliable computing platform for subsequent vehicle group thermal interference simulation.

[0062] In this embodiment of the invention, a three-dimensional array model is built based on the actual vehicle layout on site to accurately reproduce the on-site layout features such as vehicle spacing, arrangement, and vehicle orientation. This provides a simulation platform that fits the engineering reality for the subsequent reproduction of the overheating phenomenon caused by cross-train thermal interference. At the same time, the porous medium model parameters calibrated by experimental data are directly imported into the three-dimensional array to completely transmit the calibration results of the single-vehicle model. This ensures that the radiator flow resistance and heat transfer performance of each vehicle in the array are consistent with the actual measured characteristics of the real vehicle. This avoids the problem of "complete distortion of multi-vehicle simulation results due to inaccurate basic model" from the source, and thus ensures that the subsequent working condition simulation and speed optimization conclusions have engineering reference value.

[0063] Step S205: Use the train array model to simulate the working conditions in order to reproduce the over-temperature phenomenon caused by cross thermal interference of the train sets.

[0064] Specifically, step S205 includes: Step c1: After setting the ambient temperature of the vehicle array model to a preset high temperature, perform multi-vehicle flow thermal coupling simulation to reproduce the over-temperature phenomenon caused by cross-heating interference of the vehicle group; where the over-temperature phenomenon refers to the phenomenon that the high temperature exhaust gas discharged by the front vehicle is re-inhaled by the air intake of the adjacent or rear vehicle, forming a heat backflow, resulting in the radiator intake temperature being much higher than the ambient temperature.

[0065] In this embodiment, this step involves setting a preset high-temperature ambient temperature (the value of which is adaptively set according to actual needs) in the vehicle array model, combined with calm / light wind inflow conditions, and simultaneously setting boundaries such as ground heat exchange and far-field pressure and temperature to replicate the harsh working conditions at the operation site. Subsequently, all fracturing vehicles in the array are uniformly assigned full-load heat output and initial fan speed to ensure that the heat dissipation load of each vehicle is consistent with the on-site operating conditions. Steady-state flow-heat coupling simulation is then initiated to simultaneously solve the airflow and heat transfer process in the entire vehicle group area, track the diffusion and transport trajectory of high-temperature airflow, and capture the flow field characteristics of mutual airflow entrainment and hot air recirculation inlet between vehicles. Then, the radiator intake temperature and core component temperature data of each vehicle are extracted to verify the on-site pattern that "the intake temperature of the middle and rear vehicles is much higher than the ambient temperature, and the degree of overheating is higher than that of a single vehicle," to confirm the successful reproduction of the overheating phenomenon caused by cross-thermal interference.

[0066] In this embodiment of the invention, the entire process of high-temperature exhaust gas diffusion from the front vehicle and being drawn into the air intake of adjacent / rear vehicles to form heat recirculation is fully reproduced by simulation. It is clear that the core cause of "radiator intake temperature far exceeding ambient temperature" is cross-heat interference between vehicles, rather than insufficient heat dissipation capacity of a single vehicle. This provides a clear direction for subsequent targeted optimization and avoids ineffective modification costs such as blindly increasing radiator capacity or replacing hardware.

[0067] Step S206: Select multiple speed operating points at set intervals within the preset fan speed range.

[0068] In this embodiment, this step defines the fan speed range (e.g., 700-800 rpm) by considering the on-site over-temperature critical speed, the fan's rated operating range, and energy consumption constraints. Simultaneously, balancing optimization accuracy and computational cost, a fixed step size (e.g., 20 rpm) is selected as the sampling interval to balance result resolution and simulation workload. Finally, values ​​are taken at fixed intervals within the range to generate a set of independent speed operating points (e.g., 700, 720, 740, 760, 780, and 800 rpm, a total of 6 speed operating points).

[0069] Step S207: Simulate each speed operating point and extract the heat transfer performance parameters and flow resistance parameters of the porous medium region of the radiator accordingly.

[0070] In this embodiment, this step is essentially a control simulation of a single variable, where only the fan speed is changed under all operating conditions, while ambient temperature, vehicle heat load, and vehicle layout remain consistent throughout. Fluid-thermal coupling calculations are performed for each discrete speed point. Then, heat transfer performance parameters (such as air inlet / outlet temperature difference, heat exchange, and convective heat transfer coefficient) and flow resistance parameters (such as pressure drop across the porous medium and air mass flow rate) are extracted from the porous medium region of the radiator, corresponding to heat dissipation capacity and fan energy consumption costs, respectively. Finally, the heat transfer and resistance parameters corresponding to each speed are mapped one-to-one to form a speed-performance parameter mapping dataset, thus providing a direct data foundation for subsequent selection of the optimal speed through comprehensive evaluation indicators.

[0071] Furthermore, this embodiment does not require array testing of multiple speeds on the actual vehicle site. It can traverse all operating points in batches within a preset speed range through simulation alone, automatically extract core parameters and output the optimal solution. This reduces the on-site testing work that originally required several days to simulation calculations in a few hours, significantly shortening the heat dissipation optimization cycle, reducing the manpower and equipment costs of on-site debugging, and avoiding the safety risks of high-load on-site testing.

[0072] Step S208: Calculate the comprehensive performance evaluation factor of the radiator based on the heat transfer performance parameters and flow resistance parameters at each operating speed.

[0073] In this embodiment, a comprehensive characteristic evaluation factor is calculated by combining heat transfer performance parameters and flow resistance parameters. This breaks away from the crude optimization model that "uses heat dissipation capacity as the sole criterion." It ensures that the heat exchange capacity of the radiator is sufficient to offset the temperature rise caused by thermal interference of the train set, while also taking into account the flow resistance loss of the fan. This avoids the side effects of simply increasing the speed, such as high fuel consumption, high noise, and accelerated wear of fan components, thus achieving a multi-objective balance between heat dissipation effect, operating economy, and equipment life.

[0074] Specifically, step S208 includes: Step d1: For each speed operating point, calculate the preset index of the corresponding flow resistance parameter to obtain the corresponding resistance value, and calculate the ratio of the corresponding heat transfer performance parameter to the resistance value to obtain the comprehensive performance evaluation factor of the radiator at the speed operating point.

[0075] It should be noted that the specific content of the preset index can be determined adaptively according to actual needs. For example, when the index is increased, the influence of flow resistance on the evaluation result has a higher weight, and the optimization direction is biased towards reducing fan energy consumption, saving fuel, and reducing component wear; when the index is decreased, the weight of heat transfer performance is higher, and the optimization direction is biased towards maximizing heat dissipation capacity; in this embodiment, the preset index is 1 / 3.

[0076] Furthermore, since increasing the fan speed will simultaneously increase the heat exchange capacity and increase the flow resistance (increasing the fan power consumption), it is impossible to select the optimal solution based on a single indicator. In this embodiment, a comprehensive characteristic evaluation factor is obtained by calculating "heat transfer performance parameter ÷ flow resistance parameter to the power of 1 / 3". This factor represents the heat exchange effect that can be obtained under a unit fan pumping power. The larger the value, the higher the cost-effectiveness of "heat dissipation benefits and energy consumption costs" at that speed.

[0077] In this embodiment of the invention, a quantifiable and weighted single-dimensional evaluation index is constructed by using the calculation logic of "the ratio of the heat transfer performance parameter to the exponential power of the flow resistance". This solves the problem that it is difficult to directly compare the merits of different parameters under multiple operating conditions. That is, the two evaluation dimensions of heat transfer capacity and flow resistance, which are opposite in direction, are transformed into a single comprehensive evaluation value through mathematical formulas. This establishes a clear standard for subsequent judgment of the merits of operating conditions, avoids the subjective bias of human experience judgment, and to a certain extent ensures the simulation efficiency of heat dissipation of fracturing truck.

[0078] Step S209: Determine the optimal fan speed based on the comprehensive performance evaluation factor of the radiator at each speed operating point.

[0079] Specifically, step S209 includes: Step e1: Select all target speed points that meet the preset temperature requirements from all speed operating points.

[0080] In this embodiment, the preset temperature requirement is a safety red line set on site (such as the upper limit of radiator intake temperature, the maximum allowable temperature of core components, the specific content of which is set based on equipment operation specifications or on-site safety requirements). The purpose of screening is to first eliminate the operating conditions that cannot solve the over-temperature problem from all discrete speed operating conditions, define the feasible speed range that can make the train temperature meet the standard, and then screen the speed with the highest overall cost performance within the feasible set to avoid selecting an invalid solution that has "low energy consumption but cannot control the temperature".

[0081] Step e2: Based on the comprehensive performance evaluation factor of the radiator at each target speed point, determine the fan speed corresponding to the maximum value of the comprehensive performance evaluation factor of the radiator as the optimal fan speed.

[0082] It should be noted that among all feasible speeds that can control the temperature of the train set within the safe threshold, this embodiment uses the comprehensive performance evaluation factor of the radiator as the quantitative evaluation standard and selects the speed corresponding to the largest factor value. This speed can not only ensure the elimination of the risk of overheating due to heat reflow, but also maximize the heat dissipation benefits per unit fan power consumption, avoiding unnecessary energy consumption caused by blindly increasing the speed. It is the optimal operating scheme that takes into account both safety and economy.

[0083] Specifically, in this embodiment, by using the preset temperature requirement as the first screening condition, all operating speeds with insufficient heat dissipation capacity are eliminated first, thus fundamentally preventing the problem of "optimal energy efficiency but equipment overheating." This ensures that the final selected speed can completely offset the intake air temperature rise caused by cross-thermal interference of the fracturing unit, guaranteeing the temperature safety of the fracturing unit during high-power continuous operation. At the same time, among all feasible solutions that meet the heat dissipation requirements, the speed with the highest comprehensive characteristic evaluation factor is selected, which corresponds to the strongest heat exchange capacity per unit fan power consumption. This approach not only maintains the safety bottom line of not exceeding the temperature limit but also avoids the side effects of blindly increasing the speed, such as fuel waste, excessive noise, and accelerated wear of fan components. It finds a precise and optimal balance between heat dissipation reliability and operating costs.

[0084] This invention, based on the reproduction of the overheating phenomenon caused by cross-heating interference between fracturing units, conducts quantitative optimization of the core control parameter, fan speed, to alleviate the heat dissipation bottleneck caused by the thermal interference of the fracturing units at minimal cost. Specifically, it performs multi-speed operation simulations based on a real fracturing unit array model. The calculation process fully considers the thermal interference effects of actual operations such as hot air recirculation from multiple vehicles and cross-heat intake. The resulting optimal speed perfectly matches the actual heat dissipation requirements of fracturing unit operations, avoiding common problems such as "insufficient heat dissipation of a single unit at its rated speed under fracturing conditions" or "blindly increasing the speed leading to energy redundancy." The simulation process, through precise optimization of fan speed alone, can improve the effective heat exchange capacity of the cooling system, offsetting the intake air temperature rise caused by cross-heating interference between the fracturing units. This alleviates the overheating fault at the field with extremely low modification costs, not only extending the duration of high-power continuous operation of the fracturing unit but also significantly improving equipment operational stability and ensuring the simulation efficiency of fracturing unit heat dissipation.

[0085] In one specific embodiment, addressing the shortcomings of current CFD software simulations of fracturing truck cooling modules—namely, the lack of experimental benchmarks for model verification and the absence of scientific system performance evaluation indicators—a novel scheme for thermal reflux analysis and fan speed evaluation based on porous media simulation is proposed. This scheme pioneers a multi-level high-fidelity simulation verification mechanism of "single-vehicle calibration - vehicle group expansion." After constructing the basic model, single-vehicle operation is simulated, successfully reproducing the overheating phenomenon under experimental conditions and verifying the accuracy of simulation parameter settings. Based on this, simulation of vehicle group operation is performed, accurately reproducing the overheating problem caused by multi-vehicle cross-interference, fundamentally ensuring the absolute high fidelity of the complex flow field model of multiple vehicles. Furthermore, a scheme based on specific discrete intervals and a comprehensive evaluation factor j / f is also proposed. 1 / 3The optimal fan speed was determined by inputting different fan speeds after reproducing the overheating problem in the train. Specifically, this embodiment abandoned blind testing and instead manually selected speed points in 20-rpm intervals within the 700rpm to 800rpm fan speed range for discrete calculation. Finally, the optimal fan speed was determined using the radiator comprehensive performance evaluation factor j / f. 1 / 3 Scientific evaluation was conducted, with the goal of maximizing the ratio of heat transfer factor j to friction resistance factor f, to accurately determine the fan speed that balances eliminating overheating and achieving optimal energy efficiency.

[0086] It should be noted that the above solution in this embodiment needs to be completed in conjunction with 3D modeling software and Fluent simulation software. Specific implementation details are as follows: Figure 3 As shown: Step S1: Establish a single-vehicle CFD geometric model and simplify the radiator into a porous medium region.

[0087] In this embodiment, a three-dimensional geometric model of the cooling system of a single fracturing truck is constructed, including the cabin, cooling ducts, and air intake and exhaust ports. The radiator core, which consists of fine fins and flat tubes, is replaced by a homogeneous porous fluid region. Macroscopic resistance parameters and heat transfer parameters replace the fluid effects of microscopic fins, significantly reducing the number of meshes and computational costs, and providing an efficient geometric platform for subsequent vehicle-level simulation.

[0088] In one specific embodiment, the porous media model is first set up and the single-vehicle benchmark is verified. Specifically, the radiator core of a single fracturing truck is replaced with a solid block of the same size, which is defined as a porous media region in Fluent, and the initial viscous drag and inertial drag coefficients are set.

[0089] Step S2: Simulate the single-vehicle operating conditions to reproduce the over-temperature phenomenon under experimental conditions and verify the accuracy of the simulation parameter settings.

[0090] In this embodiment, the boundary conditions (ambient temperature, fan speed, coolant heat load, etc.) of a single-vehicle real-world experiment are imported to run a single-vehicle flow-thermal coupling simulation. By iteratively adjusting the viscous resistance, inertial resistance, and heat transfer coefficient of the porous medium, the simulated air pressure drop, temperature distribution, and overheating point location and values ​​are made to accurately match the experimental measured data. When the simulation can completely reproduce the experimental overheating phenomenon, the accuracy of the model parameters is verified, and the calibration of the single-vehicle model is completed.

[0091] In one specific embodiment, the boundary conditions of a single-vehicle field or bench test (such as ambient temperature 45°C, rated heat output, etc.) are input to simulate the vehicle's operating conditions. The simulation results accurately reproduce the overheating phenomenon under the experimental conditions (such as abnormal increase in local intake air temperature). By verifying the consistency between the simulated overheating values ​​and hotspot locations and the experimental data, the accuracy of the simulation parameter settings is verified.

[0092] Step S3: Based on this, the working conditions of the train set were simulated, and the over-temperature problem caused by cross thermal interference was reproduced.

[0093] In this embodiment, the calibrated single-vehicle model (including accurate porous media parameters) was directly used, and an array model of multiple fracturing trucks was built according to the actual operation layout of the oilfield. The typical high temperature, calm / light wind extreme environment and full-load operation conditions of the western oilfield were set up, and the multi-vehicle flow thermal coupling simulation was run. Finally, the core fault on site was reproduced: the high temperature cooling air and exhaust gas discharged by the front vehicle diffused slowly under low wind speed and were drawn into the air intake of adjacent or rear vehicles to form heat backflow, which caused the radiator intake temperature to be far higher than the ambient temperature and caused the equipment to overheat. It was found that the root cause of the overheating was the cross thermal interference of multiple vehicles, rather than the insufficient heat dissipation capacity of a single vehicle.

[0094] In one specific embodiment, after the model parameters are locked, an array command is used to establish a dense operation vehicle group model consisting of multiple fracturing trucks in the computational domain. The external environment is set to extreme high temperature and calm or light wind conditions. Using Fluent, the flow field characteristics of the high-temperature exhaust gas from the preceding vehicle being re-inhaled by adjacent or rear vehicles are captured. Based on this, the vehicle group's operating conditions are simulated, successfully reproducing the large-area overheating problem caused by cross-heating interference between the vehicles.

[0095] Step S4: Manually select the fan speed point in 20 rpm intervals between 700 rpm and 800 rpm, and input the different fan speeds in sequence.

[0096] In this embodiment, this step defines an effective optimization range of 700~800 rpm based on engineering experience. This range can cover the speed requirements for solving overheating without causing unnecessary energy waste due to excessive speed. Six independent operating points (i.e., 700, 720, 740, 760, 780, and 800 rpm) are discretized with a fixed step size of 20 rpm. All operating conditions change only the single variable of fan speed, while other conditions such as ambient temperature, heat load, and vehicle layout remain completely consistent to ensure the fairness of subsequent performance comparisons.

[0097] In one specific embodiment, after reproducing the overheating hazard, a multiple reference frame (MRF) model is used to simulate the cooling fan. Specifically, the fan speed is manually selected at 20 rpm intervals between 700 rpm and 800 rpm (i.e., six discrete operating points are set at fan speeds of 700, 720, 740, 760, 780, and 800 rpm respectively). Steady-state fluid-thermal coupling calculations are performed in Fluent by sequentially inputting these six different fan speeds.

[0098] Step S5: Extract the surface heat transfer coefficient and pressure drop data at each discrete rotation speed point, and calculate the Colburn heat transfer factor j and friction resistance factor f.

[0099] In this embodiment, steady-state thermal simulation is run for each speed condition. After convergence, key parameters are extracted from the porous medium region of the heat sink. Among them, the heat transfer dimension, i.e., the surface heat transfer coefficient, is further calculated to obtain the Colburn heat transfer factor j (a dimensionless number that characterizes the heat transfer intensity of the heat sink; the larger the j value, the higher the heat dissipation efficiency); the resistance dimension, i.e., the air-side pressure drop data, is further calculated to obtain the friction resistance factor f (a dimensionless number that characterizes the resistance loss of airflow penetrating the heat sink; the larger the f value, the higher the fan pumping power consumption). Finally, a dataset with a one-to-one correspondence between "speed, heat transfer factor j, and resistance factor f" is formed.

[0100] In one specific embodiment, for each calculated rotational speed point, the air convection heat transfer coefficient, mass flow rate, and air pressure drop before and after the porous medium region are extracted in Fluent post-processing; and the heat transfer factor j, which reflects the dimensionless heat transfer capacity of the air blowing through the radiator at that rotational speed, and the friction resistance factor f, which reflects the dimensionless pressure drop resistance generated by the air passing through the radiator at that rotational speed, are calculated.

[0101] Step S6: Finally, the optimal fan speed was found by evaluating the overall performance of the heatsink using the comprehensive performance evaluation factor j / f^1 / 3.

[0102] In this embodiment, the comprehensive performance evaluation index of the heat sink, j / f^1 / 3 (i.e., j / f) is used. 1 / 3 Since the fan pumping power is proportional to the cube of the flow velocity, the resistance factor f is converted to the power of 1 / 3. The physical meaning of this index is the heat exchange benefit obtainable per unit of fan pumping power (i.e., the maximum convective heat transfer obtainable with the same pump power / fan power consumption). A higher value indicates a higher overall cost-effectiveness in terms of heat dissipation and energy consumption. Specifically, by first screening all feasible speeds that can control the train temperature within a safe threshold, then calculating the comprehensive evaluation factor for each feasible speed, and taking the speed corresponding to the maximum value of the factor, we obtain the optimal fan speed that eliminates the risk of heat recirculation and overheating while achieving optimal energy consumption.

[0103] In one specific embodiment, the evaluation factors at six different rotational speeds are compared, and finally, the comprehensive performance evaluation factor j / f of the radiator is used. 1 / 3 An evaluation was conducted. Specifically, as the fan speed increased within the 700-800 rpm range, the j-factor (heat transfer) increased, but the f-factor (pressure drop resistance) increased at an even faster rate. The evaluation factor j / f was set to ensure that the overheating alarm caused by the train's backflow was eliminated (the maximum intake air temperature was reduced to within the safe threshold). 1 / 3 Once the peak fan speed is reached, the optimal fan speed can be found.

[0104] In summary, the above optimization process follows the engineering simulation paradigm of "from simple to complex, calibration before application, and safety before optimization". Specifically, it first anchors the model accuracy through single-vehicle experiments, then extends to the reproduction of real faults in the vehicle group, and finally completes parameter optimization through scientific dimensionless evaluation indicators. It combines computational efficiency and engineering practicality and is a typical technical path to solve the thermal interference problem of multiple devices operating in parallel.

[0105] This embodiment also provides a simulation optimization device for heat dissipation of fracturing vehicle units. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0106] This embodiment provides a simulation optimization device for heat dissipation of fracturing vehicle crews, such as... Figure 4 As shown, it includes: The data acquisition module 401 is used to acquire the experimental boundary conditions and experimental data of a single fracturing truck.

[0107] The first building module 402 is used to build a porous medium model corresponding to the radiator in the fracturing truck.

[0108] The parameter calibration module 403 is used to calibrate the parameters of the porous medium model using experimental boundary conditions and experimental data, so as to obtain the parameter-calibrated porous medium model.

[0109] The second construction module 404 is used to construct a vehicle array model containing multiple fracturing trucks based on the porous media model after parameter calibration.

[0110] The over-temperature reproduction module 405 is used to simulate operating conditions using a train array model in order to reproduce the over-temperature phenomenon caused by cross thermal interference between train sets.

[0111] In some alternative embodiments, the apparatus further includes: The speed selection module is used to select multiple speed operating points at set intervals within a preset fan speed range.

[0112] The operating condition simulation module is used to simulate operating conditions at various speeds and extract the heat transfer performance parameters and flow resistance parameters of the porous medium region of the radiator accordingly.

[0113] The factor calculation module is used to calculate the comprehensive performance evaluation factor of the radiator based on the heat transfer performance parameters and flow resistance parameters at each operating point of rotational speed.

[0114] The speed determination module is used to determine the optimal fan speed based on the comprehensive performance evaluation factors of the radiator at each speed operating point.

[0115] In some optional implementations, the factor calculation module includes: a calculation submodule, used to calculate the preset index of the corresponding flow resistance parameter for each speed operating point, obtain the corresponding resistance value, and calculate the ratio of the corresponding heat transfer performance parameter to the resistance value to obtain the radiator comprehensive characteristic evaluation factor for the speed operating point.

[0116] In some optional implementations, the rotational speed determination module includes: The filtering submodule is used to filter all target speed points that meet the preset temperature requirements from all speed operating points.

[0117] The determination submodule is used to determine the fan speed corresponding to the maximum value of the comprehensive performance evaluation factor of the radiator at each target speed point as the optimal fan speed.

[0118] In some alternative implementations, the parameter calibration module 403 includes: The first calibration submodule is used to set the initial model parameters of the porous media model.

[0119] The second calibration submodule is used to simulate the heat dissipation process of a single vehicle under operating conditions based on experimental boundary conditions, and obtain simulation results.

[0120] The third calibration submodule is used to verify the consistency between simulation results and experimental data, and obtain the verification results.

[0121] The fourth calibration submodule is used to adjust the initial model parameters when the verification results are inconsistent, and then return to execute the step of simulating the heat dissipation process of a single vehicle under the experimental boundary conditions until the simulation results are consistent with the experimental data, thus completing the parameter calibration of the porous medium model and obtaining the parameter-calibrated porous medium model.

[0122] In some alternative implementations, the second building module 404 includes: The first construction submodule is used to build a three-dimensional array model of multiple fracturing trucks according to the vehicle layout of the on-site operation.

[0123] The second construction submodule is used to determine the current model parameters based on the porous media model after parameter calibration.

[0124] The third construction submodule is used to import the current model parameters into the three-dimensional array model to obtain the train array model.

[0125] In some optional implementations, the over-temperature reproduction module 405 includes: a reproduction submodule, used to set the ambient temperature of the vehicle array model to a preset high temperature and then perform multi-vehicle flow thermal coupling simulation to reproduce the over-temperature phenomenon caused by cross-heating interference of the vehicle group; wherein, the over-temperature phenomenon refers to the phenomenon that the high-temperature exhaust gas discharged by the front vehicle is re-inhaled by the air intake of the adjacent or rear vehicle, forming a heat backflow, resulting in the radiator intake temperature being far higher than the ambient temperature.

[0126] The simulation optimization device for heat dissipation of fracturing trucks provided in this embodiment of the invention can execute the simulation optimization method for heat dissipation of fracturing trucks provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0127] The simulation optimization device for heat dissipation of fracturing trucks in this embodiment of the invention uses real single-vehicle experimental boundary conditions and experimental data to reverse-calibrate the parameters of the porous medium model, making the simulation model more consistent with the actual radiator characteristics. Based on the parameter calibration, a truck array model of multiple fracturing trucks is constructed to simulate the cross-heat recirculation phenomenon caused by the high-temperature exhaust gas of the front truck being sucked into the intake system of the rear truck. This provides a reliable simulation basis for subsequent heat dissipation optimization and ensures the simulation efficiency of fracturing truck heat dissipation to a certain extent.

[0128] Figure 5 This is a schematic diagram of a fracturing truck provided in an embodiment of the present invention. See below for details. Figure 5 The diagram illustrates a structural schematic suitable for implementing a fracturing truck in an embodiment of the present invention. The fracturing truck may include a processor (e.g., a central processing unit, graphics processor, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from memory 508 into a random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the fracturing truck. The processor 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0129] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows the fracturing truck to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 The fracturing truck shown is equipped with various devices; however, it should be understood that it is not required to implement or have all of the devices shown, and it may be implemented or have more or fewer devices instead.

[0130] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a memory 508, or installed from a ROM 502. When the computer program is executed by the processor 501, it performs the functions defined in the simulation optimization method for heat dissipation of fracturing trucks according to embodiments of the present invention. Figure 5 The fracturing truck shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0131] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the simulation optimization method for heat dissipation of fracturing trucks shown in the above embodiments is implemented.

[0132] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A simulation optimization method for heat dissipation in fracturing vehicle units, characterized in that, The method includes: Obtain the experimental boundary conditions and experimental data for a single fracturing truck; Construct a porous media model corresponding to the radiator in the fracturing truck; Using the experimental boundary conditions and the experimental data, the porous medium model is calibrated to obtain the parameter-calibrated porous medium model. Based on the porous media model after parameter calibration, a vehicle array model containing multiple fracturing trucks is constructed. The aforementioned train array model was used to simulate operating conditions in order to reproduce the overheating phenomenon caused by cross thermal interference between train sets.

2. The simulation optimization method for heat dissipation of fracturing vehicle units according to claim 1, characterized in that, After performing the operational condition simulation using the vehicle array model, the method further includes: Within the preset fan speed range, multiple speed operating points are selected at set intervals; Simulations were performed on each of the aforementioned speed operating points, and the heat transfer performance parameters and flow resistance parameters of the porous medium region of the radiator were extracted accordingly. Based on the heat transfer performance parameters and flow resistance parameters at each operating speed, calculate the comprehensive performance evaluation factor of the radiator. The optimal fan speed is determined based on the comprehensive performance evaluation factor of the radiator at each operating speed point.

3. The simulation optimization method for heat dissipation of fracturing vehicle units according to claim 2, characterized in that, The calculation of the comprehensive performance evaluation factor of the radiator based on the heat transfer performance parameters and the flow resistance parameters at each operating speed point includes: For each speed operating point, the preset index of the corresponding flow resistance parameter is calculated to obtain the corresponding resistance value. The ratio of the corresponding heat transfer performance parameter to the resistance value is then calculated to obtain the comprehensive performance evaluation factor of the radiator at the speed operating point.

4. The simulation optimization method for heat dissipation of fracturing vehicle units according to claim 2, characterized in that, The optimal fan speed is determined by the comprehensive performance evaluation factor of the radiator based on each operating speed point, including: From all operating speed points, select all target speed points that meet the preset temperature requirements; Based on the comprehensive performance evaluation factor of the radiator at each target speed point, the fan speed corresponding to the maximum value of the comprehensive performance evaluation factor of the radiator is determined as the optimal fan speed.

5. The simulation optimization method for heat dissipation of fracturing vehicle units according to claim 1, characterized in that, The step of calibrating the parameters of the porous medium model using the experimental boundary conditions and the experimental data includes: Set the initial model parameters for the porous medium model; The heat dissipation process under single-vehicle operating conditions was simulated based on the experimental boundary conditions, and simulation results were obtained. The simulation results are compared with the experimental data to obtain the verification results. If the verification results are inconsistent, adjust the initial model parameters and return to the step of simulating the heat dissipation process of a single vehicle under the experimental boundary conditions until the simulation results are consistent with the experimental data, thus completing the parameter calibration of the porous medium model.

6. The simulation optimization method for heat dissipation of fracturing vehicle units according to any one of claims 1 to 5, characterized in that, Based on the porous media model after parameter calibration, a vehicle array model containing multiple fracturing trucks is constructed, including: Based on the vehicle layout of the on-site operation, a three-dimensional array model of multiple fracturing trucks was established. Based on the porous media model calibrated with the aforementioned parameters, determine the current model parameters; Import the current model parameters into the three-dimensional array model to obtain the vehicle array model.

7. The simulation optimization method for heat dissipation of fracturing vehicle units according to claim 6, characterized in that, The process of using the train array model to simulate operating conditions in order to reproduce the overheating phenomenon caused by cross-heating interference between train sets includes: After setting the ambient temperature of the vehicle array model to a preset high temperature, a multi-vehicle flow thermal coupling simulation is performed to reproduce the over-temperature phenomenon caused by cross-heating interference of the vehicle group; wherein, the over-temperature phenomenon refers to the phenomenon that the high-temperature exhaust gas discharged by the front vehicle is re-inhaled by the air intake of the adjacent or rear vehicle, forming a heat backflow, resulting in the radiator intake temperature being much higher than the ambient temperature.

8. A simulation optimization device for heat dissipation of fracturing vehicle units, characterized in that, The device includes: The data acquisition module is used to acquire the experimental boundary conditions and experimental data of a single fracturing truck. The first construction module is used to construct a porous medium model corresponding to the radiator in the fracturing truck. The parameter calibration module is used to calibrate the parameters of the porous medium model using the experimental boundary conditions and the experimental data, so as to obtain the parameter-calibrated porous medium model. The second construction module is used to construct a vehicle array model containing multiple fracturing trucks based on the porous media model after parameter calibration. The over-temperature reproduction module is used to simulate operating conditions using the train array model to reproduce the over-temperature phenomenon caused by cross thermal interference between the trains.

9. A fracturing truck, characterized in that, The fracturing truck includes a controller, which includes a memory and a processor. The memory and the processor are communicatively connected. The memory stores computer instructions. The processor executes the computer instructions to perform the simulation optimization method for heat dissipation of the fracturing truck group as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the simulation optimization method for heat dissipation of fracturing vehicle as described in any one of claims 1 to 7.

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