A method for rapidly predicting the performance of a direct current plasma torch cooling system

Abstract

The application discloses a method for rapidly predicting the performance of a direct-current plasma torch cooling system. The application can simultaneously calculate the static temperature distribution of a solid domain and a fluid domain by numerical simulation of a two-dimensional multi-phase coupling magnetohydrodynamic model of axis symmetry, and can predict the region with the highest static temperature in the plasma torch cavity under the current working condition by combining the static temperature and the radial current density distribution on the interface. The application can provide an optimal reference for the arrangement of a cooling pipeline and the configuration of cooling liquid parameters, and can provide information about the internal arc structure and working performance of the direct-current plasma torch under the corresponding cooling effect, so that the global optimal design of the cooling system performance is realized.

Description

Technical Field

[0001] This invention proposes a method for rapidly predicting the performance of a DC plasma torch cooling system. Background Technology

[0002] Atmospheric pressure plasma possesses high energy density and is widely used in materials synthesis, medicine, metal surface processing and modification, etc., representing a highly efficient and controllable state of matter. Currently, plasma generation devices mainly fall into several categories: inductively coupled plasma torches, microwave-induced plasma torches, and direct current plasma torches. However, due to the characteristics of direct current plasma torches—namely, their ability to stably and continuously generate atmospheric pressure plasma at high power, their simpler structure, and smaller size—these torches have gained wider application. Because the circuit formed between the anode and cathode after plasma excitation generates a high current, resulting in significant Joule heating, direct current plasma torches are prone to ablation in the copper anode region during practical operation. Therefore, most direct current plasma torches incorporate a cooling circuit within the anode to improve the torch's lifespan. However, the structural design of the cooling circuit and the setting of coolant flow rate and inlet / outlet temperature in conventional direct current plasma torches rely heavily on experience, making it difficult to adapt to complex and variable operating conditions. Summary of the Invention

[0003] To address the problems existing in the prior art and to test and optimize the performance of the DC plasma torch cooling system, this invention proposes a rapid prediction method applicable to various operating conditions that can accurately assess the impact of cooling system configuration parameters on the plasma torch cooling effect.

[0004] This invention can be achieved through the following technical solutions:

[0005] A method for rapidly predicting the performance of a DC plasma torch cooling system, based on the commercial software Ansys-Fluent, is implemented by writing user-defined functions to couple a magnetohydrodynamic model with a standard k-epsilon turbulence model, including:

[0006] 1) The axisymmetric plasma torch and its cooling system are simplified and assumed to be two-dimensional axisymmetric;

[0007] 2) By introducing user-defined functions to describe the momentum and energy source terms in the model, and comprehensively considering the effects of radiation dissipation, Joule heating effect and electron transport enthalpy on each source term, a heat transfer model between the solid domain and the fluid domain is established through Fourier's law to improve the model's prediction accuracy for conductivity near the interface, thereby improving its calculation accuracy for heat flux at the interface and in the solid domain, and ultimately improving the prediction accuracy for the performance of the DC plasma torch cooling system.

[0008] Furthermore, the method specifically includes the following steps:

[0009] 1) Draw the mesh. Based on the cavity structure of the DC plasma torch, simplify it and draw a two-dimensional axisymmetric unstructured multiphase mesh. The mesh includes a solid computation domain and a fluid computation domain. Local refinement is performed on the anode and cathode walls of the mesh.

[0010] 2) Boundary settings: Set the boundary conditions of the calculation domain with reference to the actual working conditions, including the boundary conditions of the inlet IJ, outlet DEF, anode FGHI, cathode ABC, cooling wall GH and other walls. The heat transfer performance of the cooling wall is obtained by measuring the inlet and outlet temperatures of the coolant and combining the properties of the coolant.

[0011] 3) Model setup and initialization: Based on the characteristics of the physical model, the standard k-epsilon turbulence model combined with the second-order upwind scheme SIMPLEC pressure-velocity coupling algorithm is selected for steady-state calculation. The hybrid initialization method provided by Ansys-Flunet is used to define the initial values ​​of the flow field to accelerate the calculation convergence speed.

[0012] 4) Convergence determination: Convergence conditions are determined based on the characteristics of the calculation model and the actual accuracy requirements. The determination includes comparing the degree of decay of various residual values ​​and comparing the difference between the inlet and outlet mass flow rates.

[0013] 6) Calculation results and comparison: The converged calculation results are processed and analyzed, mainly predicting the phase interface temperature, current density and solid domain temperature distribution. By comparing with the main working parameters of the cooling wall in step 2), the heat dissipation performance of the cooling system is predicted.

[0014] Furthermore, the governing equations of the magnetohydrodynamic model are as follows:

[0015] Continuity equation

[0016]

[0017] Momentum conservation equation

[0018]

[0019] Energy conservation equation

[0020]

[0021] Where, ρ, P, μ, S r k eff T and T represent gas density, flow rate, pressure, dynamic viscosity, strain tensor, net volumetric radiation loss, effective thermal conductivity, and gas temperature, respectively. as well as These represent the Lorentz force, Joule heat, and electron transfer enthalpy, respectively. k B C p and e represent current density, magnetic induction vector, electric field strength, Boltzmann constant, hydrostatic specific heat, and elementary charge, respectively.

[0022] Furthermore, this method simultaneously calculates the static temperature distribution of the solid and fluid domains through numerical simulation of an axisymmetric two-dimensional multiphase coupled magnetohydrodynamic model, and combines the static temperature and radial current density distribution at the interface to predict the region with the highest static temperature in the plasma torch cavity under the current operating conditions.

[0023] Furthermore, the input conditions of the axisymmetric two-dimensional multiphase coupled magnetohydrodynamic model include operating current, coolant properties, coolant input and output speeds and temperatures, working gas properties and operating conditions.

[0024] Beneficial effects

[0025] This invention uses a two-dimensional axisymmetric multiphase coupled magnetohydrodynamic model to simultaneously calculate the static temperature distribution in the solid domain (plasma torch cavity) and the fluid domain (plasma jet). By combining the static temperature and radial current density distribution at the interface, it can predict the region with the highest static temperature in the plasma torch cavity (including the cavity and cavity surface) under the current operating conditions. This provides an optimized reference for the layout of cooling pipes and the configuration of coolant parameters, while also providing information on the internal arc structure and working performance of the DC plasma torch under the corresponding cooling effect, thus realizing the global optimization design of the cooling system performance.

[0026] The model in this invention has flexible input conditions (including operating current, coolant properties, coolant input and output speed and temperature, working gas properties and operating conditions, etc.), which increases the predictable application scenarios. Although the number of model meshes will change due to the influence of model size, its computational load is far less than that of the three-dimensional transient numerical model, which greatly shortens the computation time and reduces the hardware requirements of computing equipment. Attached Figure Description

[0027] Figure 1 This is a static temperature distribution diagram for operating condition A;

[0028] Figure 2 This is the static temperature distribution diagram for operating condition B;

[0029] Figure 3 This is a diagram showing the static temperature distribution within the plasma torch cavity under operating condition A.

[0030] Figure 4 This is a diagram showing the static temperature distribution within the plasma torch cavity under operating condition B.

[0031] Figure 5 Radial current density distribution on the inner wall of the ion torch under two operating conditions;

[0032] Figure 6 It is a two-phase axisymmetric two-dimensional mesh diagram. Detailed Implementation

[0033] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification.

[0034] This invention is achieved by establishing a two-dimensional axisymmetric steady-state multiphase coupled magnetohydrodynamic (MHD) numerical model. Based on the commercial software Ansys-Fluent, user-defined functions (UDFs) are written to supplement and describe the MHD model coupled with the standard k-epsilon turbulence model. Specifically, the geometrically axisymmetric plasma torch and its cooling system are simplified and assumed to be two-dimensional axisymmetric. User-defined functions (UDFs) are introduced to describe the momentum and energy source terms in the model. The effects of radiation dissipation, Joule heating, and electron transport enthalpy on each source term are comprehensively considered. A heat transfer model between the solid and fluid domains is established using Fourier's law to improve the model's prediction accuracy for conductivity near the interface, thereby improving the calculation accuracy for heat flux at the interface and in the solid domain, and ultimately improving the prediction accuracy for the performance of the DC plasma torch cooling system. Because this model adopts the two-dimensional axisymmetric assumption, it maintains high prediction accuracy while having a much smaller model size and lower computational burden than a three-dimensional model, thus making it widely applicable to various cooling loop layout schemes. Without altering the model's geometric dimensions, by providing the composition and flow rate of the incoming gas, the electrode operating current, the specific heat capacity of the coolant, and the inlet and outlet temperatures and flow rates of the coolant, the cooling system of a DC plasma torch and the development and changes of the arc within the cavity can be simulated and predicted. Ultimately, parameters such as the temperature distribution within the plasma torch cavity and at any location within the cavity, the arc structure within the cavity, and the phase interface current distribution during stable operation can be obtained. After processing, the cooling performance of the internal cooling system of the torch can be predicted based on the characteristics of the cavity temperature distribution and the cooling power calculated from the coolant operating parameters.

[0035] This model employs axisymmetry and the LTE assumption, significantly reducing computational complexity while maintaining computational accuracy. Its governing equations are shown below:

[0036] Continuity equation

[0037]

[0038] Momentum conservation equation

[0039]

[0040] Energy conservation equation

[0041]

[0042] Where, ρ, P, μ, S r k e ff and T represent gas density, flow rate, pressure, dynamic viscosity, strain tensor, net volumetric radiation loss, effective thermal conductivity, and gas temperature, respectively. as well as These represent the Lorentz force, Joule heat, and electron transfer enthalpy, respectively. k B C p and e represent current density, magnetic induction vector, electric field strength, Boltzmann constant, hydrostatic specific heat, and elementary charge, respectively.

[0043] The temperature distribution of the entire computational domain was calculated under operating conditions A and B. Operating condition A used a DC plasma torch with a nozzle diameter of 10 mm, operating current of 600 A, and nitrogen input of 40 L / min. Operating condition B used a DC plasma torch with a nozzle diameter of 8 mm, operating current of 620 A, and nitrogen input of 45 L / min.

[0044] Depend on Figure 1 , Figure 2 It can be found that the arc structure (arc length, thickness, and core area size) inside the DC plasma torch varies under different operating conditions and different geometric dimensions. This model can accurately predict the structural characteristics of the arc inside the torch when the cooling system is working.

[0045] Depend on Figure 3 , Figure 4 The static temperature distribution of the DC plasma torch cavity under the above operating conditions can be observed. By combining this with the input power of the cooling system and the temperature difference of the cooling water, the cooling performance of the cooling system can be predicted more accurately, and the area with the highest temperature in the plasma torch cavity can be located for targeted optimization design.

[0046] Similarly, by Figure 3-5 It can be found that under the above operating conditions, the location of the high temperature region in the DC plasma torch cavity coincides with the range of the radial current density trough. This explains the cause of the local high temperature and links the performance of the cooling system with the characteristics of the plasma arc structure, providing conditions for the overall evaluation of the performance of the DC plasma torch cooling system.

[0047] The working process of the method for rapidly predicting the performance of the internal cooling system of a DC plasma torch according to the present invention is as follows:

[0048] 1. Draw a two-phase axisymmetric two-dimensional mesh based on the geometry of the DC plasma torch to be evaluated and its cooling system layout, such as... Figure 6As shown, it includes a plasma torch cavity, an internal channel of the plasma torch, and an external space (the radial and axial dimensions depend on the processing requirements, but the radial dimension should not be less than 6 times the plasma torch radius, and the axial dimension should not be less than 50 mm). The mesh of the cathode surface and the inner surface area of ​​the cavity is refined.

[0049] 2. Set the boundary conditions of the model according to the operating parameters, namely: inlet IJ (incoming gas composition, incoming velocity or mass flow rate, gauge pressure, temperature), outlet DEF (room temperature, standard atmospheric pressure), cathode ABC (physical properties, operating current, temperature distribution), and cooling system GH (temperature, heat transfer coefficient, zero potential). The heat transfer coefficient ht of the cooling system can be derived from the following formula:

[0050] Q = cρV(T) out -T in )

[0051]

[0052] Where c, ρ, and V are the specific heat capacity, density, and volume of the coolant flowing per unit time, respectively, and T is the total volume of the coolant. out and T in These are the coolant outlet and inlet temperatures, respectively, and S is the surface area of ​​the cooling zone.

[0053] 3. The model is initialized using inlet boundary conditions. To ensure stable plasma excitation, the temperature should be set no lower than 8000K. Numerical simulation steady-state calculations are run, and control parameters are fine-tuned based on operating parameters and target accuracy (mainly the energy sub-convergence factor). The model is considered to have converged when the energy residual term drops below 1e-6. Subsequently, the static temperature of the flow field, the radial current distribution on the inner wall of the plasma torch, and the temperature of the plasma torch are sampled.

[0054] 4. By combining the static temperature distribution of the integrated flow field, the radial current distribution of the inner wall of the plasma torch, and the temperature of the plasma torch, statistics are performed on the regions where the temperature of the plasma torch wall and its vicinity changes drastically and remains at a high temperature for a long time during the calculation iteration step. This allows for the evaluation of the cooling system's heat dissipation effect on the plasma torch.

[0055] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for rapidly predicting the performance of a DC plasma torch cooling system, characterized in that, This method is based on the commercial software Ansys-Fluent and implements a magnetohydrodynamic model coupled with the standard k-epsilon turbulence model by writing user-defined functions, including: (1) The plasma torch and its cooling system with axisymmetric geometry are simplified and a two-dimensional axisymmetric assumption is made; (2) By introducing user-defined functions to describe the momentum source terms and energy source terms in the model, the effects of radiation dissipation, Joule heating effect and electron transport enthalpy on each source term are comprehensively considered. A heat transfer model between the solid domain and the fluid domain is established through Fourier law to improve the model's prediction accuracy for conductivity near the interface, thereby improving its calculation accuracy for heat flow at the interface and solid domain, and ultimately improving the prediction accuracy for the performance of the DC plasma torch cooling system. The method specifically includes the following steps: 1) Draw the mesh. Based on the cavity structure of the DC plasma torch, simplify it and draw a two-dimensional axisymmetric unstructured multiphase mesh. The mesh includes a solid computation domain and a fluid computation domain. Local refinement is performed on the anode and cathode walls of the mesh. 2) Boundary settings: Set the boundary conditions of the calculation domain with reference to the actual working conditions, including the boundary conditions of the inlet IJ, outlet DEF, anode FGHI, cathode ABC, cooling wall GH and other walls. The heat transfer performance of the cooling wall is obtained by measuring the inlet and outlet temperatures of the coolant and combining the properties of the coolant. 3) Model setup and initialization: Based on the characteristics of the physical model, the standard k-epsilon turbulence model combined with the second-order upwind scheme SIMPLEC pressure-velocity coupling algorithm is selected for steady-state calculation. The hybrid initialization method provided by Ansys-Flunet is used to define the initial values ​​of the flow field to accelerate the calculation convergence speed. 4) Convergence determination: Convergence conditions are determined based on the characteristics of the calculation model and the actual accuracy requirements. The determination includes comparing the degree of decay of various residual values ​​and comparing the difference between the inlet and outlet mass flow rates. 5) Calculation results and comparison: The converged calculation results are processed and analyzed, mainly predicting the phase interface temperature, current density and solid domain temperature distribution. By comparing with the main working parameters of the cooling wall in step 2), the heat dissipation performance of the cooling system is predicted. The governing equations of the magnetohydrodynamic model are as follows: Continuity equation Momentum conservation equation Energy conservation equation in, , P, , , k eff T and T represent gas density, flow rate, pressure, dynamic viscosity, strain tensor, net volumetric radiation loss, effective thermal conductivity, and gas temperature, respectively. , as well as These represent the Lorentz force, Joule heat, and electron transfer enthalpy, respectively. , , , , and e represent current density, magnetic induction vector, electric field strength, Boltzmann constant, hydrostatic specific heat, and elementary charge, respectively.

2. The method for rapidly predicting the performance of a DC plasma torch cooling system according to claim 1, characterized in that, This method uses numerical simulation of an axisymmetric two-dimensional multiphase coupled magnetohydrodynamic model to simultaneously calculate the static temperature distribution in the solid and fluid domains. By combining the static temperature and radial current density distribution at the interface, it can predict the region with the highest static temperature in the plasma torch cavity under the current operating conditions.

3. The method for rapidly predicting the performance of a DC plasma torch cooling system according to claim 2, characterized in that, The axisymmetric two-dimensional multiphase coupled magnetohydrodynamic model has input conditions including operating current, coolant properties, coolant input and output speeds and temperatures, working gas properties and operating conditions.

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