2.3.1. Heat Source from Ohmic Heating Considering Skin Effect
To calculate the temperature distribution inside the slip ring structure, firstly the heat source, The skin effect describes alternating current (AC) concentrating on a conductor’s surface, with current density decaying exponentially inward. Governed by Maxwell’s equations, it arises from electromagnetic induction and energy loss. When AC flows, it generates a time-varying self-induced magnetic field (Ampère’s law), which induces a closed-loop electric field opposing the original current (Faraday’s and Lenz’s laws), creating eddy currents. Surface eddy currents repel the original current outward, while deeper, weaker eddy currents have negligible effect. Eddy currents dissipate Joule heat via the conductor’s resistivity, further driving the exponential current decay with depth—critical for calculating slip ring temperature distributions [
8].
Quantitatively, the current density
J at a depth
d from the conductor surface follows the exponential law:
where
J0 is the current density at the surface, and
δ (skin depth) is the depth at which the current density decreases to 1/e (≈37%) of its surface value. The skin depth is determined by the conductor's properties and the frequency
f of the AC current:
where
ω = 2
πf is the angular frequency of the AC current,
μ (magnetic permeability), and σ (electrical conductivity) are material-dependent parameters.
From the above introduction on skin effect, it can be seen that higher frequencies, higher magnetic permeability, or higher electrical conductivity result in a smaller skin depth, intensifying the current concentration on the surface as well as the Joule heating. For instance, the copper conductors used in the slip ring system have a conductivity of σ = 5.8×107 S/m and magnetic permeability μ similar to the permeability of vacuum (i.e. μ = μ0 = 4π×10−7 H/m), and the conductors work under 50Hz AC current, Therefore, the skin depth can be calculated as δ = 9.3 mm, which is at the same order of magnitude of the conductors and indicates that the skin effect of the conductors cannot be neglected.
2.3.2. Heat Transfer Involving Joule Heating and Thermal Conduction
For steady-state heat transfer (where temperature does not change with time), the governing equation is based on the balancing between heat generation and dissipation [
9]. Specifically, the volumetric heat generation rate
qsrc (W/m
3) at any spatial position is always cancelled out by the volumetric heat dissipation rate (i.e., the divergence of the heat flux
qdis (W/m
2), which can be expressed as
As for the heat source
qsrc, it is generated from internal sources (e.g., Joule heating of flowing current). Herein, the heat source can be calculated by the current density
J (considering skin-effect as discussed in 2.3.1) and electric conductivity
σ using the Ohm’s law:
In engineering heat transfer simulations, the core objective is to solve the spatial varied temperature field
T (K). Therefore, the dissipated heat flux
is not directly solved but computed via the following expression:
where
k is the thermal conductivity of the material, W·m
-1·K
-1. Using the Laplace operator
, the PDE expression of heat transfer inside the simulation domain (i.e., copper conductor) in (12) is converted to:
2.3.3. Boundary Conditions Involving Convection and Radiation
Boundary conditions define heat exchange between the simulated domain and its surroundings, directly linking to ambient temperatures. This research mainly focuses on the heat transfer at the surface of solid materials, which can be classified into two pathways- Convection and Radiation.
- 1)
Convective heat transfer
Convective heat transfer is a common heat transfer mode involving fluid (gas/liquid) and solid surfaces, combining fluid macro-motion (natural convection driven by density gradients or forced convection by external forces like fans) and molecular thermal conduction. As for the convection process of slip-ring system, the heat dissipation medium (air) has low density (1.29 kg/m³), large flow space (2 m), low flow speed during natural convection (0.1 m/s) and low viscosity (1.79×10⁻⁵ Pa·s). This will lead to a high Reynolds number (~14400), indicating significant turbulent effect, and increase the computational cost and instability to directly solve the fluid-thermal behavior at the solid-fluid interface. Therefore, we uses Newton’s law simplify the thermal simulation process, and uses a single variable
h to characterize the convective outward heat flux
qc:
where
h is the convective heat transfer coefficient
h (W·m
-2·K
-1),
Ts is the temperature at the solid-fluid boundaries, and
T∞ is the air’s temperature at infinite distance (usually identical to the ambient temperature).
Considering that at steady state
qc is cancelled out by the conductive heat dissipation
qdis at the solid-air interface, the convective boundary condition can be written as:
In this equation, is the outward normal vector of the solid material’s surface. Equation (17) determines the Neumann boundary condition of convective cooling using a simple mathematical form, and can be easily realized by FEM simulation packages.
- 2)
Radiative heat transfer
Thermal radiation induced by outward infrared light is another important heat transfer pathway. Based on the gray-body approximation, the radiative outward heat flux
qr is given by:
This incorporates emissivity
εrad (ranges from 0 to 1), the Stefan-Boltzmann constant
σrad = 5.67×10⁻⁸ W·m
-²·K
-4, and ambient temperature
T∞. Under steady-state, a similar Neumann boundary condition of thermal radiation can be determined as
In summary, the Neumann boundary condition of outward thermal flux can be determined by the combination of equation
2.3.4. FEM Calculation of Temperature Rise
- 1)
Geometrical Structure
In general, the original slip-ring shown in
Figure 2 is difficult to conduct the magnetic-thermal FEM simulation. Simplification of the simulation model is critical. In this study, the simplification of SPM model is conducted under the following considerations:(1) The metal enclosure is not electrically excited, so it does not generate heat and can be neglected during the simulation (2) The thermal conductivity of copper conductor (385~401 W·m
-1·K
-1) is much higher than polymer insulation (0.2~0.5 W·m
-1·K
-1). Therefore, the insulators can be neglected during the simulation. (3) the distance between different conductors (240~300 mm) are much larger than the geometrical size of the conductors (15-30 mm). Therefore, the proximity effect can also be neglected, which means a single conductor can be used to simulate the temperature rise. The simplified model shown in
Figure 4 contains one of the copper conductor (phase A ring) and certain range of adjacent air, which reduces the size and complexity of FEM simulation.
- 2)
Material Definition
There are 2 main materials in the simulation model, e.g. air and copper, whose properties are shown in
Table 3. Considering the large fluctuations in the operating temperature, the resistance-temperature effect of copper conductor should be considered . Therefore, the electrical conductivity of the copper material is replaced by the following formula, representing the linear electrical conductivity.
where
σ represents the electrical conductivity (S·m
-1) of the conductor at temperature
T (in Kelvin).
ρ0 = 1.72 × 10
-8 Ω·m is the resistivity of the conductor at the reference temperature
Tref = 298.15K (20 °C).
α is the temperature coefficient of resistance, whose typical value for copper conductors is α = 0.0039 K
-1.
- 2)
Physical Model and Boundary Conditions
As discussed before, simulation of the temperature rise in this slip ring device is to calculate the conductor’s temperature under the excitation of certain current when heating and cooling process reaches equilibrium. The heat source in this problem comes from the Joule heat, and the main cooling method is air convection and thermal radiation on the surface of the conductor. Due to self-induction effect under AC current, there is "skin effect" in the conductor, in which the current density concentrate on the conductor’s surface.
From the above viewpoints, this simulation problem can be constructed as a coupled simulation of current and thermal fields. The current field simulation determines the spatial distribution of current density (and thus the resistance value) under 50Hz magnetic field. Thermal simulation determines the temperature distribution under Joule heating, convection cooling and infrared radiation. Coupling between current and thermal field is mainly represented by Equation (20), i.e., the temperature dependency of copper resistance.
The aforementioned simulation problem is analyzed using COMSOL software. 50Hz AC current ranges from 160A r.m.s to 1120A r.m.s. flows in through the right port of the inlet busbar and out through the lower port of the outlet busbar in
Figure 4. A certain area of the rectangular air domain is set around the conductor to provide a carrier for the simulation of the magnetic field distribution. For the heat dissipation through convective cooling, the heat transfer coefficient
h on the surface of the copper conductor is used to replace the direct simulation of turbulent flow, whose effect on temperature rise will be discussed in the following paragraphs. As for the thermal radiation effect, since the conductors are smoothly polished copper, its infrared emissivity
εrad is set to 0.05.
2) FEM Meshing and Problem Solving
For the problem of significant size difference between the overall structure and detailed parts, the locally refined adaptive mesh setting method is adopted. Specifically, the overall meshing adopts a "Finer" setting, in which the characteristic size of tetrahedral mesh dynamically varying between 25-200 mm. For refined areas such as the surface of the conductor, the maximum mesh size is reduced to 1mm to precisely calculate the current density and temperature distribution. The simplified simulation model after the self-adaptive meshing contains about 370000 tetrahedral elements.
To solve this FEM problem, the solver of generalized conjugate residual with deflated restarting (GCRO-DR) is utilized. Specifically, on the simulation workstation (AMD Ryzen 64-core CPU, 256 GB RAM, 16TB hard drive), a single simulation takes about 12 minutes, providing an efficient analysis method for the optimization design of slide rings.