Antenna Thermal Management

Project Introduction

Thermal management is essential to antenna design and operation, especially in high-power or space applications. Proper
thermal management ensures that antennas can perform optimally, remain reliable, and avoid damage due to excessive heat.
High-power antennas generate heat during transmission due to dissipating power in the antenna’s conductive components.
Efficient heat dissipation mechanisms, such as heat sinks or cooling fins, help dissipate excess heat and maintain the
antenna’s temperature within acceptable limits

Process Statement

Antennas are vital components of wireless communication systems, enabling the transmission and reception of
electromagnetic waves. When an alternating current flows through an antenna, it creates an oscillating electric field.
This oscillating field, in turn, generates a corresponding electromagnetic wave that propagates through space. The
geometry and design of the antenna determine the radiation pattern, polarization, and frequency characteristics of the
emitted or received signals. During this process, a lot of heat is generated by the ICs at work.

Antenna model

Problem Statement

# The excess temperature affects the performance of the antenna in a negative way so our customer approached us with
their newly developed antenna to study its thermal behavior and optimize them for better performance of the antenna.
# Since the antenna is to be positioned under direct sunlight the temperature inside the antenna will be more than
usual.
# With decades of experience in the computational fluid dynamics industry and highly talented professionals, we were
able to help our customers by creating a digital twin of the existing model and conducting a computational fluid
dynamics study on the model.

Simulation setup

Task Executed

  • Preparing the geometry is the first step toward the computational fluid dynamics study. The existing CAD
    geometry could cause mesh warp and convergence problems. So, we decided to recreate the entire geometry inside
    the CFD software itself for better mesh and convergence.
  • Our team recreated the entire geometry including fans, ICs, heatsinks, and detailed PCB boards.
  • During the geometry build phase we removed small features such as fillets and chamfers to reduce the mesh count
    because the higher the mesh count the higher the solving time will be.
  • No important geometry feature was ignored while recreating the geometry.
  • Three different cases were simulated with different atmospheric conditions,
  • 1.7 m/s external velocity and no radiation and no fans
  • 2 m/s external velocity with radiation and no fans
  • 2 m/s external velocity with radiation and fans
  • Once the geometry was recreated, it was verified with the customer for originality before proceeding with the
    simulation setup.
  • A detailed PCB board was created with all its layers and copper percentages and two resistor model network ICs
    were modeled for the simulation.
  • After the geometry was recreated, it was meshed using both conformal and non-conformal methods. PCB, chips,
    thermal pads, and heatsinks have meshed using conformal meshing, and outer covers and fans have meshed using the
    non-conformation meshing method.
  • Finally, the boundary conditions, thermal inputs, turbulence model, and convergence criteria were updated, and
    the model was simulated.

Problems Encountered and Resolutions

Initially, we tried using the existing CAD file for the simulation. So, we tried to mesh and solve only one
heatsink with chips but during the meshing process, we encountered a lot of skewed and poor-quality elements in
the mesh. Also, during the initial solution, the convergence curve was not as good as expected.
To overcome this issue, we decided to recreate the entire model inside the software itself but instead of
recreating the entire model at once, we proceeded one step at a time. So, we created one heatsink then one row
of heatsinks then the entire model thus higher quality mesh was achieved.
With the initial mesh, the temperature gradients on the IC were not captured properly because of the lower
number of cell count along the IC height. So, the IC mesh was refined with 8 cells along its height to capture
the correct behavior of two resistor model network chips.
Because of the non-conformal mesh between the thermal pad and the heatsink, the thermal distribution between
them was poor. We meshed the components again with mesh connectivity to solve this problem.
Initially, the k-omega turbulence model was used, and because of that the solution diverged after a certain set
of iterations. So, after brainstorming, we found the k-epsilon turbulence model will be more suited for this
application and we continued the simulation with the k-epsilon turbulence model

Results

  • The prepared model was solved in a CFD simulation software and results were extracted.
  • By studying the results, we learned that the junction temperatures of the chips were higher than the allowed
    limit.
  • Due to the lower airflow, the heatsinks were unable to dissipate the heat properly.
  • It was determined that the natural convection alone was not enough to cool the antenna while operating.

Problems Identified

  • Due to the narrow gaps between heatsink fins and lower flow airflow inside the antenna, the heat distribution
    was poor.
  • The natural convection alone was not enough to dissipate generated heat to the atmosphere.
  • Even with increased outer air velocity the system struggles with a lower dissipation rate thus causing
    components to overheat.
  • The junction temperatures of the network chips were above the design limit and this might affect their
    performance and life.

Improvements

  • Ideal fans for this application were decided based on the simulation results.
  • With forced convection along with natural convection, the temperature distribution was good.
  • With the right amount of airflow into the antenna, the junction temperatures were kept within the limits.
  • The inlet and exhaust openings for the airflow were optimized based on the flow pattern observed in the
    simulation.

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