COMPUTATIONAL METHODOLOGIES FOR THERMO-FLUID DYNAMICS

PROBLEMS IN ELECTRONICS

KITE GROUP STRUCTURE Kite Group in Italy: TORINO & CHIVASSO (HQ) Kite Group in Adriatic & Balcan: ZAGREB

KITE Group MAIN OFFICE - ITALY

KITE Group ADRIATIC & BALCAN DIVISION

•Virtual prototyping •Engineering intelligence •Software distribution

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CHALLENGES IN THERMAL DESIGN IN ELECTRONICS (1)

• During the electronic system design steps, electrical and thermo-mechanical issues are strongly coupled,

• It is important to get accurate and predictive chips operating temperature estimations during the operations.

• Three different methods to analyze the problem: 1. Analytically: substantial underestimation of temperatures

2. Experimentally: it drives costs in terms of resources and time

3. Numerically: early stage of the design, full range of operational in time and with low costs

• Heat Transfer is an Empirical Science,

• Computational multiphysics: ability to solve fluid dynamic, conductive, convective and radiative problems by means of simulations

CHALLENGES IN THERMAL DESIGN IN ELECTRONICS (2)

• Computer simulation helps in facing thermo fluid-dynamics problems

• Assumptions very key to result quality (Experience vs. CFD)

• Accurate Thermal Prediction within an electronic system is a difficult task: 1. Complex Geometries, Bends, Obstructions, etc... 2. Dozens of active and passive electronic components 3. Circuit boards as laminates of composite sheets 4. Complex phenomenology of the flow ruled by low Reynolds number.

• Select the right modeling tool is critical,

• Test validations whenever possible, actually before set-up the virtual model

• 5% - 7% accuracy of simulation to test results is acceptable.

THERMAL SIMULATION – TEST CASE / INITIAL CONDITIONS

• The problem here presented is an Info-telematic system: hi-fi system with 40W, satellite navigation, bluetooth and wifi access point,

cluster radio • Operations capability at 65 °C. • Some external surfaces always below 100 °C. • This is good case to test the computational specification of a commercial solver due to

the strong thermal interaction among components.

THERMAL SIMULATION – EXPERIMENTAL SET-UP

• six monitoring points were considered: these are the red and blue dots on the boards and aluminum heat sink

THERMAL SIMULATION – SCSTREAM

THERMAL SIMULATION – SCSTREAM

• KITE GROUP is official reseller for CRADLE (SC/Tetra, STREAM, Heat Designer) software. On these products, KITE GROUP gives to its customers Technical Support and Training.

THERMAL SIMULATION – MODELING

• The numerical model: o actual geometrical dimensions, o metal housing of 195 x 165 x 53 [mm] containing all the electronics, o a wooden box of dimensions 250 x 245 x 100 [mm] is including the

electronic system mentioned above.

• Some computational methodologies in the context of CFD are introduced in order to decrease the computing resources both in terms of hardware and time: o modeling the stratification of the circuit boards, o accurate modeling of integrated circuits, o choice of the best turbulence model characterizing the flow o methods to reduce drastically the number of degrees of freedom of the

system , by means of MultiBlock

• The circuit boards are composite elements: conductive sheets of copper alternate with layers of epoxy resin, also known as FR4.

• Several ways can be implemented to model the PCB: 1. importing within scSTREAM Gerber files of the electric circuit 2. equivalent material having anisotropic thermal conductivity

• It is needed to know: 1. The number of insulating layers in FR4, 2. number of sheets of copper and 3. the position of the thermal vias…

THERMAL SIMULATION – MODELING ( PRINTED CIRCUIT BOARDS )

• Generally the PCB thermal resistance of a PCB can be lowered by vertical interconnections of copper (in thickness, known as thermal vias).

THERMAL SIMULATION – MODELING ( THERMAL VIAS)

Array of vias

a) area with a filling of copper

b) equivalent material vias and PCB

Top view

Side view

• To model an integrated circuit (IC) it is important: 1. to define the main dimensions, 2. to identify the materials used, 3. to create the dissipative inner and 4. to modeling the welds by using a TIM

• information from the chip vendor (appropriate resistive thermal model) scSTREAM is suitable to handle those following the directives of the project DELPHI

THERMAL SIMULATION – MODELING ( INTEGRATE CIRCUITS)

• The fan was modeled not as a movable part (very expensive approach from the computational effort) but for its equivalent effect, so by inserting in the model the pressure vs flow rate curve.

THERMAL SIMULATION – MODELING ( FAN )

• To mesh the electronic system was used multi-block method: prevents an unwanted increase of the discretization elements even in areas far from the place of interest

THERMAL SIMULATION – MODELING ( MESH )

• Because of the absence of a characteristic dimension useful to describe the flow regime, the problem has been solved either in laminar that in turbulent regimes.

• A turbulence model at low Reynolds number has been used: specifically the Abe-Nagano-Kondoh (AKN) model

• It is also reported the simulation without and with radiation heat exchange mode (numerical results will be shown in the last slide)

THERMAL SIMULATION – SET-UP DEFINITION

THERMAL SIMULATION – RESULTS (1)

THERMAL SIMULATION – RESULTS (2)

THERMAL SIMULATION – RESULTS (3)

SIMULATION RESULTS vs EXPERIMENTAL DATA % DISCREPANCY

THERMAL SIMULATION – RESULTS (4)

Type of analysis DDR2 nVIDIA PCB HeatSpreader Heatsink FAN outlet air MB PCB

Laminar flow & no-radiation 1,77 1,36 3,01 3,94 5,74 5,14

AKN model & radiation 1,13 1,65 0,09 0,09 2,49 1,92

CONCLUSIONS

• It has been highlighted the opportunity to exploit a numerical approach in order to simulate thermal and fluid-dynamical behavior of a complex electronic system. • The goal mainly consists in disposing of a predictive and flexible tool for

characterising equipment during several functioning conditions.

• Using the numerical models defined, prediction accuracy is assessed against corresponding experimental measurements of component surface temperature.

• Very good correlation is found between numerical values and experimental data (mainly less than 3% of error).

• scSTREAM from SOFTWARE CRADLE is oriented towards Electronic Cooling problems

• By means of scSTREAM is very simple to create a CFD model starting from a very complex geometry