case study: design space exploration for electronic cooling simulation

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Case Study in Design Space Exploration for Electronics cooling

Designs often depend on combinations of many of these…

Physical parameters

• System dimensions• Component

dimensions• Component locations• System/component

orientations

Material parameters

• Skin/casing material• Heatsink, spreaders• Thermal interface

material• Cooling fluid material

Operational Parameters

• Ambient temperatures• Ambient pressures• Fan types • Fan speeds• Number of fans• Powers

Challenge: Multiple parameters

Each one adds more complexities and constraints to the design cycle

Increasing performance Increasing power

densities

Tight design cycles

Cost constraints

ManufacturabilityReliability

Further Challenges…

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Tablet design in ESI Presto

● Number of Parameters : 7o Material – conductivity (W/m-K)

o Midplane chassis material (15, 90)

o Gappad material (0.5, 3.5)

o Graphite sheet material (15, 1500)

o PCB conductivity (15, 55)

o Geometrico Graphite sheet thickness (100 to 500 microns)

o System power, Ambient temperature

● Total runs performed: 15

● QoI: Skin temperature, CPU max temp

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Sample Case Study – Cooling Simulation

Business Problem

Input parameters:1. Mid Chassis Conductivity2. PCB Conductivity3. Ambient Temperature4. System Power5. Graphite Thickness6. Graphite Conductivity7. Gap Pad Conductivity

Output parameters:8. Max Skin Temperature9. Max Touch Temperature10. Max CPU Temperature

ObjectivesUnderstand the interplay of input parameters vs output resultsCharacterize which parameter ranges cause extremes in output valuesAnalyze the effects of individual parameters

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General Approach• Design tests for the hypothetical requirements

• Select parameter ranges: 4th order collocations means 4 values per each parameter

• Run simulations for all evaluation points

• Transform data and export to ESI Mineset‣ Data entered as a simple CSV file containing parameter values of each

simulation

• Iterate through the process using lessons learned from the previous round

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Case Study Design Space Exploration Mineset + PRESTO for Chip Manufacturing

PRESTOSparse Grid DOE

7 Parameters4 collocations

each

MINESETMachine Learning

Using 16,384samples

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to understanding the dataStatistics is the starting point

For continuous numbers look at the distribution, extremes and statistical markers

For categories, get a histogram

Output temperatures show a Bell-curve of

distribution

Each parameter value has been seen the

same number of times

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Shows the main factors affecting this target outcome Column Importance ranking for Touch temperature

• System Power is the biggest predictor of Max Touch Temperature, followed by Ambient Temperature (Tamb) etc.

• The first two parameters provide an outsized influence to the results

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Not the same factors

Column Importance ranking for CPU temperature

• For the CPU temperature graphite conductivity is shown to be the most important factor.

• The first three parameters provide a nearly complete predictive ability for the CPU temperature

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What-if AnalysisEvidence Visualizer for target: CPU Temperature

Distribution of low ( )medium ( ) and high ( ) temperatures in the overall set

Breakdown of the CPU

temperature within ranges of the input

variables

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Posterior probability is calculated based on the data range

Keeping the Graphite Conductivity and System

Power no higher than mid-ranges, cuts the

chances of overheating in half

What-if AnalysisInterpreting the interactive results This selection:

• Graphite Conductivity between 254.16 and 752.5

• System Power between 1.083 and 2.05

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Decision Tree Analysis• At each branch the data is further subdivided based on what condition best separates the

outcome results• The nodes show the distribution of temperatures based on the rules leading to it

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Decision Tree Analysis

When Graphite Conductivity is < 254.167, resulting CPU temperature is high in 2/3 of simulation cases

Graphite Conductivity > 254.167and System Power < 2.05

guarantees no overheating

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Parallel Coordinates – Which parameter settings result in low CPU temperatures ?

Temperatures below 36 are possible only in the highlighted cases where:• Sys_Power < 2• Tamb < 31• Graphite_cond

>= 500

Selecting records with low CPU temperature (< 36)

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Parallel Coordinates – Conditions for high temperatures

The simulations resulting in high temperatures come only from settings of maximum Tamb and minimum graphite conductivity

Selecting records with high temperature (> 50)

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Summary

MinesetFeatures Used

Histograms and Statistics Understand parameter distribution and characteristics

Column Importance Calculate attributes with highest influence on resulting temperatures

What-if analyzerEnable exploration of the design space in order to determine best parameter values to minimize high temperatures

Decision Tree Find the rules that lead to either positive (low temperatures) and negative (high temperatures) outcomes

Graphic Plots For visual inspection and exploration

Actions Understand the results for better refinement on next round of simulations.

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ESI Mineset : h tt p s : / / c l o u d . e s i - g r o u p . c o m / a n a l y ti c s

ESI Presto:h tt p s : / / w w w. e s i - g r o u p . c o m / s o ft w a r e - s o l u ti o n s / v i r t u a l -

e n v i r o n m e n t / c f d - m u l ti p h y s i c s / e s i - p r e s t o

Free 14-day t r ia l : h tt p s : / / c l o u d . e s i - g r o u p . c o m / m i n e s e t / t r i a l - s i g n u p

https://cloud.esi-group.com/mineset/trial-signup

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Links:

• Mineset : https://cloud.esi-group.com/analytics

• Free trial: https://cloud.esi-group.com/mineset/trial-signup

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case study: design space exploration for electronic cooling simulation

Data & Analytics