Transcript Robust Configuration of Air Cooled Server Cabinets

Robust Configuration of Air
Cooled Server Cabinets
Nathan Rolander
SRL Lab Meeting
Spring ‘05
1
7/21/2015
Systems Realization Laboratory
Outline
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2
What is a data center & research overview
Feasibility study summary
Motivation & problem description
Flow model development
Heat transfer model development
Robust design approach
Application to server cabinet
Results & analysis
Fan configuration study
Future
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Systems Realization Laboratory
Research Goals
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Simulation based design & configuration of
server cabinets in data centers, accounting for
thermal efficiency & reliability
Computationally efficient model development
Physical validation of modeling though physical
experimentation
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Systems Realization Laboratory
What is a Data Center?
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Dedicated room of computation equipment >> servers,
switches, and accessories
Equipment housed in 2m enclosures called cabinets,
arranged in rows
Require dedicated cooling
system >> CRAC units
Heat loads over 100x a
human occupied room
Much higher environmental
regulation required
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Systems Realization Laboratory
Motivation
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Data Centers can generate MW of power, up to
20kW in a single cabinet >> very high density
25-50% of the operating costs are cooling
Lifecycle mismatch:
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New equipment introduced ~ 2 years
Center infrastructure overhauled ~ 25 years
Need to increase cooling efficiency while
maintaining reliability without center re-design
5
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Systems Realization Laboratory
How is the Equipment Cooled?
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6
Raised floor plenum
(1-8’ deep)
Cold air pumped into
plenum to distribute
to cabinets
Re-circulated through
room to return to
CRAC units
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Systems Realization Laboratory
What are the airflow patterns
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Cold aisle/Hot aisle configuration
Hot Aisle
Cold Aisle
Cold Aisle
CRAC
Unit
z
x
7
Cold Supply Air
Path
Hot Exhaust Air Path
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Systems Realization Laboratory
How are the Cabinets Cooled?
8
Plenum Active Cooling
Perforated Air Flow Cooling
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Systems Realization Laboratory
Problem Breakdown
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Data Centers are a multi-scale problem
Length Scales:
•
•
•
•
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Chips
Server
Cabinet
Data Center
~ 35 mm
~ 60 cm
~2m
~ 10’s m
2m
Focus on cabinet reconfiguration:
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~10’s m
Internal Server Layout
Cabinet Position within Center
35mm
~0.6 m
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Systems Realization Laboratory
Is Design Applicable?
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First step, completed as 6102 project
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Developed reduced order models
Applied compromise DSP
Simple 2D “cold aisle” model
Found significant effects through changing flow
parameters, not entirely intuitive
Characterized inlet vs. outlet flow rates for
rising heat fluxes
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Systems Realization Laboratory
Cold Aisle Geometry
Ambient Air
Too, Continuity
Vout1
System
Boundary
q1, Tc1
Vout2
q2, Tc2
Vout3
Vout4
Vout5
q6, Tc6
Vout6
q3, Tc3
q7, Tc7
Vout7
q4, Tc4
q8, Tc8
Vout8
Left Server
Bank
11
q5, Tc5
Cold Aisle
Vin, Tin
System
Boundary
<heat flux
input
upwards at
gray lines>
Right Server
Bank
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Systems Realization Laboratory
Cold Aisle Flow
12
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Systems Realization Laboratory
Cold Aisle Temperature
13
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Systems Realization Laboratory
Cold Aisle Parameter Sweep
80
85
75
80
Temperature [C]
75
70
70
65
65
60
2
55
4
6
50
0.5
8
55
10
1
1.5
14
60
12
Vout [m/s]
Vin [m/s]
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Systems Realization Laboratory
Cold Aisle Characterization
Vin vs Vout for Increasing Flux
1.4
1.3
1.2
Vin
1.1
1
Increasing Q
0.9
0.8
0.7
0.6
4
15
5
6
7
8
Vout
9
10
11
12
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Systems Realization Laboratory
Cold Aisle Study Conclusions
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Design variable approach is viable
Airflow and heat flux parameters work well as
design variables
Geometry needs to be considered at higher
length scales
Investigation needs to be parsed to a more
applicable problem >> Enclosed cabinet
configuration study
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Systems Realization Laboratory
Multi-Dimensional FEA Analysis
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Interesting problem for further investigation
Jitesh has looked at applying multi-scale FEA
analysis to the cold aisle problem
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2 m scale rack flow, distributed to servers
30 mm chips within 60 cm server, critical points
evaluated in greater detail
Modular, as servers are identical
Good agreement between models
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Systems Realization Laboratory
Problem Geometry
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Enclosed Cabinet
containing 10 servers
Cooling air supplied
from under floor
plenum
Cold Supply Air
Hot Exhaust Air
W
H=2m
W = 0.9 m
Server 10
Lc = 0.4 m
Server 9
Section c
Server 8
Ls
Server 7
Server 6
CRAC
Unit
H
Server 5
Cabinet
Row
Isoflux Blocks
Qa,b,c
Section b
Fan Model
x
Server 3
Server 2
Vin ~ 0-1 m/s
Section a
Single
Cabinet
18
Hs
z
Server 4
Server 1
z
Q ~ 0-200 W
x
Vin
,
Lc
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Systems Realization Laboratory
Model Development
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In design some model accuracy can be traded
for computational efficiency, required for
iterative optimization algorithms
POD based modeling approach is used for
turbulent flow modeling [Rambo]
Finite difference control volume approach is
used for heat transfer modeling
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Systems Realization Laboratory
Compete Model Overview
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Generate observations
Compute empirical basis functions of u,v,μt
using Proper Orthogonal Decomposition
Recreate complete flow field by fitting mass
fluxes at boundaries and interpolation of
coefficients
Use flow and turbulence fields, combined with
wall functions, to compute completer thermal
field using numerical finite difference approach
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Systems Realization Laboratory
FLUENT Observations
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9 Observations of
Vin = 0:0.25:2 m/s
Buoyancy effects
neglected
k-ε turbulence
model for RANS
21,701 grid cells
108,505 DoF
y
x
x
Vin = 0.95 m/s
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Systems Realization Laboratory
Traditional Galerkin Methods
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Modal expansion of basis functions:

u ( x, t )   ai (t )i ( x)
i 1
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Basis functions span entire domain, not finite
elements
Need a family of functions like a Fourier series,
or Legendre polynomials
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Systems Realization Laboratory
Traditional Galerkin Methods
23
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Good for vibrations – Fourier series are natural
choice:
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Very difficult to specify vector
valued functions for even
simplest flow problems:
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Systems Realization Laboratory
Introduction to the POD
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Fit optimal linear subspace through a series of system
observations
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Practically, assemble m observations of n DoF into a

 

matrix:
U  {u , u ,..., u }  R n x m
1
2
m

1  T
C ( x, x' )  UU  R n x n
m
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Systems Realization Laboratory
Introduction to the POD
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Maximize the projection of the basis functions onto the
observations:
max{  (u , )  | ||  ||  1}
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

C
(
x
,
x
'
)

(
x
'
)
dx
'

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
( x' )




C ( x, x ' )   u ( x )  u * ( x ' )   R n x n
2
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observations
Variational
Methods
basis functions
Vector-valued eigenvectors form empirical basis of mdimensional subspace, called POD modes
Creates the optimal linear subspace spanning the
observation domain
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Systems Realization Laboratory
Properties of the POD
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POD modes are solutions of governing equations >>
satisfy boundary conditions and constraints of problem
Each mode contains dominant system dynamics,
characterized by associated eigenvalue ~ % energy
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Systems Realization Laboratory
Flux Matching Procedure
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POD modes can be used to reconstruct any
arbitrary field within the window of observations
Data is mean centered, forming perturbations
from an ensemble average:
b
u  u   aii
*
O
i 1
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Resulting in the minimization problem:
p
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min{|| G'  ai F (i ) ||} , p  m , where G'  G  F ( u o )
i 1
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Systems Realization Laboratory
Flux Matching Procedure
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Solved with multidimensional least squares
G '   C  a 
n1
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nb
1b
G = goal vector, n x 1
C = coefficient matrix, n x b, from F ( i ) values
a = coefficient vector, 1 x b
b = number of modes used in reconstruction
n = number of boundaries to match flux at
Uses pseudo-inverse algorithm for speed &
robustness
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Systems Realization Laboratory
POD Modeling Results
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POD based flow model matched to 3 test
cases, not part of original observation set
|| ue || 2 
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|| uapprox || 2
|| uexact || 2
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Systems Realization Laboratory
Heat Transfer Modeling
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General turbulent energy equation:

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
(  E) 
ui   E  p  
t
xi
x j
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
T
 ui ( ij )eff
 keff
x j


  Sh

Viscous heating ignored, steady state solution
required
Problem: how to find effective turbulent thermal
conductivity effects, keff ?
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Systems Realization Laboratory
Effective Thermal Conductivity
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Based upon turbulent viscosity
keff  k 
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c p t
Turbulent viscosity based on k-ε RANS model
as used in FLUENT
t  C
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Prt
k2

How to find k and epsilon?
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Systems Realization Laboratory
Direct Numerical Approach
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Solve for k,є,u,v, & p at all nodes using RANS
equations and k- ε turbulence model, yielding 5
coupled PDEs:



(  ui )  0
t xi
continuity


p

( ui ) 
( ui u j )  

t
x j
xi x j
where,
k- є
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  u u j 2 u  
  ij i  
(  u 'i u ' j )
   i 
  x j xi 3 xi  x j
 u u j  2 
u 
  u 'i u ' j  t  i 
    k  t i   ij
 x
xi 
 j xi  3 



(k) 
(  kui ) 
t
xi
x j

t  k 
   
  G  Gb    YM  Sk
 k  x j  k




(  ) 
(  ui ) 
t
xi
x j

t   

2
   
  C1 (Gk  C3 Gb )  C2   S
   x j 
k
k

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Systems Realization Laboratory
POD Mode Interpolation Approach
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Construct POD modes of μt along with u and v
Fit μt modes at observations to find ai, the exact
coefficient vectors at each observation
Interpolate ai between observations, creating a
n-dimensional response surface in coefficient
space >> Required as no fluxes to match!
This is more accurate than direct averaging of
the observations, which requires an impractical
observation density, as primary modes
coefficients change smoothly over domain
7/21/2015
Systems Realization Laboratory
Effective Thermal Conductivity
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Reconstructed Keff (W/m-K) at Vin = 0.33 m/s
Kair = 0.03 W/m-K
Turbulence
effects create
a 3 order of
magnitude
increase
34
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Systems Realization Laboratory
Turbulent Wall Functions
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Sharp gradients at walls require very fine mesh
to resolve accurately
Use of “Wall Functions” to approximate effect
of turbulent boundary layer analytically to
reduce computational effort
Heat fluxes in cabinet model are at walls, thus
must be modeled as accurately as possible
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Systems Realization Laboratory
Boundary Layer Resolution
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2 approaches:
Near-wall model
approach
Wall function
approach
y
BL
BL
Wall Function
36
Large numbers of
elements required!
Wall function “bridges”
wall to adjacent elements
7/21/2015
Systems Realization Laboratory
Computing Wall Functions
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Schultz-Gronov log-law (empirical)
Re x 
 u y p

c f  0.370(log10 Rex )2.584

Find turbulent wall shear & velocity
w 
ut 
37
1
2
 u2
cf
w

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Systems Realization Laboratory
Computing Wall Functions
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Convert to numerical domain (y+ to y*)
Approximately equal in FLUENT equilibrium
boundary layers => ut+ ~ ut*
Compute k and ε:
kp 
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ut2
cm
ut 3
p 
 yp
Compute μt & keff at boundary elements:
keff  k 
c p t
Prt
t  C
k2

7/21/2015
Systems Realization Laboratory
Heat Transfer Computation
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General direct numerical solver for
0


 
T
(  E) 
 ui ( ij )eff
ui   E  p  
 keff
t
xi
x j 
x j
Transient
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Convection
Diffusion
0

  Sh

Generation
Heat flux computed using Power Law,
combining diffusive and convective heat fluxes
into single coefficient
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Systems Realization Laboratory
Power Law Stability
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Power law is approximation of analytical 1D convective
& diffusion PDE solution (exponential) with a 5th order
function
Prediction of  p by Various Schemes
1.2
Central difference
Upwind
Power law
Exponential (exact)
1
0.8
p
0.6
0.4
0.2
0
-0.2
-10
40
-8
-6
-4
-2
0
Pe
2
4
6
8
10
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Systems Realization Laboratory
Final Discretization

Control volume based finite difference
approach [Patankar]
aPP  aEE  aWW  aNN  aSS  b
aE  De A  Pee    Fe , 0
A  Pe   0, 1  0.1 Pe 
5
Balance of heat flux into the
element across each face
41
a, b
Represents the
maximum of a and b.
Pee 
Fe  ( u)e Ae
De  Ae
1
Fe
De
 xe e  xe e
P

E

keff
cp
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Systems Realization Laboratory
Solution Algorithm
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Construction of a “stiffness matrix” by scanning
through geometry mesh, reading in appropriate
element u,v,Δx,Δy,keff and computing ap
Matrix is inverted to find Temperature
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Alternating Line by Line iteration using the TDMA
algorithm, scanning in x, then y sequentially
Direct inversion using MATLAB “sparse” matrix
Complete solution in ~10 seconds
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Systems Realization Laboratory
Simulated Server Chip Temperature
Response
Cold Supply Air
Hot Exhaust Air
Tin = 15 oC, Q = 60 W
W
80
70
Section a: servers 1-2
Section b: servers 3-5
Section c: servers 6-10
Server 10
Chip Temperature (oC)
Server 9
60
Section c
Server 8
Server 7
50
Server 6
H
Server 5
40
Section b
30
Server 4
Server 3
Server 2
20
0.2
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0.3
0.4
0.7
0.6
0.5
Inlet Velocity (m/s)
0.8
0.9
Servers with similar responses grouped
together >> Section a,b,c
1
Section a
Server 1
z
x
Vin
Lc
,
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Systems Realization Laboratory
Complete Model Accuracy
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44
POD flow model ~ 5%
accurate
Thermal model slightly
too diffuse
Upper servers slightly
too hot
Conservative model
Has been improved
upon recently
Chip temperature rise above ambient Tin ( oC)
Server
CFD
Approximate Difference
1
2
3
4
5
6
7
8
9
10
39.6
25.5
24.8
22
22.1
21.5
18.9
22.8
21.9
26
39.3
25.2
22.3
21.3
20.9
20.5
20.4
19
17.7
20.8
0.2
0.3
2.5
0.7
1.3
1
1.5
3.8
4.1
5.2
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Systems Realization Laboratory
Design Approach

Integration of three constructs:
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
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45
Robust Design
POD based modeling
Compromise Decision Support Problem (DSP)
Identification and classification of variables and
parameters
Application to air cooled server cabinet
configuration problem
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Systems Realization Laboratory
Robust Design Principles

Determine superior solutions through
minimizing the effects of variation, without
eliminating their causes.
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46
Type I – minimizing variations in performance
caused by variations noise factors (uncontrollable
parameters)
Type II – minimizing variations in performance
caused by variation in control factors (design
variables)
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Systems Realization Laboratory
Robust Design Application
y

Goals:


Solution insensitivity is
obtained by minimizing
response curvature
Optimizing
Solution
Constraints:

47
Robust
Solution
Variability in control
parameters must be
accounted for to to avoid
infeasible solutions (i)
x1
x2
xoptimum
xrobust
Feasible Design
Space
(ii)
(i)
2Δx2
x1
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Systems Realization Laboratory
Finding a Robust Solution

Form Multi-objective problem:



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48
Bring desired performance mean to a target
Minimize the variation in the performance
Formulation allows flexibility, can trade off
degree of robustness obtained in solution
through goal weighting or ordering
Works for Type I & Type II robust design
simultaneously
7/21/2015
Systems Realization Laboratory
The Compromise DSP



49
The solution finding “engine” and problem
formulation method for this investigation
Hybrid of Mathematical Programming and Goal
Programming well suited to engineering
problems
Allows for multiple objectives, hence can
simultaneously bring mean to target and
minimize solution deviation
7/21/2015
Systems Realization Laboratory
Compromise DSP Structure
Given
An alternative to be improved through modification
Assumptions used to model the domain of interest
System parameters:
n
number of system variables
p
number of inequality constraints
q
number of equality constraints
m
number of system goals
Find
System Variables
xi


d
Deviation Variables i , di
Satisfy
Inequality Constraints g i ( x)  0
Equality Constraints hi ( x)  0
i = 1,…,n
i = 1,…,m
i = 1,…,p
i = 1,…,q
Goals
Ai ( x)  d i  d i  Gi
i = 1,…,m
Bounds
xi ,min  xi  xi ,max
i = 1,…,n

i

i

i

i
d  0; d  0; d d  0
i = 1,…,m
Minimize
Deviation Function: Archimedean formulation
Z   Wi  di  di 
m
50
i 1
i = 1,…,m
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Systems Realization Laboratory
Design Objective Specification

System Design Objectives => Goals




System Design Specifications => Constraints


51
Minimize flow rate of cooling air supplied to cabinet
Minimize server chip temperatures
Minimize sensitivity of configuration to changes in
cabinet operating conditions
All server chips must operate at under 85oC
Total cabinet power must meet target value
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Systems Realization Laboratory
Design Variable Classification

Classify parameters as:





52
Control Factors (x)
Signal Factors (M)
Noise Factors (Z)
Response (y)
Creates a system model of the cabinet
7/21/2015
Systems Realization Laboratory
Design Variable Classification
Cold Supply Air
Hot Exhaust Air
Control Factors, x
Inlet air velocity, Vin = x1
Section a chip power, Qa = x2
Section b chip power, Qb = x3
Section c chip power, Qc = x4
Server Cabinet
System
Noise Factors, Z
Inlet air temperature, Tin
Server 10
Server 9
Section c
Response, y
Total cabinet power
Inlet air velocity
Chip temperatures
Signal Factors, M
Inlet air velocity (smaller the better)
Chip temperatures (smaller the better)
Cabinet power (nominal the best)
W
Server 7
Server 6
H
Server 5
Section b
Server 4
Server 3
Server 2
Section a
Server 1
z
x
53
Server 8
Vin
Lc
,
7/21/2015
Systems Realization Laboratory
Cabinet Compromise DSP

Given






54
Response model of Total Cabinet Power, Inlet Air Velocity,
Server Temperature as functions of x1,x2,x3,x4, = Vin, Qa, Qb, Qc
ΔVin = 0.1 m/s
ΔQa,b,c = f(xi) = 0.1xi + 22 W,
i = 2,3,4
Target for total cabinet power, Gpower = 1800-2400 W
Target for inlet velocity, Gvin = 0.1 m/s
Target for total chip temperature sum and their total maximum
possible variation Gtemp = 300oC, δTmax = 7657oC
7/21/2015
Systems Realization Laboratory
Cabinet Compromise DSP

Find


55
The values of control factors:
x1, Inlet velocity, Vin
x2, Chip power for Section a, Qa
x3, Chip power for Section b, Qb
x4, Chip power for Section c, Qc
The values of deviation variables di+,di-,
i = 1,…,n
7/21/2015
Systems Realization Laboratory
Dealing with Constraints

The means and variability of the response are
obtained using Taylor expansions

Mean:
 y  f ( x)

56
Variance:
2
 f 
2
 y2   
 xi
i 1   xi 
n
7/21/2015
Systems Realization Laboratory
Dealing with Constraints

Constraints incur added complexity as the
variation of the system response must be
considered using a worst-case scenario
E  g j (x)   g j  0 , j = 1,…,p

Where
 gj
g j  
xi , j = 1,…,p
i 1  xi
n
57
7/21/2015
Systems Realization Laboratory
Dealing with Constraints

Variability in constraints, graphical
representation
x2
Feasible Design
Space
(ii)
(i)
2Δx2
x1
58
7/21/2015
Systems Realization Laboratory
Cabinet Compromise DSP

Subject to

The constraints:
The individual server chip temperatures cannot exceed 85oC
 Tj
 85 , j = 1,…,s

x
i 1
i
n
Tj  
The mean total cabinet power must equal value Gpower
4 x2  6 x3  10 x4  Gpower
59
7/21/2015
Systems Realization Laboratory
Cabinet Compromise DSP

Subject to
The goals:
Minimize inlet air velocity

Gvin
 d1  d1  1
x1
Bring chip temperatures to target
Gtemp
s
T
i 1
 d 2  d 2  1
i
Minimize variation of chip temperatures
60
2
  Ti 
2

 var j

j 1 i 1   x j 
 d3  d3  0
 Tmax
n
s
where, var   Vin
Qa
Qb
Qc 
7/21/2015
Systems Realization Laboratory
Cabinet Compromise DSP

Subject to

The bounds:
0.2  x1 1 [m/s]
20  xi  200 [W], i = 2,3,4

61
di di  0 , with di , di  0 , i = 1,…,m
7/21/2015
Systems Realization Laboratory
Cabinet Compromise DSP

Objective

Minimize the total objective function:
m
f  Wi (d  d ) , with
i 1
62

i

i
m
W  1 ,W  0 , i = 1,…,m
i 1
i
i
7/21/2015
Systems Realization Laboratory
Solution Algorithm


63
Adaptive Linear Programming (fmincon)
User Supplied Hessian Matrix for efficiency
 2 f
 2
 x1

 *

H
 *


 *


2 f
x1x2
2 f
x1x3
2 f
x2 2
2 f
x2 x3
*
2 f
x32
*
*
2 f 

x1x4 
2 f 

x2 x4 

2 f 
x3 x4 

2 f 
x4 2 
7/21/2015
Systems Realization Laboratory
Results

Baseline Test



64
Max total power = 1600 W
Vin = 0.54 m/s
Constraints active in Server 1
7/21/2015
Systems Realization Laboratory
Increasing Heat Loads

Inlet air velocity vs. total cabinet power level
0.9
Inlet Air Velocity (m/s)
0.8
0.7
0.6
0.5
0.4
0.3
0.2
1800
65

1900
2000
2100
2200
Total Cabinet Heat Generation (W)
2300
2400
Vin increases exponentially with power load
7/21/2015
Systems Realization Laboratory
Increasing Heat Loads

Sectional power level vs. total power level
180
Chip Power Dissipation (W)
160
Section a
Section b
Section c
140
120
100
80
60
1800
66

1900
2000
2100
2200
Total Cabinet Power (W)
2300
2400
Heat load re-distributed as airflow changes
7/21/2015
Systems Realization Laboratory
Increasing Heat Loads

Maximum chip temperature and bounds
86
Maximum Chip Temperatureo(C)
84
Mean
Upper Bound
Lower Bound
82
80
78
76
74
72
70
68
1800
67

1900
2000
2100
2200
Total Cabinet Power (W)
2300
2400
Maximum chip temperature constraint met
7/21/2015
Systems Realization Laboratory
Robust vs. Optimal Configuration
Pareto Frontier
62.3
60
(y)
Chip Heat Flux (W)
Mean Chip Temperature o(C)

62.2
62.1
62
61.9
61.8
0.8
0.85
0.9
0.95
Inlet Air Velocity (m/s)
Chip Heat Flux (W)
Chip Heat Flux (W)
68
Feasible
space
50
45
0.85
0.9
0.95
Inlet Air Velocity (m/s)
1
129
(b)
90
89
87
0.8
55
40
0.8
1
91
88
(a)
Feasible
space
0.85
0.9
0.95
Inlet Air Velocity (m/s)
1
(c)
128
127
126
125
124
0.8
Feasible
space
0.85
0.9
0.95
Inlet Air Velocity (m/s)
1
7/21/2015
Systems Realization Laboratory
Robust vs. Optimal Configuration
Pareto Frontier & increasing variability
62.3
60
(y)
Chip Heat Flux (W)
Mean Chip Temperature o(C)

62.2
62.1
62
61.9
61.8
0.8
0.85
0.9
0.95
Inlet Air Velocity (m/s)
Chip Heat Flux (W)
Chip Heat Flux (W)
69
Feasible
space
50
45
0.85
0.9
0.95
Inlet Air Velocity (m/s)
1
129
(b)
89
87
0.8
55
40
0.8
1
90
88
(a)
Feasible
space
0.85
0.9
0.95
Inlet Air Velocity (m/s)
1
(c)
128
127
126
125
124
0.8
Feasible
space
0.85
0.9
0.95
Inlet Air Velocity (m/s)
1
7/21/2015
Systems Realization Laboratory
Changing Server Fan Configuration

Changing the number of
fans on each server



Series > Pressure
Parallel > Flow Rate
Cold Supply Air
Hot Exhaust Air
W
Server 10
Server 9
Section c
Use of 1 - 3 fans / server
Server 8
Server 7
Server 6
Ls
H
Server 5
Section b
Server 4
Server 3
Isoflux Blocks
Qa,b,c
z
Fan Model
x
70
Hs
Server 2
Section a
Server 1
z
x
Vin
,
Lc
7/21/2015
Systems Realization Laboratory
Server Fan Investigation




71
Combination of discrete and continuous
parameters
Same sections (a,b,c) used for varying heat
flux and fan model, Vin = 0.5 m/s
3 fan models used >> 33 = 27 combinations >>
27 individual problems to be solved
A lot of work to be done manually!
7/21/2015
Systems Realization Laboratory
FLUENT & MATLAB

Explicit Enumeration > used MATLAB to
control FLUENT & perform analysis:






72
Generate the flow field for fan configuration
Full factorial investigation of heat transfer
Build linear response surface model
Run compromise DSP to find maximum heat fluxes
Run full factorial of fan cases
Results were analyzed for efficiency
7/21/2015
Systems Realization Laboratory
Complete Results

Total power dissipated vs. case
Total Cabinet Power vs. Case
1200
Total Cabinet Power (W)
1000
800
600
400
200
0
73
0
5
10
15
Case
20
25
30
7/21/2015
Systems Realization Laboratory
Results

Merit function vs. case
Merit Function Value vs. Case
80
79
Merit Function Value
78
77
76
Value 
75
Q
  fans 
i
0.3
, i  a, b, c
i
74
73
72
71
70
74
0
5
10
15
Case
20
25
30
7/21/2015
Systems Realization Laboratory
Results

Table of most efficient configurations
Total
Section a Section b Section c Section a Section b
fans/cabinet
no. fans/section
heat flux
0
1
1
1
35.125
37.658
1
2
1
1
36.868
40.915
2
2
2
1
38.221
44.049
3
2
2
2
38.422
47.856
4
2
2
3
37.224
50.963
5
2
3
3
36.282
54.902
6
3
3
3
35.788
58.51
75
Section c
37.295
40.284
43.398
47.682
51.409
55.848
59.366
Total
739.398
795.802
851.158
917.644
968.764
1033.02
1087.872
Case
1
2
5
14
23
26
27
7/21/2015
Systems Realization Laboratory
Results

Efficient cases vs. chip power
Case vs. Chip Power
160
140
Section a
Section b
Section c
Chip Power (W)
120
100
80
60
40
20
0
3
4
5
6
7
8
9
Case
76
7/21/2015
Systems Realization Laboratory
Results

Efficient case vs. total power
Case vs. Total Cabinet Power
1100
Total Cabinet Power (W)
1050
1000
950
900
850
800
750
700
77
3
4
5
6
Case
7
8
9
7/21/2015
Systems Realization Laboratory
Complete Validation

Comparison of results obtained using robust
design and compact model to FLUENT
Total Cabinet
Power (W)
1600
2100
2400
78
Mean Chip Temp.
Difference ( oC)
3
9
3
7/21/2015
Systems Realization Laboratory
Conclusions



79
For typical enclosed cabinet over 50% more
power than baseline can be reliably dissipated
through efficient configuration
Robust solutions account for variability in
internal & external operating conditions, model
inaccuracies & assumptions
Configuration can be accomplished without
center level re-design
7/21/2015
Systems Realization Laboratory
Experimental Validation




80
Simulated “blade”
cabinet architecture
Air cooled from
inlet at bottom
7 simulated servers
10 blades per
server
7/21/2015
Systems Realization Laboratory
Future



81
Configuration of 3D cabinet >> simulation of
experimental cabinet
Thermal profile of experimental cabinet
PIV data of experimental cabinet
7/21/2015
Systems Realization Laboratory
Questions, Comments, Death
Threats?



A train leaves Boston at 8:30 heading to New York at
50 miles per hour. Another train leaves New York
heading to Boston at 10:00 when do the trains . . .
Why did Kamikaze pilots wear helmets?
Would you want your kids to watch Star Wars Episode
I-III or IV-VI 1st?
Thank you!
82
7/21/2015
Systems Realization Laboratory
Supplemental Slides

83
You ask too many questions young one . . .
7/21/2015
Systems Realization Laboratory
PIV Teaser
84
7/21/2015
Systems Realization Laboratory
Heat Transfer Model Validation
1  cos(n L)
T0  2(TL  T0 )
sinh(n x)sinh(n y)
n 1 n L sinh(n L)

80
70
60
50
40
30
20
10
10
85
20
30
40
50
60
70
80
7/21/2015
Systems Realization Laboratory
Center Level CFD

86
Elevation CFD analysis
7/21/2015
Systems Realization Laboratory
Center Level CFD

87
Plan CFD Analysis
7/21/2015