1. Computerized, Transient Hot-Wire Thermal Conductivity (HWTC) Apparatus For Nanofluids
The 6th WSEAS International Conference on HEAT and MASS TRANSFER (WSEAS - HMT'09) Ningbo, China, January 10-12, 2009
M. Kostic & Kalyan C. Simham
Department of Mechanical Engineering
NORTHERN ILLINOIS UNIVERSITY

2. Overview INTRODUCTION OBJECTIVE THEORY OF HOT-WIRE METHOD
PRACTICAL APPLICATION OF HOT-WIRE METHOD
DESIGN OF HOT-WIRE CELL
INSTRUMENTATION
DATA ACQUISTION
CALIBRATION
UNCERTAINTY ANALYSIS
RESULTS
CONCULSIONS
RECOMMENDATIONS

3. INTRODUCTION
Nanofluids are colloidal suspensions of nanoparticles, nanofibers, nanocomposites in common fluids
They are found to have enhanced thermal properties, especially thermal conductivity
Thermal conductivity values of nanofluids may be substantially higher than related prediction by classical theories
No-well established data or prediction formula suitable to all nanofluids
Experimental thermal conductivity measurement of nanofluids is critical

4. Table 1: Summary of landmark development in nanofluids*
* (reprinted with permission; reference listed within this table are with respect to (Manna et al 2005))

5. Nanofluid Preparation Methods
One Step (Direct Evaporation and Condensation) Method
Two Step Method or Kool-aid Method
Chemical Method
Fig1: Improved new-design for the one-step, direct evaporation-condensation nanofluid production apparatus, (Kostic 2006)

6. Thermal Conductivity
Material Property
Determines ability to conduct heat
Important for thermal Management
Classification of Thermal Conductivity Measurement Techniques for Fluids
Horizontal Flat Plate Method
Vertical Coaxial Cylinder Method
Steady State Hot-Wire Method
Steady State Methods
Line Source (Hot-Wire) Method
Cylindrical Source Method
Spherical Source Method
Plane Source Method
Non-Steady State Methods

7. Transient Hot-Wire Method for Fluids
Advantages:
Fast and Accurate
Minimum Conduction and Radiation losses
Minimize (or even avoid) Convection
Classification of Hot-Wire Methods
Standard Cross Wire Method
Single Wire, Resistance Method
Potential Lead Wire Method
Parallel Wire Method

8. OBJECTIVE Design Device to Suspend Hot-Wire
Reduce Nanofluid Sample Size
Minimize End Errors
Uniform Tension on Hot-Wire
Separate Wires for Power and Voltage
Monitor Temperature
Mechanism to Calibrate Hotwire Tension
Flexibility for Cleaning and Handling
Design

9. OBJECTIVE Instrumentation Data Acquisition Calibration
Electrical Circuit
Flexible Connections
Instrumentation
Optimize to Reduce Noise
Develop Program
Data Acquisition
Calibration
Standard Fluids
Uncertainty Analysis
Thermal Conductivity

10. Principle of Hot-Wire Method
An infinitely long and thin, ideal continuous line source dissipating heat into an infinite medium, with constant heat generation
General Fourier’s Equation
Boundary Conditions
Where
T is the final temperature,
T0 is the initial temperature,
r is the radial distance and
t is the time
q is heat flux
is thermal diffusivity
kf is Thermal Conductivity
and
Ideal case:
Line source has an infinite thermal conductivity and zero heat capacity

11. series expansion of the exponential integration
The temperature change at a radial distance r, from the heat source is conforms to a simple formula by applying boundary conditions
series expansion of the exponential integration
= is the Euler’s constant
Where,
At any fixed radial distance, in two instances in time the equation, the temperature change can be represented as

12. Practical application of hot-wire method
A plot of temperature against the natural logarithm of time results in a straight line, the slope being propositional to kf
Thermal Conductivity
Practical application of hot-wire method
The ideal case of continuous line is approximated with a finite wire embedded in a finite medium
Figure 2.1 Typical plot of temperature change against time for hot-wire experiment (Johns et al 1988)

13. Nanofluids Thermal Conductivity Methods By Other Authors
Author, Year
Nanofluid Thermal Conductivity Measurement Method
Wang et al (1999)
Horizontal flat plate method
Lee et al (1999), Yu et al (2003) and Vadasz (2006)
Vertical, single wire, hot-wire method
Assael et al (2004)
Two wires, hot-wire method
Manna et al (2005)
Thermal comparator
Ma (2006)
Horizontal, single wire, hot-wire method
Simham (2008)

14. Hot-wire Method for Nanofluid
Nanofluids are electrically conducting fluids
Availability of nanofluids
Thermal expansion of wire
Cleaning of the cell
Hot-Wire Method for Electrically Conducting Fluids
Problems identified by Nagasaka and Nagashima (1981)
Possible current flow through the liquid, resulting in ambiguous measurement of heat generated in the wire,
Polarization of the wire surface,
Distortion of small voltage signal due to combination of electrical system with metallic cell through the liquid.

15. is due to the presence of the insulation layer on the wire
Where,
is due to the presence of the insulation layer on the wire
shifts (i.e. offsets) the plot of
against ln (t), without changing the slope

16. Insulation Coating Influence on Thermal Conductivity Measurement
Yu and Choi (2006)
The results of numerical simulation and experimental test show that, for most of the engineering applications, the relative measurement error of the thermal conductivity caused by the insulation coating are very small if the slopes of the temperature rise – logarithmic time diagram are calculated for large time values
No correction to insulation coating is necessary even for the conditions that the insulation coating thickness is comparable to the wire radius, and that the thermal conductivity of the insulation coating is lower than that of the measured medium

17. Reasons For Adapting Single Wire Method
Simplicity of Operation
Low Cost
Easy Insulation Coating
Easy Construction
Design Optimized

18. Selected Design Parameters
Size of the wire (i.e., Wire radius)
Type of insulation coating
Length of the wire
Sample size (length and radius of the cell)
Selected Design Parameters
Wire Diameter 50.8 µm
Teflon Insulation coating thickness 25.4 µm
Measured length of wire (after fabrication) is m
Diameter of bounding wall is m
Length of sample is m

20.
Fig 2: Cross-sectional front view of improved transient hot-wire thermal Conductivity Cell

21. Fig 2: Top half cross-sectional front view of transient hot-wire thermal conductivity cell

22.
Fig 3: Bottom half cross-sectional front view of transient hot-wire thermal conductivity cell

23. Fig 4: Cross sectional top view of the hot-wire cell at the middle

24. Fig 5: Isometric view of transient hot-wire thermal conductivity cell
Fig 5: Isometric view of transient hot-wire thermal conductivity cell

25.
Fig 6: Left-side view of transient hot-wire thermal conductivity cell without the outer cell, base plate and protection pins

26. Fig 7: Calibration position of the hot-wire cell
Fig 7: Calibration position of the hot-wire cell

27. Spring Assembly = 0.0056 m = 0.1997 N Where,
ΔZcal
Zcal
ΔZ0
(1) Spring Rod
(2) Locking Nut
Cell Cap
(3) Tension Spring
(4) Sliding Tube
Fwa
Spring Constant ζs
Initial Spring Force Fsi
Spring Assembly
Where,
Weight of spring rod,W1 = N
Weight of locking nut, W2 = N
Weight of tension spring,W3 = N
Weight of sliding tube,W4 = N
= N
= m

28.
Fig 8: Fabricated transient hot-wire thermal conductivity apparatus cell

29. Instrumentation
Figure 5.1 Schematics of electrical circuit with data acquisition system

30. Measurement Procedure
The wire is heated with electrical constant power supply at step time
The wire simultaneously serves as the heating element and as the temperature sensor
The change in resistance of the wire due to heating is measured in time using a Wheatstone bridge circuit
The temperature increase of the wire is determined from its change in resistance
Thermal conductivity is determined from the heating power and the slope of temperature change in logarithmic time

31. Signal Analysis Bridge Balance Resistance of the hot wire
The bridge voltage output
The Resistance change of Hot-Wire

32. The Temperature change of Hot-Wire
The Voltage Drop Across the Hot-Wire
Heat Flux per Unit Length at any Instant of Time
Thermal Conductivity

33. Computerized Data Acquisition
Data acquisition hardware and software are optimized to minimize signal noise and enhance gathering and processing of useful data
Types of Data Measured
Bridge voltage output
Bridge voltage input
Hot-wire Voltage
Temperature of fluid
Programming in LabVIEW
A program has been written in LabVIEW application software to automatically calculate thermal conductivity

34. Data Acquisition Hardware
PCI – 6024E, Multifunctional DAQ Board
(E–series family, PCI, PCMCIA bus, 16 single-ended/ 8 differential channel analog inputs, 12 bit input resolution, 200 kS/s maximum sampling rate, ± 0.05 V to ± 10 V input range, 2 analog inputs, 12 bit output resolution, 10 kSamples/s output range, 8 digital I/O, two 24 bit counter timer, digital trigger)
SCXI – 1000, 4 Slot Signal Conditioning Chassis
(shielded enclosure for SCXI module, low – noise environment for signal conditioning, forced air cooling, timing circuit)
SCXI – 1102, 32 Differential Channel Thermocouple Input Module
(programmatic input range of ± 100 mV to ± 10 V per channel, overall gain of 1 – 100, hardware scanning of cold junction sensor, 2 Hz low pass filtering per channel, relay multiplexer, over voltage protection of ± 42 V, 333 kS/s maximum sampling rate, 0-50 ºC operation environment temperature)
SCXI – 1303, 32 Channel Isothermal Terminal Block for Thermocouple modules
(SCXI front end mountable terminal block for SCXI-1100 and SCXI-1102/B/C, cold junction compensation sensor, open-thermocouple detection circuitry, isothermal construction for minimizing errors due to thermal gradient, cold junction accuracy for 15-35 ºC is 0.5 ºC and for 0-15 ºC & 25-50 ºC is 0.85 ºC, repeatability is 0.35 ºC)

35. Data Acquisition Hardware
SCXI – 1122, 16 Differential Channel Isolated Universal Input Module
(DC input coupling, nominal range ± 250 V to ± 5 mV with overall gain of 0.01 to 2000, over voltage protection at 250 Vrms, maximum working voltage in each input should remain with 480 Vrms of ground and 250 Vrms of any other channel, cold junction compensation, bridge compensation, isolated voltage and current excitation, low pass filter setting at 4 kHz or 4 Hz, shunt calibration, 16 relay multiplexer, 100 Samples/s (at 4 kHz filter) and 1 Sample/s (at 4 Hz filter), two V excitation level sources)
SCXI – 1322, Shielded Temperature Sensor Terminal Block
(SCXI front end mountable terminal block for SCXI -1122, on board cold junction sensor)
SCXI – 1349, Shielded Cable Assembly
(adapter to connect SCXI systems to plug-in data acquisition devices, mounting bracket for secure connection to the SCXI chassis)
SH68-68-EP, Noise Rejecting, Shielded Cable
(Connects 68-pin E Series devices (not DAQ cards) to 68-pin accessories, individually shielded analog twisted pairs for reduced crosstalk with high-speed boards)

36.
Figure 5.3: LabVIEW Program Algorithm for Thermal Conductivity Measurement

37. Calibration Reference Temperature
Two Standard Fluids Ethylene Glycol and Water
Reference Temperature
Resistances of the Wheatstone bridge circuit are measured as ,
= Ω
= m
= Ω
= Ω
Where,
= Ω
= Ω/°C
is the the slope of dRw vs T
= 8.22 Ω

38. for ethylene glycol and distilled water
Figure 6.1: Wire temperature change against time (in logarithmic scale)
for ethylene glycol and distilled water

39.
Figure 6.2: Heat input per unit length against time (for ethylene glycol and water)

40. Figure 6.3: Calibration data from time (1 s – 10 s), shows the
selected time range for data reduction as 2s – 6 s, for ethylene glycol and water

41. Ethylene glycol, shows the bias and precision error in measurement
Figure 6.4: Results of repeatability measurement of thermal conductivity for
Ethylene glycol, shows the bias and precision error in measurement

42. for distilled water, shows the bias and precision error in measurement
Figure 6.5: Results of repeatability measurement of thermal conductivity
for distilled water, shows the bias and precision error in measurement

43. Calibration Results
Table 6.1: Uncertainty in repeatability of measured thermal conductivity
Fluid
Reference
[W/m°C]
Measured
Bias Error
Precision Error
(95 %)
Uncertainty
Ethylene Glycol
(32.5 °C)
0.254
0.253 %
2.03 %
2.06 %
Distilled water
(~ 26 °C)
0.612
0.619
1.2 %
2.23 %
2.52 %

44. Uncertainty in Thermal Conductivity
Rearranging in terms of the measured resistance change in the wire
Uncertainty

45. Uncertainty in Heat Input per Unit Length
is the precision error in the average heat input per unit length
Uncertainty in Wire Voltage

46. Uncertainty in Total Resistance Change
Uncertainty in Measured Bridge Voltage Input
Uncertainty in Measured Bridge Voltage Output

47. Uncertainty in Resistances
Uncertainty in Multimeter
Uncertainty in Resistance R1
Uncertainty in Resistance R2
Uncertainty in Resistance R3
Uncertainty in Resistance R3

48. Uncertainty in Temperature Coefficient of Resistance
Figure 6.7 Calibration of Temperature Coefficient of Resistance of Teflon Coated Platinum Hot-Wire

49. Uncertainty in Length of Hot-Wire
Uncertainty in Slope of Total Resistance Change
against Logarithmic Time

50. Table 7.2: Percentage uncertainties Uncertainty (%) 1.629 2.274 1.627
0.231
3.245

51. Nanofluid thermal conductivity Measurement
Nanoparticles:
Copper, particle size 35 nm
Base Fluid:
Ethylene glycol and Water
Concentration:
1 volumetric %
Physical Stabilization:
Ultrasonication

52. Copper in Ethylene Glycol Nanofluid
Figure 7.1: Nanofluid thermal conductivity measurement of 1 vol % of copper in ethylene glycol

53. Copper In Water Nanofluid
Figure 7.2: Nanofluid thermal conductivity measurement of 1 vol % of copper in water

54. Improvements in Design
Overall volume of the cell after fabrication is 35 ml
Four wire arrangement to measure voltage drop independently from power wiring
Incorporated a spring to provide a uniform tension and avoid any slackness due to expansion
Effective off-centering mechanical design provides additional room for wiring and thermocouples
Three thermocouples to verify the uniformity of the fluid temperature
Electrical connection junctions are arranged on the cell for flexibility in connections and handling
Boundary induced errors are minimized

55. Conclusion Designed and Fabricated a Hot-wire cell with improvements
Designed and Fabricated a Wheatstone bridge for Hot-wire cell
Optimized Data Acquisition Hardware
Developed a LabVIEW Program for Measuring Thermal Conductivity
Calibrated the Apparatus with Standard Fluids

56. Conclusion Bias Error is within 1.5 % Precision Error is within 2.5 %
Total Uncertainty within 3.5 % at 95 % Probability
Enhancement in Thermal Conductivity with Copper in Ethylene glycol is 13 %
Enhancement in Thermal Conductivity with Copper in Water is 16 %

57. RECOMMENDATIONS
The uncertainty analysis shows that the resistors are the major contributors of error. This error can be reduced by using very high precision resistors with extremely small temperature coefficient of resistance.
In the present study, temperature coefficient of resistance was determined through calibration over limited temperature range. Precise calibration under well controlled conditions with a larger temperature range would be beneficial.
At present, the resistances are manually measured. This process can be automated in future.
The data acquisition and LabVIEW® can be programmed to evaluate curvature of temperature versus logarithmic-time dependence (at initial heat-capacity and later convection non-linear regions), and automate evaluation if linear range relevant for thermal conductivity measurement.
The hot-wire tension can be more accurately controlled using a micrometer in place of the fixed calibration gauge.

58. Acknowledgements
The authors acknowledge support by National Science Foundation (Grant No. CBET ).
The authors are also grateful for help in mechanical design and fabrication to Mr. Al Metzger, instrument maker and technician supervisor at NIU.

59. Thank You