fire resistive materials: microstructure
DESCRIPTION
Fire Resistive Materials: MICROSTRUCTURE. Performance Assessment and Optimization of Fire Resistive Materials NIST July 14, 2005. Microstructure Experimental 3-D Tomography 2-D optical, SEM Confocal microscopy Modeling 3-D Reconstruction Parameters Porosity Pore Sizes Contact Areas. - PowerPoint PPT PresentationTRANSCRIPT
Fire Resistive Materials: MICROSTRUCTURE
Performance Assessment and Optimization of Fire Resistive Materials
NISTJuly 14, 2005
MicrostructureExperimental
3-D Tomography2-D optical, SEM
Confocal microscopy
Modeling3-D Reconstruction
ParametersPorosity
Pore SizesContact Areas
Properties(all as a function of T)
ThermalHeat CapacityConductivity
DensityHeats of Reaction
AdhesionPull-off strength
Peel strengthAdhesion energy
Fracture toughness
EquipmentTGA/DSC/STASlug calorimeter
DilatometerBlister apparatus
Materials Science-Based Studies of Fire Resistive Materials
EnvironmentalInterior
Temperature, RH, load
ExteriorTemperature, RH, UV, load
Performance PredictionLab scale testingASTM E119 Test
Real structures (WTC)
Importance of Microstructure• As with most materials, microstructure of FRMs will significantly
influence many performance properties– Adhesion and mechanical properties (fracture toughness)– Heat transfer
• Conduction• Convection (of steam and hot gases)• Radiation
• What are some critical microstructural parameters for porous FRMs?– Porosity (density, effective thermal conductivity)– Pore size (radiation transfer)– Pore connectivity (convection, radiation)
• What are some applicable techniques for characterizing the microstructure of FRMs?– Optical microscopy– Scanning electron microscopy (SEM)– X-ray microtomography
Optical Microscopy• Can apply to cast or
fracture surfaces• Minimal specimen
preparation required• Can estimate total coarse
porosity and “maximum” pore size
• Two-dimensional so limited information on porosity connectivity
Scanning Electron Microscopy
• Higher resolution view than optical microscopy
• Tradeoffs between magnification and having a representative field of view
• More specimen preparation may be required
• Two-dimensional
X-ray Microtomography• Inherently three-
dimensional• Intensity of signal
based on x-ray transmission (local density) of material
• Voxels dimensions of 10 μm readily available– 1 μm at specialized
facilities (e.g., ESRF in France)
BFRL Experience with Microtomography• With CSTB and ESRF (France), in 2001, created
the visible cement dataset, a first-of-its-kind view of the 3-D microstructure of hydrating cement paste and plaster of Paris– http://visiblecement.nist.gov
• With FHWA, Penn State, and others, have imaged thousands of three-dimensional coarse and fine aggregates as part of the ongoing VCCTL consortium
• In 2004, with Penn State, imaged a variety of fire resistive materials including fiber-based, gypsum-based, and intumescent materials
X-ray Microtomography of Unexposed Gypsum-Based FRM
From Center for Quantitative Imaging, Penn State Univ.
X-ray Microtomography of Flame-Exposed Intumescent Coating FRM
From Center for Quantitative Imaging, Penn State Univ.
Microstructure Thermal Conductivity
• Segment 3-D microstructures into pores and solids (binary image)
• Extract a 200x200x200 voxel subvolume from each microstructure data set
• Separate and quantify volume of each “pore” (erosion/dilation, watershed segmentation-Russ, 1988, Acta Stereologica)
• Input segmented subvolume into finite difference program to compute thermal conductivity (compare to measured values)
Three-Dimensional X-ray Microtomography
• Three-dimensional images of isolated poresGypsum-based Fiber/cement-based
Thermal Conductivity Computation
-Use finite difference technique with conjugate gradient solver (Garboczi, 1998, NISTIR)-Put a temperature gradient across the sample and solve for heat flow at each node-Compute equivalent k value for composite material
Q = -kA (dT/dx)
Porosity: kpore
“Solid”: ksolid
Q-Need to know values for kpore and ksolid (itself microporous)
Thermal Conductivity of Porous Solids
ppppvpvpkk solidPS
3/23/2
3/23/2
1)(1
where • v = kpore/ksolid
• ksolid = thermal conductivity of solid material,
• p = porosity = (max –matl)/max
max = density of solid material in the porous system, matl = density of the porous material, and
• kpore = thermal conductivity of pore = kgas-cond + krad
Theory of Russell (1935, J Amer Ceram Soc)
Radiation Term
3
316 ETrkrad
where• σ = Stefan-Boltzmann constant
(5.669x10-8 W/m2/K4),• E = emissivity of solid (1.0 for a black body), • T = absolute temperature (K), and
• r = radius of pore (m)
kpore= krad + kgas-cond
For spherical pores (Loeb, 1954, J Amer Ceram Soc):
Applicability of Russell/Loeb Theory
FRM A and B are both fiber/portland cement-basedPore sizes estimated as “maximum radius” from optical microscopy
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 200 400 600 800 1000 1200
Temperature (oC)
k [W
(/m•K
)]
FRM-A 0.5 mm pores
FRM-A (measured)
FRM-B (measured)
FRM-B 0.75 mm pores
Microstructure Modeling Results: Gypsum-Based Material FRM C
Complication- gypsum to anhydrite conversionkgypsum ≈ 1.2 W/m•K kanhydrite≈ 4.8 W/m•K (Horai, 1971)
0.05
0.10
0.15
0.20
0.25
0.30
0 200 400 600 800 1000 1200
Temperature ( oC)
k [W
/(m•K
)]
Measured data
Model (gypsum/anhydrite)
Model (constant k-gypsum)
Microstructure Modeling Results: Fiber/Cement-Based Material FRM B
Complications– Anisotropy of microstructure– Radiation transfer through connected pores (Flynn and Gorthala, 1997)
0.000.050.100.150.200.250.300.350.400.450.50
0 200 400 600 800 1000 1200
Temperature ( oC)
k (W
/m•K
)
Measured data
No erosions (x,y)
No erosions (z)
k vs. Porosity and Pore SizeFRM ρ
(kg/m3)
Porosity Poreradius (mm)
k (23 oC)
[W/(m•K)]
k (1000 oC)
[W/(m•K)]
A-fiber 313.7 87.5 % 0.5 0.0534 0.3708
B-fiber 236.8 91.2 % 0.75 0.0460 0.5010
C-gypsum 292.4 87.2 % 0.2 0.0954 0.2618
Summary• Microstructure is paramount to thermal
performance of FRMs• Powerful microstructure characterization
techniques exist and are becoming more commonplace
• Computational techniques are readily available for predicting thermal conductivity from microstructure-based inputs
• Opens possibilities for microstructure-based design and optimization of new and existing FRMs