Presentation is loading. Please wait.

Presentation is loading. Please wait.

Fire Resistive Materials: MICROSTRUCTURE Performance Assessment and Optimization of Fire Resistive Materials NIST July 14, 2005.

Similar presentations


Presentation on theme: "Fire Resistive Materials: MICROSTRUCTURE Performance Assessment and Optimization of Fire Resistive Materials NIST July 14, 2005."— Presentation transcript:

1 Fire Resistive Materials: MICROSTRUCTURE Performance Assessment and Optimization of Fire Resistive Materials NIST July 14, 2005

2 Microstructure Experimental 3-D Tomography 2-D optical, SEM Confocal microscopy Modeling 3-D Reconstruction Parameters Porosity Pore Sizes Contact Areas Properties (all as a function of T) Thermal Heat Capacity Conductivity Density Heats of Reaction Adhesion Pull-off strength Peel strength Adhesion energy Fracture toughness Equipment TGA/DSC/STA Slug calorimeter Dilatometer Blister apparatus Materials Science-Based Studies of Fire Resistive Materials Environmental Interior Temperature, RH, load Exterior Temperature, RH, UV, load Performance Prediction Lab scale testing ASTM E119 Test Real structures (WTC)

3 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

4 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

5 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

6 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)

7 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.govhttp://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

8 X-ray Microtomography of Unexposed Gypsum-Based FRM From Center for Quantitative Imaging, Penn State Univ.

9 X-ray Microtomography of Flame- Exposed Intumescent Coating FRM From Center for Quantitative Imaging, Penn State Univ.

10 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)

11 Three-Dimensional X-ray Microtomography Three-dimensional images of isolated pores Gypsum-based Fiber/cement-based

12 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: k pore “Solid”: k solid Q -Need to know values for k pore and k solid (itself microporous)

13 Thermal Conductivity of Porous Solids where v = k pore /k solid k solid = 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 k pore = thermal conductivity of pore = k gas-cond + k rad Theory of Russell (1935, J Amer Ceram Soc)

14 Radiation Term where σ = Stefan-Boltzmann constant (5.669x10 -8 W/m 2 /K 4 ), E = emissivity of solid (1.0 for a black body), T = absolute temperature (K), and r = radius of pore (m) k pore = k rad + k gas-cond For spherical pores (Loeb, 1954, J Amer Ceram Soc):

15 Applicability of Russell/Loeb Theory FRM A and B are both fiber/portland cement-based Pore sizes estimated as “maximum radius” from optical microscopy

16 Microstructure Modeling Results: Gypsum-Based Material FRM C Complication- gypsum to anhydrite conversion k gypsum ≈ 1.2 W/mK k anhydrite ≈ 4.8 W/mK (Horai, 1971)

17 Microstructure Modeling Results: Fiber/Cement-Based Material FRM B Complications –Anisotropy of microstructure –Radiation transfer through connected pores (Flynn and Gorthala, 1997)

18 k vs. Porosity and Pore Size FRMρ (kg/m 3 ) PorosityPore radius (mm) k (23 o C) [W/(mK)] k (1000 o C) [W/(mK)] A-fiber % B-fiber % C-gypsum %

19 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


Download ppt "Fire Resistive Materials: MICROSTRUCTURE Performance Assessment and Optimization of Fire Resistive Materials NIST July 14, 2005."

Similar presentations


Ads by Google