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Fire Resistive Materials: MICROSTRUCTURE

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.

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Fire Resistive Materials: MICROSTRUCTURE

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  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 Environmental Interior Temperature, RH, load Exterior Temperature, RH, UV, load Performance Prediction Lab scale testing ASTM E119 Test Real structures (WTC) Materials Science-Based Studies of Fire Resistive Materials

  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.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. Porosity: kpore “Solid”: ksolid Q Thermal Conductivity Computation Q = -kA (dT/dx) • 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 -Need to know values for kpore and ksolid (itself microporous)

  13. where v = kpore/ksolid ksolid = thermal conductivity of solid material, p = porosity = (rmax –rmatl)/rmax rmax = density of solid material in the porous system, rmatl = density of the porous material, and kpore = thermal conductivity of pore = kgas-cond + krad Thermal Conductivity of Porous Solids Theory of Russell (1935, J Amer Ceram Soc)

  14. Radiation Term For spherical pores (Loeb, 1954, J Amer Ceram Soc): 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

  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 kgypsum≈ 1.2 W/m•K kanhydrite≈ 4.8 W/m•K (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

  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

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