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This paper1 explores some of the important considerations in devising a practical and consistent framework and methodology for working with experiments and experimental data in connection with modeling and prediction. The paper outlines a pragmatic and versatile “real-space” approach within which experimental and modeling uncertainties (correlated and uncorrelated, systematic and random, aleatory and epistemic) are treated to mitigate risk in modeling and prediction. The elements of data conditioning, model conditioning, model validation, hierarchical modeling, and extrapolative prediction under uncertainty are examined. An appreciation can be gained for the constraints and difficulties at play in devising a viable end-to-end methodology. The considerations and options are many, and a large variety of viewpoints and precedents exist in the literature, as surveyed here. Rationale is given for the various choices taken in assembling the novel real-space end-to-end framework.

Silica aerogels have 1/3 the thermal conductivity of the best commercial composite insulations, or ~13 mW/m-K at 25 °C. However, aerogels are transparent in the near IR region of 4-7 μm, which is where the radiation peak from a thermal-battery stack occurs. Titania and carbon-black powders were examined as thermal opacifiers, to reduce radiation at temperatures between 300°C and 600°C, which spans the range of operating temperature for most thermal batteries. The effectiveness of the various opacifiers depended on the loading, with the best overall results being obtained using aerogels filled with carbon black. Fabrication and strength issues still remain, however.