Data-Driven Variational Reduced Models Using Learned Radial Basis Functions
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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presentation for MORe 2024
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This poster will be presented at IMAGE as well as for a Sandia-UT student poster session next week
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work on random tensors, all UUR
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AGU 2024 poster
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Most temperature measurement methods rely upon the assumption of a single uniform source as part of the analysis in inferring an observed temperature T from the surface of a material. This is true for the radiometric and spectroscopic methods we rely upon in shock physics. However, there are obvious cases where this is a poor assumption that can cause large errors in analysis, such as the measurement of emission from a surface composed of more than one material, such as a rusted surface of Fe and Fe 2 O 3 , or a multi-material surface that could be produced through alloying methods (e.g., mokume gane) or 3D printing. Here we perform an initial examination of the effects of varied surface properties upon the analysis using idealized test data to constrain when we may have better confidence in the resulting calculations. We also examine the structures of the fits to test whether, in principle, comparing the fit with ideal gray bodies would reveal such a structure and enable the researcher to identify when a two-temperature fitting model should be used instead of a single- T model or a wavelength-dependent emissivity model.
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