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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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Uncertainty Quantification for Equation of State using Heterogeneous Data Sources
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Uncertainty Quantification for a TNT Equation of State Including the Kinetic Burn Model
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Uncertainty quantification of unconfined spill fire data by coupling Monte Carlo and artificial neural networks
Not Available
Uncertainty Quantification of the 1-D SFR Thermal Stratification Model via the Latin Hypercube Sampling Monte Carlo Method
Not provided.
Validation and Uncertainty Quantification for Two-Phase Natural Circulation Flows Using TRACE Code
Not provided.
Uncertainty Quantification of the Decay Gamma Source in Mesh-Based Shutdown Dose Rate Calculations
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Uncertainty quantification for computational modelling of laser powder bed fusion
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Computational prediction of dielectric breakdown strength of a transformer paper in oil with uncertainty quantification
The determination of the dielectric breakdown strengths of microstructurally heterogeneous materials has been a primarily experimental endeavor. We report the development of a microstructure-level model for computationally predicting the breakdown strength and analyzing the interactions between electromagnetic pulses (EMP) and the constituents in a composite of cellulose-based paper and mineral oil found in electrical transformers. The model allows explicit simulation of the material breakdown process by tracking the transition of dielectric constituents from non-conductive to conductive states. The focus is on the electric fields induced in the materials and the overall conditions for dielectric breakdown (defined as the onset of avalanche) caused by the electric field induced in the composite. Responses to three distinct pulse shapes, i.e., Steep Front (SF), Lightning (L), and AC with spectra spanning 60–9 × 105 Hz are considered. It is found that the breakdown strength of the material is significantly affected by microstructure heterogeneities, the spatial variations of the constituent properties, and the pulse shapes. A probabilistic characterization of the breakdown strength is computationally obtained and compared with experimental measurements. Although one particular material is analyzed, the model and approach are applicable to other heterogeneous materials as well.
Parameter inference and uncertainty quantification with diffusion models: extending CDI to 2D spatial conditioning
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Modeling with uncertainty quantification reveals the essentials of a non-canonical algal carbon-concentrating mechanism
The thermoacidophilic red alga Cyanidioschyzon merolae survives its challenging environment likely in part by operating a carbon-concentrating mechanism (CCM). Here, we demonstrated that C. merolae 's cellular affinity for CO 2 is stronger than the affinity of its rubisco for CO 2 . This finding provided additional evidence that C. merolae operates a CCM while lacking the structures and functions characteristic of CCMs in other organisms. To test how such a CCM could function, we created a mathematical compartmental model of a simple CCM, distinct from those we have seen previously described in detail. The results of our modeling supported the feasibility of this proposed minimal and non-canonical CCM in C. merolae . To facilitate the robust modeling of this process, we measured and incorporated physiological and enzymatic parameters into the model. Additionally, we trained a surrogate machine-learning model to emulate the mechanistic model and characterized the effects of model parameters on key outputs. This parameter exploration enabled us to identify model features that influenced whether the model met the experimentally derived criteria for functional carbon concentration and efficient energy usage. Such parameters included cytosolic pH, bicarbonate pumping cost and kinetics, cell radius, carboxylation velocity, number of thylakoid membranes, and CO 2 membrane permeability. Our exploration thus suggested that a non-canonical CCM could exist in C. merolae and illuminated the essential features generally necessary for CCMs to function.
Uncertainty quantification for virtual diagnostic of particle accelerators
Not Available
Uncertainty quantification in the machine-learning inference from neutron star probability distribution to the equation of state
Not provided.
Low-shot, Semi-supervised, Uncertainty Quantification Enabled Model for High Consequence HSI Data.
Abstract not provided.
Analytical Data-Driven Method for Geomagnetic Disturbance Uncertainty Quantification Compensation
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Uncertainty Quantification in Stochastic Economic Dispatch using Gaussian Process Emulation
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