SEARCH · Search NASA
Results for “Uncertainty quantification”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Uncertainty quantification for computational modelling of laser powder bed fusion
Explore the source record for details and available documents.
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
Explore the source record for details and available documents.
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
Explore the source record for details and available documents.
Uncertainty Quantification in Stochastic Economic Dispatch using Gaussian Process Emulation
Explore the source record for details and available documents.
Debiased Uncertainty Quantification Approach for Probabilistic Transient Stability Assessment
Not provided.
A General Framework of Rotational Sparse Approximation in Uncertainty Quantification
Not provided.
Uncertainty Quantification for Electronic Hamiltonian
This program will generate random points for electrons within the dimensions given by a parameter input file. Based on these randomly generated electron positions and the nuclear positions given by a position input file it will generate a value for the total electronic energy of an isolated system. This total electronic energy is calculated using the electronic Hamiltonian for a monoatomic system with atoms having the same number of protons and neutrons. The size of the system is defined by the parameter input file. The program will do this many times to generate a distribution of theoretically possible electronic total energies of the system. A user can then compare the total electronic energy given by their electronic structure method to make sure it falls within the distribution of theoretically possible values.
Monte Carlo Uncertainty Quantification in UF 6 Cylinder Neutron Emissions
In order to provide timely detection of the diversion of nuclear material, the international community needs to develop technology for safeguarding the entire fuel cycle. As such, there is significant interest in securing the front end of the fuel cycle through precise and accurate material accountancy prior to fuel irradiation. One particularly attractive solution for such problems is the development of unattended enrichment verification monitors for uranium hexafluoride (UF 6 ) transit cylinders. Systems such as the Unattended Cylinder Verification Station (UCVS) promise to provide independent nuclear material accountancy (i.e. measurements of relative enrichment, 235 U mass and total uranium mass) for all UF 6 cylinders entering and/or leaving any enrichment or fuel fabrication facilities.
Status Report on Uncertainty Quantification and Sensitivity Analysis Tools in the Geologic Disposal Safety Assessment (GDSA) Framework
The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Fuel Cycle Technology (FCT) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). Two high priorities for SFWST disposal R&D are design concept development and disposal system modeling. These priorities are directly addressed in the SFWST Geologic Disposal Safety Assessment (GDSA) control account, which is charged with developing a geologic repository system modeling and analysis capability, and the associated software, GDSA Framework, for evaluating disposal system performance for nuclear waste in geologic media. GDSA Framework is supported by SFWST Campaign and its predecessor the Used Fuel Disposition (UFD) campaign.