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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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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Parameterization Study of Sew-EZ Materials: Types #6 and #8

Two material types identified by Sew-EZ were tested in various configurations, and under various conditions, by Sandia National Laboratories (SNL). The primary focus of this study was to assess the filtration performance of these two materials and identify if they perform similarly to certified N95 respirators. Testing was conducted on two systems which use distinctly different techniques to characterize the aerosol penetration characteristics of materials: a) R&D Filtration System: A large-scale R&D filtration system was used with testing parameters that mimicked NIOSH guidelines, where possible. Efficiency data as a function of particle size was attained using NaC1 as the test aerosol and a Scanning Mobility Particle Sizer (SMPS) for measurements. A more detailed system description can be found in Omana et al. 2020. b) Automated Tester: A commercial, automated filter tester (100Xs, Air Techniques International) was used to provide penetration/efficiency data for Sew EZ materials. The 100Xs aerosolizes a polydisperse NaC1 aerosol with a consistent concentration and size profile. The 100Xs manual (Air Techniques International 2018) states, "The aerosol particle size and distribution are designed to meet all requirements as defined in the relevant sections of NIOSH 42 CFR, Part 84 (pg. 32)."

36 MATERIALS SCIENCE↗
DOE OSTI2020

The Preston-Tonks-Wallace (PTW) Model Parameterization of AU

We have produced the Preston-Tonks-Wallace viscoplasticity model parameters for gold (Au), using the quasi-static and split Hopkinson pressure bar (SHPB) experimental data from MST division at LANL. (The experiments were performed in 2015.) We will give some specifications of the samples and the PTW parameters in the following sections.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Results from Fuel Matrix Degradation Model Parameterization Experiments and Model Development Activities

This work is being performed as part of the DOE NE Spent Fuel and Waste Science and Technology Campaign, Argillite and Crystalline Rock R&D work packages: SF-20AN01030101 and SF-20AN01030201 respectively. This document is submitted in fulfillment of milestone M4SF-20AN010301013 for Argillite R&D and the milestone M4SF-20AN010302013 for Crystalline R&D

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

A library of AI-assisted FAIR water cycle and related disturbance datasets to enable model training, parameterization and validation

This whitepaper is responsive to focal area Data acquisition and assimilation enabled by machine learning, AI, and advanced methods. Here we describe how FAIR (Findable, Accessible, Reusable, Interoperable) datasets related to water cycle extremes are essential for successful implementation of ML in Earth System and other models. We also describe how AI can be used to acquire and integrate water cycle data related to extreme events to create a library of FAIR datasets for training and evaluating algorithms.

58 GEOSCIENCES↗
DOE OSTI2021

Modeling Noise: Paths toward AI-Enabled Stochastic Earth System Models and Parameterizations

Some of the key challenges in Earth system prediction arise from an uncertain representation of unpredictable natural variability, across time and space scales. This variability, hereafter referred to as noise, represents the quantity against which the strength of a signal of interest is measured to assess predictability. Underestimating or overestimating this noise in Earth system models can lead to issues ranging between overconfidence in an erroneous prediction and a lack thereof in an otherwise accurate one. Noise is part of the physical processes in the Earth system and can amplify or damp a signal of interest in a complex manner that is not systematically characterized. Specifically, while there has been significant progress in our understanding of multiscale interactions among known modes of variability (potentially predictable signals), how these are impacted by the noise is not clear. Recent effort toward stochastic parametrization schemes that provide a representation of noise due to uncertain sub-grid processes in climate models (Berner et al., 2017 and references therein) has shown promise not only in reducing biases and improving probabilistic prediction but also in improving the representation of natural variability in the model toward what is observed. However, today’s standard schemes for stochastic parametrization are still simplistic as they do not take the state of the atmosphere into account (other than by the use of multiplicative noise for model tendencies). Furthermore they require hand-tuning and trial-and-error testing for the magnitude of the added noise, which is difficult in a chaotic system that is as complex as the Earth system.

58 GEOSCIENCES↗
DOE OSTI2021

Toward the Development of New Parameterizations for Surface Fluxes

For more than 50 years Monin-Obukhov Similarity Theory (MOST) has been used to represent near-surface exchanges of heat, moisture, and momentum in NWP models. However, limitations of MOST are well-known. In brief, MOST assumes a homogenous near-surface flux layer, which is oftentimes not valid, and suffers from autocorrelation. Limitations such as these motivate the need to modify the functional forms of the similarity equations used to represent fluxes in NWP models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Synthesis of observed and simulated rain microphysics to inform a new Bayesian statistical framework for microphysical parameterization in climate models (Final Scientific Report)

This project aimed to investigate a new approach to bulk microphysics schemes. We identified the need to move beyond the fixed structural assumptions and approximation of existing schemes. For example, most schemes assume some functional form for the rain drop size distribution (e.g. a gamma or exponential distribution). Most schemes then evolve some number of statistical moments of that distribution via various processes, such as evaporation, sedimentation, collision-coalescence, and collisional breakup. These microphysical processes, in turn, are typically some fixed functional form derived from either some other (more detailed) model, or using some single-particle rates that are then integrated over the size distribution. While these process rate functions may have some free parameters to adjust, in other cases doing so is impossible (e.g. when the process rates are analytical or piecewise solutions to some target function). Our goal was to build and test a scheme that assumed no size distribution form, and used a series of power laws as the basis for the process rates. Moments of the size distribution would be predicted, but no underlying size distribution would be specified. Any number or choice of size distribution moments could be used, and any number of power laws could be employed to model the process rates. Thus, our approach is flexible, and can seamlessly scale across levels of complexity, as demanded by the data. Bayesian inference then would provide the formalism to estimate the model parameters and structure, allowing for robust uncertainty quantification. We proposed testing this framework in idealized simulations using bin microphysical schemes as a data source. We also proposed using real data to inform our microphysics scheme, and also that we would integrate our scheme into WRF.

58 GEOSCIENCES↗
DOE OSTI2022

Compare $\overline{ν}$ parameterizations for 235 U(n,f) with fitted fission barriers

In this document, we compare observables computed with CGMF to some available data for 235 U(n,f), to investigate the multi-chance fission probabilities (fission barriers from CoH were refit) and two TKE parametrization. The four parametrizations include i) default CGMF multi-chance fission probabilities with bends in TKE for all fission chances, ii) default CGMF multi-chance fission probabilities with a TKE bend in only first-chance fission iii) refitted multi-chance fission probabilities with bends in TKE for all fission chances, and iv) refitted multi-chance fission probabilities with a bend in TKE for only first-chance fission. In all figures where the four parametrizations are on the same plot, the ones with bends in TKE for all chance fission are in black, with only first-chance bend in red, the default multi-chance fission probabilities are dashed and the new multi-chance fission probabilities are the solid lines.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗