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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Baryonic Effects in Cosmic Shear Tomography: PCA Parameterization and the Importance of Extreme Baryonic Models

Baryonic effects are amongst the most severe systematics to the tomographic analysis of weak lensing data which is the principal probe in many future generations of cosmological surveys like LSST, Euclid etc.. Modeling or parameterizing these effects is essential in order to extract valuable constraints on cosmological parameters. In a recent paper, Eifler et al. (2015) suggested a reduction technique for baryonic effects by conducting a principal component analysis (PCA) and removing the largest baryonic eigenmodes from the data. In this article, we conducted the investigation further and addressed two critical aspects. Firstly, we performed the analysis by separating the simulations into training and test sets, computing a minimal set of principle components from the training set and examining the fits on the test set. We found that using only four parameters, corresponding to the four largest eigenmodes of the training set, the test sets can be fitted thoroughly with an RMS $\sim 0.0011$. Secondly, we explored the significance of outliers, the most exotic/extreme baryonic scenarios, in this method. We found that excluding the outliers from the training set results in a relatively bad fit and degraded the RMS by nearly a factor of 3. Therefore, for a direct employment of this method to the tomographic analysis of the weak lensing data, the principle components should be derived from a training set that comprises adequately exotic but reasonable models such that the reality is included inside the parameter domain sampled by the training set. The baryonic effects can be parameterized as the coefficients of these principle components and should be marginalized over the cosmological parameter space.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Using Deep Neural Networks as Cost-Effective Surrogate Models for Super-Parameterized E3SM Radiative Transfer

Deep neural networks (DNNs) are implemented in Super-Parameterized Energy Exascale Earth System Model (SP-E3SM) to imitate the shortwave and longwave radiative transfer calculations. These DNNs were able to emulate the radiation parameters with an accuracy of 90–95% at a cost of 8–10 times cheaper than the original radiation parameterization. Here, a comparison of time-averaged radiative fluxes and the prognostic variables manifested qualitative and quantitative similarity between the DNN emulation and the original parameterization. It has also been found that the differences between the DNN emulation and the original parameterization are comparable to the internal variability of the original parameterization. Although the DNNs developed in this investigation emulate the radiation parameters for a specific set of initial conditions, the results justify the need of further research to generalize the use of DNNs for the emulations of full model radiation and other parameterization for seasonal predictions and climate simulations.

58 GEOSCIENCES↗
DOE OSTI2015

Parameterized Receptacle-Pin Contact Model

This report describes a simple, quasi-static, closed-form, parameterized model that predicts the contact forces acting between axially-engaging electrical contact receptacles and a pin. This approach is useful for design studies and reduced-order mechanism modeling, where receptacle-pin insertion forces have traditionally been difficult to quantify without high-fidelity (e.g. rigid body dynamics, finite element analysis) simulations. A Matlab implementation of the model is provided and is demonstrated for three receptacle geometries. Results are compared to rigid body dynamics simulations for the first two geometries and experimental insertion force measurements for the third.

42 ENGINEERING↗
DOE OSTI2019

Investigating old-growth ponderosa pine physiology using tree-rings, δ 13 C, δ 18 O, and a process-based model

In dealing with predicted changes in environmental conditions outside those experienced today, forest managers and researchers rely on process-based models to inform physiological processes and predict future forest growth responses. The carbon and oxygen isotope ratios of tree-ring cellulose (d13Ccell, d18Ocell) reveal long-term, integrated physiological responses to environmental conditions. We incorporated a submodel of d18Ocell into the widely used Physiological Principles in Predicting Growth (3-PG) model for the first time, to complement a recently added d13Ccell submodel. We parameterized the model using previously reported stand characteristics and long-term trajectories of tree-ring growth, d13Ccell, and d18Ocell collected from the Metolius AmeriFlux site in central Oregon (upland trees). We then applied the parameterized model to a nearby set of riparian trees to investigate the physiological drivers of differences in observed basal area increment (BAI) and d13Ccell trajectories between upland and riparian trees. The model showed that greater available soil water and maximum canopy conductance likely explain the greater observed BAI and lower d13Ccell of riparian trees. Unexpectedly, both observed and simulated d18Ocell trajectories did not differ between the upland and riparian trees, likely due to similar d18O of source water isotope composition. The d18Ocell submodel with a Peclet effect improved model estimates of d18Ocell because its calculation utilizes 3-PG growth and allocation processes. Because simulated stand-level transpiration (E) is used in the d18O submodel, aspects of leaf-level anatomy such as the effective path length for transport of water from the xylem to the sites of evaporation could be estimated.

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 OSTI2021

Material Models and Credibility for System Level Abnormal Mechanical ModSim Applications

The purpose of this document is to provide evidence for assessing the adequacy of parameterized material models for a collection of materials used in a finite element analyses setting. “Adequacy” is relative to the intended use of the material in particular analyses. The intended application of the material models covered within this document is for system level abnormal mechanical solid mechanics analyses. Generally, material model parameterizations should be valid from temperatures of approximately -50 to 70° C, across a range of strain rates, and (depending on details of the parts involved) large deformations. Each material covered in this document is presented in its own chapter with a common format across materials. Model assumptions, limitations, existing validation results, readiness for use with uncertainty quantification, general usage guidance, and failure considerations are all provided along with specific parameterization inputs suitable for the finite element analysis code Sierra/Solid Mechanics.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Sensitivity of U.S. summer precipitation to model resolution and convective parameterizations across gray zone resolutions: Sensitivity Across Gray Zone Resolutions

Simulating summer precipitation is a significant challenge for climate models that rely on cumulus parameterizations to represent moist convection processes. Motivated by recent advances in computing that support very high-resolution modeling, this study aims to systematically evaluate the effects of model resolution and convective parameterizations across the gray zone resolutions. Simulations usingthe Weather Research and Forecasting model were conducted at grid spacings of 36 km, 12 km, and 4 km fortwo summers over the conterminous U.S. The convection-permitting simulations at 4 km grid spacing are most skillful in reproducing the observed precipitation spatial distributions and diurnal variability. Notable differences are found between simulations with the traditional Kain-Fritsch (KF) and the scale-aware Grell-Freitas (GF) convection schemes, with the latter more skillful in capturing the nocturnal timing in the Great Plains and North American monsoon regions. The GF scheme also simulates a smoother transition from convective to large-scale precipitation as resolution increases, resulting in reduced sensitivity to model resolution compared to the KF scheme. Nonhydrostatic dynamics has a positive impact on precipitation over complex terrain even at 12 km and 36 km grid spacings. With nudging of the winds toward observations, we show that the conspicuous warm biases in the Southern Great Plains are related to precipitation biases induced by large-scale circulation biases, which are insensitive to model resolution. Overall, notable improvements in simulating summer rainfall and its diurnal variability through convection-permitting modeling and scale-aware parameterizations suggest promising venues for improving climate simulations of water cycle processes.

54 ENVIRONMENTAL SCIENCES↗