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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Research in Integrated Assessment Inter-Model Development, Testing and Diagnostics (Final Technical Report)

This is the final report on the second phase (spanning 2013-2018) of a project focused on further establishing an integrated assessment modeling (IAM) community research program on IAM model development, diagnostic testing, and inter-comparisons (PIAMDDI), including multi-model scenario ensemble analyses. The program was dedicated to improving the science of integrated assessment by doing cutting edge research in critical scientific areas of IAM development, and integrating that research with research programs on improved methods for model diagnostics, inter-comparison and ensemble-like analyses. We continued to link this program closely to other climate change research programs in the U.S. and abroad, and to make the work highly transparent, accessible and credible. As anticipated, progress in the scientific research areas informed the model comparison methods development and scenario tasks, and the comparisons and ensemble activities helped set priorities for the research areas.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Plant roots alter microbial functional genes supporting root litter decomposition

Decomposition of soil organic carbon is central to the global carbon cycle and profoundly affected by plant roots. While root “priming” of decomposition has been extensively investigated, it is not known how plants alter the molecular ecology of soil microbial decomposers. We disentangled the effects of Avena fatua, a common annual grass, on 13 C-labeled root litter decomposition and quantified multiple genetic characteristics of soil bacterial and fungal communities. In our study, plants consistently suppressed rates of root litter decomposition. Microbes from planted soils had relatively more genes coding for low molecular weight compound degradation enzymes, while those from unplanted had more macromolecule degradation genes. Higher abundances of “water stress” genes in planted soils suggested that microbes experienced plant-induced water stress. We developed a conceptual model based on Mantel analyses of our extensive data set. This model indicates that plant root effects on the multiple soil environmental and microbial mechanisms involved in root litter decomposition act through changing the functional gene profiles of microbial decomposers living near plant roots.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

WaterTAP3 Model Input Data for NAWI's Eight Source Water Baseline Analyses

This folder contains the input data for the WaterTAP3 model that was used for the eight NAWI (National Alliance for Water Innovation) source water baselines studies published in the Environmental Science and Technology special issue: Technology Baselines and Innovation Priorities for Water Treatment and Supply. There are also eight other separate DAMS submissions, one per source water, that include the model results for the published studies. In this data submission, all model inputs across the eight baselines are included. The data structure and content are described in a README.txt file. For more details on how to use the data in WaterTAP3 please refer to the model documentation and GitHub site found at "WaterTAP3 Github" linked in the submission resources.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1993

A discussion of plausible solar irradiance variations, 1700-1992

From satellite observations the solar total irradiance is known to vary. Sunspot blocking, facular emission, and network emission are three identified causes for the variations. In this paper we examine several different solar indices measured over the past century that are potential proxy measures for the Sun's irradiance. These indices are (1) the equatorial solar rotation rate, (2) the sunspot structure, the decay rate of individual sunspots, and the number of sunspots without umbrae, and (3) the length and decay rate of the sunspot cycle. Each index can be used to develop a model for the Sun's total irradiance as seen at the Earth. Three solar indices allow the irradiance to be modeled back to the mid-1700s. The indices are (1) the length of the solar cycle, (2) the normalized decay rate of the solar cycle, and (3) the mean level of solar activity. All the indices are well correlated, and one possible explanation for their nearly simultaneous variations is changes in the Sun's convective energy transport. Although changes in the Sun's convective energy transport are outside the realm of normal stellar structure theory (e.g., mixing length theory), one can imagine variations arising from even the simplest view of sunspots as vertical tubes of magnetic flux, which would serve as rigid pillas affecting the energy flow patterns by ensuring larger-scale eddies. A composite solar irradiance model, based upon these proxies, is compared to the northern hemisphere temperature depatures for 1700-1992. Approximately 71% of the decadal variance in the last century can be modeled with these solar indices, although this analysis does not include anthropogenic or other variations which would affect the results. Over the entire three centuries, approx. 50% of the variance is modeled. Both this analysis and previous similar analyses have correlations of model solar irradiances and measured Earth surface temperatures that are significant at better than the 95% confidence level. To understand our present climate variations, we must place the anthropogenic variations in the context of natural variability from solar, volcanic, oceanic, and other sources.

Hoyt, Douglas V.↗
DOE OSTI2016

Multi-dimensional rheology-based two-phase model for sediment transport and applications to sheet flow and pipeline scour

Sediment transport is fundamentally a two-phase phenomenon involving fluid and sediments; however, many existing numerical models are one-phase approaches, which are unable to capture the complex fluid-particle and inter-particle interactions. In the last decade, two-phase models have gained traction; however, there are still many limitations in these models. For example, several existing two-phase models are confined to one-dimensional problems; in addition, the existing two-dimensional models simulate only the region outside the sand bed. This paper develops a new three-dimensional two-phase model for simulating sediment transport in the sheet flow condition, incorporating recently published rheological characteristics of sediments. The enduring-contact, inertial, and fluid viscosity effects are considered in determining sediment pressure and stresses, enabling the model to be applicable to a wide range of particle Reynolds number. A k − ε turbulence model is adopted to compute the Reynolds stresses. In addition, a novel numerical scheme is proposed, thus avoiding numerical instability caused by high sediment concentration and allowing the sediment dynamics to be computed both within and outside the sand bed. The present model is applied to two classical problems, namely, sheet flow and scour under a pipeline with favorable results. For sheet flow, the computed velocity is consistent with measured data reported in the literature. For pipeline scour, the computed scour rate beneath the pipeline agrees with previous experimental observations. However, the present model is unable to capture vortex shedding; consequently, the sediment deposition behind the pipeline is overestimated. Sensitivity analyses reveal that model parameters associated with turbulence have strong influence on the computed results.

42 ENGINEERING↗
DOE OSTI2011

Dynamic (G2) Model Design Document, 24590-WTP-MDD-PR-01-002, Rev. 12

The Hanford Tank Waste Treatment and Immobilization Plant (WTP) Statement of Work (Department of Energy Contract DE-AC27-01RV14136, Section C) requires the contractor to develop and use process models for flowsheet analyses and pre-operational planning assessments. The Dynamic (G2) Flowsheet is a discrete-time process model that enables the project to evaluate impacts to throughput from eventdriven activities such as pumping, sampling, storage, recycle, separation, and chemical reactions. The model is developed by the Process Engineering (PE) department, and is based on the Flowsheet Bases, Assumptions, and Requirements Document (24590-WTP-RPT-PT-02-005), commonly called the BARD. The terminologies of Dynamic (G2) Flowsheet and Dynamic (G2) Model are interchangeable in this document. The foundation of this model is a dynamic material balance governed by prescribed initial conditions, boundary conditions, and operating logic. The dynamic material balance is achieved by tracking the storage and material flows within the plant as time increments. The initial conditions include a feed vector that represents the waste compositions and delivery sequence of the Tank Farm batches, and volumes and concentrations of solutions in process equipment before startup. The boundary conditions are the physical limits of the flowsheet design, such as piping, volumes, flowrates, operation efficiencies, and physical and chemical environments that impact separations, phase equilibriums, and reaction extents. The operating logic represents the rules and strategies of running the plant.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2018

NASA Observations and Modeling During ICE-POP

Recap: NASA-Specific Objectives for ICE-POP: Provide real-time observational and NWP data in support of ICE-POP, participate in significant international science effort; GPM (Global Precipitation Measurement) Ground Validation and NASA Weather Program -Direct/physical validation of active/passive satellite-based snowfall retrieval algorithms over coastline and mountains; melting layer interaction with terrain -Physics of snow, coupling to snow water equivalent rate and satellite remote sensor retrieval algorithm assumptions - -Size distributions, types/habit, water equivalent, profiles -NU-WRF (NASA-Unified Weather Research and Forecasting) Model plus Observational analyses: Movement toward “level IV products” leverage intensive and multi-faceted NWP (Numerical Weather Prediction) component -Model precipitation processes (liquid, mixed phase and frozen); Build model testing database for further active/passive remote sensing algorithm development (e.g., satellite data simulators) -"Integrated" validation of products in operational context.

Precipitation Science↗
NASA NTRS2023

Observational Constraints Reduce Model Spread but Not Uncertainty in Global Wetland Methane Emission Estimates

The recent rise in atmospheric methane (CH 4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH 4 source, estimates of global wetland CH 4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH 4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH 4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH 4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH 4 year −1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH 4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.

Kuang-Yu Chang↗
DOE OSTI2017

Al/Zr/U-10Mo Foil Sensitivity Analyses

This report describes modeling work performed to analyze typical uranium (U)-10 wt percent molybdenum (Mo) foils co-rolled with zirconium (Zr) and clad in aluminum (Al). The purpose of the analyses was to predict foil integrity, while accounting for the mechanical properties, thickness, and roughness of the Zr-layer coating.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Toward a Holistic Performance Evaluation of Large Language Models Across Diverse AI Accelerators

Artificial intelligence (AI) methods have become critical in scientific applications to help accelerate scientific discovery. Large language models (LLMs) are being considered a promising approach to address some challenging problems because of their superior generalization capabilities across domains. The effectiveness of the models and the accuracy of the applications are contingent upon their efficient execution on the underlying hardware infrastructure. Specialized Al accelerator hardware systems have recently become available for accelerating Al applications. However, the comparative performance of these AI accelerators on large language models has not been previously studied. In this paper, we systematically study LLMs on multiple AI accelerators and GPUs and evaluate their performance characteristics for these models. We evaluate these systems with (i) a micro-benchmark using a core transformer block, (ii) a GPT-2 model, and (iii) an 1,I,M-driven science use case, GenSLM. We present our findings and analyses of the models' performance to better understand the intrinsic capabilities of AI accelerators. Furthermore, our analysis takes into account key factors such as sequence lengths, scaling behavior, and sensitivity to gradient accumulation steps.

Emani, Murali↗
DOE OSTI2015

Impact of the Normalization Condition and Model Information on Evaluated Prompt Fission Neutron Spectra and Associated Uncertainties

Low evaluated uncertainties compared to experimental information and a strong model impact were observed in some prompt fission neutron spectrum (PFNS) evaluations that include mean values and covariances stemming from a rigid model. Here, we show by studying the (PU)-P-239 PFNS ENDF/B-VII.1 evaluation via generalized least-squares analyses that strong model correlations in combination with the normalization condition on the estimated PFNS and its covariances result in surprisingly low evaluated uncertainties. Furthermore, the model changes the evaluated results by > 1 sigma of combined experimental uncertainties near the average outgoing neutron energy (similar to 2 MeV). We show both analytically and by means of representative numerical examples that the normalization condition on the spectrum and its covariances naturally leads to uncertainties reduced by a fully positively correlated scaling uncertainty

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRSDate not supplied

PolarMERRA: A collaborative effort for NASA’s polar reanalysis

The polar atmosphere is often poorly represented in global models, adversely impacting analyses in these regions and forecasts on much larger scales. PolarMERRA is an initiative that brings together the modeling expertise from NASA’s global modeling and assimilation office (GMAO) and observational expertise from NASA’s Cryospheric sciences lab to assess and improve the representation of polar processes in the NASA’s modelling system in preparation for upcoming and next generation reanalyses. Model improvements in resolution, physics, and data assimilation have been guided by assessment of model skill in the polar regions. Preliminary results from a novel validation framework indicate a reduction in near surface air temperature, turbulent fluxes, and precipitation biases.

Chelsea Parker↗
DOE OSTI2024

Parametrized uncertainties in the spectral function model of neutrino charged-current quasielastic interactions for oscillation analyses

A substantial fraction of systematic uncertainties in neutrino oscillation experiments stem from the lack of precision in modeling the nuclear target in neutrino-nucleus interactions. Whilst this has driven significant progress in the development of improved nuclear models for neutrino scattering, it is crucial that the models used in neutrino data analyses be accompanied by parameters and associated uncertainties that allow the coverage of plausible nuclear physics. Based on constraints from electron scattering data, we propose such a set of parameters, which can be applied to nuclear shell models, and test their application to the Benhar [] spectral function model. The parametrization is validated through a series of maximum likelihood fits to cross section measurements made by the T2K and MINERvA experiments, which also permit an exploration of the power of near-detector data to provide constraints on the parameters in neutrino oscillation analyses. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

A Quantitative Analysis of the Relationship Between Radiation Therapy Use and Travel Time

Purpose: To model and quantify the relationship between radiation therapy (RT) use and travel time to RT services. Methods and Materials: Population-based registries and databases were used to identify both incident cancer patient and patients receiving RT within 1 year of diagnosis (RT1y) in British Columbia, Canada, between 1992 and 2011. The effects of age, gender, diagnosis year, income, prevailing wait time, and travel duration for RT on RT1y were assessed. Significant factors from univariate analyses were included in a multivariable logistic regression model. The shape of the travel time–RT1y curve was represented by generalized additive and segmented regression models. Analyses were conducted for breast, lung, and genitourinary cancer separately and for all cancer sites combined. Results: After adjustment for age, gender, diagnosis year, income, and prevailing wait times, increasing travel time to the closest RT facility had a negative impact RT1y. The shape of the travel time–RT1y curve varied with cancer type. For breast cancer, the odds of RT1y were constant for the first 2 driving hours and decreased at 17% per hour thereafter. For lung cancer, the odds of RT1y decreased by 16% after 20 minutes and then decreased at 6% per hour. Genitourinary cancer RT1y was relatively independent of travel time. For all cancer sites combined, the odds of RT1y were constant within the first 2 driving hours and decreased at 7% per hour thereafter. Conclusions: Travel time to receive RT has a different impact on RT1y for different tumor sites. The results provide evidence-based insights for the configuration of catchment areas for new and existing cancer centers providing RT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Evaluation of multiple reduced-order models to enhance confidence in global sensitivity analyses

Variance-based global sensitivity analysis (e.g., the Sobol' sensitivity index) can be used to identify the important parameters over the entire parameter space. However, one often cannot afford the computational costs of sampling-based approaches in combination with expensive high-fidelity forward models. Reduced-order models (ROM) can substantially accelerate calculation of these sensitivities. However, it is usually difficult to determine what type of ROM should be used and how accurately the ROM represents the high-fidelity model (HFM) results. Here in this paper, we propose to concurrently use multiple ROMs as a way to assess the robustness of the model-reduction method. Two sets of HFM simulations are needed, one set for building ROMs and the other for validating ROMs. Our goal is to keep the total number of HFM simulations to a minimum. Ideally some of the HFM simulations in the first set can be shared by different ROMs. Based on validation results, the ROMs can be combined with different schemes. We demonstrate that we can achieve the goal by using four different ROMs and still considerably save computational time compared to using traditional HFM simulation for calculating sensitivity indices. We apply the approach to an example problem of a large-scale geological carbon dioxide storage system, in which the objective is to calculate a sensitivity index to identify important parameters. For this problem, the locally best ROM provides better estimates than the weighted average from all ROMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Insights into Cation Ordering of Double Perovskite Oxides from Machine Learning and Causal Relations

This work investigates origins of cation ordering in double perovskites using first-principles theory computations combined with machine learning (ML) and causal relations. We have considered various oxidation states of A, A', B, and B' from the family of transition metal ions to construct a diverse compositional space. A conventional framework employing traditional ML classification algorithms such as Random Forest (RF) coupled with appropriate features including geometry-driven and key structural modes leads to accurate prediction (~98%) of A-site cation ordering. We have evaluated the accuracy of ML models by employing analyses of decision paths, assignments of probabilistic confidence bound, and finally a direct non-Gaussian acyclic structural equation model to investigate causality. Our study suggests that structural modes are crucial for classifying layered, columnar, and rock-salt ordering. The charge difference between A and A' is the most important feature for predicting clear layered ordering, which in turn depends on the B and B' charge separation. We have also designed mathematical relationships with these features to derive energy differences to form clear layered ordering. Here, the trilinear coupling between tilt, in-phase rotation, and A-site antiferroelectric displacement in the Landau free-energy expansion becomes the necessary condition behind formation of A-site cation ordering.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Railcar Dynamics Model of the ENSA/DOE Multimodal Transportation Campaign Rail Conveyance System

Railcar dynamics models can be applied to calculate loads transmitted to used nuclear fuel (UNF) during normal conditions of transportation (NCT). This work details the development of a railcar dynamics model for the Equipos Nucleares of Spain-U.S. Department of Energy (ENSA/DOE) multimodal transportation campaign. The model was developed using NUCARS and represents the Kasgro 12-axle railcar and the ENSA ENUN-32P cask and cradle used during the campaign. The dynamics of the cask and cradle are captured by modeling them as a single degree-of-freedom (SDOF) system with optimized stiffness and damping parameters. The model was tuned using a statistical design method and manual parameter adjustment, and validated by comparing the predicted response to data collected from captive track tests. The SDOF cask and cradle model simplifies downstream analyses used to predict loads on UNF. Previous methods fed the railcar deck motion into computationally expensive finite element models of the cask and cradle; capturing the dynamics of the cask and cradle in the railcar model bypasses this step. The model provides information useful for understanding the shock and vibration environment of UNF during rail transportation, and the agreement achieved between model and test provides confidence that the methods used in this work could be used for other vehicle configurations such as the ATLAS railcar.

Kadooka, Kevin↗
NASA NTRS2004

Preliminary Convective-Radiative Heating Environments for a Neptune Aerocapture Mission

Convective and radiative heating environments have been computed for a three-dimensional ellipsled configuration which would perform an aerocapture maneuver at Neptune. This work was performed as part of a one-year Neptune aerocapture spacecraft systems study that also included analyses of trajectories, atmospheric modeling, aerodynamics, structural design, and other disciplines. Complementary heating analyses were conducted by separate teams using independent sets of aerothermodynamic modeling tools (i.e. Navier-Stokes and radiation transport codes). Environments were generated for a large 5.50 m length ellipsled and a small 2.88 m length ellipsled. Radiative heating was found to contribute up to 80% of the total heating rate at the ellipsled nose depending on the trajectory point. Good agreement between convective heating predictions from the two Navier-Stokes solvers was obtained. However, the radiation analysis revealed several uncertainties in the computational models employed in both sets of codes, as well as large differences between the predicted radiative heating rates.

Hollis, Brian R.↗