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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
NASA NTRS1993

Chromospheric heating by acoustic shocks - A confrontation of GHRS observations of Alpha Tauri (K5 III) with ab initio calculations

We compare ab initio calculations of semiforbidden C II line profiles near 2325 A with recently published observations of the inactive red giant Alpha Tau (K5 III) obtained using the GHRS on board the Hubble Space Telescope. Our one-dimensional, time-dependent calculations assume that the chromosphere is heated by stochastic acoustic shocks generated by photospheric convection. We calculate various models using results from traditional (mixing length) convection zone calculations as input to hydrodynamical models. The semiforbidden C II line profiles and ratios provide sensitive diagnostics of chromospheric velocity fields, electron densities, and temperatures. We identify major differences between observed and computed line profiles which are related to basic gas dynamics and which are probably not due to technical modeling restrictions. If the GHRS observations are representative of chromospheric conditions at all epochs, then one (or more) of our model assumptions must be incorrect. Several possibilities are examined. We predict time variability of semiforbidden C II lines for comparison with observations. Based upon data from the IUE archives, we argue that photospheric motions associated with supergranulation or global pulsation modes are unimportant in heating the chromosphere of Alpha Tau.

Judge, P. G.↗
NASA NTRS1994

An evaluation with the Fourier Amplitude Sensitivity Test (FAST) of which land-surface parameters are of greatest importance in atmospheric modeling

Land-surface parameterizations based on a statistical-dynamical have been suggested recently to improve the representation of the surface forcing from heterogeneous land in atmospheric models. With this approach, land-surface characteristics are prescribed by probability density functions (PDFs) rather than single 'representative' values as in 'big-leaf' parameterizations. Yet the use of many PDFs results in an increased computational burden and requires the complex problem of representing covariances between PDFs to be addressed. In this study, a sensitivity analysis of a land-surface parameterization for atmospheric modeling was performed to evaluate the surface parameters most important to the variability of surface heat fluxes. The Fourier amplitude sensitivity test (FAST) used for this analysis determines the relative contribution of individual input parameters to the variance of energy fluxes resulting from a heterogeneous surface. By simultaneously varying all parameters according to their individual probability density functions, the number of computations needed is very much reduced by this technique. This analysis demonstrates that most of the variability of surface heat fluxes may be described by the distributions of relative stomatal conductance and surface roughness. Thus, the statistical-dynamical approach may be simplified by the use of only these two probability density functions.

Collins, Dan C.↗
NASA NTRS2000

An Evaluation of an Analytical Simulation of an Airplane with Tailplane Icing by Comparison to Flight Data

This report presents the assessment of an analytical tool developed as part of the NASA/FAA Tailplane Icing Program. The analytical tool is a specialized simulation program called TAILSM4 which was developed to model the effects of tailplane icing on the flight dynamics Twin Otter Icing Research Aircraft. This report compares the responses of the TAILSIM program directly to flight test data. The comparisons should be useful to potential users of TAILSIM. The comparisons show that the TAILSIM program qualitatively duplicates the flight test aircraft response during maneuvers with ice on the tailplane. TAILSIM is shown to be quantitatively "in the ballpark" in predicting when Ice Contaminated Tailplane Stall will occur during pushover and thrust transition maneuvers. As such, TAILSIM proved its usefulness to the flight test program by providing a general indication of the aircraft configuration and flight conditions of concern. The aircraft dynamics are shown to be modeled correctly by the equations of motion used in TAILSIM. However, the general accuracy of the TAILSIM responses is shown to be less than desired primarily due to inaccuracies in the aircraft database. The high sensitivity of the TAILSIM program responses to small changes in load factor command input is also shown to be a factor in the accuracy of the responses. A pilot model is shown to allow TAILSIM to produce more accurate responses and contribute significantly to the usefulness of the program. Suggestions to improve the accuracy of the TAILSIM responses are to further refine the database representation of the aircraft aerodynamics and tailplane flowfield and to explore a more realistic definition of the pilot model.

Hiltner, Dale W.↗
NASA NTRS2011

Analysis of Oxygen Spectral Lines in the 1.27 Micron Band for the ASCENDS Mission

The ASCENDS mission requires simultaneous laser remote sensing of CO2 and O2 in order to convert CO2 column number densities to average column CO2 mixing ratios (XCO2). As such, the CO2 column number density and the O2 column number density will be utilized to derive the average XCO2 column. NASA Langley Research Center, working with its partners, is developing O2 lidar technology in the 1.26-1.27- m band for surface pressure measurements. The O2 model optical depth calculation is very sensitive to knowledge of the transmitted wavelengths and to the choice of Voigt input parameters. Modeling using the HITRAN database is being carried out to establish the evolution of candidate O2 absorption lines as a function of atmospheric parameters such as altitude, temperature, and pressure. Preliminary results indicate limitations of the Voigt profile and the need to utilize more advanced models which take into account line mixing, line narrowing, and speed dependence. In this paper, we evaluate alternative lineshape models to establish the optimum lineshapes which better account for the variability of individual O2 absorption lines at various atmospheric conditions. The combination of our modeling efforts with accurate laboratory measurements is anticipated to aid in achieving the desired CO2 mixing ratio measurement accuracy requirement of for the ASCENDS mission.

Pliutau, Denis↗
NASA NTRSDate not supplied

Aeroacoustic Characterization of Optimum Hovering Rotors using Artificial Neural Networks

This work illustrates the use of artificial neural network modeling in the aerodynamic and aeroacoustic characterization of optimum hovering rotors over a broad range of design and operating conditions. Design of Experiments was used to create input feature spaces over eight input factors: the number of rotor blades, rotor radius, rotor rotation rate, design thrust condition, collective pitch, airfoil camber, the location of maximum camber, and the airfoil thickness. A low-fidelity tool chain was then used at the discrete data points defined by the designed input feature spaces to analytically design optimum hovering rotors and simulate aerodynamic and aeroacoustic quantities. This allowed for the generation of data sets over which to train and test the artificial neural network prediction models. Prediction models were trained over the data sets for the actual thrust generated by the rotor, power loading, tonal thickness and loading noise at the fundamental blade passage frequency, and broadband self-noise at seventeen one-third octave bands between 1 kHz and 40 kHz. These prediction models were validated by testing over data previously unseen by the models to quantify their capability for generalization to new data within the design feature space. The models were then used to study the effect each input feature had on the aeroacoustics and aerodynamics of optimum hovering rotors and physical insights were concluded to further explain the effect of each input. This characterization study showed that tonal noise and power loading were most sensitive to the number of rotor blades and the rotor rotation rate and that broadband noise was most sensitive to collective pitch and the design thrust condition.

Christopher S Thurman↗
DOE OSTI2016

Modelling Accident Tolerant Fuel Concepts

The catastrophic events that occurred at the Fukushima-Daiichi nuclear power plant in 2011 have led to widespread interest in research of alternative fuels and claddings that are proposed to be accident tolerant. The United States Department of Energy (DOE) through its Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has funded an Accident Tolerant Fuel (ATF) High Impact Problem (HIP). The ATF HIP is a three-year project to perform research on two accident tolerant concepts. The final outcome of the ATF HIP will be an in-depth report to the DOE Advanced Fuels Campaign (AFC) giving a recommendation on whether either of the two concepts should be included in their lead test assembly scheduled for placement into a commercial reactor in 2022. The two ATF concepts under investigation in the HIP are uranium silicide fuel and iron-chromium-aluminum (FeCrAl) alloy cladding. Utilizing the expertise of three national laboratory participants (Idaho National Laboratory, Los Alamos National Laboratory, and Argonne National Laboratory), a comprehensive multiscale approach to modeling is being used that includes atomistic modeling, molecular dynamics, rate theory, phase-field, and fuel performance simulations. Model development and fuel performance analysis are critical since a full suite of experimental studies will not be complete before AFC must prioritize concepts for focused development. In this paper, we present simulations of the two proposed accident tolerance fuel systems: U3Si2 fuel with Zircaloy-4 cladding, and UO2 fuel with FeCrAl cladding. Sensitivity analyses are completed using Sandia National Laboratories’ Dakota software to determine which input parameters (e.g., fuel specific heat) have the greatest influence on the output metrics of interest (e.g., fuel centerline temperature). We also outline the multiscale modelling approach being employed. Considerable additional work is required prior to preparing the recommendation report for the Advanced Fuels Campaign.

Accident Tolerant Fuel↗
DOE OSTI2016

Preliminary Modeling of Accident Tolerant Fuel Concepts under Accident Conditions

The catastrophic events that occurred at the Fukushima-Daiichi nuclear power plant in 2011 have led to widespread interest in research of alternative fuels and claddings that are proposed to be accident tolerant. Thus, the United States Department of Energy through its NEAMS (Nuclear Energy Advanced Modeling and Simulation) program has funded an Accident Tolerant Fuel (ATF) High Impact Problem (HIP). The ATF HIP is funded for a three-year period. The purpose of the HIP is to perform research into two potential accident tolerant concepts and provide an in-depth report to the Advanced Fuels Campaign (AFC) describing the behavior of the concepts, both of which are being considered for inclusion in a lead test assembly scheduled for placement into a commercial reactor in 2022. The initial focus of the HIP is on uranium silicide fuel and iron-chromium-aluminum (FeCrAl) alloy cladding. Utilizing the expertise of three national laboratory participants (INL, LANL, and ANL) a comprehensive mulitscale approach to modeling is being used including atomistic modeling, molecular dynamics, rate theory, phase-field, and fuel performance simulations. In this paper, we present simulations of two proposed accident tolerant fuel systems: U3Si2 fuel with Zircaloy-4 cladding, and UO2 fuel with FeCrAl cladding. The simulations investigate the fuel performance response of the proposed ATF systems under Loss of Coolant and Station Blackout conditions using the BISON code. Sensitivity analyses are completed using Sandia National Laboratories’ DAKOTA software to determine which input parameters (e.g., fuel specific heat) have the greatest influence on the output metrics of interest (e.g., fuel centerline temperature). Early results indicate that each concept has significant advantages as well as areas of concern. Further work is required prior to formulating the proposition report for the Advanced Fuels Campaign.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Distribution Substation Planning Toolkit (dsp-toolkit) v1.0

The Distribution Substation Planning Toolkit (DSP Toolkit) is a software suite designed to streamline the planning and optimization of distribution substations. This toolkit offers a comprehensive set of tools and APIs for data curation, short-term electric load forecasting, and weather-sensitive load adjustment, making it an essential resource for utility companies, engineers, and researchers. Features • Data Preprocessing and Curation: Efficiently manage and preprocess large datasets to ensure high-quality input for analysis. • Short-Term Load Forecasting: Utilize data-driven models to predict short-term electric loads accurately. • Weather-Sensitive Modeling: Automatically adjust load forecasts based on weather data to predict future peak demands more precisely. Uses The DSP Toolkit is ideal for planning and optimizing distribution substations, providing a user-friendly interface and comprehensive documentation. It is suitable for both novice and experienced users, facilitating efficient and accurate planning processes. Advantages • Efficiency: Automates complex planning tasks, reducing manual effort and minimizing errors. • Scalability: Handles large datasets and complex models, making it suitable for large-scale projects. • Community and Support: Open-source with active community contributions, ensuring continuous improvement and support. • Extensibility: Easily extendable with custom modules and plugins, allowing users to tailor the toolkit to their specific needs. The DSP Toolkit stands out by offering a robust, flexible, and user-friendly solution for distribution substation planning. Public Abstract

Li, Han [Lawrence Berkeley National Laboratory (LB↗
DOE OSTI2015

SENSITIVITY OF COSMIC-RAY PROTON SPECTRA TO THE LOW-WAVENUMBER BEHAVIOR OF THE 2D TURBULENCE POWER SPECTRUM

In this study, a novel ab initio cosmic ray (CR) modulation code that solves a set of stochastic transport equations equivalent to the Parker transport equation, and that uses output from a turbulence transport code as input for the diffusion tensor, is introduced. This code is benchmarked with a previous approach to ab initio modulation. The sensitivity of computed galactic CR proton spectra at Earth to assumptions made as to the low-wavenumber behavior of the two-dimensional (2D) turbulence power spectrum is investigated using perpendicular mean free path expressions derived from two different scattering theories. Constraints on the low-wavenumber behavior of the 2D power spectrum are inferred from the qualitative comparison of computed CR spectra with spacecraft observations at Earth. Another key difference from previous studies is that observed and inferred CR intensity spectra at 73 AU are used as boundary spectra instead of the usual local interstellar spectrum. Furthermore, the results presented here provide a tentative explanation as to the reason behind the unusually high galactic proton intensity spectra observed in 2009 during the recent unusual solar minimum.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2015

System and Method for Providing Model-Based Alerting of Spatial Disorientation to a Pilot

A system and method monitor aircraft state parameters, for example, aircraft movement and flight parameters, applies those inputs to a spatial disorientation model, and makes a prediction of when pilot may become spatially disoriented. Once the system predicts a potentially disoriented pilot, the sensitivity for alerting the pilot to conditions exceeding a threshold can be increased and allow for an earlier alert to mitigate the possibility of an incorrect control input.

Conner, Kevin J↗
NASA NTRS2004

Employing Sensitivity Derivatives for Robust Optimization under Uncertainty in CFD

A robust optimization is demonstrated on a two-dimensional inviscid airfoil problem in subsonic flow. Given uncertainties in statistically independent, random, normally distributed flow parameters (input variables), an approximate first-order statistical moment method is employed to represent the Computational Fluid Dynamics (CFD) code outputs as expected values with variances. These output quantities are used to form the objective function and constraints. The constraints are cast in probabilistic terms; that is, the probability that a constraint is satisfied is greater than or equal to some desired target probability. Gradient-based robust optimization of this stochastic problem is accomplished through use of both first and second-order sensitivity derivatives. For each robust optimization, the effect of increasing both input standard deviations and target probability of constraint satisfaction are demonstrated. This method provides a means for incorporating uncertainty when considering small deviations from input mean values.

Newman, Perry A.↗
NASA NTRS1990

Energy input and HI spin temperatures in low pressure regions

Two recent (unpublished) HI emission/absorption studies carried out with good sensitivity using the Arecibo 21 cm beam are discussed. One study (Colgan, Salpeter and Terzian) looked for high velocity clouds of our own Galaxy in absorption in the directions of 63 of the brightest continuum sources reachable with the Arecibo telescope. HI emission mapping in the neighborhood of these directions was also carried out. The other study (Corbelli and Schneider) looked for absorption along lines of sight to about 50 weaker sources which pass within a few diameters of nearby disk galaxies. Neither study detected any absorption.

Corbelli, E.↗
DOE OSTI2021

Local and Global Sensitivity Analysis of a Reactive Transport Model Simulating Floodplain Redox Cycling

Reactive transport models (RTMs) are essential tools that simulate the coupling of advective, diffusive, and reactive processes in the subsurface, but their complexity makes them difficult to understand, develop and improve without accompanying statistical analyses. Although global sensitivity analysis (SA) can address these issues, the computational cost associated with most global SA techniques limits their use with RTMs. In this study, we apply distance-based generalized sensitivity analysis (DGSA), a novel and computationally efficient method of global SA, to a floodplain-scale RTM and compare DGSA results to those from local SA. Our test case focuses on the impact of 17 uncertain environmental parameters on spatially and temporally variable redox conditions within a floodplain aquifer. The input parameters considered include flow and diffusion rates, geochemical reaction rates, and the spatial distribution of sediment facies. Sensitivity was evaluated for three distinct components of the model response, encompassing both multidimensional and categorical output. Parameter rankings differ between local SA and DGSA, due to nonlinear effects of individual parameters and interaction effects between parameters. DGSA results show that fluid residence time, which is controlled by aquifer permeability, generally exerts a stronger control on redox conditions than do geochemical reaction rates. Sensitivity indices also demonstrate that sulfate reduction is key for establishing and maintaining reducing conditions throughout the aquifer. Furthermore, these results provide insights into the key drivers of heterogeneous redox processes within floodplain aquifers, as well as the main sources of uncertainty when modeling complex subsurface systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Field Sensitivity Analysis of Turbulence Model Parameters for Flow Over a Wing

Reynolds-averaged-Navier-Stokes (RANS) turbulence models are a critical tool in computational-fluid-dynamics simulations of aerodynamic systems, but simulation results can be highly sensitive to RANS-model parameter choices. Sensitivity analysis can be used to quantify these impacts, and the objective of this study is to demonstrate field sensitivity analysis with respect to ten parameters in the 2003 Menter shear-stress-transport (SST) turbulence model. Here, the analysis is demonstrated for an application relevant to wind energy, namely, flow over a NACA 0015 wing at 12 degree angle of attack and a Reynolds number of 1.5 x 10 6 . We quantify sensitivity using Sobol indices and the mean-squared gradient, which are estimated using polynomial chaos and active subspace models, respectively. Our results indicate that there are substantial spatial variations in parameter sensitivities, with different sets of most-sensitive parameters near the wing, as well as in the downstream wake, consistent with the physical interpretations of the turbulence model inputs. We show that, for this particular turbulence model and flow, simultaneous dimension reduction is possible across all quantities of interest, enabling efficient exploration of model outcomes. Ultimately, this analysis provides new insights into turbulence model parameter sensitivities in incompressible flows, and also demonstrates the implementation of field sensitivity analysis for applications relevant to aerodynamics simulations.

17 WIND ENERGY↗
DOE OSTI2021

Coarse woody debris decomposition assessment tool: Model development and sensitivity analysis

Coarse woody debris (CWD) is an important component in forests, hosting a variety of organisms that have critical roles in nutrient cycling and carbon (C) storage. We developed a process-based model using literature, field observations, and expert knowledge to assess woody debris decomposition in forests and the movement of wood C into the soil and atmosphere. The sensitivity analysis was conducted against the primary ecological drivers (wood properties and ambient conditions) used as model inputs. The analysis used eighty-nine climate datasets from North America, from tropical (14.2° N) to boreal (65.0° N) zones, with large ranges in annual mean temperature (26.5°C in tropical to -11.8°C in boreal), annual precipitation (6,143 to 181 mm), annual snowfall (0 to 612 kg m -2 ), and altitude (3 to 2,824 m above mean see level). The sensitivity analysis showed that CWD decomposition was strongly affected by climate, geographical location and altitude, which together regulate the activity of both microbial and invertebrate wood-decomposers. CWD decomposition rate increased with increments in temperature and precipitation, but decreased with increases in latitude and altitude. CWD decomposition was also sensitive to wood size, density, position (standing vs downed), and tree species. The sensitivity analysis showed that fungi are the most important decomposers of woody debris, accounting for over 50% mass loss in nearly all climatic zones in North America. The model includes invertebrate decomposers, focusing mostly on termites, which can have an important role in CWD decomposition in tropical and some subtropical regions. The role of termites in woody debris decomposition varied widely, between 0 and 40%, from temperate areas to tropical regions. Woody debris decomposition rates simulated for eighty-nine locations in North America were within the published range of woody debris decomposition rates for regions in northern hemisphere from 1.6° N to 68.3° N and in Australia.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Estimation of 3D Woven Design Sensitivities Using a Rapid Multiscale Analysis Technique

Highly-refined finite element models of three-dimension (3D) woven composite systems currently require excessive computational demands that limit their use in sensitivity analysis, uncertainty quantification, and optimization. An alternative analysis methodology was developed using the NASA Multiscale Analysis Tool (NASMAT) where multiscale models of a 3D woven composite (including inter-tow matrix voids and constituent failure) can be completed on a single central processing unit (CPU) on the order of ~30s. To develop inputs and validation data for the NASMAT model, coupon and acid-digesting testing and x-ray computed tomography were performed. The NASMAT inputs were parameterized using a set of 25 input variables and distributions. These inputs were randomly sampled to generate a total of 100,000 NASMAT analyses that could be used to understand the influence of different material and geometric properties on the warp and weft-direction stiffness and strength. These analyses (including pre/post-processing) were performed in less than eight hours on a 120 CPU cluster. The computational efficiency of the NASMAT model enabled a sensitivity analysis to be performed, and dominant input variables were able to be identified. Key results were consistent with theoretical and experimental observations for the specific 3D woven system studied in this work.

NASMAT↗
DOE OSTI2018

Dynamic, Intermediate Soil Carbon Pools May Drive Future Responsiveness to Environmental Change

Accurately capturing dynamic soil response to disturbance effects in agroecosystem models remains elusive, thereby limiting projections of climate change mitigation potential. Perennial grasses cultivated in zero‐tillage management systems hold promise as sustainable agroecosystems. High‐yielding tropical C 4 grasses often have extensive rooting systems, and the belowground processes of root turnover, aggregate formation, and mineral stabilization drove rapid C accumulation after cultivation in a recent study. We sought (i) to understand and constrain the size and responsiveness of dynamic, intermediate‐cycling C pools contributing to the observed C accrual rates, and (ii) to simulate C stocks over time under the disturbance of elevated temperature using soil incubation at multiple temperatures and physical fractionation via density and sonication. Three‐pool transfer modeling of soil incubations revealed small pools of readily available (i.e., days to months) microbial substrate that were responsive to temperature, time since cultivation, and inputs. Larger, kinetically slow‐cycling pools were more indicative of long‐term (i.e., years to decades) changes in C stock and strongly connected to measured changes in physical fractions. Combining the sensitivity of readily available microbial substrate with three‐pool transfer modeling of the physical fractions over time since cultivation revealed that dynamic transfers of inputs occurred between the free organic and aggregate‐protected fractions, and from these fractions to the mineral‐associated dense fraction. Under 5°C temperature elevation, increased transfer rates outweighed elevated decomposition losses to sustain soil C accrual into the future. To effectively plan managed landscapes and monitor sustainable agroecosystems for climate change mitigation, tools must incorporate the complexity of soil response to change. Core Ideas Dynamic aspects of soil C are inadequately represented in most models. Intermediate‐cycling pools are more responsive to disturbances than thought. Root inputs are rapidly moved from particulate debris to mineral‐bound stable pools. Temperature increased the transfer rate to stable pools and sustained C accumulation Climate change mitigation projections must capture dynamic responses in agroecosystems.

Crow, Susan E.↗
DOE OSTI2022

Uncertainty Quantification Framework for Predicting Material Response with Large Number of Parameters: Application to Creep Prediction in Ferritic-Martensitic Steels Using Combined Crystal Plasticity and Grain Boundary Models

This paper presents an uncertainty quantification (UQ) framework for the physics-based model prediction of material response with a large number of parameters. The application problem presented in this work is that of predicting creep in Grade 91 steel at 600°C. The material response is defined with a physically based microstructural model with constitutive equations emulating several observed phenomena in Grade 91 and embodied into an explicit geometry mesoscale finite element model for prior austenite grains and grain boundaries. Creep within the grains and in grain boundaries are represented by crystal plasticity for dislocation motion and a physics-based model for cavity growth and nucleation, respectively. The creep behavior of this material is influenced by several parameters, some of which have a wide range of variation based on experimental data. UQ combined with microstructural modeling can discover the core microstructural causes of experimental variability, leading to improved materials with lower variability in critical long-term material properties. In this study, we investigate the model's uncertainty to identify material properties that may be modified during production to increase creep life and analyze different components of the crystal plasticity model for improvements. For this purpose, a quantity of interest is defined as time to minimum creep rate, which correlates well to the creep failure of the material. A deep neural network model was trained and validated to be used as a surrogate for the finite element model. Then, a variance-based sensitivity analysis is performed on the surrogate model to find the Sobol indices of the input parameters in respect to the output quantity of interest. The Sobol indices are used to reduce the dimensionality of the model. Generalized polynomial chaos expansion is used on the reduced basis models to propagate the uncertainty from the input parameters to the quantity of interest using the deep neural network surrogate model. These results are benchmarked against uncertainty propagation using Monte Carlo simulations. In conclusion, the UQ performed through the reduced basis model captures almost all the uncertainty in the model with significantly fewer simulations, making it possible to perform the UQ directly via simulations with the finite element model rather than surrogate machine-learned models.

36 MATERIALS SCIENCE↗