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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Model predictions of latitude-dependent ozone depletion due to aerospace vehicle operations

Results are presented from a two-dimensional model of the stratosphere that simulates the seasonal movement of ozone by both wind and eddy transport, and contains all the chemistry known to be important. The calculated reductions in ozone due to NO2 injection from a fleet of supersonic transports are compared with the zonally averaged results of a three-dimensional model for a similar episode of injection. The agreement is good in the northern hemisphere, but is not as good in the southern hemisphere. Both sets of calculations show a strong corridor effect in that the predicted ozone depletions are largest to the north of the flight corridor for aircraft operating in the northern hemisphere.

Borucki, W. J.↗
NASA NTRS1976

Model predictions of latitude-dependent ozone depletion due to supersonic transport operations

Results are presented from a two-dimensional model of the stratosphere that simulates the seasonal movement of ozone by both wind and eddy transport, and contains all the chemistry known to be important. The calculated reductions in ozone due to NO2 injection from a fleet of supersonic transports are compared with the zonally averaged results of a three-dimensional model for a similar episode of injection. The agreement is good in the northern hemisphere, but is not as good in the southern hemisphere. Both sets of calculations show a strong corridor effect in that the predicted ozone depletions are largest to the north of the flight corridor for aircraft operating in the northern hemisphere.

Borucki, W. J.↗
DOE OSTI2026

Development and assessment of models for turbulent Rayleigh-Taylor mixing using the macroscopic forcing method

Reynolds-Averaged Navier Stokes (RANS) simulations are a popular method for designing ICF experiments, and accurate mixing models are crucial for these simulations to give good predictions. To this end, the present work seeks to demonstrate the Macroscopic Forcing Method (MFM) as a tool for both improving existing RANS models as well as assessing RANS model forms. First, MFM analysis from Lavacot et al. (Phys. Rev. Fluids, 2025) is used to develop the k–L–F model, an extension of the k–L model of Dimonte and Tipton (Phys. Fluids, 2006) that incorporates nonlocality through addition of a turbulent species flux transport equation. MFM is then applied to the k–L–F model along with the k–L and BHR–4 models to assess their forms and compare the model-implied eddy diffusivity moments to those measured from high-fidelity simulations. Furthermore, the analysis reveals that models incorporating nonlocality (k–L–F and BHR–4) match the high-fidelity simulation data better than purely local models (k–L), both in terms of mean fields and eddy diffusivity moments. However, all of the considered RANS models struggle to match temporal moments at high Atwood numbers, highlighting the importance of temporal nonlocality in these regimes and the need for additional improvement even among models incorporating nonlocality.

general physics↗
NASA NTRS1999

Remote Sensing of Smoke and its Direct Radiative Forcing in South-America

Smoke aerosol, organic and black carbon particles suspended in the air, are capable to absorb and reflect solar radiation to space - direct radiative forcing. Depending on the ratio of scattering to absorption, and on the surface spectral properties, the net smoke effect can be warming or cooling of the climate system. Data from the AVIRIS spectrometer flown on the ER-2 at altitude of 20 km, and of the AERONET ground based sun/sky radiometers are used to assess the smoke aerosol optical thickness over the land and the direct radiative forcing at the top of the atmosphere and at ground level, respectively. The data are collected during the SCAR-B experiment in Brazil. In contrast to model results of the smoke effect, we show using the AVIRIS data, that even though the surface albedo is high (0.17 - average on the solar spectrum) and the smoke single scattering albedo is low (0.88), smoke does increase the instantaneous reflectance of sunlight to space by 30-40 W/m(exp 2) for an increase in the smoke optical thickness of 0.5. The AERONET data are used to derive, simultaneously, the effect of smoke on the downward radiation. Models using spectrally averaged smoke optical properties and averaged surface reflectance, predicted very small smoke forcing. The reason is that smoke is mainly opaque in short wavelengths (e.g.< 0.7 micron) while the surface is mainly bright in longer wavelengths (> 0.7 micron). Modeling the effect of smoke on climate forcing cannot be done using the spectrally average properties. It requires the knowledge of the smoke and surface spectral properties.

Kaufman, Yoram↗
NASA NTRS2006

An Initial Non-Equilibrium Porous-Media Model for CFD Simulation of Stirling Regenerators

The objective of this paper is to define empirical parameters (or closwre models) for an initial thermai non-equilibrium porous-media model for use in Computational Fluid Dynamics (CFD) codes for simulation of Stirling regenerators. The two CFD codes currently being used at Glenn Research Center (GRC) for Stirling engine modeling are Fluent and CFD-ACE. The porous-media models available in each of these codes are equilibrium models, which assmne that the solid matrix and the fluid are in thermal equilibrium at each spatial location within the porous medium. This is believed to be a poor assumption for the oscillating-flow environment within Stirling regenerators; Stirling 1-D regenerator models, used in Stirling design, we non-equilibrium regenerator models and suggest regenerator matrix and gas average temperatures can differ by several degrees at a given axial location end time during the cycle. A NASA regenerator research grant has been providing experimental and computational results to support definition of various empirical coefficients needed in defining a noa-equilibrium, macroscopic, porous-media model (i.e., to define "closure" relations). The grant effort is being led by Cleveland State University, with subcontractor assistance from the University of Minnesota, Gedeon Associates, and Sunpower, Inc. Friction-factor and heat-transfer correlations based on data taken with the NASAlSunpower oscillating-flow test rig also provide experimentally based correlations that are useful in defining parameters for the porous-media model; these correlations are documented in Gedeon Associates' Sage Stirling-Code Manuals. These sources of experimentally based information were used to define the following terms and parameters needed in the non-equilibrium porous-media model: hydrodynamic dispersion, permeability, inertial coefficient, fluid effective thermal conductivity (including themal dispersion and estimate of tortuosity effects}, and fluid-solid heat transfer coefficient. Solid effective thermal conductivity (including the effect of tortuosity) was also estimated. Determination of the porous-media model parameters was based on planned use in a CFD model of Infinia's Stirling Technology Demonstration Convertor (TDC), which uses a random-fiber regenerator matrix. The non-equilibrium porous-media model presented is considered to be an initial, or "draft," model for possible incorporation in commercial CFD codes, with the expectation that the empirical parameters will likely need to be updated once resulting Stirling CFD model regenerator and engine results have been analyzed. The emphasis of the paper is on use of available data to define empirical parameters (and closure models) needed in a thermal non-equilibrium porous-media model for Stirling regenerator simulation. Such a model has not yet been implemented by the authors or their associates. However, it is anticipated that a thermal non-equilibrium model such as that presented here, when iacorporated in the CFD codes, will improve our ability to accurately model Stirling regenerators with CFD relative to current thermal-equilibrium porous-media models.

Tew, Roy↗
NASA NTRS2022

A Dynamic Nonlinear Subgrid-Scale Model for Large-Eddy Simulation of Complex Turbulent Flows

We present a new dynamic nonlinear subgrid-scale (SGS) model for large-eddy simulations (LES) and apply it to compute a flow involving pressure gradients, surface curvature and separation, for which data from a direct numerical simulation are available for comparison. The model, inspired by the triple model idea of Bardina et al. (“Improved Turbulence Models Based on Large Eddy Simulation of Homogeneous, Incompressible, Turbulent Flows,” Report No. TF-19, Thermosciences Division, Department of Mechanical Engineering, Stanford University, 1983), includes a Galilean-invariant term called the modified Leonard stress tensor, and two nonlinear terms comprised of the products of the strain-rate and rotation-rate tensors for an improved representation of the subgrid-scale dissipation, backscatter and anisotropy effects. The model does not employ any ad hoc averaging or clipping procedures, and does not require the specification of a characteristic length scale; hence, it naturally avoids the ambiguities associated with defining a proper length scale for anisotropic grids. Results from the wall-resolved LES of flow past a Gaussian bump using the new model demonstrate improved prediction of skin-friction, flow separation, mean flow profiles and turbulent quantities when compared to implicit LES as well as explicit LES using the Vreman SGS model on the same grid.

Turbulence↗
NASA NTRS1992

3-D orbital evolution model of outer asteroid belt

The evolution of minor planets in the outer part of the asteroid belt is considered. In the framework of the semi-averaged elliptic restricted three-dimensional three-body model, the boundary of regions of the Hill's stability is found. As was shown in our work, the Jacobian integral exists.

Solovaya, Nina A.↗
DOE OSTI2025

Investigating late-stage particle production in pp collisions with balance functions

Balance functions have been regarded in the past as a method of investigating the late-stage hadronization found in the presence of a strongly-coupled medium. They are also used to constrain mechanisms of particle production in large and small collision systems. Charge balance functions for inclusive and identified particle pairs are reported as a function of charged particle multiplicity in proton–proton collisions simulated with the PYTHIA8 and the EPOS4 models. The charge balance functions of inclusive, pion, kaon, and proton pairs exhibit amplitudes and shapes that depend on particle species and differ significantly in the two models due to the different particle production mechanisms implemented in PYTHIA and EPOS. The shapes and amplitudes also evolve with multiplicity in both models. In addition, the evolution of the longitudinal rms width and that of balance functions integrals with multiplicity (and average transverse momentum) feature significant differences in the two models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1984

Average ozone profiles for 1979 from the Nimbus 7 SBUV instrument

Monthly average ozone profiles from the first year of operation of the solar backscattered ultraviolet (SBUV) instrument on the Nimbus 7 satellite (November 1978 through October 1979) are tabulated for 10 deg latitude bands from 80 deg S to 80 deg N. For each month and latitude zone layer ozone amounts, standard deviations for 12 Umkehr layers (approximately 0-60 km), and the average total ozone for each zone are listed. The ozone mixing ratio and number density at the center of each layer are also given. It is found that the average SBUV profiles agree with average Umkehr profiles measured at Boulder to within 10 percent over most of the range. The Krueger-Minizner model is found to be a good representative ozone model for 45 deg N, agreeing with an annual average of measured profiles to better than 10 percent between 20 and 60 km.

Mcpeters, R. D.↗
DOE OSTI2020

QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants

Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. Although it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. Here we tested the feasibility of enabling QTG-Finder to work on species that have few or no known causal genes by using orthologs of known causal genes as training set. The model trained with orthologs could recall about 64% of Arabidopsis and 83% of rice causal genes when the top 20% ranked genes were considered, which is similar to the performance of models trained with known causal genes. The average precision was 0.027 for Arabidopsis and 0.029 for rice. We further extended the algorithm to include polymorphisms in conserved non-coding sequences and gene presence/absence variation as additional features. Using this algorithm, QTG-Finder2, we trained and cross-validated Sorghum bicolor and Setaria viridis models. The S. bicolor model was validated by causal genes curated from the literature and could recall 70% of causal genes when the top 20% ranked genes were considered. Furthermore, we applied the S. viridis model and public transcriptome data to prioritize a plant height QTL and identified 13 candidate genes. QTL-Finder2 can accelerate the discovery of causal genes in any plant species and facilitate agricultural trait improvement.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2000

Intercomparison of Models Representing Direct Shortwave Radiative Forcing by Sulfate Aerosols

The importance of aerosols as agents of climate change has recently been highlighted. However, the magnitude of aerosol forcing by scattering of shortwave radiation (direct forcing) is still very uncertain even for the relatively well characterized sulfate aerosol. A potential source of uncertainty is in the model representation of aerosol optical properties and aerosol influences on radiative transfer in the atmosphere. Although radiative transfer methods and codes have been compared in the past, these comparisons have not focused on aerosol forcing (change in net radiative flux at the top of the atmosphere). Here we report results of a project involving 12 groups using 15 models to examine radiative forcing by sulfate aerosol for a wide range of values of particle radius, aerosol optical depth, surface albedo, and solar zenith angle. Among the models that were employed were high and low spectral resolution models incorporating a variety of radiative transfer approximations as well as a line-by-line model. The normalized forcings (forcing per sulfate column burden) obtained with the several radiative transfer models were examined, and the discrepancies were characterized. All models simulate forcings of comparable amplitude and exhibit a similar dependence on input parameters. As expected for a non-light-absorbing aerosol, forcings were negative (cooling influence) except at high surface albedo combined with small solar zenith angle. The relative standard deviation of the zenith-angle-averaged normalized broadband forcing for 15 models-was 8% for particle radius near the maximum in this forcing (approx. 0.2 microns) and at low surface albedo. Somewhat greater model-to-model discrepancies were exhibited at specific solar zenith angles. Still greater discrepancies were exhibited at small particle radii and much greater discrepancies were exhibited at high surface albedos, at which the forcing changes sign; in these situations, however, the normalized forcing is quite small quite small. Discrepancies among the models arise from inaccuracies in Mie calculations, differing treatment of the angular scattering phase function, differing wavelength and angular resolution, and differing treatment of multiple scattering. These results imply the need for standardized radiative transfer methods tailored to the direct aerosol forcing problem. However, the relatively small spread in these results suggests that the uncertainty in forcing arising from the treatment of radiative forcing of a well-characterized aerosol at well-specified surface albedo is smaller than some of the other sources of uncertainty in estimates of direct forcing by anthropogenic sulfate aerosols and anthropogenic aerosols generally.

Boucher, O.↗
NASA NTRS2013

On the Performance of Global Magnetohydrodynamic Models in the Earth's Magnetosphere

We study the performance of four magnetohydrodynamic models (BATS-R-US, GUMICS, LFM, Open GGCM) in the Earths magnetosphere. Using the Community Coordinated Modeling Centers Run-on-Request system, we compare model predictions with magnetic field measurements of the Cluster, Geotail and Wind spacecraft during a multiple substorm event. We also compare model cross polar cap potential results to those obtained from the Super Dual Auroral Radar Network(SuperDARN) and the model magnetopause standoff distances to an empirical magnetopause model. The correlation coefficient (CC) and prediction efficiency (PE) metrics are used to objectively evaluate model performance quantitatively. For all four models, the best performance outside geosynchronous orbit is found on the dayside. Generally, the performance of models decreases steadily down stream from the Earth. On the dayside most CCs are above 0.5 with CCs for Bx and Bz close to 0.9 for three out of four models. In the magnetotail at a distance of about -130 Earth radii from Earth, the prediction efficiency of all models is below that of using an average value for the prediction with the exception of Bz. Bx is most often best predicted and correlated both on the dayside and the nightside close to the Earth whereas in the far tail the CC and PE for Bz are substantially higher than other components in all models. We also find that increasing the resolution or coupling an additional physics module does notautomatically increase the model performance in the magnetosphere.

geomagnetic tail↗
NASA NTRS1981

Comparison of the Nimbus-4 BUV ozone data with the Ames two-dimensional model

Predictions by the Ames two-dimensional model of altitude, latitude and seasonal variations in ozone distribution are compared with the first two years of Nimbus 4 backscattered ultraviolet (BUV) measurements in a preliminary attempt at model verification. The ozone observations consist of mixing ratios on the 1-, 2-, 5-, and 10-mbar pressure surfaces zonally and time averaged to obtain seasonal means for 1970 and 1971. The model is based on chemical reaction and photolysis rate constants recommended by the NASA Panel for Data Evaluation (1979), diurnally averaged for latitudes from 80 deg N to 80 deg S and altitudes from 0 to 60 km with 5 deg horizontal and 2.5 km vertical grid spacings. The large altitude, latitude and seasonal variations observed in the data are found to agree well with model predictions. Examination of the sensitivity of the model predictions to various assumed parameters indicates that improvements in agreement may be obtained by variations of the odd chlorine mixing ratio, odd-nitrogen level and transport parameters.

Borucki, W. J.↗
DOE OSTI2025

Learning error distribution kernel‐enhanced neural network methodology for multi‐intersection signal control optimization

Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)-based models. For example, developing AI-driven dynamic traffic system models that accurately capture high-resolution traffic attributes and formulate robust control algorithms for traffic signal optimization is difficult. Additionally, uncertainties in traffic system modeling and control processes can further complicate traffic signal system controllability. To partially address these challenges, this study presents a novel, hybrid neural network model enhanced with a probability density function kernel shaping technique to formulate traffic system dynamics better and improve comprehensive traffic network modeling and control. The numerical experimental tests were conducted, and the results demonstrate that the proposed control approach outperforms the baseline control strategies and reduces overall average delays by 11.64% on average. By leveraging the capabilities of this innovative model, this study aims to address major challenges related to traffic congestion and energy inefficiency toward more effective and adaptable AI-based traffic control systems.

Wang, Hong [Oak Ridge National Laboratory (ORNL), ↗
DOE OSTI2020

Climate Influences on Capacity Expansion Planning with Application to the Western U.S

Electric power system planners utilize a variety of planning tools to inform decisions concerning generation and transmission additions to the electric grid, the need for operational changes, and to evaluate potential stressors on the system. Numerous factors contribute to the planning process including projected fuel and technology costs, policy and load profiles. There is also a growing recognition of the interdependency of the electric grid with other natural and engineered systems. Here we explore how future climate change and hydropower operability might influence decisions related to electricity capacity expansion planning and operations. To do so we assemble a multi-model framework. Specifically, water resource modeling is used to simulate climate impacts on future water supply for thermoelectric and hydropower generation. Separately, temperature impacts on electricity load are evaluated. Together, these climate factors spatially constrain a capacity expansion model that projects generation and transmission additions to the grid. The projected new capacity-builds are then evaluated on their operations, reliability, and cost under average and extreme climate conditions using production cost modeling. This coupled framework is demonstrated on the electric grid in the Western U.S., supporting capacity expansion planning by WECC, the North American Electric Reliability Corporation (NERC) regional entity responsible for reliability assurance of the Western Interconnection. This region was selected in part because the West is unique in that it has high potential for renewable penetrations and is experiencing large retirements/displacements of baseload resources, primarily coal, leading to possible operational challenges in terms of changing resource mix and the need for resource flexibility. Toward this challenge, planning scenarios encompass a range of alternative energy, climate and drought futures. In this context we explore answers to two strategic questions: 1) How does changing climate influence electricity expansion planning (generation and transmission) and future operations, including type and capacity of new builds, system reliability, cost and environmental impacts? 2) How does the representation of hydropower in the modeling framework influence the evaluation of bulk power system operations? Results indicate that climate has a measurable influence on recommendations concerning the capacity, type and location of new generation and transmission additions, with up to 17 GW additional capacity needed by 2038 to meet peak loads (~6.6% increase over capacity-builds based on historical climate). The extent of additional infrastructure needs is strongly influenced by future water availability for hydropower and the potential deployment of demand response technologies. Systems designed for future climate conditions were found to maintain high system reliability under a range of electricity and water availability scenarios (including significant drought), with minimal system curtailments. Additional capacity needs due to higher load tend to increase cumulative 20-year investment and operating costs by $\$$5-$\$$17 billion and generation costs increase by 9 to 19%. Finally, changing the representation of hydropower flexibility has a relatively small influence on capacity expansion in the Western Interconnection through 2038, but hydropower flexibility impacts generation costs to a similar extent as climate.

13 HYDRO ENERGY↗
NASA NTRS1988

Using data tagging to improve the performance of Kanerva's sparse distributed memory

The standard formulation of Kanerva's sparse distributed memory (SDM) involves the selection of a large number of data storage locations, followed by averaging the data contained in those locations to reconstruct the stored data. A variant of this model is discussed, in which the predominant pattern is the focus of reconstruction. First, one architecture is proposed which returns the predominant pattern rather than the average pattern. However, this model will require too much storage for most uses. Next, a hybrid model is proposed, called tagged SDM, which approximates the results of the predominant pattern machine, but is nearly as efficient as Kanerva's original formulation. Finally, some experimental results are shown which confirm that significant improvements in the recall capability of SDM can be achieved using the tagged architecture.

Rogers, David↗

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