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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Importance of an Ensemble Approach for Modeling Aerosol‐Convection Interactions

Aerosol-convection interactions modulate cloud microphysics, thermodynamics, and updraft intensity, contributing to climate-scale aerosol-radiative forcing. However, quantifying aerosol indirect effects in mixed-phase deep convection remains challenging due to uncertainties in parameterized physics and initial conditions driving nonlinear evolution of convective processes. This study investigates the convective updraft sensitivity to random initial temperature perturbations using an idealized ensemble modeling framework informed by in situ thermodynamic and aerosol observations from the DOE TRACER field campaign. We analyze the impact of small-scale initial perturbations on updraft velocity and supersaturation, and determine the ensemble size required to minimize stochastic internal variability. Results show that minor thermodynamic perturbations can produce updraft variability comparable to aerosol-induced changes reported in prior work. An ensemble of 10 members sufficiently reduces variability, enabling robust investigation of aerosol-related updraft invigoration and informing the design of future ensemble-based aerosol-convection interaction studies in environments with significant spatiotemporal mesoscale thermodynamic and aerosol heterogeneity.

aerosol-convection interactions↗
DOE OSTI2022

Effects of random forest modeling decisions on biogeochemical time series predictions

Abstract Random forests (RF) are an increasingly popular machine learning approach used to model biogeochemical processes in the Earth system. While RF models are robust to many assumptions that complicate deterministic models, there are several important parameterization decisions for appropriate use and optimal model fit. We explored the role that parameter decisions, including training/testing data splitting strategies, variable selection, and hyperparameters play on RF goodness‐of‐fit by constructing models using 1296 unique parameter combinations to predict concentrations of nitrate, a key nutrient for biogeochemical cycling in aquatic ecosystems. Models were built on long‐term, publicly available water quality and meteorology time series collected by the National Estuarine Research Reserve monitoring network for two contrasting ecosystems representing freshwater and brackish estuaries. We found that accounting for temporal dependence when splitting data into training and testing subsets was key for avoiding over‐estimation of model predictive power. In addition, variable selection, the ratio of training to testing data, and to a lesser degree, variables per split and number of trees, were significant parameters for optimizing RF goodness‐of‐fit. We also explored how model parameter decisions influenced interpretation of the relative importance of predictors to the model, and model predictor‐dependent variable relationships, with results suggesting that both data structure and model parameterization influence these factors. Because much of the current RF literature is written for the computational and statistical science communities, the primary goal of this study is to provide guidelines for aquatic scientists new to machine learning to apply RF techniques appropriately to aquatic biogeochemical datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

As a genre of physics-informed machine learning, differentiable process-based hydrologic models (abbreviated as δ or delta models) with regionalized deep-network-based parameterization pipelines were recently shown to provide daily streamflow prediction performance closely approaching that of state-of-the-art long short-term memory (LSTM) deep networks. Meanwhile, δ models provide a full suite of diagnostic physical variables and guaranteed mass conservation. Here, we ran experiments to test (1) their ability to extrapolate to regions far from streamflow gauges and (2) their ability to make credible predictions of long-term (decadal-scale) change trends. We evaluated the models based on daily hydrograph metrics (Nash–Sutcliffe model efficiency coefficient, etc.) and predicted decadal streamflow trends. For prediction in ungauged basins (PUB; randomly sampled ungauged basins representing spatial interpolation), δ models either approached or surpassed the performance of LSTM in daily hydrograph metrics, depending on the meteorological forcing data used. They presented a comparable trend performance to LSTM for annual mean flow and high flow but worse trends for low flow. For prediction in ungauged regions (PUR; regional holdout test representing spatial extrapolation in a highly data-sparse scenario), δ models surpassed LSTM in daily hydrograph metrics, and their advantages in mean and high flow trends became prominent. In addition, an untrained variable, evapotranspiration, retained good seasonality even for extrapolated cases. The δ models' deep-network-based parameterization pipeline produced parameter fields that maintain remarkably stable spatial patterns even in highly data-scarce scenarios, which explains their robustness. Combined with their interpretability and ability to assimilate multi-source observations, the δ models are strong candidates for regional and global-scale hydrologic simulations and climate change impact assessment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Minimizing thickness variation in monolithic U-10Mo fuel foil and Zr interlayer during hot rolling: A microstructure-based finite element method analysis

Low-enriched uranium alloyed with 10 wt. % molybdenum (U-10Mo) has been identified as a promising alternative to highly enriched uranium fuel for the United States’ high performance research reactors. The monolithic U-10Mo fuel plate consists of a metallic U-10Mo fuel foil with a 25 µm Zr interlayer and a relatively thick cladding of aluminum alloy 6061. The Zr interlayer is typically applied during the hot co-rolling process, and this process dictates the uniformity of the Zr interlayer. Thickness variation observed in the U-10Mo and Zr interlayer has been attributed to several sources: the initial grain size of the U-10Mo castings, can materials, rolling temperature, inhomogeneous molybdenum content, and porosity in the cast U-10Mo. This thickness variation limits the ability to meet the dimensional specification; thus, a better understanding of the factors causing the nonuniform thickness is needed. In this work, we used a novel, microstructure-based finite element method to model the hot rolling process to address these concerns. Grain microstructures in U-10Mo were tessellated and explicitly considered in the finite element model. Each grain was assigned a random material property to mimic the grain strength variations induced by different grain orientations. Simulations were performed using six steel can thicknesses, four grain sizes, and with or without a Zr interlayer to investigate the influences of those variables on the thickness nonuniformity. The simulation results showed that a thinner steel can and finer U-10Mo grain size reduce thickness variations in both the U-10Mo fuel foil and Zr interlayer. The direct findings from the simulations and analysis can be used to optimize the hot rolling schedule, reduce fabrication defects, and meet the dimensional specifications. The proposed microstructure-based finite element model can be also coupled with experimental microstructure characterization data, images, and models to simulate multi-pass hot rolling.

36 MATERIALS SCIENCE↗
NASA NTRS1977

An ILLIAC program for the numerical simulation of homogeneous incompressible turbulence

An algorithm and ILLIAC computer program, developed for the simulation of homogeneous incompressible turbulence in the presence of an applied mean strain, are described. The turbulence field is represented spatially by a truncated triple Fourier series (spectral method) and followed in time using a fourth-order Runge-Kutta algorithm. These include: (1) transformation of variables suggested by Taylor's sudden-distortion theory; (2) implicit viscous diffusion by use of an integrating factor; (3) implicit pressure calculation suggested by Taylor's sudden-distortion theory, and (4) inexpensive control of aliasing by random and phased coordinate shifts.

Rogallo, R. S.↗
NASA NTRS1977

Driver steering dynamics measured in car simulator under a range of visibility and roadmaking conditions

A simulation experiment was conducted to determine the effect of reduced visibility on driver lateral (steering) control. The simulator included a real car cab and a single lane road image projected on a screen six feet in front of the driver. Simulated equations of motion controlled apparent car lane position in response to driver steering actions, wind gusts, and road curvature. Six drivers experienced a range of visibility conditions at various speeds with assorted roadmaking configurations (mark and gap lengths). Driver describing functions were measured and detailed parametric model fits were determined. A pursuit model employing a road curvature feedforward was very effective in explaining driver behavior in following randomly curving roads. Sampled-data concepts were also effective in explaining the combined effects of reduced visibility and intermittent road markings on the driver's dynamic time delay. The results indicate the relative importance of various perceptual variables as the visual input to the driver's steering control process is changed.

Allen, R. W.↗
NASA NTRS1986

Nonlinear system controller design based on domain of attaction: An application to CELSS analysis and control

Nonlinear system controller design based on the domain of attraction is presented. This is particularly suited to investigating Closed Ecological Life Support Systems (CELSS) models. In particular, the dynamic consequences of changes in the waste storage capacity and system mass, and how information is used for control in CELSS models are examined. The models' high dimensionality and nonlinear state equations make them difficult to analyze by any other technique. The domain of attraction is the region in initial conditions that tend toward an attractor and it is delineated by randomly selecting initial conditions from the region of state space being investigated. Error analysis is done by repeating the domain simulations with independent samples. A refinement of this region is the domain of performance which is the region of initial conditions meeting a performance criteria. In nonlinear systems, local stability does not insure stability over a larger region. The domain of attraction marks out this stability region; hence, it can be considered a measure of a nonlinear system's ability to recovery from state perturbations. Considering random perturbations, the minimum radius of the domain is a measure of the magnitude of perturbations for which recovery is guaranteed. Design of both linear and nonlinear controllers are shown. Three CELSS models, with 9 to 30 state variable, are presented. Measures of the domain of attraction are used to show the global behavior of these models under a variety of design and controller scenarios.

Babcock, P. S., IV↗
NASA NTRS1987

The correspondence of surface climate parameters with satellite and terrain data

One of the goals of the research was to develop a ground sampling stragegy for calibrating remotely sensed measurements of surface climate parameters. The initial sampling strategy involved the stratification of the terrain based on important ancillary surface variables such as slope, exposure, insolation, geology, drainage, fire history, etc. For a spatially heterogeneous population, sampling error is reduced and efficiency increased by stratification of the landscape into more homogeneous sub-areas and by employing periodic random spacing of samples. These concepts were applied in the initial stratification of the study site for the purpose of locating and allocating instrumentation.

Dozier, Jeff↗
NASA NTRS1989

Comment on 'Distribution of whistler mode bursts at Venus' by F.L. Scarf, K.F. Jordan, and C.T. Russell

Consideration is given to the study of the 100-Hz electric field noise observed from the Pioneer Venus Orbiter (PVO) by Scarf et al. (1987). It is suggested that the study incorrectly interprets the results as evidence for lightning and volcanism at Venus. Statistical evidence is presented, suggesting that the distribution of the noise is essentially random, within the zone of occurrence of ion troughs, and decreases at the lowest PVO altitudes, closest to the neutral atmosphere. It is found that the topographic clustering reported by Scarf et al. is an artifact of the variable spatial distribution of troughs and associated noise.

Taylor, Harry A., Jr.↗
NASA NTRS1990

Oscillating flow loss test results in Stirling engine heat exchangers

The results are presented for a test program designed to generate a database of oscillating flow loss information that is applicable to Stirling engine heat exchangers. The tests were performed on heater/cooler tubes of various lengths and entrance/exit configurations, on stacked and sintered screen regenerators of various wire diameters and on Brunswick and Metex random fiber regenerators. The test results were performed over a range of oscillating flow parameters consistent with Stirling engine heat exchanger experience. The tests were performed on the Sunpower oscillating flow loss rig which is based on a variable stroke and variable frequency linear drive motor. In general, the results are presented by comparing the measured oscillating flow losses to the calculated flow losses. The calculated losses are based on the cycle integration of steady flow friction factors and entrance/exit loss coefficients.

Koester, G.↗
NASA NTRS1990

Multiversion software reliability through fault-avoidance and fault-tolerance

In this project we have proposed to investigate a number of experimental and theoretical issues associated with the practical use of multi-version software in providing dependable software through fault-avoidance and fault-elimination, as well as run-time tolerance of software faults. In the period reported here we have working on the following: We have continued collection of data on the relationships between software faults and reliability, and the coverage provided by the testing process as measured by different metrics (including data flow metrics). We continued work on software reliability estimation methods based on non-random sampling, and the relationship between software reliability and code coverage provided through testing. We have continued studying back-to-back testing as an efficient mechanism for removal of uncorrelated faults, and common-cause faults of variable span. We have also been studying back-to-back testing as a tool for improvement of the software change process, including regression testing. We continued investigating existing, and worked on formulation of new fault-tolerance models. In particular, we have partly finished evaluation of Consensus Voting in the presence of correlated failures, and are in the process of finishing evaluation of Consensus Recovery Block (CRB) under failure correlation. We find both approaches far superior to commonly employed fixed agreement number voting (usually majority voting). We have also finished a cost analysis of the CRB approach.

Vouk, Mladen A.↗
NASA NTRS1998

Cloud Scaling Properties and Cloud Parameterization

Cloud liquid and cloud traction variability is studied as a function of horizontal scale in the ECMWF forecast model during several 10-day runs at the highest available model resolution, recently refined from approximately 60 km (T213) down to approximately 20 km (T639). At higher resolutions, model plane-parallel albedo biases are reduced, so that models may be tuned to have larger, more realistic, cloud liquid water amounts, However, the distribution of cloud liquid assumed -within- each gridbox, for radiative and thermodynamic computations, depends on ad hoc assumptions that are not necessarily consistent with observed scaling properties, or with scaling properties produced by the model at larger scales. To study the larger-scale cloud properties, ten locations on the Earth are chosen to coincide with locations having considerable surface data available for validation, and representing a variety of climatic regimes, scaling exponents are determined from a range or scales down to model resolution, and are re-computed every three hours, separately for low, medium and high clouds, as well as column-integrated cloudiness. Cloud variability fluctuates in time, due to diurnal, synoptic and other' processes, but scaling exponents are found to be relatively stable. various approaches are considered for applying computed cloud scaling to subgrid cloud distributions used for radiation, beyond simple random or maximal overlap now in common use. Considerably more work is needed to compare model cloud scaling with observations. This will be aided by increased availability of high-resolution surface, aircraft and satellite data, and by the increasing resolution of global models,

Cahalan, R. F.↗
NASA NTRS2001

Constraints on PSC Particle Microphysics Derived From Lidar Observations

Based on extensive T-matrix computations of light scattering by polydispersions of randomly oriented, rotationally symmetric nonspherical particles, we analyze existing lidar observations of polar stratospheric clouds (PSCs) and derive several constraints on PSC particle microphysical properties. We show that sharp-edged nonspherical particles (finite circular cylinders) exhibit less variability of lidar backscattering characteristics with particle size and aspect ratio than particles with smooth surfaces (spheroids). For PSC particles significantly smaller than the wavelength, the backscatter color index Alpha and the depolarization color index Beta are essentially shape-independent. Observations for type Ia PSCs can be reproduced by spheroids with aspect ratios larger than 1.2, oblate cylinders with diameter-to-length ratios greater than 1.6, and prolate cylinders with length-to-diameter ratios greater than 1.4. The effective equal-volume-sphere radius for type la PSCs is about 0.8 microns or larger. Type Ib PSCs are likely to be composed of spheres or nearly spherical particles with effective radii smaller than 0.8 microns. Observations for type II PSCs are consistent with large ice crystals (effective radius greater than 1 micron modeled as cylinders or prolate spheroids.

Liu, Li↗
NASA NTRS2014

Global-Scale Comparison of Passive (SMOS) and Active (ASCAT) Satellite Based Microwave Soil Moisture Retrievals with Soil Moisture Simulations (MERRA-Land)

Global surface soil moisture (SSM) datasets are being produced based on active and passive microwave satellite observations and simulations from land surface models (LSM). This study investigates the consistency of two global satellite-based SSM datasets based on microwave remote sensing observations from the passive Soil Moisture and Ocean Salinity (SMOS;SMOSL3 version 2.5) and the active Advanced Scatterometer (ASCAT; version TUWien- WARP 5.5) with respect to LSM SSM from the MERRA-Land data product. The relationship between the global-scale SSM products was studied during the 2010-2012 period using (1) a time series statistics (considering both original SSM data and anomalies), (2) a space-time analysis using Hovmoller diagrams, and (3) a triple collocation error model. The SMOSL3 and ASCAT retrievals are consistent with the temporal dynamics of modeled SSM (correlation R (is) greater than 0.70 for original SSM) in the transition zones between wet and dry climates, including the Sahel, the Indian subcontinent, the Great Plains of North America, eastern Australia, and southeastern Brazil. Over relatively dense vegetation covers, a better consistency with MERRA-Land was obtained with ASCAT than with SMOSL3. However, it was found that ASCAT retrievals exhibit negative correlation versus MERRA-Land in some arid regions (e.g., the Sahara and the Arabian Peninsula). In terms of anomalies, SMOSL3 better captures the short term SSM variability of the reference dataset (MERRA-Land) than ASCAT over regions with limited radio frequency interference (RFI) effects (e.g., North America, South America, and Australia). The seasonal and latitudinal variations of SSM are relatively similar for the three products, although the MERRALand SSM values are generally higher and their seasonal amplitude is much lower than for SMOSL3 and ASCAT. Both SMOSL3 and ASCAT have relatively comparable triple collocation errors with similar spatial error patterns: (i) lowest errors in arid regions (e.g., Sahara and Arabian Peninsula), due to the very low natural variability of soil moisture in these areas, and Central America, and (ii) highest errors over most of the vegetated regions (e.g., northern Australia, India, central Asia, and South America). However, the ASCAT SSM product is prone to larger random errors in some regions (e.g., north-western Africa, Iran, and southern South Africa). Vegetation density was found to be a key factor to interpret the consistency with MERRA-Land between the two remotely sensed products (SMOSL3 and ASCAT) which provides complementary information on SSM. This study shows that both SMOS and ASCAT have thus a potential for data fusion into long-term data records.

ASCAT↗
NASA NTRSDate not supplied

Planning Bias: Planning as a Source of Sampling Bias

Many data-driven planning methods are trained on data generated by planners. It is well known that many statistical learning methods are sensitive to sampling bias, and yet there has been little or no attention to planning as a sampling method and its role in introducing sampling bias into planner-generated training data. Recently, it has been demonstrated that A**,* in the presence of problems with variable heuristic error, prefers some solutions over other equally cost-optimal solutions. But, as we discuss in this paper, mitigation may not be as simple as resolving arbitrary tie-breaking by sampling from ties uniformly at random. In this paper, we formalize an intuition of planning bias. We focus on problems which output a single solution. Diverse planning only complicates the problem by generalizing it to bias in the set of sets; we show how it is subject to bias in the single solution. We make some useful observations about deterministic algorithms in contrast to non-deterministic algorithms. We explain how information entropy may be a good way to measure planning bias, and discuss some issues in evaluating practical approaches to measurement. We address the intuition that uniform random tiebreaking should mitigate bias; and sketch a novel approach to constructing an appropriate random distribution for duplicate detection during forward search for unbiased A*. Finally, we suggest directions for future work.

Planning Scheduling Algorithms↗
DOE OSTI2025

Achievable Rates for Concatenated Square Gottesman-Kitaev-Preskill Codes

The Gottesman-Kitaev-Preskill (GKP) codes are known to achieve optimal rates under displacement noise and pure-loss channels, which establishes theoretical foundations for its optimality. However, such optimal rates are only known to be achieved at a discrete set of noise strengths with the current self-dual symplectic lattice construction. In this work, we develop a new coding strategy using concatenated continuous variable-discrete variable encodings to go beyond past results and establish GKP’s optimal rate over all noise strengths. In particular, for displacement noise, the rate is obtained through a constructive approach by concatenating GKP codes with a quantum polar code and analog decoding. For a pure-loss channel, we prove the existence of capacity-achieving GKP codes through a random coding approach. These results highlight the capability of concatenation-based GKP codes and provides new methods for constructing good GKP lattices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Contributions of vegetation heterogeneity within tower footprint to CO 2 flux estimations through graph neural network modeling

Net ecosystem exchange of CO 2 (Fc) measured directly by eddy covariance towers is based on various assumptions, including large, flat and homogenous land cover type. In reality, often a tower site is not large enough for flux measurements, and landscapes consist of patches of different land cover types within the flux footprint. In addition, some portions of fluxes are contributed by different cover types when a footprint exceeds the size of the target ecosystem. The contributions of non-dominant patches to Fc are often ignored. Here, in this study, we propose a novel integrated modeling framework that combines random forest (RF) and XGBoost with a residual correction module based on a deep graph convolutional network (DeeperGCN) to simulate Fc for seven flux measurement sites in southwest Michigan. High-resolution remote sensing vegetation indices, soil properties, meteorological variables, and footprint-weighted spatial features were used as model inputs at three spatial resolutions (10 m, 20 m, 30 m), and their importance in predicting Fc with DeeperGCN was assessed. We found that residual correction using DeeperGCN significantly improved prediction accuracy, with the R 2 increasing from 0.9098 to 0.9479 for RF and from 0.9235 to 0.9433 for XGBoost. At site level, the maximum improvement in R 2 reached 0.1617. Paired t-tests confirmed that these improvements were statistically significant (p < 0.05). Among all predictors, leaf area index and incoming shortwave radiation emerged as the dominant drivers of spatial residual variation, followed by precipitation, relative humidity, and selected vegetation indices. The 20 m resolution yielded the best balance between model performance and computational efficiency. In conclusion, our modeling framework effectively captures both spatial heterogeneity and nonlinear interactions, offering a robust solution for spatially explicit flux modeling in structurally diverse ecosystems beyond the study sites.

footprint model↗
DOE OSTI2026

High-Performance Semiempirical Excited-State Molecular Dynamics Powered by Graphics Processing Units

Here, this Letter introduces excited-state molecular dynamics in PYSEQM, a GPU-accelerated semiempirical quantum chemistry engine implemented in PyTorch. The new module enables Born–Oppenheimer molecular dynamics (BOMD) using configuration-interaction singles and random phase approximation for excited states, allowing long trajectories and large statistical ensembles to be simulated efficiently on a single GPU. We also implement an extended Lagrangian excited-state BOMD (XL-ESMD) scheme that propagates auxiliary electronic variables, enabling relaxed ground and excited-state convergence thresholds without compromising energy conservation. The excited-state BOMD implementation scales smoothly from small chromophores to a nearly 900-atom dendrimer (taking 6.5 s per MD step). PYSEQM also supports batched execution, allowing many geometries or trajectories to be evaluated in a single GPU launch, substantially increasing throughput and making ensemble-based protocols routine. As a demonstration, we compute absorption, emission, and infrared spectra from trajectories propagated on the ground and first excited states. The XL-ESMD scheme yields identical spectra at significantly lower computational cost, establishing the role of extended Lagrangian based dynamics for efficient excited-state BOMD simulations. Beyond raw performance, PYSEQM’s PyTorch foundation provides automatic differentiation for forces, efficient GPU batching, and seamless interfacing with machine learning models. These capabilities position PYSEQM as a practical platform for machine learning-augmented excited-state dynamics and lay the foundation for future data-driven nonadiabatic excited-state dynamics modeling of ultrafast spectroscopic probes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗