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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Prediction of Hydroxymethylfurfural Yield in Glucose Conversion through Investigation of Lewis Acid and Organic Solvent Effects

Hydroxymethylfurfural (HMF) is one of the important renewable platform compounds that can be obtained from biomass feedstocks through glucose conversion catalyzed by Brønsted and Lewis acids. However, it is challenging to enhance the HMF yield due to side reactions. In this study, a systematic approach combining theory and experiment was performed to investigate the influence of Lewis acids and organic solvents on the HMF yield. For the Lewis acid effect, a relationship between chemical hardness and experimental HMF yields was found in the rate-limiting step of glucose-to-fructose isomerization for six metal chlorides; HMF production was promoted when the metal chloride and a substrate had a similar chemical hardness. To study the organic solvent effect, a multivariate model was developed based on the insights gained from the mechanistic study of fructose dehydration, to predict HMF yields in a given water-organic cosolvent system. It showed a reliable accuracy in evaluating HMF yields with a mean absolute error (MAE) of 3.0% with respect to experimental HMF yields for 13 solvents, and also predicted HMF yields with a MAE of 10.7% for four new solvents. Chemical interpretation of the model revealed that it is desirable to use a solvent capable of stabilizing the carbocation intermediates with low proton transfer activity and high hydrogen bond basicity, to maximize the HMF yield. This multivariate model informs experimentalists about rational selection of solvents with very low computational costs needed to calculate only six variables for each solvent. It can be expanded to other catalytic systems such as heterogeneous Brønsted–Lewis bifunctional catalysts and enables optimization of reaction conditions to obtain other useful platform molecules through biomass conversion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1982

Winter wheat stand density determination and yield estimates from handheld and airborne scanners

Radiance measurements from handheld (Exotech 100-A) and air-borne (Daedalus DEI 1260) radiometers were related to wheat (Triticum aestivum L.) stand densities (simulated winter wheat winterkill) and to grain yield for a field located 11 km northwest of Sidney, Montana, on a Williams loam soil (fine-loamy, mixed Typic Argiborolls) where a semidwarf hard red spring wheat cultivar was needed to stand. Radiances were measured with the handheld radiometer on clear mornings throughout the growing season. Aircraft overflight measurements were made at the end of tillering and during the early stem extension period, and the mid-heading period. The IR/red ratio and normalized difference vegetation index were used in the analysis. The aircraft measurements corroborated the ground measurements inasmuch as wheat stand densities were detected and could be evaluated at an early enough growth stage to make management decision. The aircraft measurements also corroborated handheld measurements when related to yield prediction. The IR/red ratio, although there was some growth stage dependency, related well to yield when measured from just past tillering until about the watery-ripe stage.

Aase, J. K.↗
DOE OSTI2022

A Comparison of Preconstruction and Operational Wake Loss Estimates for Land-Based Wind Plants

Recent studies suggest that biases between wind plant pre-construction energy yield estimates and actual energy production are decreasing over time. However, variability in energy yield prediction accuracy across different projects and wind energy consultants remains high. Wake effects are one of the largest categories comprising the pre-construction energy yield assessment process. To assess the accuracy of wake loss predictions, we compare pre-construction wake loss estimates provided by 8 consultants to the estimated operational wake losses for 10 North American wind plants, as part of the Wind Plant Performance Prediction (WP3) Benchmark project. We estimate operational wake losses using supervisory control and data acquisition (SCADA) data by comparing total wind plant energy production to the potential energy production based on the power produced by freestream wind turbines. In the presentation, we will discuss the overall wake loss prediction bias as well as the project-to-project variability in the prediction accuracy. Further, we will highlight challenges encountered when estimating operational wake losses, including the impact of complex terrain and the presence of neighboring wind plants.

benchmark↗
DOE OSTI2024

STFM: Accurate Spatio-Temporal Fusion Model for Weather Forecasting

Meteorological prediction is crucial for various sectors, including agriculture, navigation, daily life, disaster prevention, and scientific research. However, traditional numerical weather prediction (NWP) models are constrained by their high computational resource requirements, while the accuracy of deep learning models remains suboptimal. In response to these challenges, we propose a novel deep learning-based model, the Spatiotemporal Fusion Model (STFM), designed to enhance the accuracy of meteorological predictions. Our model leverages Fifth-Generation ECMWF Reanalysis (ERA5) data and introduces two key components: a spatiotemporal encoder module and a spatiotemporal fusion module. The spatiotemporal encoder integrates the strengths of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), effectively capturing both spatial and temporal dependencies. Meanwhile, the spatiotemporal fusion module employs a dual attention mechanism, decomposing spatial attention into global static attention and channel dynamic attention. This approach ensures comprehensive extraction of spatial features from meteorological data. The combination of these modules significantly improves prediction performance. Experimental results demonstrate that STFM excels in extracting spatiotemporal features from reanalysis data, yielding predictions that closely align with observed values. In comparative studies, STFM outperformed other models, achieving a 7% improvement in ground and high-altitude temperature predictions, a 5% enhancement in the prediction of the u/v components of 10 m wind speed, and an increase in the accuracy of potential height and relative humidity predictions by 3% and 1%, respectively. This enhanced performance highlights STFM’s potential to advance the accuracy and reliability of meteorological forecasting.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2003

Efficient, Low-Cost Fan System Research for General Aviation and Commuter Aircraft

This document reports research investigations into efficient, low-cost fan system concepts for high bypass turbofans for future general aviation and commuter aircraft. The research specifically addressed lower pressure ratio fans for good propulsive efficiencies in the 200 to 400 knot flight speed regime. Aerodynamic design analyses yielded predicted efficiency in area of 91 to 92 percent (adiabatic). Low-cost manufacturing studies yielded an aluminum blisk rotor and investment cast stator having lowest cost. Structural design analyses yielded a design having excellent vibratory characteristics and the ability to pass One- and Four-pound bird strikes satisfactorily. The low speed and low pressure fans of the study are estimated to have 24 to 30 EPNdB lower community noise levels than larger, high pressure ratio transonic fans.

Merrill, G. L.↗
NASA NTRSDate not supplied

HIFiRE Flight 2 Unstart Reliability Analysis

The objective of this work was to assess the unstart reliability of the HIFiRE Flight 2 system. To do this, a quantification of margins and uncertainties framework was used for comparing model predictions of the predicted combustion induced shock location to the predicted last stable shock location within the isolator. Uncertainty sources included parametric uncertainty in the flight conditions, the heat release model, and turbulence modeling, as well as model verification errors. Additionally, an estimate of model-form uncertainty was established by comparing the model to measured ground test data. A computationally efficient non-intrusive polynomial chaos approach was used to propagate parametric uncertainty through the computational flu-ids dynamics models of both the ground test configuration and the flight vehicle. Compared to direct-connect ground test data, computational fluid dynamics predictions yielded about two duct heights of model-form uncertainty. This was applied to a prediction of flight vehicle unstart margin at the Mach 6.5 flight condition. Building up all of the computational model uncertainty, including parametric uncertainty, verification errors, and the determined model-form uncertainty, a 95% probability level based confidence ratio, or a ratio of total uncertainty to a statistical margin measure, was found to be 0.31 for the flight system.

Thomas K West↗
DOE OSTI2026

Presupernova O–C Shell Mergers Could Produce More 44 Ti than the Explosion

The formation of 44 Ti in massive stars is thought to occur during explosive nucleosynthesis; however, recent studies have shown it can be produced during O–C shell mergers prior to core collapse. We investigate how mixing according to 3D macrophysics derived from hydrodynamic simulations impacts presupernova O–C shell merger nucleosynthesis and if it can dominate explosive supernova production of 44 Ti and other radioactive isotopes. We compare a range of observations and models of explosive 44 Ti yields to preexplosive multizone mixing–burning nucleosynthesis simulations of an O–C shell merger in a 15 M ⊙ Z = 0.02 stellar model with mixing conditions corresponding to different 3D hydrodynamic mixing scenarios. Radioactive species produced in the O shell have a multidex spread in preexplosive yield predictions across different 3D mixing scenarios of 1.54 and 2.14 dex on average, depending on mass cut. 44 Ti has the largest spread of 4.78 and 4.81 dex depending on mass cut. Further, we show that the preexplosive production of 44 Ti can be larger than the explosive production of models and can match observations. Our results also show that 3D mixing physics enhances 44 Ti in 1D models without modifying 56 Ni yields. We conclude that quantitative predictions of 44 Ti and other radioactive species more broadly require an understanding of the 3D hydrodynamic mixing conditions present during the O–C shell merger.

Issa, Joshua [University of Victoria, BC (Canada);↗
NASA NTRS2008

Multiple-Instance Regression with Structured Data

We present a multiple-instance regression algorithm that models internal bag structure to identify the items most relevant to the bag labels. Multiple-instance regression (MIR) operates on a set of bags with real-valued labels, each containing a set of unlabeled items, in which the relevance of each item to its bag label is unknown. The goal is to predict the labels of new bags from their contents. Unlike previous MIR methods, MI-ClusterRegress can operate on bags that are structured in that they contain items drawn from a number of distinct (but unknown) distributions. MI-ClusterRegress simultaneously learns a model of the bag's internal structure, the relevance of each item, and a regression model that accurately predicts labels for new bags. We evaluated this approach on the challenging MIR problem of crop yield prediction from remote sensing data. MI-ClusterRegress provided predictions that were more accurate than those obtained with non-multiple-instance approaches or MIR methods that do not model the bag structure.

learning↗
NASA NTRS1990

A macro-micromechanics analysis of a notched metal matrix composite

A macro-micromechanics analysis was formulated to determine the matrix and fiber behavior near the notch tip in a center-notched metal matrix composite. Results are presented for a boron/aluminum monolayer. The macro-level analysis models the entire notched specimen using a three dimensional finite element program which uses the vanishing-fiber-diameter model to model the elastic-plastic behavior of the matrix and the elastic behavior of the fiber. The micro-behavior is analyzed using a Discrete Fiber-Matrix (DFM) model containing one fabric and the surrounding matrix. The dimensions of the DFM model were determined by the ply thickness and the fiber volume fraction and corresponded to the size of the notch-tip element in the macro-level analysis. The boundary conditions applied to the DFM model were determined from the macro-level analysis. Stress components within the DFM model were calculated and stress distributions are presented along selected planes and surfaces within the DFM model, including the fiber-matrix interface. Yielding in the matrix was examined at the notch tip in both the macro- and micro-level analyses. The DFM model predicted higher stresses (24 percent) in the fiber compared to the global analysis. In the notch-tip element, the interface stresses indicated that a multi-axial criterion may be required to predict interfacial failure. The DFM analysis predicted yielding to initiate in the notch-tip element at a stress level 28 percent lower than predicted by the global analysis.

Bigelow, C. A.↗
DOE OSTI2024

Nitrogen fertilization effects on aged Miscanthus × giganteus stands: Exploring biomass yield, yield components, and biomass prediction using in–season morphological traits

For sustainable biomass production of Miscanthus × giganteus (hereafter miscanthus), understanding the impact of stand age and nitrogen (N) fertilization on biomass yield is crucial. This study investigated the effects of varying N fertilization rates (0, 56, 112, and 168 kg N ha –1 ) on yield components (tiller height, density, and weight) and their correlations with end-of-season biomass yield in miscanthus. We also explored end-of-season biomass yield prediction using in-season traits (canopy height, leaf area index, and leaf chlorophyll content [LCC]). The study was conducted at two sites in Illinois: a previously unfertilized 10-year-old miscanthus research stand at Urbana and a 16-year-old commercial stand at Pesotum with a history of annual 56N application. Results from 2018 to 2021 in Urbana and 2020 to 2021 in Pesotum showed increased biomass yields with N fertilization, varying by rate, year, and location. Biomass yield in Pesotum peaked at 56N, while in Urbana, it increased significantly at 112 kg N ha –1 . Biomass yield was strongly correlated with tiller height and weight measured at Urbana across N rates. Morphological traits measured every 2–3 weeks during the 2020 and 2021 growing seasons showed that canopy height was the strongest single predictor of miscanthus biomass yield, followed by LCC. Mid-August to September measurements of these traits were the best predictors of biomass yield. Multiple regressions involving the canopy height and LCC further improved yield predictions. We conclude that while N enhances biomass yields of aging miscanthus, the optimum rate depends on the site, environmental conditions, and management history.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1996

Long-Wavelength Instability in Marangoni Convection

Our experiments in thin liquid layers (approximately 0.1 mm thick) heated from below reveal a well-defined long-wavelength instability: at a critical temperature difference across the layer, the depth of the layer in the center of the cell spontaneously decreases until the liquid-air interface ruptures and a dry spot forms. The onset of this critical instability occurs at a temperature difference across the liquid layer that is 35% smaller than that predicted in earlier theoretical studies of a single layer model. Our analysis of a two-layer model yields predictions in accord with the observations for liquid layer depths greater than or equal to 0.15 mm, but for smaller depths there is an increasing difference between our predictions and observations (the difference is 25% for a layer 0.06 mm thick). In microgravity environments the long-wavelength instability observed in our terrestrial experiments is expected to replace cellular convection as the primary instability in thick as well as thin liquid layers heated quasistatically from below.

VanHook, Stephen J.↗
DOE OSTI2025

Conformal Hierarchical Simulation-Based Inference with Local Validity

Trustworthy and interpretable uncertainty quantification is a long-standing challenge in artificial intelligence. Simulation-based inference (SBI) comprises a broad swath of approaches for estimating latent parameters with uncertainties. Although flexible neural density estimators in SBI can be remark- ably expressive capturing highly structured, high-dimensional posteriors their credible regions can be badly mis-calibrated and are often only accompanied by heuristic coverage checks. We present the first SBI framework that delivers finite-sample local valid coverage guarantees that hold in the neighborhood of each observation. Our framework can couple any off-the-shelf hierarchical SBI engine with a confor- mal Bayesian post-processing step that operates on the posterior predictive density. A kernel-weighted conformity score adapts the conformal quantile to the local geometry of the data, yielding prediction sets that are simultaneously (i) marginally calibrated, (ii) locally valid, and (iii) hierarchical, handling global and observation-specific parameters in a single pass. Through experiments on synthetic data and benchmarks from neuroscience and physics, we show that our approach attains 1 − α coverage, where prior SBI methods under- or over-cover. Our approach also maintains a competitive, credible set size with minimal computational overhead. Finally, our approach can be used to make predictions on real data and give valid credible regions modulo weight-initialization-based model mis-specification.

Trivedi, Shubhendu [Fermilab]↗
NASA NTRS1976

Wheat productivity estimates using LANDSAT data

The author has identified the following significant results. Objective measurements of percent green wheat cover on May 21 were significantly correlated with yield, as were measurements of green LAI and LANDSAT data. Three data sets from the Finney test site were analyzed from LANDSAT passes on 22 November 1974, 15 April 1975, and 21 May 1975. After mean signal values in each band were computed for each sufficiently large wheat field, the mean values were correlated with the farmer estimates of wheat grain yield in order to assess relative information content. It is clear that the single best spectral temporal band for predicting yield is the 15 April red band (0.6-0.7 microns, band 5), with the 15 April green band (0.5-0.6 microns, band 4) a close second.

Nalepka, R. F.↗
DOE OSTI2022

Long‐term yields in annual and perennial bioenergy crops in the Midwestern United States

Abstract Many yield predictions in perennial bioenergy species have been made based on data collected during the establishment phase of growth or a limited number of long‐term studies. Few studies compare multiple perennial crops with the dominant agricultural vegetation of the landscape over long time periods. Here, we present the results of 11 years of perennial crop management on fertile agricultural soils in central Illinois, compared with conventional row crop maize/soybean ( Zea mays L., Glycine max L.) production. We examined the long‐term productivity and drought susceptibility of Miscanthus x giganteus Greef et. Deu. ex. Hodkinson et Renvoize (miscanthus), Panicum virgatum L., Cave‐in‐Rock cultivar (switchgrass), and a native prairie mix, in contrast to annual maize/soybean agriculture. Long‐term yields for miscanthus and switchgrass failed to reach initial predictions made during the establishment phase; however, in miscanthus, the 11 th year of production shows little progressive yield loss with age, exceeding the modeled limit for the onset of age‐related decline. Harvest timing and differences in yields from hand and machine harvests in perennial crops likely contribute to overestimates of potential yields. Application of fertilizer to mature miscanthus resulted in significant increases in yield after a severe drought, though modeled effects of management and drought in miscanthus point to a more complex mechanism for yield response.

09 BIOMASS FUELS↗
DOE OSTI2022

Editorial: Artificial Intelligence and Machine Learning Applications in Plant Genomics and Genetics

In plants, as in other species, genotypic variation in combination with environmental variation leads to differences in the biochemical makeup of cells, measurable as molecular phenotypes. These in turn influence physiological and developmental traits such as organ formation and plant growth, and eventually traits relevant in agriculture, such as yield and stress tolerance. Thus, relating genotypes to phenotypes yields fundamental insights into the regulation of important processes in plant development and physiology, but also provides the ability to predict yield and quality traits in specific environments, which is essential in basic plant science and molecular breeding of resilient plants in changing environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Multiaxial Plastic Deformation of Zircaloy-4 Nuclear Fuel Cladding Tubes

Here, this work is motivated by the desire to devise an internal pressure test that can mimic a displacement-controlled loading scenario and demonstrate how to apply the multiaxial stress and strain data from the test to develop an elastic/plastic constitutive model for a thin-walled tubular component. This is achieved by conducting simultaneous measurements of tangential and axial strain during the pressure test and integrating these strain measures into a feedback loop with the pressure controller. It is shown how data from such a test can be used to develop a large mechanical property data set relevant to biaxial loading conditions. The data obtained have high confidence evidenced by their low variability and alignment with other literature studies. Additionally, data from these internal pressure tests combined with full-tube axial tensile tests allow for the derivation of the Hill anisotropic yield function. The developed Hill yield function is validated by comparing the plastic strain ratios from the full-tube tension tests and by comparing the predicted yield stress in the tangential direction with measured values from ring tension tests in a previous study.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2007

PREDICTS

PREDICTS is a computer program that predicts the frequencies, as functions of time, of signals to be received by a radio science receiver in this case, a special-purpose digital receiver dedicated to analysis of signals received by an antenna in NASA s Deep Space Network (DSN). Unlike other software used in the DSN, PREDICTS does not use interpolation early in the calculations; as a consequence, PREDICTS is more precise and more stable. The precision afforded by the other DSN software is sufficient for telemetry; the greater precision afforded by PREDICTS is needed for radio-science experiments. In addition to frequencies as a function of time, PREDICTS yields the rates of change and interpolation coefficients for the frequencies and the beginning and ending times of reception, transmission, and occultation. PREDICTS is applicable to S-, X-, and Ka-band signals and can accommodate the following link configurations: (1) one-way (spacecraft to ground), (2) two-way (from a ground station to a spacecraft to the same ground station), and (3) three-way (from a ground transmitting station to a spacecraft to a different ground receiving station).

Zhou, Hanying↗
NASA NTRS1979

Regressions by leaps and bounds and biased estimation techniques in yield modeling

The author has identified the following significant results. It was observed that OLS was not adequate as an estimation procedure when the independent or regressor variables were involved in multicollinearities. This was shown to cause the presence of small eigenvalues of the extended correlation matrix A'A. It was demonstrated that the biased estimation techniques and the all-possible subset regression could help in finding a suitable model for predicting yield. Latent root regression was an excellent tool that found how many predictive and nonpredictive multicollinearities there were.

Marquina, N. E.↗