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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Enhanced spectral efficiency using bandwidth switchable SAW filtering for mobile satellite communications systems

Currently proposed mobile satellite communications systems require a high degree of flexibility in assignment of spectral capacity to different geographic locations. Conventionally this results in poor spectral efficiency which may be overcome by the use of bandwidth switchable filtering. Surface acoustic wave (SAW) technology makes it possible to provide banks of filters whose responses may be contiguously combined to form variable bandwidth filters with constant amplitude and phase responses across the entire band. The high selectivity possible with SAW filters, combined with the variable bandwidth capability, makes it possible to achieve spectral efficiencies over the allocated bandwidths of greater than 90 percent, while retaining full system flexibility. Bandwidth switchable SAW filtering (BSSF) achieves these gains with a negligible increase in hardware complexity.

Peach, Robert↗
NASA NTRS1992

Evolution of catalytic RNA in the laboratory

We are interested in the biochemistry of existing RNA enzymes and in the development of RNA enzymes with novel catalytic function. The focal point of our research program has been the design and operation of a laboratory system for the controlled evolution of catalytic RNA. This system serves as working model of RNA-based life and can be used to explore the catalytic potential of RNA. Evolution requires the integration of three chemical processes: amplification, mutation, and selection. Amplification results in additional copies of the genetic material. Mutation operates at the level of genotype to introduce variability, this variability in turn being expressed as a range of phenotypes. Selection operates at the level of phenotype to reduce variability by excluding those individuals that do not conform to the prevailing fitness criteria. These three processes must be linked so that only the selected individuals are amplified, subject to mutational error, to produce a progeny distribution of mutant individuals. We devised techniques for the amplification, mutation, and selection of catalytic RNA, all of which can be performed rapidly in vitro within a single reaction vessel. We integrated these techniques in such a way that they can be performed iteratively and routinely. This allowed us to conduct evolution experiments in response to artificially-imposed selection constraints. Our objective was to develop novel RNA enzymes by altering the selection constraints in a controlled manner. In this way we were able to expand the catalytic repertoire of RNA. Our long-range objective is to develop an RNA enzyme with RNA replicase activity. If such an enzyme had the ability to produce additional copies of itself, then RNA evolution would operate autonomously and the origin of life will have been realized in the laboratory.

Joyce, Gerald F.↗
DOE OSTI2020

Long interior carrier lifetime in selective-area InAs nanowires on silicon

Catalyst-free, position-controlled indium arsenide (InAs) nanowires (NWs) of variable diameters were grown on Si (111) by selective-area epitaxy (SAE). Ultrafast pump-probe spectroscopy was conducted, from which carrier recombination mechanisms on the NW surface and interior were resolved and characterized. NWs grown using SAE demonstrated high optical quality, showing minority carrier lifetimes more than two-fold longer than that of the randomly-positioned (RP) NWs. The extracted SAE-InAs NW interior recombination lifetime was found to be as long as 7.2 ns , 13X longer than previous measurements on RP-NWs; and the surface recombination velocity 4154 cm · s - 1 . Transmission electron microscopy revealed a high density of stacking defects within the NWs, suggesting that interior recombination lifetime can be further increased by improving NW interior crystalline quality.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Cataclysmic Variables in the First Year of the Zwicky Transient Facility

Using selection criteria based on amplitude, time, and color, we have identified 329 objects as known or candidate cataclysmic variables (CVs) during the first year of testing and operation of the Zwicky Transient Facility. Of these, 90 are previously confirmed CVs, 218 are strong candidates based on the shape and color of their light curves obtained during 3–562 days of observation, and the remaining 21 are possible CVs but with too few data points to be listed as good candidates. Almost half of the strong candidates are within 10 deg of the galactic plane, in contrast to most other large surveys that have avoided crowded fields. The available Gaia parallaxes are consistent with sampling the low mass transfer CVs, as predicted by population models. Our follow-up spectra have confirmed Balmer/helium emission lines in 27 objects, with four showing high-excitation He ii emission, including candidates for an AM CVn, a polar, and an intermediate polar. Our results demonstrate that a complete survey of the Galactic plane is needed to accomplish an accurate determination of the number of CVs existing in the Milky Way.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1973

Basic principles of variable speed drives

An understanding of the principles which govern variable speed drive operation is discussed for successful drive application. The fundamental factors of torque, speed ratio, and power as they relate to drive selection are discussed. The basic types of variable speed drives, their operating characteristics and their applications are also presented.

Loewenthal, S. H.↗
DOE OSTI2019

Multivariable Analysis of Aerodynamic Forces on Slotted Airfoils for Wind Turbine Blades

Improvement of the aerodynamic performance for cambered airfoils with leading-edge slots is investigated in this work. This concept is proven both computationally and experimentally in recent years. Five design variables of interest are slot's length, slot's width or thickness, inlet angle, exit angle, and the vertical position. The objective is to perform design of experiment and optimization studies on these variables and evaluate the behavior of the objective functions, namely lift and lift over drag ratio (LoD), within the appropriate ranges of the independent variables. Simulations are mainly carried out at the Reynolds number of 1.6 × 10 6 and the angles of attack (AoA) of 6 deg for NACA 4412 airfoil. However, some of the analyses are repeated at Reynolds number of 3.2 × 10 6 and AoA of 0 and 8 deg to show the scalability of the results. Results indicate that the proper selection of three of the design variables, i.e., length, inlet angle, and vertical position, can have a significant impact on both lift and LoD, while the other two variables seem less influential. For the combination of the operating conditions and the values of the design variables considered in this investigation, a LoD improvement as large as 11% is observed.

Energy & Fuels↗
DOE OSTI2023

Applying NIR and MIR spectroscopy for C and soil property prediction in northern cold-region ecosystems. Which approach works better?

Here, developing reliable predictions of soil attributes is necessary to understand northern cold-region climate-soil feedback. Calibration models using near-infrared (NIR) and mid-infrared (MIR) spectroscopy were developed to predict eight commonly measured soil properties for 119 soil samples representing a range of vegetation types, parent materials, and soil types spanning >23° of latitude from southeast Alaska to the Canadian high Arctic. In order to obtain a more accurate prediction, this study compared the performance of linear and non-linear calibration techniques, including lasso regression (Lasso), support vector machine (SVM), random forest (RF) and classic partial least squares (PLS) to predict different soil properties of these soils. Comparing the four models, we noticed that their performance was quite similar for MIR overall, while NIR achieved better results with a PLS model for our dataset. PLS coupled with MIR showed a better performance for soil parameters, such as total organic carbon (TOC), total nitrogen (TN), cation exchange capacity (CEC) and clay (R-squared of 0.9, 0.81, 0.80, and 0.84) when compared with NIR (R-squared of 0.85, 0.72, 0.81 and 0.68). However, using either MIR or NIR spectroscopy, PLS predictions for bulk density (BD) and sand content were not accurate. The variable importance analysis based on the PLS model successfully estimated the relative contribution of wavelengths influencing soil property predictions most. Overall, TOC, TN, CEC and clay mineral predictions are closely related to the occurrence of specific spectral bands in the MIR region. For example, wavelengths at 2978 and 1761 cm -1 for TOC and TN, as well as at 3064 cm -1 for CEC, were selected as the most influential predictor variables. We demonstrated that MIR spectroscopy is a powerful tool for more extensive monitoring in soils of the northern cold climate region; however, NIR could be utilized for rapid estimates when the highest accuracy is not essential.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI’s waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

Laura Duncanson↗
DOE OSTI2019

A strategy for the selection of monovalent antibodies that span protein dimer interfaces

Ligand-induced dimerization is the predominant mechanism through which secreted proteins activate cell surface receptors to transmit essential biological signals. Cytokines are a large class of soluble proteins that dimerize transmembrane receptors into precise signaling topologies, but there is a need for alternative, engineerable ligand scaffolds that specifically recognize and stabilize these protein interactions. Recombinant antibodies can potentially serve as robust and versatile platforms for cytokine complex stabilization, and their specificity allows for tunable modulation of dimerization equilibrium. Here, we devised an evolutionary strategy to isolate monovalent antibody fragments that bridge together two different receptor subunits in a cytokine–receptor complex, precisely as the receptors are disposed in their natural signaling orientations. To do this, we screened a naive antibody library against a stabilized ligand– receptor ternary complex that acted as a “molecular cast” of the natural receptor dimer conformation. Our selections elicited “stapler” single-chain variable fragments (scFvs) of antibodies that specifically engage the interleukin-4 receptor heterodimer. The 3.1 Å resolution crystal structure of one such stapler revealed that, as intended, this scFv recognizes a composite epitope between the two receptors as they are positioned in the complex. Extending our approach, we evolved a stapler scFv that specifically binds to and stabilizes the interface between the interleukin-2 cytokine and one of its receptor subunits, leading to a 15-fold enhancement in interaction affinity. This demonstration that scFvs can be selected to recognize epitopes that span protein interfaces presents new opportunities to engineer structurally defined antibodies for a broad range of research and therapeutic applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2009

Liquid-Propellant Rocket Engine Throttling: A Comprehensive Review

Liquid-Propellant Rocket Engines (LREs) are capable of on-command variable thrust or thrust modulation, an operability advantage that has been studied intermittently since the late 1930s. Throttleable LREs can be used for planetary entry and descent, space rendezvous, orbital maneuvering including orientation and stabilization in space, and hovering and hazard avoidance during planetary landing. Other applications have included control of aircraft rocket engines, limiting of vehicle acceleration or velocity using retrograde rockets, and ballistic missile defense trajectory control. Throttleable LREs can also continuously follow the most economical thrust curve in a given situation, compared to discrete throttling changes over a few select operating points. The effects of variable thrust on the mechanics and dynamics of an LRE as well as difficulties and issues surrounding the throttling process are important aspects of throttling behavior. This review provides a detailed survey of LRE throttling centered around engines from the United States. Several LRE throttling methods are discussed, including high-pressure-drop systems, dual-injector manifolds, gas injection, multiple chambers, pulse modulation, throat throttling, movable injector components, and hydrodynamically dissipative injectors. Concerns and issues surrounding each method are examined, and the advantages and shortcomings compared.

Casiano, Matthew↗
NASA NTRS1989

Bandwidth-limited robust nonlinear sliding control of pointing and tracking maneuvers

It is shown how multiaxial spacecraft tracking and pointing maneuvers with known control bandwidth and given tracking error bounds can be implemented by variable-structure control in the presence of uncertain vehicle and target dynamics. It is shown how to select a nonlinear sliding surface relating attitude and rate variables, as well as a Lyapunov function in the surface variables that absorbs multiplicative model undertainties, thereby simplifying the computation of control corrections. It is then shown how a boundary layer envelope can be designed, within which the components of the surface error dynamics can be modeled as the outputs of designer-selected decoupled low-pass filters. Closed-loop stability conditions accounting for the coupling between the attitude error dynamics and the surface error dynamics are then obtained.

Dwyer, Thomas A. W., III↗
DOE OSTI2022

Heuristic Computational Model for Predicting Lignin Solubility in Tailored Organic Solvents

Lignin is a random heteropolymer that has been extensively studied as a renewable source of aromatic precursors for high-value chemicals, biofuels, and bioplastics. A key challenge in lignin valorization is the structural and compositional heterogeneity of lignin feedstocks. Solvent-based approaches are commonly used to fractionate lignin to reduce this heterogeneity, but solvent selection can be challenging due to variability in lignin composition. In this work, we developed computational methods to predict good and poor organic solvents as a function of lignin composition. We analyzed 28 different linear pentamer structures, 18 from known libraries and 10 hypothetical polymers, and calculated their activity coefficients in 50 different organic solvents by using the conductor-like screening model for realistic solvents. We used these data to train a regression model that enabled the extensive investigation of the impact of solvent and monolignol compositions on predicted lignin solubility. The exhaustive exploration of solubility trends using model predictions revealed sets of solvents, identified using Kamlet–Taft parameters, that are predicted to promote lignin dissolution regardless of lignin composition. We further identified solvents expected to selectively isolate lignin fractions enriched in certain subunits. Furthermore, these results establish heuristic guidelines for solvent selection that can be used to tailor fractionation processes for lignin feedstocks of distinct composition or to design new processes that isolate fractions with higher proportions of selected subunits.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2003

Probabilistic Evaluation of Advanced Ceramic Matrix Composite Structures

The objective of this report is to summarize the deterministic and probabilistic structural evaluation results of two structures made with advanced ceramic composites (CMC): internally pressurized tube and uniformly loaded flange. The deterministic structural evaluation includes stress, displacement, and buckling analyses. It is carried out using the finite element code MHOST, developed for the 3-D inelastic analysis of structures that are made with advanced materials. The probabilistic evaluation is performed using the integrated probabilistic assessment of composite structures computer code IPACS. The affects of uncertainties in primitive variables related to the material, fabrication process, and loadings on the material property and structural response behavior are quantified. The primitive variables considered are: thermo-mechanical properties of fiber and matrix, fiber and void volume ratios, use temperature, and pressure. The probabilistic structural analysis and probabilistic strength results are used by IPACS to perform reliability and risk evaluation of the two structures. The results will show that the sensitivity information obtained for the two composite structures from the computational simulation can be used to alter the design process to meet desired service requirements. In addition to detailed probabilistic analysis of the two structures, the following were performed specifically on the CMC tube: (1) predicted the failure load and the buckling load, (2) performed coupled non-deterministic multi-disciplinary structural analysis, and (3) demonstrated that probabilistic sensitivities can be used to select a reduced set of design variables for optimization.

Abumeri, Galib H.↗
DOE OSTI2020

Arctic Tipping Points Triggering Global Change (LDRD Final Report)

The Arctic is warming and feedbacks in the coupled Earth system may be driving the Arctic to tipping events that could have critical downstream impacts for the rest of the globe. In this project we have focused on analyzing sea ice variability and loss in the coupled Earth system Summer sea ice loss is happening rapidly and although the loss may be smooth and reversible, it has significant consequences for other Arctic systems as well as geopolitical and economic implications. Accurate seasonal predictions of sea ice minimum extent and long-term estimates of timing for a seasonally ice-free Arctic depend on a better understanding of the factors influencing sea ice dynamics and variation in this strongly coupled system. Under this project we have investigated the most influential factors in accurate predictions of September Arctic sea ice extent using machine learning models trained separately on observational data and on simulation data from five E3SM historical ensembles. Monthly averaged data from June, July, and August for a selection of ice, ocean, and atmosphere variables were used to train a random forest regression model. Gini importance measures were computed for each input feature with the testing data. We found that sea ice volume is most important earlier in the season (June) and sea ice extent became a more important predictor closer to September. Results from this study provide insight into how feature importance changes with forecast length and illustrates differences between observational data and simulated Earth system data. We have additionally performed a global sensitivity analysis (GSA) using a fully coupled ultra- low resolution configuration E3SM. To our knowledge, this is the first global sensitivity analysis involving the fully-coupled E3SM Earth system model. We have found that parameter variations show significant impact on the Arctic climate state and atmospheric parameters related to cloud parameterizations are the most significant. We also find significant interactions between parameters from different components of E3SM. The results of this study provide invaluable insight into the relative importance of various parameters from the sea ice, atmosphere and ocean components of the E3SM (including cross-component parameter interactions) on various Arctic-focused quantities of interest (QOIs).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Bayesian modeling of inconsistent plastic response due to material variability

The advent of fabrication techniques such as additive manufacturing has focused attention on the considerable variability of material response due to defects and other microstructural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. To account for material response variability through variations in physical parameters, we adapt a recent Bayesian embedded modeling error calibration technique. We use Bayesian model selection to determine the most plausible of a variety of plasticity models and the optimal embedding of parameter variability. To expedite model selection, we develop an adaptive importance-sampling-based numerical integration scheme to compute the Bayesian model evidence. In conclusion, we demonstrate that the new framework provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters.

36 MATERIALS SCIENCE↗
NASA NTRS2013

Optimized tuner selection for engine performance estimation

A methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. Theoretical Kalman filter estimation error bias and variance values are derived at steady-state operating conditions, and the tuner selection routine is applied to minimize these values. The new methodology yields an improvement in on-line engine performance estimation accuracy.

Simon, Donald L.↗
NASA NTRS2004

Ionospheric Variability and Storms on Mars

The goal of this grant was to conduct the first-ever study of ionospheric variability on Mars. To do so, we used data from the Radio Science (RS) experiment onboard the Mars Global Surveyor (MGS) satellite. Dr. David Hinson of the RS team at Stanford University was a most helpful and valuable colleague throughout the studies we conducted. For the initial RS datasets available from the MGS mission, there were no severe storms caused by solar wind activity, so we concentrated on day-to-day effects. This turned out to be a wise approach since understanding "normal variability" had to be done before any claim could be made about "space weather" effects. Our approach was three-fold: (1) select a good dataset for characterization of ionosphere variability at Mars, one for which excellent terrestrial data were also available. This turned out to be the period 9-27 March 1999; (2) once the variability at Mars was described, develop and use a new photochemical model of the martian ionosphere to find the extent to which solar variability on those days caused or contributed to the observed patterns; (3) use the results from the above, together with additional datasets from the MGS/RS experiment, to describe some practical consequences that the martian ionosphere would have upon NASA s proposed navigation and communications systems for Mars. The results of these studies showed that: (a) solar variability is the dominant source of ionospheric variability at Mars (during periods of quiet solar wind), (b) that current models do a good job in portraying such effects at the height of the ionospheric peak electron density, and (c) that ionospheric structure on Mars can affect attempts at precise position-fixing at Mars should relatively high (GPS-like) frequencies not be used in a Mars communications and navigation system.

Mendillo, Michael↗