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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 NTRS1984

On-orbit frequency stability analysis of the GPS NAVSTAR-1 quartz clock and the NAVSTARs-6 and -8 rubidium clocks

An on-orbit frequency stability performance analysis of the GPS NAVSTAR-1 quartz clock and the NAVSTARs-6 and -8 rubidium clocks is presented. The clock offsets were obtained from measurements taken at the GPS monitor stations which use high performance cesium standards as a reference. Clock performance is characterized through the use of the Allan variance, which is evaluated for sample times of 15 minutes to two hours, and from one day to 10 days. The quartz and rubidium clocks' offsets were corrected for aging rate before computing the frequency stability. The effect of small errors in aging rate is presented for the NAVSTAR-8 rubidium clock's stability analysis. The analysis includes presentation of time and frequency residuals with respect to linear and quadratic models, which aid in obtaining aging rate values and identifying systematic and random effects. The frequency stability values were further processed with a time domain noise process analysis, which is used to classify random noise process and modulation type.

Mccaskill, T. B.↗
DOE OSTI2022

A new multi-model absolute difference-based sensitivity (MMADS) analysis method to screen non-influential processes under process model and parametric uncertainty

Process-based models have been widely used for hydrologic modeling, and it is a common practice to use sensitivity analysis methods for excluding non-influential hydrologic processes from further investigation and/or model improvement. This study develops a new method called multi-model absolute difference-based sensitivity (MMADS) analysis method to screen non-influential system processes and parameters. MMADS is conceptually similar to the Morris method for addressing parametric uncertainty, but has a unique feature to address both process model uncertainty (i.e., a process may be represented by multiple process models) and process model parameter uncertainty (i.e., parameters associated with a process model are random). MMADS first evaluates absolute differences of a quantity of interest (i.e., a system model output) by varying process models and/or process model parameter values, and then calculates the mean and variance of the differences for investigating process influence. The mean measures overall influence of the process on the quantity of interest, and the variance estimates influence of nonlinear effects of the process and/or its interactions with other processes. MMADS is an extension of the Morris method from a parameter space to a joint parameter-model space for explicitly addressing both process model uncertainty and model parameter uncertainty. The performance of MMADS is evaluated by using two numerical experiments. One experiment is based on Sobol’s G*-function with ten product elements, and has analytical solutions of the MMADS mean and variance of absolute differences. The other experiment is for groundwater flow modeling which considers three processes (i.e., recharge, geology, and snowmelt) that interact with each other. Finally, results indicate that MMADS is computationally efficient and can identify non-influential processes of complex hydrological systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Near Surface Nucleation and Particle Mediated Growth of Colloidal Au Nanocrystals

During non-classical growth of nanostructures via assembly of primary nuclei, nucleation and assembly are assumed to be distinct processes: nanoparticles nucleate randomly and aggregate to form extended structures through Brownian motion in the presence of long-range attractive interactions. Here we investigate the relationship between these two processes by using in situ AFM, in situ, ex situ and cryo TEM and UV-Vis spectroscopy to observe growth of colloidal gold and simulations to develop a mechanistic model of the process. Our results reveal an inexorable link between nucleation and assembly with nuclei forming almost exclusively within a ~ 1 nm interfacial region of existing particles. The new particles immediately close the gap either through a diffusive jump or via growth of a neck between the seed and new particle, generating aggregates exhibiting features commonly attributed to oriented attachment of independently nucleated particles. The results demonstrate that creation of initial particle interfaces leads to local environments that redirect growth towards non-classical processes.

Cheng, Yingwen↗
DOE OSTI2025

S AP F LOWER : an automated tool for sap flow data preprocessing, gap-filling, and analysis using deep learning

Sap flow, a critical process in plant water use and ecosystem water cycles, is often measured using thermal dissipation probes (TDP) due to their ease of installation and continuous data collection. However, sap flow data frequently include noise, outliers, and gaps, creating challenges for analysis and requiring substantial manual processing. We developed S AP F LOWER , a tool that automates data preprocessing, model training, gap-filling, sapwood area scaling and modeling, and water use analysis. It integrates autocleaning, machine learning and deep learning models (e.g. random forest, Gaussian process regression, long short-term memory (LSTM), bidirectional LSTM (BiLSTM)), and efficient workflows to process sap flow data. S AP F LOWER can remove over 90% of noisy data while preserving legitimate variations and achieve high accuracy in gap-filling based on user-determined parameters. Random forest, LSTM, and BiLSTM models reduced root mean square error to 10% or less for long-term gaps. Model training and prediction can be performed efficiently within seconds. S AP F LOWER significantly enhances the efficiency and accessibility of TDP data analysis by automating complex tasks, enabling researchers without programming expertise to employ advanced techniques. Future improvements will focus on species-specific corrections for TDP and support for additional measurement methods. S AP F LOWER is openly available on GitHub (https://github.com/JiaxinWang123/SapFlower) and Zenodo (doi: 10.5281/zenodo.13665919).

ecosystem water balance↗
DOE OSTI2019

Stationary Spatial Charging Demand Distribution for Commercial Electric Vehicles in Urban Area

The electric vehicle (EV) is an increasingly popu- lar mobility solution today. It is especially appealing for urban commercial usage including serving commuters and delivering goods. Commercial EVs, defined as electric taxi fleets in this paper, contribute a lot to the energy consumption and emission. One critical component to facilitate the adoption of EVs is to provide sufficient charging infrastructures for road users to alleviate the range anxiety. In this study, we develop an analytical model to profile the stationary spatial distribution of charging demand for any given fleet of commercial EVs, which serves as the necessary input for planning the location of charging infrastructures. Our model considers the movement of EVs as a random walk, with the underlying Markov process driven by the spatial demand that the commercial EV fleet needs to serve. We then define the electricity consumption and charging models and develop the probability distribution that an EV may need to recharge its battery at any given location in the city, based on the stationary distribution of the random walk process. We calibrate the performance parameters of EVs from the trip record and present numerical experiments based on New York City taxi trip data and compare the solution from simulation with our analytical solution. The result shows that analytical solution is quite close to the simulation result. This paper provides insights to the city agencies and private companies to make charging infrastructure location planning.

Qian, Xinwu↗
NASA NTRS1981

AVE-SESAME program for the REEDA System

The REEDA system software was modified and improved to process the AVE-SESAME severe storm data. A random access file system for the AVE storm data was designed, tested, and implemented. The AVE/SESAME software was modified to incorporate the random access file input and to interface with new graphics hardware/software now available on the REEDA system. Software was developed to graphically display the AVE/SESAME data in the convention normally used by severe storm researchers. Software was converted to AVE/SESAME software systems and interfaced with existing graphics hardware/software available on the REEDA System. Software documentation was provided for existing AVE/SESAME programs underlining functional flow charts and interacting questions. All AVE/SESAME data sets in random access format was processed to allow developed software to access the entire AVE/SESAME data base. The existing software was modified to allow for processing of different AVE/SESAME data set types including satellite surface and radar data.

Hickey, J. S.↗
NASA NTRS2008

Nanotip Carpets as Antireflection Surfaces

Carpet-like random arrays of metal-coated silicon nanotips have been shown to be effective as antireflection surfaces. Now undergoing development for incorporation into Sun sensors that would provide guidance for robotic exploratory vehicles on Mars, nanotip carpets of this type could also have many uses on Earth as antireflection surfaces in instruments that handle or detect ultraviolet, visible, or infrared light. In the original Sun-sensor application, what is required is an array of 50-micron-diameter apertures on what is otherwise an opaque, minimally reflective surface, as needed to implement a miniature multiple-pinhole camera. The process for fabrication of an antireflection nanotip carpet for this application (see Figure 1) includes, and goes somewhat beyond, the process described in A New Process for Fabricating Random Silicon Nanotips (NPO-40123), NASA Tech Briefs, Vol. 28, No. 1 (November 2004), page 62. In the first step, which is not part of the previously reported process, photolithography is performed to deposit etch masks to define the 50-micron apertures on a silicon substrate. In the second step, which is part of the previously reported process, the non-masked silicon area between the apertures is subjected to reactive ion etching (RIE) under a special combination of conditions that results in the growth of fluorine-based compounds in randomly distributed formations, known in the art as "polymer RIE grass," that have dimensions of the order of microns. The polymer RIE grass formations serve as microscopic etch masks during the next step, in which deep reactive ion etching (DRIE) is performed. What remains after DRIE is the carpet of nano - tips, which are high-aspect-ratio peaks, the tips of which have radii of the order of nanometers. Next, the nanotip array is evaporatively coated with Cr/Au to enhance the absorption of light (more specifically, infrared light in the Sun-sensor application). The photoresist etch masks protecting the apertures are then removed by dipping the substrate into acetone. Finally, for the Sun-sensor application, the back surface of the substrate is coated with a 57-nm-thick layer of Cr for attenuation of sunlight.

Bae, Youngsam↗
DOE OSTI2018

A Quantum Random Number Generator Based on the 100-Mbit/s Poisson Photocount Statistics

A quantum random number generator is experimentally realized. The initial randomness source is a sequence of photocounts from quasi-single-photon radiation detected with a SiPM (Silicon Photo Multiplier) silicon avalanche detector array. The use of a SiPM provides a reliable control of the quantum nature of the Poisson photocount statistics. A special algorithm with nonexponential complexity allows one to extract from the Poisson process all the randomness contained in it, namely, a random uniform sequence of 0 and 1.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1991

Texture analysis of radiometric signatures of new sea ice forming in Arctic leads

Analysis of 33.6-GHz, high-resolution, passive microwave images suggests that new sea ice accumulating in open leads is characterized by a unique textural signature which can be used to discriminate new ice forming in this environment from adjacent surfaces of similar radiometric temperature. Ten training areas were selected from the data set, three of which consisted entirely of first-year ice, four entirely of multilayer ice, and three of new ice in open leads in the process of freezing. A simple gradient operator was used to characterize the radiometric texture in each training region in terms of the degree to which radiometric gradients are oriented. New ice in leads has a sufficiently high proportion of well-oriented features to distinguish it uniquely from first-year ice and multiyear ice. The predominance of well-oriented features probably reflects physical processes by which new ice accumulates in open leads. Banded structures, which are evident in aerial photographs of new ice, apparently give rise to the radiometric signature observed, in which the trend of brightness temperature gradients is aligned parallel to lead trends. First-year ice and multiyear ice, which have been subjected to a more random growth and process history, lack this banded structure and therefore are characterized by signatures in which well-aligned elements are less dominant.

Eppler, Duane T.↗
DOE OSTI2019

Lignin valorization process control under feedstock uncertainty through a dynamic stochastic programming approach

The randomness introduced by reactants is an issue when processing renewable bioresources. In this paper, we apply tools from dynamic stochastic programming theory to the biochemical process. Instead of introducing extra preprocessing units, we consider the inherent randomness of the process and optimize in expectation the performance of the system. In a general setting, this is a multistage stochastic optimization problem and we investigate its approximate solution via two approaches, namely stochastic dual dynamic programming (SDDP) and the finite state, finite action Markov decision process (MDP) framework. These two methods are implemented to a case study of lignin valorization that is crucial to a cost-effective biorefinery process using biomass as the feedstock.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Dynamical transitions in a modulated Landau–Zener model with finite driving fields

Highlights: •The rigorous dynamics of a modulated Landau–Zener model is investigated. •Nonadiabatic evolution of the model could realize complete population transfer. •Imperfect effects caused by both the amplitude and phase damping are estimated. -- Abstract: We investigate a special time-dependent quantum model which assumes the Landau–Zener driving form but with an overall modulation of the intensity of the pulsing field. We demonstrate that the dynamics of the system, including the two-level case as well as its multi-level extension, is exactly solvable analytically. Differing from the original Landau–Zener model, the nonadiabatic effect of the evolution in the present driving process does not destroy the desired population transfer. As the sweep protocol employs only the finite driving fields which tend to zero asymptotically, the cutoff error due to the truncation of the driving pulse to the finite time interval turns out to be negligibly small. Furthermore, we investigate the noise effect on the driving protocol due to the dissipation of the surrounding environment. The losses of the fidelity in the protocol caused by both the phase damping process and the random spin flip noise are estimated by solving numerically the corresponding master equations within the Markovian regime.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2005

A First and Second Order Moment Approach to Probabilistic Control Synthesis

This paper presents a robust control design methodology based on the estimation of the first two order moments of the random variables and processes that describe the controlled response. Synthesis is performed by solving an multi-objective optimization problem where stability and performance requirements in time- and frequency domains are integrated. The use of the first two order moments allows for the efficient estimation of the cost function thus for a faster synthesis algorithm. While reliability requirements are taken into account by using bounds to failure probabilities, requirements related to undesirable variability are implemented by quantifying the concentration of the random outcome about a deterministic target. The Hammersley Sequence Sampling and the First- and Second-Moment- Second-Order approximations are used to estimate the moments, whose accuracy and associated computational complexity are compared numerically. Examples using output-feedback and full-state feedback with state estimation are used to demonstrate the ideas proposed.

Crespo, Luis G.↗
DOE OSTI2021

Synergistic Enzyme Mixtures to Realize Near-Complete Depolymerization in Biodegradable Polymer/Additive Blends

Embedding catalysts inside of plastics affords accelerated chemical modification with programmable latency and pathways. Nanoscopically embedded enzymes can lead to near-complete degradation of polyesters via chain-end mediated processive depolymerization. The overall degradation rate and pathways have a strong dependence on the morphology of semicrystalline polyesters. Yet, most studies to date focus on pristine polymers instead of mixtures that contain additives and other components despite their nearly universal use in plastic production. Here, for this study, additives are introduced to purposely change the morphology of polycaprolactone (PCL) by increasing the bending and twisting of crystalline lamellae. These morphological changes immobilize chain ends preferentially at the crystalline/amorphous interfaces and limit chain-end accessibility by the embedded processive enzyme. This chain-end redistribution reduces the polymer-to-monomer conversion from >95% to less than 50%, causing formation of highly crystalline plastic pieces, including microplastics. By synergizing both random chain scission and processive depolymerization, it is feasible to navigate morphological changes in polymer/additive blends and to achieve near-complete depolymerization. The random scission enzymes in the amorphous domains create new chain ends that are subsequently bound and depolymerized by processive enzymes. Present studies further highlight the importance to consider how the host polymer's morphologies affect the reactions catalyzed by embedded catalytic species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Microstructure and crystallographic texture of high frequency electric resistance welded X65 pipeline steel

Here, microstructure and crystallographic texture and their correlation with impact toughness of high-frequency electric resistance welded (HF-ERW) X65 Grade pipeline steel have been investigated. X65 base metal after HF-ERW has formed a typical hourglass-shaped weld joint with four different regions, including the thermomechanical-affected zone (TMAZ), fine-grained heat affected zone (FGHAZ), coarse-grained heat affected zone (CGHAZ), and bondline (Weld Zone). X65 base metal shows prominent (113) [110] and (112) [110] texture components due to thermomechanical-controlled processing. TMAZ depicted random texture with a minor intensity of Cube component (001)[110] due to the partial recrystallization during welding. In the FGHAZ and CGHAZ, near the bondline, the base metal texture has been converted to rotated Goss (110) [110], Goss (110) [001], and rotated cube (001) [110] due to the recrystallization of the austenite and the shear deformation in the austenite temperature range. A high density of {100} and {110} cleavage planes is observed in the CGHAZ, which is often found to be the fracture location for the impact toughness testing.

36 MATERIALS SCIENCE↗
DOE OSTI2017

A pseudo-thermodynamic description of dispersion for nanocomposites

Dispersion in polymer nanocomposites is determined by the kinetics of mixing and chemical affinity. Compounds like reinforcing filler/elastomer blends display some similarity to colloidal solutions in that the filler particles are close to randomly dispersed through processing. It is attractive to apply a pseudo-thermodynamic approach taking advantage of this analogy between the kinetics of mixing for polymer compounds and thermally driven dispersion for colloids. In order to demonstrate this pseudo-thermodynamic approach, two polybutadienes and one polyisoprene were milled with three carbon blacks and two silicas. These samples were examined using small-angle x-ray scattering as a function of filler concentration to determine a pseudo-second order virial coefficient, A 2 , which is used as an indicator for compatibility of the filler and polymer. It is found that A 2 follows the expected behavior with lower values for smaller primary particles indicating that smaller particles are less compatible and more difficult to mix. The measured values of A 2 can be used to specify repulsive interaction potentials for coarse grain DPD simulations of filler/elastomer systems. In addition, new methods to quantify the filler percolation threshold and filler mesh size as a function of filler concentration are obtained. Moreover, the results represent a new approach to understanding and predicting compatibility in polymer nanocomposites based on a pseudo-thermodynamic approach.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Exploring the potential of using L-Band InSAR for the mapping of flooded vegetation in tropical wetlands

Wetlands play a critical role in global water and carbon cycles, yet monitoring their water extent remains difficult, particularly beneath dense vegetation. SAR-based techniques such as backscatter thresholding are limited by complex scattering mechanisms, while fully polarimetric SAR (PolSAR) data capable of detecting doublebounce scattering remain scarce. To address these challenges, this study evaluates the potential of Interferometric SAR (InSAR) for mapping water surfaces beneath vegetation, termed flooded vegetation, using the Atrato floodplain in Colombia as a case study. We develop an automated workflow combining InSAR fringe detection with local phase homogeneity analysis and random sampling of processing parameters to generate probabilistic flooded vegetation maps. Applied to ALOS PALSAR-1 L-band image pairs from 2007–2011, the workflow captures seasonal fluctuations in flooded extent ranging from 500 to 1,500 km2. Compared to other L-band SAR inundation products, the InSAR-based maps identify broader flooded areas, with ~70% agreement in pairwise comparisons. Around 84% of detections align with existing wetland inventories and seasonal changes correspond with regional hydrological indicators, including terrestrial water storage anomalies and water gauge measurements. PolSAR analysis shows that InSAR complements backscatter-based methods by detecting inundation in areas with weak double-bounce signals. These findings suggest that combining InSAR with backscatter-based methods can improve detection of flooded vegetation, which is especially relevant for the upcoming NISAR mission that will offer frequent global L-band observations.

Coastal inundation↗