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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Fidelity Analysis of Sampled Imaging Systems

Many modeling, simulation and performance analysis studies of sampled imaging systems are inherently incomplete because they are conditioned on a discrete-input, discrete-output model that only accounts for blurring during image acquisition and additive noise. For those sampled imaging systems where the effects of digital image acquisition, digital filtering and reconstruction are significant, the modeling, simulation and performance analysis should be based on a more comprehensive continuous-input, discrete-processing, continuous-output end-to-end model. This more comprehensive model should properly account for the low-pass filtering effects of image acquisition prior to sampling, the potentially important noiselike effects of the aliasing caused by sampling, additive noise due to device electronics and quantization, the generally high-boost filtering effects of digital processing, and the low-pass filtering effects of image reconstruction. This model should not, however, be so complex as to preclude significant mathematical analysis, particularly the mean-square (fidelity) type of analysis so common in linear system theory. We demonstrate that, although the mathematics of such a model is more complex, the increase in complexity is not so great as to prevent a complete fidelity-metric analysis at both the component level and at the end-to-end system level: that is, computable mean-square-based fidelity metrics are developed by which both component-level and system-level performance can be quantified. In addition, we demonstrate that system performance can be assessed qualitatively by visualizing the output image as the sum of three component images, each of which relates to a corresponding fidelity metric. The cascaded, or filtered, component accounts for the end-to-end system filtering of image acquisition, digital processing, and image reconstruction; the random noise component accounts for additive random noise, modulated by digital processing and image reconstruction filtering; and the aliased noise component accounts for the frequency folding effect of sampling, modulated by digital processing and image reconstruction filtering.

Park, Stephen K.↗
DOE OSTI2023

Influence of energy density on the microstructure, growth orientation, and anisotropy of magnetic properties in additively manufactured Fe-3.8wt%Si transformer steels

Fe-3.8wt%Si transformer steels were processed using two different additive manufacturing (AM) techniques, laser powder bed fusion (LPBF) and directed energy deposition (DED). While the LPBF processed samples exhibited a strong <001> orientation of the BCC grains along the build axis, the DED processed samples exhibited a randomized texture along the build axis. DED processed samples showed substantially coarser columnar grains as compared to their LPBF counterparts. Here, the columnar grains exhibited a substantial number of low-angle sub-grain boundaries. All samples exhibited very good soft magnetic properties, with saturation magnetization (M s ) values ranging from 205 - 232 emu/gm, and coercivity (H c ) values ranging from 1.2 – 4.2 Oe. The Coercivity (H c ) values were significantly lower when the magnetic field was applied parallel to the build axis, as compared to being perpendicular, which can be rationalized based on the columnar nature of the grains, resulting in a higher number density of grain boundaries in case of the field applied perpendicular to the build axis.

36 MATERIALS SCIENCE↗
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 OSTI2014

SU-F-18C-15: Model-Based Multiscale Noise Reduction On Low Dose Cone Beam Projection

Purpose: To improve image quality of low dose cone beam CT for patient positioning in radiation therapy. Methods: In low dose cone beam CT (CBCT) imaging systems, Poisson process governs the randomness of photon fluence at x-ray source and the detector because of the independent binomial process of photon absorption in medium. On a CBCT projection, the variance of fluence consists of the variance of noiseless imaging structure and that of Poisson noise, which is proportional to the mean (noiseless) of the fluence at the detector. This requires multiscale filters to smoothen noise while keeping the structure information of the imaged object. We used a mathematical model of Poisson process to design multiscale filters and established the balance of noise correction and structure blurring. The algorithm was checked with low dose kilo-voltage CBCT projections acquired from a Varian OBI system. Results: From the investigation of low dose CBCT of a Catphan phantom and patients, it showed that our model-based multiscale technique could efficiently reduce noise and meanwhile keep the fine structure of the imaged object. After the image processing, the number of visible line pairs in Catphan phantom scanned with 4 ms pulse time was similar to that scanned with 32 ms, and soft tissue structure from simulated 4 ms patient head-and-neck images was also comparable with scanned 20 ms ones. Compared with fixed-scale technique, the image quality from multiscale one was improved. Conclusion: Use of projection-specific multiscale filters can reach better balance on noise reduction and structure information loss. The image quality of low dose CBCT can be improved by using multiscale filters.

60 APPLIED LIFE SCIENCES↗
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 OSTI2016

Bayesian estimation of Karhunen–Loève expansions; A random subspace approach

One of the most widely-used statistical procedures for dimensionality reduction of high dimensional random fields is Principal Component Analysis (PCA), which is based on the Karhunen-Lo eve expansion (KLE) of a stochastic process with finite variance. The KLE is analogous to a Fourier series expansion for a random process, where the goal is to find an orthogonal transformation for the data such that the projection of the data onto this orthogonal subspace is optimal in the L 2 sense, i.e, which minimizes the mean square error. In practice, this orthogonal transformation is determined by performing an SVD (Singular Value Decomposition) on the sample covariance matrix or on the data matrix itself. Sampling error is typically ignored when quantifying the principal components, or, equivalently, basis functions of the KLE. Furthermore, it is exacerbated when the sample size is much smaller than the dimension of the random field. In this paper, we introduce a Bayesian KLE procedure, allowing one to obtain a probabilistic model on the principal components, which can account for inaccuracies due to limited sample size. The probabilistic model is built via Bayesian inference, from which the posterior becomes the matrix Bingham density over the space of orthonormal matrices. We use a modified Gibbs sampling procedure to sample on this space and then build a probabilistic Karhunen-Lo eve expansions over random subspaces to obtain a set of low-dimensional surrogates of the stochastic process. We illustrate this probabilistic procedure with a finite dimensional stochastic process inspired by Brownian motion.

97 MATHEMATICS AND COMPUTING↗
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 OSTI2015

Mesh refinement for uncertainty quantification through model reduction

We present a novel way of deciding when and where to refine a mesh in probability space in order to facilitate uncertainty quantification in the presence of discontinuities in random space. A discontinuity in random space makes the application of generalized polynomial chaos expansion techniques prohibitively expensive. The reason is that for discontinuous problems, the expansion converges very slowly. An alternative to using higher terms in the expansion is to divide the random space in smaller elements where a lower degree polynomial is adequate to describe the randomness. In general, the partition of the random space is a dynamic process since some areas of the random space, particularly around the discontinuity, need more refinement than others as time evolves. In the current work we propose a way to decide when and where to refine the random space mesh based on the use of a reduced model. The idea is that a good reduced model can monitor accurately, within a random space element, the cascade of activity to higher degree terms in the chaos expansion. In turn, this facilitates the efficient allocation of computational sources to the areas of random space where they are more needed. For the Kraichnan–Orszag system, the prototypical system to study discontinuities in random space, we present theoretical results which show why the proposed method is sound and numerical results which corroborate the theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
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 OSTI2016

Stochastic Games for Continuous-Time Jump Processes Under Finite-Horizon Payoff Criterion

In this paper we study two-person nonzero-sum games for continuous-time jump processes with the randomized history-dependent strategies under the finite-horizon payoff criterion. The state space is countable, and the transition rates and payoff functions are allowed to be unbounded from above and from below. Under the suitable conditions, we introduce a new topology for the set of all randomized Markov multi-strategies and establish its compactness and metrizability. Then by constructing the approximating sequences of the transition rates and payoff functions, we show that the optimal value function for each player is a unique solution to the corresponding optimality equation and obtain the existence of a randomized Markov Nash equilibrium. Furthermore, we illustrate the applications of our main results with a controlled birth and death system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
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↗