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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
DOE OSTI2024

Computationally efficient and error aware surrogate construction for numerical solutions of subsurface flow through porous media

Limiting the injection rate to restrict the pressure below a threshold at a critical location can be an important goal of simulations that model the subsurface pressure between injection and extraction wells. The pressure is approximated by the solution of Darcy’s partial differential equation for a given permeability field. The subsurface permeability is modeled as a random field since it is known only up to statistical properties. This induces uncertainty in the computed pressure. Solving the partial differential equation for an ensemble of random permeability simulations enables estimating a probability distribution for the pressure at the critical location. These simulations are computationally expensive, and practitioners often need rapid online guidance for real-time pressure management. An ensemble of numerical partial differential equation solutions is used to construct a Gaussian process regression model that can quickly predict the pressure at the critical location as a function of the extraction rate and permeability realization. The Gaussian process surrogate analyzes the ensemble of numerical pressure solutions at the critical location as noisy observations of the true pressure solution, enabling robust inference using the conditional Gaussian process distribution. Our first novel contribution is to identify a sampling methodology for the random environment and matching kernel technology for which fitting the Gaussian process regression model scales as O ( n log n ) instead of the typical O ( n 3 ) rate in the number of samples n used to fit the surrogate. The surrogate model allows almost instantaneous predictions for the pressure at the critical location as a function of the extraction rate and permeability realization. Our second contribution is a novel algorithm to calibrate the uncertainty in the surrogate model to the discrepancy between the true pressure solution of Darcy’s equation and the numerical solution. Finally, although our method is derived for building a surrogate for the solution of Darcy’s equation with a random permeability field, the framework broadly applies to solutions of other partial differential equations with random coefficients.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

The visible laser absorption property of chromium-doped polyvinyl alcohol films: synthesis, optical and dielectric properties

New films for visible laser filter were fabricated from poly(vinyl alcohol), PVA, and various levels of chrome(III) chloride, CrCl{sub 3}, by a known simple process named casting. The structure order degree, complex interaction, as well as optical filtering characteristic of the PVA + xCrCl{sub 3} (x = 0.037, 0.37, 3.7, 18.5 and 37 wt%) samples were investigated via the diffraction of the X-ray, Fourier transform infrared spectroscopy and UV/Vis/NIR spectrum, respectively, while the behavior of optical limiting was tested by set-up laser limiting measurement. The increment of Cr{sup 3+}-doping level in the PVA gives some results as, (I) a marked increase in the degree of random structure of the samples, and (II) two clearly absorption peaks are seen around 428 nm and 608 nm with a progressive increase in the intensity and a slight shift to a higher wavelength. (III) Three distinguished energy gaps were clearly observed, as well as, (IV) the dielectric spectroscopy of the samples was decreased with increasing the Cr{sup 3+}-doping, which contributes to the strong interaction among the mixtures. Furthermore, at concentrations above 0.37 wt% of Cr{sup 3+}-doping, a notable decrease in the output detected laser power was observed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Present and future potential of krypton-85 for the detection of clandestine reprocessing plants for treaty verification

Burnup calculations are applied to determine the amount of krypton-85 that is produced during the irradiation of nuclear fuel. Since krypton-85 is most likely released into the atmosphere during reprocessing to separate plutonium, atmospheric transport modeling is used to calculate the worldwide distribution of krypton-85 concentrations stemming from emissions from declared reprocessing plants. Our results are the basis for scenarios in which emissions from clandestine reprocessing facilities have to be detected against various background levels. We conclude that today’s background imposes heavily on the ability to detect small and medium plutonium separation rates; only high separation rates of 1 SQ per week and higher have a chance to be detected with feasible outlay. A fixed network of monitoring stations seems too costly; instead the high number of samples that are required rather calls for mobile sampling procedures, where air samples are collected at random locations over the world and are analyzed in regional laboratories for their krypton-85 concentration. Furthermore, it is argued that krypton-85 emissions from declared reprocessing activities have to be significantly lowered to enable a worldwide verification of the absence of even smaller clandestine reprocessing. For each scenario the number of samples that have to be taken for probable detection is calculated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Elemental profiling and genomewide association studies reveal genomic variants modulating ionomic composition in Populus trichocarpa leaves

Samples were collected from a population of 1,089 black cottonwood genotypes (P. trichocarpa) assembled from native stands to encompass the central portion of the natural range of the species, stretching from 38.8° to 54.3° N Q13 latitude from California, USA, to British Columbia, Canada. Establishment of the common garden, growth conditions, and site maintenance have been described by Muchero et al (2015). In this study, leaf samples for ionomic profiling were collected from 4-year-old trees, during the growing season, in July 2012, from a field located in Clatskanie, Oregon, USA (46°6′11″N 123°12′13″W). The field site was located in a protected alluvial floodplain containing a uniform Wauna-Locoda silt loam soil area characterized by an acidic pH, in Columbia County, Oregon. A subset of 584 out of the 1,089 P. trichocarpa genotypes were represented in this sampling. These genotypes were randomly selected to represent the geographical distribution of the population. A single fully mature (LPI 7-9) leaf on the south side of the tree exposed to full sunlight conditions was removed from the tree within a 6-hour window centering on solar noon and immediately frozen under dry ice before processing. Leaf samples of 584 P. trichocarpa genotypes were finely ground to 40 mm particle size using a mortar and pestle, and ionomic composition was analyzed using ICP-MS. In total, 20 elements were profiled, including aluminum (Al27), arsenic (As75), boron (B11), cadmium (Cd111), calcium (Ca43), cobalt (Co), copper (Cu), iron (Fe57), magnesium (Mg25), manganese (Mn55), molybdenum (Mo), nickel (Ni60), phosphorus (P31), potassium (K39), rubidium (Rb85), selenium (Se82), sodium (Na23), strontium (Sr88), sulfur (S34), and zinc (Zn66), following a protocol established by Ziegler et al. (2013). For each sample, 75mg of powder was digested overnight in 2.5 mL HNO3 containing 20 parts per billion (ppb) indium as an internal standard, following the protocol described in Ziegler et al. (2013). Following a dilution, concentration of the 20 elements was measured using an Elan 6000 DRC-e mass spectrometer (Perkin-Elmer SCIEX) connected to a PFA microflow nebulizer (Elemental Scientific) and Apex HF desolvator (Elemental Scientific). One measurement per sample per genotype was done. For subsequent analyses, the quantifications were converted to total element concentration.

CBI ionomics, GWAS, plasma-mass spectrometry, neut↗
NASA NTRS1985

Long term monitoring of methane in the atmosphere by multiplex gas chromatography

Methane is of interest in the study of the Earth's atmosphere because of its implication in the future global warming of the surface. This warming is produced by the absorption of infrared energy by trace gases. It has been estimated that in the next 40 to 50 years, methane could contribute 20 to 25 pct. as much atmospheric warming as that expected from carbon dioxide increases. Studies to examine sources, sinks, and cycles of methane will require analytical methods capable of continuous unattended measurement with temporal resolution of an hour or less for weeks at a time. Gas chromatography (GC) is one of the most practical methods available to conduct the analysis of air, but limitations in this technique still exist which can be alleviated with multiplex GC (MGC). MGC is a technique where many samples are pseudo-randomly introduced to the chromatograph without regard to the length of time required for an analysis. The resulting data must then be reduced using computational methods such as cross correlation. In the technique reported, a tube packed with silver oxide was used at the inlet of the GC column to create concentration pulses of methane in a sample stream of air. By using only one carrier, i.e., ambient air, an effective and accurate method to monitor the variations in concentration of methane in the atmosphere over long periods of time was developed. Methane in ambient air was monitored for an eight day period and an interesting temporal variability was found. This work has shown the utility of a relatively simple MGC for the analysis of a real environmental sample.

Valentin, Jose R.↗
NASA NTRS2010

Analyzing Non Stationary Processes in Radiometers

The lack of well-developed techniques for modeling changing statistical moments in our observations has stymied the application of stochastic process theory for many scientific and engineering applications. Non linear effects of the observation methodology is one of the most perplexing aspects to modeling non stationary processes. This perplexing problem was encountered when modeling the effect of non stationary receiver fluctuations on the performance of radiometer calibration architectures. Existing modeling approaches were found not applicable; particularly problematic is modeling processes across scales over which they begin to exhibit non stationary behavior within the time interval of the calibration algorithm. Alternatively, the radiometer output is modeled as samples from a sequence random variables; the random variables are treated using a conditional probability distribution function conditioned on the use of the variable in the calibration algorithm. This approach of treating a process as a sequence of random variables with non stationary stochastic moments produce sensible predictions of temporal effects of calibration algorithms. To test these model predictions, an experiment using the Millimeter wave Imaging Radiometer (MIR) was conducted. The MIR with its two black body calibration references was configured in a laboratory setting to observe a third ultra-stable reference (CryoTarget). The MIR was programmed to sequentially sample each of the three references in approximately a 1 second cycle. Data were collected over a six-hour interval. The sequence of reference measurements form an ensemble sample set comprised of a series of three reference measurements. Two references are required to estimate the receiver response. A third reference is used to estimate the uncertainty in the estimate. Typically, calibration algorithms are designed to suppress the non stationary effects of receiver fluctuations. By treating the data sequence as an ensemble collection, it is possible to apply temporal algorithms which exacerbate the non stationary effects. By varying the algorithm, information about the properties of the non stationary receiver fluctuations is obtained. Comparisons of analytical calculations and statistical analysis of data demonstrate impressive agreement.

Racette, Paul↗
DOE OSTI2025

How Representative Are Uncrewed Aircraft System Measurements of the Convective Boundary Layer?

Abstract Uncrewed aircraft systems (UAS) demonstrate significant potential for filling data gaps in the atmospheric boundary layer. However, the extent to which UAS observations—typically vertical profiles taken over 15 min—are representative of the boundary layer as a whole remains poorly characterized. Using large eddy simulations (LES) of the daytime convective boundary layer (CBL), we quantify random errors in UAS measurements that occur due to insufficient statistical convergence of the time average to the true ensemble mean. Random errors in first‐order moments increase as the CBL becomes increasingly unstable, and are largest near the surface for most quantities. Errors are on the order of 2–6 m for wind speed, 15–60 for wind direction, 0.2–3 K for potential temperature, and 0.1–1 g for specific humidity, with errors in turbulent fluxes on the order of 50%–100%. Sampling strategies that mitigate random errors are discussed in light of our results.

Greene, Brian R. [Now at Verisk Extreme Event Solu↗
NASA NTRS2023

Magnetic Anisotropy and Stress Dependent Epoxy Wetting in FeNi-based Metal Amorphous Nanocomposite

Recently developed FeNi-based metal amorphous nanocomposites (MANCs) used in high-speed motors (HSMs) exhibit reduced eddy current losses while maintaining good mechanical properties and glass-forming abilities. Magnetic anisotropy in (Fe 70 Ni 30 ) 80 Nb 4 B 14 Si 2 amorphous magnetic ribbon (AMR) in the as-cast state and upon conventional (T CA ) and strain (T SA ) annealing heat treatment is investigated. From ribbon samples in as-cast condition, quenched in stress from planar flow casting (PFC) induced as-cast curvature derived uniaxial magnetic anisotropy. Stress relief by conventional furnace annealing at T CA > 350 °C achieved isotropic properties in the bulk. Annealing about the primary crystallization temperature, T CA ~ 450 °C, resulted in the formation of both FCC and BCC nanocrystallites and evolution to isotropic bulk magnetic properties confirming the random anisotropy model. In samples strain annealed at T SA = 440 °C at various tensions, relatively large controlled induced uniaxial anisotropy is achieved. The largest magnetic anisotropy occurs in annealing under the stress of 250 MPa yielding an anisotropy field of 7.1 x 10 4 A/m. Surface anisotropy observed by MOKE differs from bulk anisotropy due to image contrast from closure domains. Epoxy coatings are important for improved bonding, mechanical properties, and resistivity in tape-wound MANC cores for HSMs. Using a sessile droplet method, the equilibrium contact angle of an epoxy droplet on a tensile stress annealed MANC exhibits stress dependent surface energies. Anisotropic wetting in FeNi-based MANC heat treated at T CA = 440 °C mimics surface magnetic anisotropy observed by MOKE.

soft magnetic materials↗
DOE OSTI2021

An extended polynomial chaos expansion for PDF characterization and variation with aleatory and epistemic uncertainties

This paper presents an extended polynomial chaos formalism for epistemic uncertainties and a new framework for evaluating sensitivities and variations of output probability density functions (PDF) to uncertainty in probabilistic models of input variables. An ”extended” polynomial chaos expansion (PCE) approach is developed that accounts for both aleatory and epistemic uncertainties, modeled as random variables, thus allowing a unified treatment of both types of uncertainty. We explore in particular epistemic uncertainty associated with the choice of prior probabilistic models for input parameters. A PCE-based Kernel Density (KDE) construction provides a composite map from the PCE coefficients and germ to the PDF of quantities of interest (QoI). Here, the sensitivities of these PDF with respect to the input parameters are then evaluated. Input parameters of the probabilistic models are considered. By sampling over the epistemic random variable, a family of PDFs is generated and the failure probability is itself estimated as a random variable with its own PCE. Integrating epistemic uncertainties within the PCE framework results in a computationally efficient paradigm for propagation and sensitivity evaluation. Two typical illustrative examples are used to demonstrate the proposed approach.

Aleatory uncertainty↗
NASA NTRS1986

multiplex gas chromatography: A novel analytical technique for future planetary studies

Determination of molecular species comprised of the biogenic elements in the atmospheres of planets and moons of the solar system is one the foremost requirements of the exobiologist studying chemical evolution and the origin of life. Multiplex chromatography is a technique where many samples are pseudo-randomly introduced to the chromatograph without regard to elution of preceding components. The resulting data are then reduced using mathematical techniques such as cross correlation or Fourier Transforms. To demonstrate the utility of this technique for future solar system exploration, chemical modulators were developed. Several advantages were realized from this technique in combination with these modulators: improvement in detection limits of several orders of magnitude, improvement in the analysis of complex mixtures by selectively modulating some of the components present in the sample, increase in the number of analyses that can be conducted in a given period of time, and reduction in the amount of expendables needed to run an analysis. In order to apply this technique in a real application, methane in ambient air was monitored continuously over a period of one week. By using ambient air as its own carrier all expendables beyond power were eliminated.

Valentin, J. R.↗
DOE OSTI2024

An Empirical Quantile Estimation Approach for Chance-Constrained Nonlinear Optimization Problems

We investigate an empirical quantile estimation approach to solve chance-constrained nonlinear optimization problems. Our approach is based on the reformulation of the chance constraint as an equivalent quantile constraint to provide stronger signals on the gradient. In this approach, the value of the quantile function is estimated empirically from samples drawn from the random parameters, and the gradient of the quantile function is estimated via a finite-difference approximation on top of the quantile-function-value estimation. We establish a convergence theory of this approach within the framework of an augmented Lagrangian method for solving general nonlinear constrained optimization problems. The foundation of the convergence analysis is a concentration property of the empirical quantile process, and the analysis is divided based on whether or not the quantile function is differentiable. In contrast to the sampling-and-smoothing approach used in the literature, the method developed in this paper does not involve any smoothing function and hence the quantile-function gradient approximation is easier to implement and there are less accuracy-control parameters to tune. Furthermore, we demonstrate the effectiveness of this approach and compare it with a smoothing method for the quantile-gradient estimation. Numerical investigation shows that the two approaches are competitive for certain problem instances.

Applied Probability↗
DOE OSTI2020

A data-driven approach to sampling matrix selection for compressive sensing

Sampling is a fundamental aspect of any implementation of compressive sensing. Typically, the choice of sampling method is guided by the reconstruction basis. However, this approach can be problematic with respect to certain hardware constraints and is not responsive to domain-specific context. We propose a method for defining an order for a sampling basis that is optimal with respect to capturing variance in data, thus allowing for meaningful sensing at any desired level of compression. We focus on the Walsh-Hadamard sampling basis for its relevance to hardware constraints, but our approach applies to any sampling basis of interest. We illustrate the effectiveness of our method on the Physical Sciences Inc. Fabry-Perot interferometer sensor multispectral dataset, the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset, and a Colorado State University Swiss Ranger depth image dataset. The spectral datasets consist of simulant experiments, including releases of chemicals such as GAA and SF6. We combine our sampling and reconstruction with the adaptive coherence estimator (ACE) and bulk coherence for chemical detection and we incorporate an algorithmic threshold for ACE values to determine the presence or absence of a chemical. We compare results across sampling methods in this context. We have successful chemical detection at a compression rate of 90%. For all three datasets, we compare our sampling approach to standard orderings of sampling basis such as random, sequency, and an analog of sequency that we term `frequency.' In one instance, the peak signal to noise ratio was improved by over 30% across a test set of depth images.

compressive sensing, hyperspectral imaging, Walsh-↗
NASA NTRS1991

Gas chromatographic concepts for the analysis of planetary atmospheres

Over the last few years, new gas chromatographic (GC) concepts were developed for use on board spacecraft or any other restricted environments for determining the chemical composition of the atmosphere and surface material of various planetary bodies. Future NASA Missions include an entry probe that will be sent to Titan and various spacecraft that will land on Mars. In order to be able to properly respond to the mission science requirements and physical restrictions imposed on the instruments by these missions, GC analytical techniques are being developed. Some of these techniques include hardware and mathematical techniques that will improve GC sensitivity and increase the sampling rate of a GC descending through a planetary atmosphere. The technique of Multiplex Gas Chromatography (MGC) is an example of a technique that was studied in a simulated Titan atmosphere. In such an environment, the atmospheric pressure at instrument deployment is estimated to be a few torr. Thus, at such pressures, the small amount of sample that is acquired might not be enough to satisfy the detection requirements of the gas chromatograph. In MGC, many samples are pseudo-randomly introduced to the chromatograph without regard to elution of preceding components. The resulting data is then reduced using mathematical techniques such as cross-correlation of Fourier Transforms. Advantages realized from this technique include: improvement in detection limits of several orders of magnitude and increase in the number of analyses that can be conducted in a given period of time. Results proving the application of MGC at very low pressures emulating the same atmospheric pressures that a Titan Probe will encounter when the instruments are deployed are presented. The sample used contained hydrocarbons that are expected to be found in Titan's atmosphere. In addition, a new selective modulator was developed to monitor water under Martian atmospheric conditions. Since this modulator is selective only to water, the need for a GC column is eliminated. This results in further simplification of the instrument.

Valentin, J. R.↗
DOE OSTI2018

Crystallographic and magnetic investigations of textured bismuth ferrite lead titanate layers

In present work we demonstrate the synthesis of textured 60BiFeO 3 –40PbTiO 3 (60:40 BFPT) by using the templated grain growth technique with platelets of BaTiO 3 . Synthesised 60:40 BFPT has been examined by High-energy x-ray diffraction utilizing a synchrotron x-ray source. The crystallographic structure refinement results revealed the co-existence of monoclinic and tetragonal phases. By employing an external electric field, the mixed monoclinic/tetragonal phases transformed to the predominantly tetragonal phase. In addition, a crystallographic texture refinement was completed on unpoled state of 60:40 BFPT which showed the sample had 1.3 multiples of random distribution (MRD) $\{100\}$ crystallographic textured in tetragonal phase. However, magnetic measurements showed isotropic ferromagnetism for the sample which is not in agreement with the crystallographic texture properties of the sample. Low temperature magnetic transition has been found in zero field cooled—field cooled (ZFC-FC) measurements. This could be due to the possible existence of a secondary magnetic phase, which dominated the magnetic result.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Motion Planning Algorithms for Safety and Quantum Computing Efficiency

Motion planning remains a fundamental problem in robotics. Sampling-based algorithms use randomization to allow efficient solutions to this complex problem. As mobile robots and autonomous vehicles become more prevalent in everyday life, motion planning must be applied to increasingly challenging scenarios. Safety has become a paramount concern in motion planning for ensuring robotic applications enrich human lives. To date, many motion planning techniques to increase safety in the face of uncertain and dynamic environments have been developed. This dissertation first addresses distributional safety of Rapidly-Exploring Random Trees (RRT) through our algorithm W-Safe RRT. To acknowledge distributional uncertainty and poor modeling, W-Safe RRT uses the Wasserstein metric to provide a probabilistic bound on the distributional distance between a robot and obstacles. Human-interpretable environmental agent classification allows online safety margin adaptation. We propose and analyze an integrating region method for online classification that increases actor labeling accuracy based on behavioral feature values when compared to state of the art methods. The method performs class assignments based on local maximum likelihood in a created behavioral feature-space, allowing a notion of classification uncertainty. Model-based methods with safety guarantees can quickly become computationally in tractable, especially with multiple agents, higher dimensions, and plentiful unknowns. Sampling based algorithms have been parallelized for computation with multi-core computers and GPUs. We consider the use of quantum algorithms and computers for sampling-based motion planning for the first time. Quantum computing performs operations on superpositions of states and can solve certain problems much more efficiently than classical computers, but introduces previously unseen challenges. With Quantum-RRT, we recast the motion planning problem into a database-search structure and use Quantum Amplitude Amplification to find reachable states in the database with a quadratic performance increase over classical methods. We address two error sources with this method: quantum measurement and quantum oracle errors. We then extend this method to Parallel Quantum-RRT, which uses a manager-worker architecture with multiple parallel quantum workers to increase database search efficiency. We compare algorithm architectures and characterize probabilities of multiple workers finding solutions. Lastly, we test in simulation the quantum algorithms against classical versions in a wide variety of scenarios, concluding that a similar parallelization improvement is to be found in the quantum case as was found in the parallelization of classical RRT.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Evaluation of Rock Powdering Methods to Obtain Fine-grained Samples for CHEMIN, a Combined XRD/XRF Instrument

A miniature XRD/XRF (X-ray diffraction / X-ray fluorescence) instrument, CHEMIN, is currently being developed for definitive mineralogic analysis of soils and rocks on Mars. One of the technical issues that must be addressed to enable remote XRD analysis is how best to obtain a representative sample powder for analysis. For powder XRD analyses, it is beneficial to have a fine-grained sample to reduce preferred orientation effects and to provide a statistically significant number of crystallites to the X-ray beam. Although a two-dimensional detector as used in the CHEMIN instrument will produce good results even with poorly prepared powder, the quality of the data will improve and the time required for data collection will be reduced if the sample is fine-grained and randomly oriented. A variety of methods have been proposed for XRD sample preparation. Chipera et al. presented grain size distributions and XRD results from powders generated with an Ultrasonic/Sonic Driller/Corer (USDC) currently being developed at JPL. The USDC was shown to be an effective instrument for sampling rock to produce powder suitable for XRD. In this paper, we compare powder prepared using the USDC with powder obtained with a miniaturized rock crusher developed at JPL and with powder obtained with a rotary tungsten carbide bit to powders obtained from a laboratory bench-scale Retsch mill (provides benchmark mineralogical data). These comparisons will allow assessment of the suitability of these methods for analysis by an XRD/XRF instrument such as CHEMIN.

Chipera, S. J.↗
DOE OSTI2022

Differentially Private Synthesis and Sharing of Network Data Via Bayesian Exponential Random Graph Models

Abstract Network data often contain sensitive relational information. One approach to protecting sensitive information while offering flexibility for network analysis is to share synthesized networks based on the information in originally observed networks. We employ differential privacy (DP) and exponential random graph models (ERGMs) and propose the DP-ERGM method to synthesize network data. We apply DP-ERGM to two real-world networks. We then compare the utility of synthesized networks generated by DP-ERGM, the DyadWise Randomized Response (DWRR) approach, and the Synthesis through Conditional distribution of Edge given nodal Attribute (SCEA) approach. In general, the results suggest that DP-ERGM preserves the original information significantly better than two other approaches in network structural statistics and inference for ERGMs and latent space models. Furthermore, DP-ERGM satisfies node DP through modeling the global network structure with ERGM, a stronger notion of privacy than the edge DP under which DWRR and SCEA operate.

graph synthesis↗