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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 OSTI2023

Plasma Properties in the Earth's Magnetosheath Near the Subsolar Magnetopause: Implications for Geocoronal Density Estimates

Combined in situ ion measurements and remote sensing of energetic neutral atoms are used to determine the geocoronal Hydrogen density at large (∼10 R E ) distances from the Earth. This method for determining the geocoronal density requires global magnetospheric modeling. Observations in the Earth's subsolar magnetosheath from the Magnetospheric Multiscale mission are used to determine the accuracy of using global models to predict the geocoronal density. On average, gas dynamic and magnetohydrodynamic (MHD) models and observations are in reasonable agreement, with differences <25%. In addition, the MHD model subsolar magnetopause is about 0.5 R E sunward of the observed location. However, variations around averages are large (up to a factor of 2), indicating that global models introduce relatively large uncertainties in geocoronal density estimates. Finally, the critical ion flux in the Interstellar Boundary Explorer IBEX‐Hi energy range is often minimally affected by fluctuations of a factor of 2 in the density.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Kernel Density Estimators for SAMPLER Analyses [Slides]

This presentation discusses Sampler, specifically in terms of uncertainty quantification and states that for many simulation tasks nominal input parameters have complicated influence on the uncertainty in the final result. Sampler provides a convenient means to perform stochastic uncertainty quantification, brute force sampling parameter space and tallying changes in outputs, and it can be coupled with any SCALE modules. Perturbed responses can be collected and analyzed with standard statistical measures; however, two problems have been identified. The first problem is that for Monte Carlo simulations the individual response uncertainties are ignored, and the second problem is that features of distributions are easily hidden in histograms. They found that the solution is to add Kernel Density Estimator (KDE) analysis to the Sampler output. In conclusion, Sampler is a unique tool for stochastic uncertainty propagation, and it can be coupled with any module in SCALE. Current functionality is limited to histograms, and it may not adequately account for sample uncertainties and/or not adequately display underlying distribution features. Work is in progress to implement KDE in the Sampler module and no additional flagging is needed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Next-Cycle Optimal Dilute Combustion Control via Online Learning of Cycle-to-Cycle Variability Using Kernel Density Estimators

Dilute combustion using exhaust gas recirculation (EGR) presents a cost-effective method for increasing the efficiency of spark-ignition (SI) engines. However, the maximum amount of EGR that can be used at a given condition is limited by a rapid increment of cycle-to-cycle variability (CCV). This study describes a methodology to design a model-based stochastic optimal controller to adjust the cycle-to-cycle fuel injection quantity in order to reduce CCV and further extend the dilute limit. Given the complexity and chaotic nature of combustion events, the controller was enhanced with online learning in order to identify the statistical properties of combustion efficiency, which are needed to generate predictions for next-cycle events. This study showed that a kernel density estimator (KDE) can be used to learn the combustion properties in real time and can be incorporated into the feedback policy in order to calculate the optimal control command. Experimental results suggested that the dilute limit can be extended from 18.5% to 21% EGR fraction at an operating condition relevant for highway cruising. Additionally, the proposed controller can achieve a large CCV reduction with less fuel enrichment compared to previous methods, overall contributing to an increase in 0.2% indicated fuel conversion efficiency.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2009

Comparison of Mars Atmospheric Density Estimates from Models to Measurements from Mars Global Surveyor (MGS) Data

A recent study (Desai, 2008) has shown that the actual landing sites of Mars Pathfinder, the Mars Exploration Rovers (Spirit and Opportunity) and the Phoenix Mars Lander have been further downrange than predicted by models prior to landing Desai's reconstruction of their entries into the Martian atmosphere showed that the models consistently predicted higher densities than those found upon entry, descent and landing. Desai's results have raised a question as to whether there is a systemic problem within Mars atmospheric models. Proposal is to compare Mars atmospheric density estimates from Mars atmospheric models to measurements made by Mars Global Surveyor (MGS). Comparison study requires the completion of several tasks that would result in a greater understanding of reasons behind the discrepancy found during recent landings on Mars and possible solutions to this problem.

Justh, Hilary L.↗
DOE OSTI2018

A Novel Non-Parametric Density Estimation Approach to Measuring Muon Ionization Cooling and Reverse Emittance Exchange in the MICE Experiment

The Muon Ionization Cooling Experiment (MICE) located at Rutherford Ap- pleton Laboratory in the U.K. has demonstrated muon ionization beam cooling for the rst time. A beam of muons in MICE is produced from high-energy proton beam collision with a xed target, generating pions which in turn decay into muons. Pion-decay muons, thus, are tertiary particles and, as a result, occupy a large vol- ume in position{momentum phase space. To t the muon beam into smaller and more cost-eective accelerating devices, muon beam phase{space volume needs to be reduced (beam cooling). Ionization beam cooling, which before MICE has never been demonstrated experimentally for muons, is the only technique fast enough to be used for muons within their short lifetime. Ionization cooling occurs when muons traverse an absorbing material and lose momentum through ionization energy loss. The cooling eect in MICE is measured using two scintillating-ber tracking detec- tors. These trackers, one upstream and one downstream of the absorber, reconstruct and measure the position and momentum coordinates of each muon. Given the pre- cision MICE needed to demonstrate beam cooling, it is necessary to develop analysis tools that can account for any eects that may lead to inaccurate measurement of cooling, such as non{linear eects in beam optics. Non{parametric density estima- tion techniques, such as kernel density estimation (KDE), provide a basis for creating analysis tools that are robust against these eects, directly calculating the muon beam phase-space density and volume for demonstrating beam cooling. This thesis focuses on the application of KDE to the measurement of beam cooling in MICE. The KDE technique is validated using known distributions and is applied to sim- ulated and experimental MICE data corresponding to the various magnet, optics, and absorber congurations. Using the KDE technique, muon beam cooling in the four{dimensional transverse phase space, as well as reverse emittance exchange using MICE data have been demonstrated.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Sampling Size Optimization for Bioburden Density Estimation in Planetary Protection

Planetary protection (PP) is a discipline that focuses on minimizing the biological contamination of spacecraft to ensure compliance with international policy. Precise estimation of bioburden - the total number of microbes in or on spacecraft hardware – and the bioburden density are of utmost importance for PP. Such estimation is the way concordance with requirements is demonstrated, and it is critical for quantifying the potential risk of inadvertently contaminating other planetary bodies. Although a suite of molecular techniques have been used to thoroughly characterize and profile the microbiome of various cleanroom environments and spacecraft, the gold standard remains the physical enumeration of microbes via culturing of samples directly taken from spacecraft and associated surfaces. However, due to technical, budgetary, and programmatic constraints, only a manageable portion (around 10%) of the entire spacecraft surface is directly sampled with cotton swabs or wipes. To generate the bioburden current best estimate (CBE) for components not directly verifiable, the accepted approach is to apply a NASA-defined bioburden estimate based on the components’ manufacturing or assembly environment. This approach utilizes a prespecified bioburden density estimation that applies a maximum value across the total surface area of the specified component. For hardware components that underwent similar assembly processes, an implied bioburden is adopted for all components, based on a direct verification of a representative component within the same lot. Once all components have a CBE, the bioburden estimates are generated. In previous publication [ 1], we have shown that statistical risks quantifying the accuracy of the estimates for sampled, prespecified, and implied components can be derived and ranked. For mean squared error (MSE) function, the risks are available analytically and hence a cost function can be obtained to optimize the risks with respect to the sampling area and sampling cost. Since the sampling area and sampling cost are two complimentary variables, their sum will have a well-defined minimum. This paper presents the multivariate optimization of the integrated risk of an empirical Bayes estimator to determine the optimal sampling schedule for a given number of components. It is assumed that given a number of components, N, the bioburden density for each component can either be sampled, implied, or prespecified. The multivariate optimization searches through different options to sample, imply or prespecify the bioburden density for a component, and account for the component’s surface area and cost of sampling. The idea of the optimization is based on the observation that the statistical risk of using an estimator is a monotonically decreasing function of the sampled area. The larger the sampled area, the lower the risk of using the estimator as the estimator becomes more and more accurate as the sampling area increases. On the other hand, the cost of sampling is monotonically increasing as the sampled surface grows. This makes the risk and total cost of sampling complimentary variables which can be counterbalanced to achieve an optimal overall value with respect to the sampled surface. In this paper, the integrated risk has been used to quantify the accuracy of the estimator. This risk has been selected because it depends on neither the true value of the parameter nor on the collected data. The cost of each sample was also available to obtain the total cost of sampling of N components. The paper will present the results based on computer-simulated data as well as the data collected during the InSight mission. The computer-simulated data have N components with randomly generated total areas and each component assigned to one of the three categories according to the method of estimating of bioburden density: sampled, implied, or prespecified. The cost of sampling is also available. The cost of sampling is estimated based on a cost model provided by the planetary protection group at JPL. For this paper, the overall cost was assumed to be a linear function of exposure. The optimization process finds the allocation of the components to the three categories that minimizes the tradeoff between integrated risk and total cost. For the InSight data, a set of components is selected representing all three categories, and optimization is performed to determine if the performed allocation was optimal or if a better allocation could have been obtained. To the best of our knowledge, this work is the first attempt not only perform an accurate estimation of bioburden density but also do it in an optimal way.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Out-of-distribution detection with non-parametric density estimation for models predicting processing history of uranium ore concentrates

The rapid advancement in machine learning (ML) and computer vision (CV) coincides with the growth of interest in deploying these ML/CV models in numerous fields from medicine to social science. Similar to those areas, we have witnessed a great number of works in materials science employing ML/CV models – neural networks in particular – in their studies in recent years. These models have proven to obtain accurate performance in various tasks. However, these models struggle to attain a similar performance when encountering test samples coming from a distribution that is different from the training set. More importantly, they fail without providing any warning to the users. Therefore, we propose a framework for detecting out-of-distribution (OOD) samples to alert users when a human intervention might be necessary in this work. Specifically, we explore the use of a non-parametric density estimation method to detect OOD samples. Here, we assess OOD detection capability of the proposed framework on ML models developed for categorizing precipitation routes of U 3 O 8 when encountering OOD datasets that contain samples (1) undergone different imaging acquisition process, (2) undergone different material synthesis process, and (3) different materials than ID set. Through those experiments, we achieve an average area under the receiver operating characteristic (AUROC) of at least 91% on average in detecting OOD samples. With minimal overhead cost and superior performance, the proposed framework enables a reliable and safe system when deploying in real-world scenarios.

Convolutional neural networks↗
DOE OSTI2016

Daniell method for power spectral density estimation in atomic force microscopy

An alternative method for power spectral density (PSD) estimation—the Daniell method—is revisited and compared to the most prevalent method used in the field of atomic force microscopy for quantifying cantilever thermal motion—the Bartlett method. Both methods are shown to underestimate the Q factor of a simple harmonic oscillator (SHO) by a predictable, and therefore correctable, amount in the absence of spurious deterministic noise sources. However, the Bartlett method is much more prone to spectral leakage which can obscure the thermal spectrum in the presence of deterministic noise. By the significant reduction in spectral leakage, the Daniell method leads to a more accurate representation of the true PSD and enables clear identification and rejection of deterministic noise peaks. This benefit is especially valuable for the development of automated PSD fitting algorithms for robust and accurate estimation of SHO parameters from a thermal spectrum.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Accelerated Monte Carlo Fission Source Convergence with Fission Matrix and Kernel Density Estimators

Accurate solution of the k-eigenvalue form of the Boltzmann radiation transport equation is required in many application areas, including nuclear reactor analysis and criticality safety. Monte Carlo calculations are free of the discretization errors inherent to deterministic methods and therefore offer the most accurate solutions. However, converging the fission source can be difficult for many problems, resulting in either a substantial computational overhead or potential inaccuracies that negate many of the benefits of using Monte Carlo methods. Difficulty in converging the fission source occurs for two distinct reasons. The first is the existence of a high dominance ratio in the problem (i.e. the largest and second largest eigenvalues have approximately the same magnitude). The second difficulty arises due to undersampling of the fission source because an insufficient number of particle histories are simulated. The first difficulty is shared by deterministic solvers when using power iteration, while the second difficulty is unique to Monte Carlo solvers. In practice, both difficulties may be present in the same problem, compounding the difficulty in achieving a converged distribution. In this paper, we consider a new approach to accelerate the convergence of the fission source based on combining the use of fission matrix methods with kernel density estimators (KDEs). It has been observed that deterministic fission matrix methods show the potential to accelerate the convergence of the fission source in Monte Carlo criticality calculations but may introduce significant additional noise for difficult problems. In this paper, we have demonstrated the use of a KDE approach in combination with the fission matrix acceleration. Although the addition of the KDE initially slows the convergence of the fission matrix acceleration, it decreases the magnitude of the noise while still offering significantly improved convergence relative to analog Monte Carlo. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2016

Study of the Spacecraft Potential Under Active Control and Plasma Density Estimates During the MMS Commissioning Phase

Each spacecraft of the recently launched magnetospheric multiscale MMS mission is equipped with Active Spacecraft Potential Control (ASPOC) Instruments, which control the spacecraft potential in order to reduce spacecraft charging effects. ASPOC typically reduces the spacecraft potential to a few volts. On several occasions during the commissioning phase of the mission, the ASPOC instruments were operating only on one spacecraft at a time. Taking advantage of such intervals, we derive photoelectron curves and also perform reconstructions of the uncontrolled spacecraft potential for the spacecraft with active control and estimate the electron plasma density during those periods. We also establish the criteria under which our methods can be applied.

Andriopoulou, M.↗
DOE OSTI2020

Spectral-density estimation with the Gaussian integral transform

The spectral-density operator $\hat{ρ}(ω) = δ(ω–\hat{H})$ plays a central role in linear response theory as its expectation value, the dynamical response function, can be used to compute scattering cross sections. In this work, we describe a near optimal quantum algorithm providing an approximation to the spectral density with energy resolution $\Delta$ and error $\epsilon$ using $O(\sqrt{\text{log}_2 (1/ε)[\text{log}_2 (1 / Δ) + \text{log}_2 (1/ε)]/ Δ)}$ operations. This is achieved without using expensive approximations to the time-evolution operator, but instead exploiting qubitization to implement an approximate Gaussian integral transform of the spectral density. Finally, we also describe appropriate error metrics to assess the quality of the spectral function approximations more generally.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Bayesian Framework for Bioburden Density Estimation in Planetary Protection

To comply with the international planetary protection policy set forth by the Committee on Space Research and NASA Agency level requirements, spacecraft destined to biologically sensitive planetary bodies have to minimize terrestrial biological contamination. Analysis, testing and inspection are the standard forward verification activities that are used to demonstrate compliance with the biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA Approved Planetary Protection Methods for Culture Based Assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the 1970’s Viking Lander Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimate factors in the following variables: the observed bioburden count, representative volume processed, sampling efficiencies. Notably, to account for error in the approach, a 0 observed count is arbitrarily changed to a count of 1 for each hardware grouping. The data generated by spacecraft bioburden verification campaigns in the past have resulted in <80% of wipes and <90% of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a gamechanger for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, Empirical Bayes statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of the flight hardware surface. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for Empirical Bayes analyses. Several non-informative priors are currently under investigation to assess fitness including use of these priors to serve as a foundation to build off of NASA specification values or a basis of risk to account for unknowns during the integration and testing process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g. vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using Empirical Bayes approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2012

SIZE AND DENSITY ESTIMATION FROM IMPACT TRACK MORPHOLOGY IN SILICA AEROGEL: APPLICATION TO DUST FROM COMET 81P/WILD 2

A large number of cometary dust particles were captured with low-density silica aerogel during the NASA Stardust mission. The dust particles penetrated into the aerogel and formed various track shapes. To estimate the properties of the dust particles, such as density and size, based on the morphology of the tracks, we carried out systematic experiments testing impacts into low-density aerogel at 6 km s{sup -1} using projectiles of various sizes and densities. We found that the maximum track diameter and the ratio of the track length to the maximum track diameter in aerogel are good indicators of projectile size and density, respectively. Based on these results, we estimated the size and density of individual dust particles from comet 81P/Wild 2. The average density of the 'fluffy' dust particles and the bulk density of all dust particles were obtained as 0.35 {+-} 0.07 and 0.49 {+-} 0.18 g cm{sup -3}, respectively. These statistical data provided the content of monolithic and coarse grains in the Stardust particles, {approx}30 wt%. Combining this result with some mid-infrared observational data, we found that the content of crystalline silicates is {approx}50 wt% or more of non-volatile material.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1981

Ultrasonic velocity for estimating density of structural ceramics

The feasibility of using ultrasonic velocity as a measure of bulk density of sintered alpha silicon carbide was investigated. The material studied was either in the as-sintered condition or hot isostatically pressed in the temperature range from 1850 to 2050 C. Densities varied from approximately 2.8 to 3.2 g cu cm. Results show that the bulk, nominal density of structural grade silicon carbide articles can be estimated from ultrasonic velocity measurements to within 1 percent using 20 MHz longitudinal waves and a commercially available ultrasonic time intervalometer. The ultrasonic velocity measurement technique shows promise for screening out material with unacceptably low density levels.

Klima, S. J.↗
DOE OSTI2019

Local and statistical maps of lightning-generated wave power density estimated at the Van Allen Probes footprints from the World-Wide Lightning Location Network database

We introduce a new method that uses the World-Wide Lightning Location Network (WWLLN) to estimate both the local and the drift lightning power density at the Van Allen Probes footprints during 4.3 years (~2x10 8 strokes.). The ratio of the drift power density to the local power density defines a time-resolved WWLLN-based model of lightning-generated wave (LGW) power density ratio, $\mathcal{R_{WWLLN}}$. $\mathcal{R_{WWLLN}}$ is computed every ~34s. This ratio multiplied by the time-resolved LGW intensity measured by the Probes allows direct computation of pitch angle diffusion coefficients used in radiation belts codes. According to Statistical analysis, the median power density ratio is $\mathcal{\hat{R}_{WWLLN}}$ = 0.3 - 4 over the Americas. Elsewhere, $\mathcal{\hat{R}_{WWLLN}}$ > 1 in general. Over oceans, $\mathcal{\hat{R}_{WWLLN}}$ is larger than ~10 and varies with season (x2.5 from winter to summer). The yearly median $\mathcal{\hat{R}_{WWLLN}}$ decays as $\mathcal{\hat{R}_{WWLLN}}$~9.9/L 0.91 . The strong geographical and temporal variation should be kept in assessing effects in space. $\mathcal{R_{WWLLN}}$> 1 suggests significant LGW effects in the inner belt.

58 GEOSCIENCES↗
NASA NTRS2022

Quantifying Feedback from Narrow Line Region Outflows in Nearby Active Galaxies. IV. The Effects of Different Density Estimates on the Ionized Gas Masses and Outflow Rates

Active galactic nuclei (AGN) can launch outflows of ionized gas that may influence galaxy evolution, and quantifying their full impact requires spatially resolved measurements of the gas masses, velocities, and radial extents. We previously reported these quantities for the ionized narrow-line region outflows in six low-redshift AGN, where the gas velocities and extents were determined from Hubble Space Telescope long-slit spectroscopy. However, calculating the gas masses required multicomponent photoionization models to account for radial variations in the gas densities, which span ∼6 orders of magnitude. To simplify this method for larger samples with less spectral coverage, we compare these gas masses with those calculated from techniques in the literature. First, we use a recombination equation with three different estimates for the radial density profiles. These include constant densities, those derived from [S ii], and power-law profiles based on constant values of the ionization parameter (U). Second, we use single-component photoionization models with power-law density profiles based on constant U, and allow U to vary with radius based on the [O iii]/Hβ ratios. We find that assuming a constant density of nH = 10(^2) cm(^−3) overestimates the gas masses for all six outflows, particularly at small radii where the outflow rates peak. The use of [S ii] marginally matches the total gas masses, but also overestimates at small radii. Overall, single-component photoionization models where U varies with radius are able to best match the gas mass and outflow rate profiles when there are insufficient emission lines to construct detailed models.

Mitchell Revalski↗