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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Characterizing Tradeoffs in Memory, Accuracy, and Speed for Chemistry Tabulation Techniques

Chemistry tabulation is a common approach in practical simulations of turbulent combustion at engineering scales. Linear interpolants have traditionally been used for accessing precomputed multidimensional tables but suffer from large memory requirements and discontinuous derivatives. Higher-degree interpolants address some of these restrictions but are similarly limited to relatively low-dimensional tabulation. Artificial neural networks (ANNs) can be used to overcome these limitations but cannot guarantee the same accuracy as interpolants and introduce challenges in reproducibility and reliable training. These challenges are enhanced as the physics complexity to be represented within the tabulation increases. Here, we assess the efficiency, accuracy, and memory requirements of Lagrange polynomials, tensor product B-splines, and ANNs as tabulation strategies. We analyze results in the context of nonadiabatic flamelet modeling where higher dimension counts are necessary. While ANNs do not require structuring of data, providing benefits for complex physics representation, interpolation approaches often rely on some structuring of the table. Interpolation using structured table inputs that are not directly related to the variables transported in a simulation can incur additional query costs. This is demonstrated in the present implementation of heat losses. We show that ANNs, despite being difficult to train and reproduce, can be advantageous for high-dimensional, unstructured datasets relevant to nonadiabatic flamelet models. Furthermore we demonstrate that Lagrange polynomials show significant speedup for similar accuracy compared to B-splines.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Predicting U.S. federal fleet electric vehicle charging patterns using internal combustion engine vehicle fueling transaction statistics

Utilizing fueling transactions from internal combustion engine vehicles (ICEVs), the authors estimated how frequently midday public charging would be required for U.S. federal fleet battery electric vehicles (BEVs). Fueling transaction summary statistics are more widely available than trip-level telematics data, making this methodology more accessible and transferable to other researchers and fleet managers considering BEV replacements. For example, readers can easily apply a linear model using only the count of back-to-back fueling events at gas stations over 57 straight-line miles apart to predict days exceeding range. This linear regression predicted binned days exceeding 250 miles at 80% accuracy on a hold-out test set from the same fleet as the training data and 66 % accuracy on a new fleet displaying different driving behaviors. The authors additionally provide linear equations for days exceeding 200 and 300 miles as alternative range estimates to account for differences in BEV range and temperature impacts. Beyond the single-feature linear models which readers can apply, the authors tuned and trained other machine learning models on a variety of fueling transaction statistics including consecutive transaction distances, transaction distance from garage, estimated miles traveled from fuel economy and fuel quantity, and transaction periodicity. Utilizing a subset of 1678 light-duty federal fleet vehicles which contained daily vehicle miles traveled (VMT) in addition to fueling statistics, the authors determined which fueling transaction statistics were most relevant in predicting driving days exceeding 250 miles (an approximation of BEV rated driving range). In support of the U.S. federal fleet transition to zero-emission vehicles (ZEVs), the authors used these statistics and machine learning models to predict the frequency of BEV midday charging. After training models on the subset with VMT, the authors predicted days exceeding rated range for 112,902 light-duty vehicles operating in similar circumstances in the federal fleet using a Support Vector Regressor (SVR). In conclusion, they then used the projections as part of the ZEV Planning and Charging (ZPAC) tool to identify optimal candidates for BEVs for the federal fleet. An anonymized version of ZPAC is included in the supplementary materials.

25 ENERGY STORAGE↗
DOE OSTI2016

FERMI LAT STACKING ANALYSIS OF SWIFT LOCALIZED GRBs

We perform a comprehensive stacking analysis of data collected by the Fermi Large Area Telescope (LAT) of γ -ray bursts (GRBs) localized by the Swift spacecraft, which were not detected by the LAT but which fell within the instrument’s field of view at the time of trigger. We examine a total of 79 GRBs by comparing the observed counts over a range of time intervals to that expected from designated background orbits, as well as by using a joint likelihood technique to model the expected distribution of stacked counts. We find strong evidence for subthreshold emission at MeV to GeV energies using both techniques. This observed excess is detected during intervals that include and exceed the durations typically characterizing the prompt emission observed at keV energies and lasts at least 2700 s after the co-aligned burst trigger. By utilizing a novel cumulative likelihood analysis, we find that although a burst’s prompt γ -ray and afterglow X-ray flux both correlate with the strength of the subthreshold emission, the X-ray afterglow flux measured by Swift ’ s X-ray Telescope at 11 hr post trigger correlates far more significantly. Overall, the extended nature of the subthreshold emission and its connection to the burst’s afterglow brightness lend further support to the external forward shock origin of the late-time emission detected by the LAT. These results suggest that the extended high-energy emission observed by the LAT may be a relatively common feature but remains undetected in a majority of bursts owing to instrumental threshold effects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

A cascaded model of spectral distortions due to spectral response effects and pulse pileup effects in a photon-counting x-ray detector for CT

Purpose: Energy discriminating, photon-counting detectors (PCDs) are an emerging technology for computed tomography (CT) with various potential benefits for clinical CT. The photon energies measured by PCDs can be distorted due to the interactions of a photon with the detector and the interaction of multiple coincident photons. These effects result in distorted recorded x-ray spectra which may lead to artifacts in reconstructed CT images and inaccuracies in tissue identification. Model-based compensation techniques have the potential to account for the distortion effects. This approach requires only a small number of parameters and is applicable to a wide range of spectra and count rates, but it needs an accurate model of the spectral distortions occurring in PCDs. The purpose of this study was to develop a model of those spectral distortions and to evaluate the model using a PCD (model DXMCT-1; DxRay, Inc., Northridge, CA) and various x-ray spectra in a wide range of count rates. Methods: The authors hypothesize that the complex phenomena of spectral distortions can be modeled by: (1) separating them into count-rate independent factors that we call the spectral response effects (SRE), and count-rate dependent factors that we call the pulse pileup effects (PPE), (2) developing separate models for SRE and PPE, and (3) cascading the SRE and PPE models into a combined SRE+PPE model that describes PCD distortions at both low and high count rates. The SRE model describes the probability distribution of the recorded spectrum, with a photo peak and a continuum tail, given the incident photon energy. Model parameters were obtained from calibration measurements with three radioisotopes and then interpolated linearly for other energies. The PPE model used was developed in the authors’ previous work [K. Taguchi et al., “Modeling the performance of a photon counting x-ray detector for CT: Energy response and pulse pileup effects,” Med. Phys. 38(2), 1089–1102 (2011)]. The agreement between the x-ray spectra calculated by the cascaded SRE+PPE model and the measured spectra was evaluated for various levels of deadtime loss ratios (DLR) and incident spectral shapes, realized using different attenuators, in terms of the weighted coefficient of variation (COV{sub W}), i.e., the root mean square difference weighted by the statistical errors of the data and divided by the mean. Results: At low count rates, when DLR < 10%, the distorted spectra measured by the DXMCT-1 were in agreement with those calculated by SRE only, with COV{sub W}'s less than 4%. At higher count rates, the measured spectra were also in agreement with the ones calculated by the cascaded SRE+PPE model; with PMMA as attenuator, COV{sub W} was 5.6% at a DLR of 22% and as small as 6.7% for a DLR as high as 55%. Conclusions: The x-ray spectra calculated by the proposed model agreed with the measured spectra over a wide range of count rates and spectral shapes. The SRE model predicted the distorted, recorded spectra with low count rates over various types and thicknesses of attenuators. The study also validated the hypothesis that the complex spectral distortions in a PCD can be adequately modeled by cascading the count-rate independent SRE and the count-rate dependent PPE.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Prime VI

SAND2025-03757O Prime VI is a distribution-of-disease outbreak model calibration code based on variational inference. It accompanies a publication for submission to Statistics in Medicine journal, and the code will be maintained for open-source use on Sandia's GitLab. The software provides methods for calibrating an epidemiological model to measured case-count data for a multitude of correlated spatial regions. The code solves a Bayesian inverse problem for model calibration where the posterior over-model parameters are approximated through a custom implementation of variational inference. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Safta, Cosmin↗
DOE OSTI2016

X-ray Characterization of a Multichannel Smart-Pixel Array Detector

The Voxtel VX-798 is a prototype X-ray pixel array detector (PAD) featuring a silicon sensor photodiode array of 48 x 48 pixels, each 130 mu m x 130 mu m x 520 mu m thick, coupled to a CMOS readout application specific integrated circuit (ASIC). The first synchrotron X-ray characterization of this detector is presented, and its ability to selectively count individual X-rays within two independent arrival time windows, a programmable energy range, and localized to a single pixel is demonstrated. During our first trial run at Argonne National Laboratory's Advance Photon Source, the detector achieved a 60 ns gating time and 700 eV full width at half-maximum energy resolution in agreement with design parameters. Each pixel of the PAD holds two independent digital counters, and the discriminator for X-ray energy features both an upper and lower threshold to window the energy of interest discarding unwanted background. This smart-pixel technology allows energy and time resolution to be set and optimized in software. It is found that the detector linearity follows an isolated dead-time model, implying that megahertz count rates should be possible in each pixel. Measurement of the line and point spread functions showed negligible spatial blurring. When combined with the timing structure of the synchrotron storage ring, it is demonstrated that the area detector can perform both picosecond time-resolved X-ray diffraction and fluorescence spectroscopy measurements.

CMOS ASIC↗
DOE OSTI2020

Improving the Performance of Integrated Power-Hardware-in-the-Loop and Quasi-Static Time-Series Simulations

This paper introduces a novel multirate cosimulation architecture that overcomes previous challenges integrating faster real-time, microseconds-scale, power-hardware-in-the-loop (PHIL) with larger scale but slower, real-time, seconds-scale, quasi-static time-series (QSTS) simulation. Specifically, an intermediate reduced-equivalent electromagnetic transient (EMT) model duplicates the key power system topology to capture high-speed dynamics and converge hardware-power system interactions between QSTS updates. Exchanging multidimensional parameter vectors (loads, control status, etc.) between the QSTS and EMT models enables capturing the interactions of the rich, high-node-count (thousands of electrical nodes) QSTS model with the hardware. This architecture offers full spatial resolution from QSTS, including individual load dynamics, actual distribution management system controls, and nodal voltages. Simultaneously, the intermediate EMT model provides more detailed high-speed transients of key distributed energy resource hardware interactions. In addition, we use careful PHIL interface design and the exchange of complex power data, rather than current, to further improve performance. This cosimulation architecture is demonstrated by testing a simulated distribution system with an interconnected 500-kVA advanced photovoltaic (PV) inverter in PHIL. In conclusion, the architecture successfully captured PV local volt-volt ampere reactive (volt-VAR) control interactions with the larger network under time-varying electrical and weather conditions.

42 ENGINEERING↗
DOE OSTI2021

A Bayesian nonparametric analysis for zero-inflated multivariate count data with application to microbiome study

High-throughput sequencing technology has enabled researchers to profile microbial communities from a variety of environments, but analysis of multivariate taxon count data remains challenging. Here, we develop a Bayesian nonparametric (BNP) regression model with zero inflation to analyse multivariate count data from microbiome studies. A BNP approach flexibly models microbial associations with covariates, such as environmental factors and clinical characteristics. The model produces estimates for probability distributions which relate microbial diversity and differential abundance to covariates, and facilitates community comparisons beyond those provided by simple statistical tests. We compare the model to simpler models and popular alternatives in simulation studies, showing, in addition to these additional community-level insights, it yields superior parameter estimates and model fit in various settings. The model's utility is demonstrated by applying it to a chronic wound microbiome data set and a Human Microbiome Project data set, where it is used to compare microbial communities present in different environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

ThermoPore: Predicting part porosity based on thermal images using deep learning

Part qualification is often a critical and labor-intensive process in additive manufacturing, particularly in the detection of defects such as porosity, which stands to benefit significantly from advancements in machine learning. We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts based on thermal images acquired during the build. The quantification task builds upon the established Convolutional Neural Network model architecture to predict pore count and the localization task leverages the spatial and temporal attention mechanisms of the novel Video Vision Transformer model to indicate areas of expected porosity. Our model for porosity quantification achieved a R 2 score of 0.57 and our model for porosity localization produced an average Intersection over Union (IoU) score of 0.32 and a maximum of 1.0. This work is setting the foundations of part porosity “Digital Twins” based on additive manufacturing monitoring data and can be applied downstream to reduce time-intensive post-inspection and testing activities during part qualification and certification. In addition, we seek to accelerate the acquisition of crucial insights normally only available through ex-situ part evaluation by means of machine learning analysis of in-situ process monitoring data.

Deep learning↗
NASA NTRS1991

Galaxy formation and evolution

The presence of high z quasars and radio galaxies tells us that galaxy formation began at z greater than 5, but leaves unanswered the question of when the bulk of galaxies formed. Recent near infrared number counts of galaxies strongly favor a cosmological geometry with q(sub 0) = 0.5 and lambda = 0. Such a model grossly underpredicts blue galaxy counts. Spectroscopy shows that the excess blue galaxies at B = 24 are dwarfs at z approximately equals 0.4 which are no longer seen at the present time. These dwarfs must contain a large amount of baryonic matter which is not included in current estimates of baryonic omega .

Cowie, Lennox L.↗
DOE OSTI2016

Deadtime Determination of Helium Detector Using MCNP Code

In detector systems, the minimum time separation between the events is called detector's deadtime. The The intrinsic properties of the detector and the pulse processing circuitry's characteristics are the sources of deadtime. Researchers have been working on improving a detector deadtime model that can implicitly characterize a detection system's behavior while reducing counting errors. There are two commonly known dead-time models: the 'Paralyzing' and the 'Non-paralyzing' models. In reality, detection systems fit neither of these idealized models perfectly, instead falling somewhere between the two models. The paralyzing model is mathematically expressed by eq. 1, where m is the measured count rate, n is the true count rate and t is dead-time. This model assumes that each event during the dead-time will reset it to a fixed duration, thus extending the deadtime. The dead-time extension depends on the count rate. According to the non-paralyzing model, deadtime is fixed after each detected event, and all events occurring during deadtime are lost. The fraction of time during which an apparatus is sensitive is 1-mt. Therefore, the fraction of the true number of events can be recorded. In this study, a Canberra {sup 3}He neutron detector (0.5NH1/1K) deadtime value was estimated using a combination of experimental subcritical assembly measurements and theoretical MCNP simulations. The deadtime of the Canberra 10 mm diameter {sup 3}He tube detector was calculated by comparing measured counts from different locations in the subcritical assembly at Missouri University of Science and Technology Reactor (MSTR) with MCNP (Monte Carlo N-Particle Code) simulations. The deadtime calculations are provided for both the paralyzing and non-paralyzing models for the Canberra 10 mm diameter {sup 3}He tube detector. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

SU-E-T-430: Modeling MLC Leaf End in 2D for Sliding Window IMRT and Arc Therapy

Purpose: To develop a 2D geometric model for MLC accounting for leaf end dose leakage for dynamic IMRT and Rapidarc therapy. Methods: Leaf-end dose leakage is one of the problems for MLC dose calculation and modeling. Dosimetric leaf gap used to model the MLC and to count for leakage in dose calculation, but may not be accurate for smaller leaf gaps. We propose another geometric modeling method to compensate for the MLC round-shape leaf ends dose leakage, and improve the accuracy of dose calculation and dose verification. A triangular function is used to geometrically model the MLC leaf end leakage in the leaf motion direction, and a step function is used in the perpendicular direction. Dose measurements with different leaf gap, different window width, and different window height were conducted, and the results were used to fit the analytical model to get the model parameters. Results: Analytical models have been obtained for stop-and-shoot and dynamic modes for MLC motion. Parameters a=0.4, lw'=5.0 mm for 6X and a=0.54, lw'=4.1 mm for 15x were obtained from the fitting process. The proposed MLC leaf end model improves the dose profile at the two ends of the sliding window opening. This improvement is especially significant for smaller sliding window openings, which are commonly used for highly modulated IMRT plans and arc therapy plans. Conclusion: This work models the MLC round leaf end shape and movement pattern for IMRT dose calculation. The theory, as well as the results in this work provides a useful tool for photon beam IMRT dose calculation and verification.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Dynamic logistic regression and variable selection: Forecasting and contextualizing civil unrest

Civil unrest can range from peaceful protest to violent furor, and researchers are working to monitor, forecast, and assess such events to allocate resources better. Twitter has become a real-time data source for forecasting civil unrest because millions of people use the platform as a social outlet. Additionally, daily word counts are used as model features, and predictive terms contextualize the reasons for the protest. To forecast civil unrest and infer the reasons for the protest, we consider the problem of Bayesian variable selection for the dynamic logistic regression model and propose using penalized credible regions to select parameters of the updated state vector. This method avoids the need for shrinkage priors, is scalable to high-dimensional dynamic data, and allows the importance of variables to vary in time as new information becomes available. A substantial improvement in both precision and F1-score using this approach is demonstrated through simulation. Finally, we apply the proposed model fitting and variable selection methodology to the problem of forecasting civil unrest in Latin America. Our dynamic logistic regression approach shows improved accuracy compared to the static approach currently used in event prediction and feature selection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

A method to improve the sensitivity of neutron porosity measurement based on D-T source

Compensated porosity logging tool utilizing deuterium-tritium (D-T) source shows a lower sensitivity to the variation of formation porosity compared with that adopting Am-Be source. In order to improve the sensitivity, the factors of an infinite homogeneous formation influencing slowing-down length and the near to far counts ratio are analyzed in this paper. Then Monte Carlo simulation method is used to build well logging models to study the responses of a neutron porosity logging tool to hydrogen index and formation density. It shows that in addition to hydrogen index, the variation of the density also has a great impact on slowing-down length and the ratio which reduces the response sensitivity to porosity. A new model depicts the relationship between the count ratio and porosity is proposed. Finally, when the model is used to process the measured ratio, the ratio shows improved dynamic range and sensitivity to porosity compared with the values without processing.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Morphotype-resolved characterization of microalgal communities in a nutrient recovery process with ARTiMiS flow imaging microscopy

Microalgae-driven nutrient recovery represents a promising technology for phosphorus removal from wastewater while simultaneously generating biomass that can be valorized to offset treatment costs. As full-scale processes come online, system parameters including biomass composition must be carefully monitored to optimize performance and prevent culture crashes. In this study, flow imaging microscopy (FIM) was leveraged to characterize microalgal community composition in near real-time at a full-scale municipal wastewater treatment plant (WWTP) in Wisconsin, USA, and population and morphotype dynamics were examined to identify relationships between water chemistry, biomass composition, and system performance. Two FIM technologies, FlowCam and ARTiMiS, were evaluated as monitoring tools. ARTiMiS provided a more accurate estimate of total system biomass, and estimates derived from particle area as a proxy for biovolume yielded better approximations than particle counts. Deep learning classification models trained on annotated image libraries demonstrated equivalent performance between FlowCam and ARTiMiS, and convolutional neural network (CNN) classifiers proved significantly more accurate when compared to feature table-based dense neural network (DNN) models. Across a two-year study period, Scenedesmus spp. appeared most important for phosphorus removal, and were negatively impacted by elevated temperatures and increase in nitrite/nitrate concentrations. Chlorella and Monoraphidium also played an important role in phosphorus removal. For both Scenedesmus and Chlorella, smaller morphological types were more often associated with better system performance, whereas larger morphotypes likely associated with stress response(s) correlated with poor phosphorus recovery rates. Furthermore, these results demonstrate the potential of FIM as a critical technology for high-resolution characterization of industrial microalgal processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Detection of special nuclear materials with the associate particle technique

In the frame of the French trans-governmental R and D program against chemical, biological, radiological, nuclear and explosives (CBRN-E) threats, CEA is studying the detection of Special Nuclear Materials (SNM) by neutron interrogation with fast neutrons produced by an associated particle sealed tube neutron generator. The deuterium-tritium fusion reaction produces an alpha particle and a 14 MeV neutron almost back to back, allowing tagging neutron emission both in time and direction with an alpha particle position-sensitive sensor embedded in the generator. Fission prompt neutrons and gamma rays induced by tagged neutrons which are tagged by an alpha particle are detected in coincidence with plastic scintillators. This paper presents numerical simulations performed with the MCNP-PoliMi Monte Carlo computer code and with post processing software developed with the ROOT data analysis package. False coincidences due to neutron and photon scattering between adjacent detectors (cross talk) are filtered out to increase the selectivity between nuclear and benign materials. Accidental coincidences, which are not correlated to an alpha particle, are also taken into account in the numerical model, as well as counting statistics, and the time-energy resolution of the data acquisition system. Such realistic calculations show that relevant quantities of SNM (few kg) can be distinguished from cargo and shielding materials in 10 min acquisitions. First laboratory tests of the system under development in CEA laboratories are also presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Plant responses to increasing CO 2 reduce estimates of climate impacts on drought severity

Rising atmospheric CO 2 will make Earth warmer, and many studies have inferred that this warming will cause droughts to become more widespread and severe. However, rising atmospheric CO 2 also modifies stomatal conductance and plant water use, processes that are often are overlooked in impact analysis. We find that plant physiological responses to CO 2 reduce predictions of future drought stress, and that this reduction is captured by using plant-centric rather than atmosphere-centric metrics from Earth system models (ESMs). The atmosphere-centric Palmer Drought Severity Index predicts future increases in drought stress for more than 70% of global land area. This area drops to 37% with the use of precipitation minus evapo-transpiration (P-E), a measure that represents the water flux available to downstream ecosystems and humans. The two metrics yield consistent estimates of increasing stress in regions where precipitation decreases are more robust (southern North America, northeastern South America, and southern Europe). The metrics produce diverging estimates elsewhere, with P-E predicting decreasing stress across temperate Asia and central Africa. The differing sensitivity of drought metrics to radiative and physiological aspects of increasing CO 2 partly explains the divergent estimates of future drought reported in recent studies. Further, use of ESM output in offline models may double-count plant feedbacks on relative humidity and other surface variables, leading to overestimates of future stress. The use of drought metrics that account for the response of plant transpiration to changing CO 2 , including direct use of P-E and soil moisture from ESMs, is needed to reduce uncertainties in future assessment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Resurgence, Painlevé equations and conformal blocks

We discuss some physical consequences of the resurgent structure of Painlevé equations and their related conformal block expansions. Here, the resurgent structure of Painlevé equations is particularly transparent when expressed in terms of physical conformal block expansions of the associated tau functions. Resurgence produces an intricate network of inter-relations; some between expansions around different critical points, others between expansions around different instanton sectors of the expansions about the same critical point, and others between different non-perturbative sectors of associated spectral problems, via the Bethe-gauge and Painlevé-gauge correspondences. Resurgence relations exist both for convergent and divergent expansions, and can be interpreted in terms of the physics of phase transitions. These general features are illustrated with three physical examples: correlators of the 2d Ising model, the partition function of the Gross–Witten–Wadia matrix model, and the full counting statistics of one dimensional fermions, associated with Painlevé VI, Painlevé III and Painlevé V, respectively.

97 MATHEMATICS AND COMPUTING↗