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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Development of a narrow stopband filter for spectroscopic fast ion deuterium-alpha measurements

A new technique to attenuate the unshifted deuterium Balmer-alpha (D-alpha) emission is developed and tested for the fast ion D-alpha (FIDA) diagnostic. The unshifted D-alpha emission, at λ = 656.1 nm, is around three orders of magnitude higher than the desired FIDA emission. Blocking the strong emission feature is essential to prevent blooming and light smearing on the CCD chip and scattered light contamination. The new method is a notch filter approach that utilizes the reflection from ultra-narrow bandpass filters to block the saturating signal before it enters the spectrometer. Collimated light from the fibers is reflected off the filter at a 15° angle of incidence. Measurements show that a center wavelength transmission of 0.006 and a blocking full width at half maximum of ~ 1 nm are achieved by using a 200 μm fiber and a 20 mm focal length collimator with two filters.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

The Simons Observatory: validation of reconstructed power spectra from simulated filtered maps for the small aperture telescope survey

We present a transfer function-based method to estimate angular power spectra from filtered maps for cosmic microwave background (CMB) surveys. This is especially relevant for experiments targeting the faint primordial gravitational wave signatures in CMB polarisation at large scales, such as the Simons Observatory (SO) small aperture telescopes. While timestreams can be filtered to mitigate the contamination from low-frequency noise, usual methods that calculate the mode coupling at individual multipoles can be challenging for experiments covering large sky areas or reaching few-arcminute resolution. The method we present here, although approximate, is more practical and faster for larger data volumes. We validate it through the use of simulated observations approximating the first year of SO data, going from half-wave plate-modulated timestreams to maps, and using simulations to estimate the mixing of polarisation modes induced by an example of time-domain filtering. We show its performance through an example null test and with an end-to-end pipeline that performs inference on cosmological parameters, including the tensor-to-scalar ratio r. The performance demonstration uses simulated observations at multiple frequency bands. We find that the method can recover unbiased parameters for our simulated noise levels.

CMBR experiments↗
DOE OSTI2023

Multi-regime mixing modeling for local extinction and re-ignition in turbulent non-premixed flame by using LES/FDF method

Local extinction and re-ignition occur in turbulent non-premixed combustion when the Damköhler number is not large enough and the combustion is not fully mixing controlled. The occurrence of local extinction introduces locally extinguished flame holes followed by a premixed flame propagation toward the hole center to potentially reignite the flame. The co-existence of the non-premixed and premixed combustion regimes complicates the modeling since traditional combustion models are mostly for a single regime. In this work, we examine the effect of multi-regime mixing modeling in the transported filtered density function (FDF) method on the predictions of local extinction and reignition. Predictions of local extinction and reignition remain a challenge for the FDF method despite the progress made in the past. To account for the multi-regime combustion, two different mixing timescale models for non-premixed and premixed combustion are combined. A flame index based on the gradients of fuel and oxidizer is used to define a weighting factor to blend the two mixing timescale models. A turbulent jet non-premixed flame with substantial local extinction, the Sydney piloted jet flame L, is adopted as a test case to examine the performance of the multi-regime model in large-eddy simulation/FDF modeling. It is found that the traditional non-premixed mixing timescale model when combined with the modified Curl mixing leads to global extinction for the Sydney flame L without the presence of the premixed combustion regime. After accounting for the multi-regime combustion with proper detection of the different combustion regimes, the predictions for the flame statistics and the amount of local extinction are significantly improved. Furthermore, it suggests the need of including multi-regime combustion for the predictions of local extinction and re-ignition in turbulent non-premixed combustion configurations.

42 ENGINEERING↗
DOE OSTI2022

Implementation and optimization of the PTOLEMY transverse drift electromagnetic filter

The PTOLEMY transverse drift filter is a new concept to enable precision analysis of the energy spectrum of electrons near the tritium β-decay endpoint. Here, we detail the implementation and optimization methods for successful operation of the filter for electrons with a known pitch angle. We present the first demonstrator that produces the required magnetic field properties with an iron return-flux magnet. Two methods for the setting of filter electrode voltages are detailed. The challenges of low-energy electron transport in cases of low field are discussed, such as the growth of the cyclotron radius with decreasing magnetic field, which puts a ceiling on filter performance relative to fixed filter dimensions. Additionally, low pitch angle trajectories are dominated by motion parallel to the magnetic field lines and introduce non-adiabatic conditions and curvature drift. To minimize these effects and maximize electron acceptance into the filter, we present a three-potential-well design to simultaneously drain the parallel and transverse kinetic energies throughout the length of the filter. These optimizations are shown, in simulation, to achieve low-energy electron transport from a 1 T iron core (or 3 T superconducting) starting field with initial kinetic energy of 18.6 keV drained to < 10 eV (< 1 eV) in about 80 cm. This result for low field operation paves the way for the first demonstrator of the PTOLEMY spectrometer for measurement of electrons near the tritium endpoint to be constructed at the Gran Sasso National Laboratory (LNGS) in Italy.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

A regularized-interface method as a unified formulation for simulations of high-pressure multiphase flows

The injection of multi-species fluids into high-pressure and high-temperature environments beyond the species' critical points is commonly found in engineering applications. At these conditions, for immiscible species, both subcritical interfacial dynamics and supercritical mixing can coexist due to variations in temperature around the mixture critical point. The modeling of these complex transcritical phenomena for large-scale configurations is so far not possible. To address this issue, we propose the Regularized-Interface Method (RIM) as a unified formulation that can describe both sub- and supercritical processes as well as the transition between them. The proposed method is derived via filtering of the nanoscale interface-resolving formulation based on van der Waals' linear gradient theory. Thus, this approach allows for the consistent modeling of interfacial dynamics that vanishes at supercritical conditions, while significantly reducing the temporal and spatial resolution constraints of the original nanoscale formulation. The resulting RIM formulation is examined in interface-capturing simulations of sub-, trans-, and supercritical fuel injection processes, involving droplets and jets. Furthermore, these results highlight the importance of resolving spatio-temporal transitions from subcritical interfacial dynamics to supercritical mixing in high-pressure multiphase simulations, in contrast to commonly employed diffused-interface methods, where interfacial dynamics are often neglected.

Interface capturing↗
DOE OSTI2023

BeyondPlanck: VII. Bayesian estimation of gain and absolute calibration for cosmic microwave background experiments

We present a Bayesian calibration algorithm for cosmic microwave background (CMB) observations as implemented within the global end-to-end BEYONDPLANCK framework and applied to the Planck Low Frequency Instrument (LFI) data. Following the most recent Planck analysis, we decomposed the full time-dependent gain into a sum of three nearly orthogonal components: one absolute calibration term, common to all detectors, one time-independent term that can vary between detectors, and one time-dependent component that was allowed to vary between one-hour pointing periods. Each term was then sampled conditionally on all other parameters in the global signal model through Gibbs sampling. The absolute calibration is sampled using only the orbital dipole as a reference source, while the two relative gain components were sampled using the full sky signal, including the orbital and Solar CMB dipoles, CMB fluctuations, and foreground contributions. We discuss various aspects of the data that influence gain estimation, including the dipole-polarization quadrupole degeneracy and processing masks. Comparing our solution to previous pipelines, we find good agreement in general, with relative deviations of -0.67% (-0.84%) for 30 GHz, 0.12% (-0.04%) for 44 GHz and -0.03% (-0.64%) for 70 GHz, compared to Planck PR4 and Planck 2018, respectively. We note that the BEYONDPLANCK calibration was performed globally, which results in better inter-frequency consistency than previous estimates. Additionally, WMAP observations were used actively in the BEYONDPLANCK analysis, which both breaks internal degeneracies in the Planck data set and results in an overall better agreement with WMAP. Finally, we used a Wiener filtering approach to smoothing the gain estimates. We show that this method avoids artifacts in the correlated noise maps as a result of oversmoothing the gain solution, which is difficult to avoid with methods like boxcar smoothing, as Wiener filtering by construction maintains a balance between data fidelity and prior knowledge. Although our presentation and algorithm are currently oriented toward LFI processing, the general procedure is fully generalizable to other experiments, as long as the Solar dipole signal is available to be used for calibration.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Filterbank Multicarrier Communications for High Mobility Underwater Acoustic Links

This paper presents a new method of designing prototype filters that are robust against time and frequency dispersive (i.e., doubly dispersive) underwater acoustic (UWA) channels. The proposed design uses a modified form of Slepian sequences as a basis set to arrive at a design that is maximally compact along both time and frequency/Doppler dimensions. This keeps the inter-symbol interference confined to a small number of adjacent data symbols, both across time and frequency dimensions. It also minimizes the ramp-up (at the beginning) and ramp-down (at the end) of each synthesized data packet. Here, we test our design by making use of the BELLHOP/VirTex channel simulator and compare its performance with that of the widely used orthogonal frequency division multiplexing (OFDM) method. Our emphasis is on scenarios where the channel impulse response extends to a few hundreds of milli-seconds and the relative speed of communicating vehicles can be as large as 10 m/s. We find that while OFDM fails in majority of these scenarios, the proposed design continues to work well in all the scenarios that we simulate.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Dynamic Matrix Completion Based State Estimation in Distribution Grids

The power distribution network is undergoing tremendous transformation due to an increase in the penetration of renewable energy resources and electric vehicles. These changes have resulted in greater uncertainty and dynamics in the distribution grid states. Therefore, the ability to track and monitor system states has become a critical need for accurate and timely control actions. In this paper, we propose two dynamic sparsity-based state estimation approaches for distribution systems: (1) locally weighted matrix completion (LW-MC) and (2) Bayesian matrix completion with Kalman filter prediction (BMC-KF). The performance of the proposed dynamic state estimation strategies is compared with the classic/static matrix completion (static-MC) approach using the IEEE 37 and IEEE 123 bus test systems. Finally, results indicate that BMC-KF approach outperforms both LW-MC as well as static-MC even when 30% of the measurement data is available. Computational complexity associated with both approaches is quantified.

42 ENGINEERING↗
DOE OSTI2025

Nonlinear Ensemble Filtering with Diffusion Models: Application to the Surface Quasigeostrophic Dynamics

The intersection between classical data assimilation methods and novel machine learning techniques has attracted significant interest in recent years. Here, we explore another promising solution in which diffusion models are used to formulate a robust nonlinear ensemble filter for sequential data assimilation. Unlike standard machine learning methods, the proposed ensemble score filter (EnSF) is completely training free and can efficiently generate a set of analysis ensemble members. Here, in this study, we apply the EnSF to a surface quasigeostrophic model and compare its performance against the popular local ensemble transform Kalman filter (LETKF), which makes Gaussian assumptions in the analysis step. Numerical tests demonstrate that EnSF maintains stable performance in the absence of localization and for a variety of experimental settings. We find that while LETKF maintains optimal performance in the case of linear observations of the entire state and a perfect model, EnSF shows improvements over LETKF when nonlinear observations are assimilated and the system is subject to unexpected model errors. A spectral decomposition of the analysis results in this nonlinear observation regime shows that the largest improvements over LETKF occur at large scales (small wavenumbers), where LETKF lacks sufficient ensemble spread. Overall, this initial application of EnSF to a geophysical model of intermediate complexity motivates further development of the algorithm for more realistic problems.

Artificial intelligence↗
DOE OSTI2024

Cast Film Production with Polyethylene Recycled from a Post-Industrial Printed Multilayer Film by Solvent-Targeted Recovery and Precipitation

The solvent-targeted recovery and precipitation (STRAP) process separates and recovers the constituent resins in multilayer plastic packaging films by selective polymer dissolution. In this work, three different experimental methods were considered to recover a polyethylene (PE) resin from a printed multilayer film by STRAP. The methods consisted of (1) a filter bag system, (2) a Soxhlet extraction, and (3) a jacketed dissolution vessel. Cast films were produced with the PE recovered from each method and were analyzed for color, mechanical properties, and number of impurities. High-quality recycled PE cast films can be produced by increasing the solvent to plastic ratio, including a filter pore size of 100 μm, and optimizing temperature control in STRAP. Furthermore, this study demonstrates that STRAP polymers can be recycled back into plastic films, enabling the potential circularity of these packaging materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Early season prediction of within-field crop yield variability by assimilating CubeSat data into a crop model

Accurate early season predictions of crop yield at the within-field scale can be used to address a range of crop production, management, and precision agricultural challenges. While the remote sensing of within-field insights has been a research goal for many years, it is only recently that observations with the required spatio-temporal resolutions, together with efficient assimilation methods to integrate these into modeling frameworks, have become available to advance yield prediction efforts. Here we explore a yield prediction approach that combines daily high-resolution CubeSat imagery with the APSIM crop model. The approach employs APSIM to train a linear regression that relates simulated yield to simulated leaf area index (LAI). That relationship is then used to identify the optimal regression date at which the LAI provides the best prediction of yield: in this case, approximately 14 weeks prior to harvest. Instead of applying the regression on satellite imagery that is coincident, or closest to, the regression date, our method implements a particle filter that integrates CubeSat-based LAI into APSIM to provide end-of-season high-resolution (3 m) yield maps weeks before the optimal regression date. The approach is demonstrated on a rainfed maize field located in Nebraska, USA, where suitable collections of both imagery and in-situ data were available for assessment. The procedure does not require in-field data to calibrate the regression model, with results showing that even with a single assimilation step, it is possible to provide yield estimates with good accuracy up to 21 days before the optimal regression date. Yield spatial variability was reproduced reasonably well, with a strong correlation to independently collected measurements (R 2 = 0.73 and rRMSE = 12%). When the field averaged yield was compared, our approach reduced yield prediction error from 1 Mg/ha (control case based on a calibrated APSIM model), to 0.5 Mg/ha (using satellite imagery alone), and then to 0.2 Mg/ha (results with assimilation up to three weeks prior to the optimal regression date). Such a capacity to provide spatially explicit yield predictions early in the season has considerable potential to enhance digital agricultural goals and improve end-of-season yield predictions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Imposing equilibrium on experimental 3-D stress fields using Hodge decomposition and FFT-based optimization

Here, we present a methodology to impose micromechanical constraints, i.e. stress equilibrium at grain and sub-grain scale, to an arbitrary (non-equilibrated) voxelized stress field obtained, for example, by means of synchrotron X-ray diffraction techniques. The method consists in finding the equilibrated stress field closest (in L 2 -norm sense) to the measured non-equilibrated stress field, via the solution of an optimization problem. The extraction of the divergence-free (equilibrated) part of a general (non-equilibrated) field is performed using the Hodge decomposition of a symmetric matrix field, which is the generalization of the Helmholtz decomposition of a vector field into the sum of an irrotational field and a solenoidal field. The combination of: a) the Euler–Lagrange equations that solve the optimization problem, and b) the Hodge decomposition, gives a differential expression that contains the bi-harmonic operator and two times the curl operator acting on the experimental stress field. These high-order derivatives can be efficiently performed in Fourier space. The method is applied to filter the non-equilibrated parts of a synthetic piecewise constant stress fields with a known ground truth, and stress fields in Gum Metal, a beta-Ti-based alloy measured in-situ using Diffraction Contrast Tomography (DCT). In both cases, the largest corrections were obtained near grain boundaries.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Comparing Gravity Waves in a Kilometer‐Scale Run of the IFS to AIRS Satellite Observations and ERA5

Abstract Atmospheric gravity waves (GWs) impact the circulation and variability of the atmosphere. Sub‐grid scale GWs, which are too small to be resolved, are parameterized in weather and climate models. However, some models are now available at resolutions at which these waves become resolved and it is important to test whether these models do this correctly. In this study, a GW resolving run of the European Center for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), run with a 1.4 km average grid spacing (TCo7999 resolution), is compared to observations from the Atmospheric Infrared Sounder (AIRS) instrument, on NASA's Aqua satellite, to test how well the model resolves GWs that AIRS can observe. In this analysis, nighttime data are used from the first 10 days of November 2018 over part of Asia and surrounding regions. The IFS run is resampled with AIRS's observational filter using two different methods for comparison. The ECMWF ERA5 reanalysis is also resampled as AIRS, to allow for comparison of how the high resolution IFS run resolves GWs compared to a lower resolution model that uses GW drag parametrizations. Wave properties are found in AIRS and the resampled models using a multi‐dimensional S‐Transform method. Orographic GWs can be seen in similar locations at similar times in all three data sets. However, wave amplitudes and momentum fluxes in the resampled IFS run are found to be significantly lower than in the observations. This could be a result of horizontal and vertical wavelengths in the IFS run being underestimated.

Meteorology & Atmospheric Sciences↗
DOE OSTI2021

Quantitative imaging and automated fuel pin identification for passive gamma emission tomography

Compliance of member States to the Treaty on the Non-Proliferation of Nuclear Weapons is monitored through nuclear safeguards. The Passive Gamma Emission Tomography (PGET) system is a novel instrument developed within the framework of the International Atomic Energy Agency (IAEA) project JNT 1510, which included the European Commission, Finland, Hungary and Sweden. The PGET is used for the verification of spent nuclear fuel stored in water pools. Advanced image reconstruction techniques are crucial for obtaining high-quality cross-sectional images of the spent-fuel bundle to allow inspectors of the IAEA to monitor nuclear material and promptly identify its diversion. In this work, we have developed a software suite to accurately reconstruct the spent-fuel cross sectional image, automatically identify present fuel rods, and estimate their activity. Unique image reconstruction challenges are posed by the measurement of spent fuel, due to its high activity and the self-attenuation. While the former is mitigated by detector physical collimation, we implemented a linear forward model to model the detector responses to the fuel rods inside the PGET, to account for the latter. The image reconstruction is performed by solving a regularized linear inverse problem using the fast-iterative shrinkage-thresholding algorithm. We have also implemented the traditional filtered back projection (FBP) method based on the inverse Radon transform for comparison and applied both methods to reconstruct images of simulated mockup fuel assemblies. Higher image resolution and fewer reconstruction artifacts were obtained with the inverse-problem approach, with the mean-square-error reduced by 50%, and the structural-similarity improved by 200%. We then used a convolutional neural network (CNN) to automatically identify the bundle type and extract the pin locations from the images; the estimated activity levels finally being compared with the ground truth. The proposed computational methods accurately estimated the activity levels of the present pins, with an associated uncertainty of approximately 5%.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Going Forward with the Nancy Grace Roman Space Telescope Transient Survey: Validation of Precision Forward-modeling Photometry for Undersampled Imaging

The Nancy Grace Roman Space Telescope (Roman) is an observatory for both wide-field observations and coronagraphy that is scheduled for launch in the mid-2020s. Part of the planned survey is a deep, cadenced field or fields that enable cosmological measurements with type Ia supernovae (SNe Ia). With a pixel scale of 0$_.^"$11, the Wide Field Instrument will be undersampled, presenting a difficulty for precisely subtracting the galaxy light underneath the SNe. We use simulated data to validate the ability of a forward-model code (such codes are frequently also called “scene-modeling” codes) to perform precision supernova photometry for the Roman SN survey. Our simulation includes over 760,000 image cutouts around SNe Ia or host galaxies (~10% of a full-scale survey). To have a realistic 2D distribution of underlying galaxy light, we use the VELA simulated high-resolution images of galaxies. We run each set of cutouts through our forward-modeling code which automatically measures time-dependent SN fluxes. Given our assumed inputs of a perfect model of the instrument point-spread functions and calibration, we find biases at the millimagnitude level from this method in four red filters (Y106, J129, H158, and F184), easily meeting the 0.5% Roman inter-filter calibration requirement for a cutting-edge measurement of cosmological parameters using SNe Ia. Simulated data in the bluer Z087 filter shows larger ~ 2–3 mmg biases, also meeting this requirement, but with more room for improvement. Our forward-model code has been released on Zenodo.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A Model-Reference Adaptive Direct-Power Control Scheme for Grid-Interactive Inverters

This paper develops an adaptive direct power control scheme for grid-interactive inverters while the control parameters are adaptively adjusted as the grid and filter parameters vary under different conditions. To demonstrate the efficacy and robustness of the adaptive operation, the performance of the adaptive controller is compared with a typical direct power controller by estimating the filter and grid impedances using a model reference parameter estimation technique with small signal injection. The simulation results verify that the developed methodology can track the reference power setpoints under output circuit parameter uncertainties while the typical design leads to instability. To improve the performance of the adaptive technique and eliminate the need for filter parameter estimation, a Lyapunov stability criterion-based model-reference adaptive direct power controller is designed to track the filter impedance. The developed method was tested using MATLAB/Simulink software to demonstrate the effectiveness of the proposed method. The results verify that adaptively tuning the control parameter can still maintain stable operation if the control gains are poorly selected while applying the classical method leads to abnormal operation or instability.

Gursoy, Mehmetcan↗
DOE OSTI2022

Adaptive Internal Model Based Current Control with Embedded Active Damping of a Three-Phase Grid-Connected Inverter with LCL Filter for PV Application

This paper presents an internal model based controller with embedded active damping for a three-phase grid-connected inverter with LCL filter. A strategy to control the grid current based on the internal model principle is presented in this paper. It is known that a third-order LCL filter apart from providing better attenuation also poses the problem of initiating unwanted resonance oscillations especially during transients. To address this problem, an active damping strategy based on virtual resistance emulation on the grid side filter is implemented. Also, to ensure effective active damping, an online parameter update strategy is implemented for the filter parameters. The proposed method provides better attenuation to oscillation damping and achieves good tracking performance without adversely affecting the bandwidth. The architecture is verified based on computer simulations via MATLAB/Simulink and PLECS domain and various important case study results are presented.

grid-connected inverter↗
DOE OSTI2024

Semantic Stealth: Crafting Covert Adversarial Patches for Sentiment Classifiers Using Large Language Models

Deep learning models have been shown to be vulnerable to adversarial attacks, in which perturbations to their inputs cause the model to produce incorrect predictions. As opposed to adversarial attacks in computer vision, where small changes introduced to pixel values can drastically alter a model's output while remaining imperceptible to humans, text-based attacks are difficult to conceal due to the discrete nature of tokens. Consequently, unconstrained gradient-based attacks often produce adversarial examples that lack semantic meaning, rendering them detectable through visual inspection or perplexity filters. In contrast to methods that rely on gradient-based optimization in the embedding space, we propose an approach that leverages a Large Language Model's ability to generate grammatically correct and semantically meaningful text to craft adversarial patches that seamlessly blend in with the original input text. These patches can be used to alter the behavior of a target model, such as a text classifier. Since our approach does not rely on gradient backpropagation, it only requires access to the target model's confidence scores, making it a grey-box attack. We demonstrate the feasibility of our approach using open-source LLMs, including Intel's Neural Chat, Llama2, and Mistral-Instruct, to generate adversarial patches capable of altering the predictions of a distilBERT model fine-tuned on the IMDB reviews dataset for sentiment classification.

Roa Carvajal, Maria↗