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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS1972

On estimating the Venus spin vector

The improvement in spin vector and probe position estimates one may reasonably expect from the processing of such data is indicated. This was done by duplicating the ensemble calculations associated with a weighed least squares with a priori estimation technique applied to range rate data that were assumed to be unbiased and uncorrelated. The weighting matrix was assumed to be the inverse of the covariance matrix of the noise on the data. Attention is focused primarily on the spin vector estimation.

Argentiero, P. D.↗
DOE OSTI2021

Tidal Energy Resource Characterization, Bottom Lander Measurements, Cook Inlet, AK, 2021

These datasets are from tidal resource characterization measurements collected on the Terrasond High Energy Oceanographic Mooring (THEOM) from 1 July 2021 to 30 August 2021 (60 days) in Cook Inlet, Alaska. The lander was deployed at 60.7207031 N, 151.4294998 W in ~50 m of water. The dataset contains raw and processed data from the following two instruments: 1. A Nortek Signature 500 kHz acoustic Doppler current profiler (ADCP). Data were recorded in 4 Hz in the beam coordinate system from all 5 beams. Processed data has been averaged into 5 minutes bins and converted to the East-North-Up (ENU) coordinate system. 2. A Nortek Vector acoustic Doppler velocimeter (ADV). Data were recorded at 8 Hz in the beam coordinate system. Processed data has been averaged into 5 minutes bins and converted to the Streamwise - Cross-stream - Vertical (Principal) coordinate system. Turbulence statistics were calculated from 5-minute bins, with an FFT length equal to the bin length, and saved in the processed dataset. Data was read and analyzed using the DOLfYN (version 1.0.2) python package and saved in MATLAB (.mat) and netCDF (.nc) file formats. Files containing analyzed data (".b1") were standardized using the TSDAT (version 0.4.2) python package. NetCDF files can be opened using DOLfYN (e.g., `dat = dolfyn.load(''*.nc")`) or the xarray python package (e.g. `dat = xarray.open_dataset("*.nc"). All distances are in meters (e.g., depth, range, etc), and all velocities in m/s. See the DOLfYN documentation linked in the submission, and/or the Nortek documentation for additional details.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

Fast transformations between configuration state function and Slater determinant bases for direct configuration interaction

A hybrid configuration state function (CSF) and Slater determinant (SD) basis full configuration interaction (CI) program was developed to simultaneously take advantage of fast SD basis algorithms for σ = Hc formation and the smaller CI vector length and more robust convergence offered by a CSF basis. Graphical processing unit acceleration of the direct CSF-SD and SD-CSF basis transformation algorithms ensures that the combined transformation time per iteration relative to σ formation is small (~15%). In addition to the obvious benefits of reducing the memory footprint of the CI vector, additional computational savings are demonstrated that rely directly on the size of the CI basis, in one particular case reducing the CI time-to-solution of a HF-CAS-(16,16)-CI/6-31G calculation of ethylene from 1954.79 s to 956 s by using a CSF basis, a 2.0× speedup.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Gluon Sivers function from forward exclusive χ c 1 photoproduction on unpolarized protons

Exclusive production of a χ c 1 axial vector quarkonia in photon-proton scattering at high energies requires a C -odd t -channel exchange. In the limit of vanishing momentum transfer this occurs either via the exchange of a photon, the Primakoff process, where the spin of the proton does not change. For axial-vector meson production, as a consequence of the Landau-Yang theorem, the Primakoff cross section is finite as t → 0 . Alternatively, a C -odd spin dependent Odderon can be exchanged, which involves a spin flip of the proton. The resulting cross section is related to the square of the collinear trigluon correlator or the k ⊥ -moment of the gluon Sivers function. Using two models for the gluon Sivers function from the literature we compute the ratio of Sivers to Primakoff cross sections and the angular coefficient λ θ governing the angular distribution of the χ c 1 → J / ψ + γ decay as functions of x . We point out that these observables constrain the magnitude of the gluon Sivers function at small x which could be accessed in electron-proton scattering and ultraperipheral proton-proton and nucleus-proton collisions. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Simple, low-cost and accurate data-driven geophysical forecasting with learned kernels

Modelling geophysical processes as low-dimensional dynamical systems and regressing their vector field from data is a promising approach for learning emulators of such systems. We show that when the kernel of these emulators is also learned from data (using kernel flows, a variant of cross-validation), then the resulting data-driven models are not only faster than equation-based models but are easier to train than neural networks such as the long short-term memory neural network. In addition, they are also more accurate and predictive than the latter. When trained on geophysical observational data, for example the weekly averaged global sea-surface temperature, considerable gains are also observed by the proposed technique in comparison with classical partial differential equation-based models in terms of forecast computational cost and accuracy. When trained on publicly available re-analysis data for the daily temperature of the North American continent, we observe significant improvements over classical baselines such as climatology and persistence-based forecast techniques. Although our experiments concern specific examples, the proposed approach is general, and our results support the viability of kernel methods (with learned kernels) for interpretable and computationally efficient geophysical forecasting for a large diversity of processes.

58 GEOSCIENCES↗
NASA NTRS2002

Complex physiological and molecular processes underlying root gravitropism

Gravitropism allows plant organs to guide their growth in relation to the gravity vector. For most roots, this response to gravity allows downward growth into soil where water and nutrients are available for plant growth and development. The primary site for gravity sensing in roots includes the root cap and appears to involve the sedimentation of amyloplasts within the columella cells. This process triggers a signal transduction pathway that promotes both an acidification of the wall around the columella cells, an alkalinization of the columella cytoplasm, and the development of a lateral polarity across the root cap that allows for the establishment of a lateral auxin gradient. This gradient is then transmitted to the elongation zones where it triggers a differential cellular elongation on opposite flanks of the central elongation zone, responsible for part of the gravitropic curvature. Recent findings also suggest the involvement of a secondary site/mechanism of gravity sensing for gravitropism in roots, and the possibility that the early phases of graviresponse, which involve differential elongation on opposite flanks of the distal elongation zone, might be independent of this auxin gradient. This review discusses our current understanding of the molecular and physiological mechanisms underlying these various phases of the gravitropic response in roots.

NASA Discipline Plant Biology↗
DOE OSTI2020

Vectorization techniques for probability distribution functions using VecCore

Probability distribution functions (PDFs) are very used in modeling random processes and physics simulations. Improving the performance of algorithms that generate many random numbers under complex PDFs is often a very challenging task when methods as direct functions are not available. In this work we present general strategies on how to vectorize some PDFs using VecCore library. We show the results for the Exponential, Gaussian, discrete Poisson and Gamma probability distributions.

Chaparro Amaro, Oscar R.↗
DOE OSTI2022

Euler-Rodrigues Parameters: A Quantum Circuit to Calculate Rigid-Body Rotations

The use of vectorial parameterization to create geometrical representations in computational models has a large number of applications. One particular application is the calculation of the 3D rotational motion of rigid bodies, that could be used for the spatial location estimation from objects. Provided the algebraic nature of this problem, it could benefit from Quantum Computing, in particular several vectors could be superposed to be transformed with a single operation, providing a quantum processing advantage. In this article, we propose an implementation of a Quantum Computing algorithm to compute Euler-Rodrigues Parameters to model rigid body rotations to transform arbitrary functions, rotating multiple vectors in superposition. We developed this algorithm using Qiskit, taking into account the limitations imposed by the current Noisy Intermediate Scale Quantum (NISQ) devices, such as the reduced number of qubits available and the limited coherence time.

Pelaez, Emilio↗
DOE OSTI2025

Machine learning-powered data cleaning for LEGEND: a semi-supervised approach using affinity propagation and support vector machines

Neutrinoless double-beta decay ($0\nu\beta\beta$) is a rare nuclear process that, if observed, will provide insight into the nature of neutrinos and help explain the matter-antimatter asymmetry in the Universe. The large enriched germanium experiment for neutrinoless double-beta decay (LEGEND) will operate in two phases to search for $0\nu\beta\beta$. The first (second) stage will employ 200 (1000) kg of High-Purity Germanium (HPGe) enriched in 76 Ge to achieve a half-life sensitivity of 10 27 (10 28 ) years. In this study, we present a semi-supervised data-driven approach to remove non-physical events captured by HPGe detectors powered by a novel artificial intelligence model. We utilize affinity propagation to cluster waveform signals based on their shape and a support vector machine to classify them into different categories. We train, optimize, and test our model on data taken from a natural abundance HPGe detector installed in the Full Chain Test experimental stand at the University of North Carolina at Chapel Hill. We demonstrate that our model yields a maximum sacrifice of physics events of $0.024 ^{+0.004}_{-0.003} \%$ after data cleaning. Our model is being used to accelerate data cleaning development for LEGEND-200 and will serve to improve data cleaning procedures for LEGEND-1000.

artificial intelligence↗
DOE OSTI2024

Dark matter relic density in strongly interacting dark sectors with light vector mesons

Stable dark matter particles may arise as pseudo-Goldstone bosons from the confinement of dark quarks interacting via a non-Abelian gauge force. Their relic abundance is determined not by annihilations into visible particles but by dark pion number-changing processes within the dark sector, such as 3 π D → 2 π D . However, if the dark vector mesons ρ D are light enough for 3 π D → π D ρ D annihilations to be kinematically allowed, this process dominates and significantly delays freeze-out. As a result, the preferred dark matter mass scale increases and bounds from the Bullet Cluster can be evaded. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2021

Automated Data Accountability for Missions in Mars Rover Data

As the Mars Curiosity Rover transmits data to the JPL Ground Data System (GDS), it frequently observes data loss and corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the downlink process. As new missions are launched, the GDSA team redistributes analysts to these new missions, causing shortages in previous missions. The GDSA team can significantly benefit from the automation and optimization of the downlink process of telemetry data. In fact, there is a need for a better understanding of why the data is corrupted, so that the GDSA team can best determine the root cause of the issues in the GDS. This paper presents machine learning and deep learning based approaches to automate and optimize the detection of data loss. We first created a pipeline to automatically accumulate data from the telemetry databases (MAROS, Telemetry Data Storage, and GDS Elastic Search Database) in the downlink process. With our newly created datasets, we perform feature selection to supplement the GDSA understanding of the downlink process and provide supplemental analysis on the importance of different features. We implement various machine learning and deep learning based models, including support vector machines, ensemble methods, and deep neural networks and evaluate their accuracies in identifying whether a downlink process is complete or incomplete. We utilize fast hyperparameter optimization methods that allow our models to quickly be re-trained, allowing them to quickly be tuned and optimized on daily incoming data in real time. This hyperparameter optimization also allows our methods to be quickly integrated into other JPL missions. Our results show that our best-performing machine learning and deep learning based models outperform the existing GDSA detection software by 6 accuracy points and can aid analysts by providing insights into the data accountability problem. Since these various machine learning and deep learning approaches vary significantly in interpretability, we provide a discussion on the tradeoffs between their performance and trustworthiness in helping detect issues in data transmission.

Divsalar, Dariush↗
NASA NTRS2025

Improving Adhesive Bondline Time of Flight Predictions During Autoclave Cure Utilizing Machine Learning

Composite materials are increasingly being used in aerospace applications due to their superior strength-to-weight ratio compared to commonly used metals. A current limitation to widespread adoption is the certification of adhesively bonded joints. One approach to improving adhesive bonding in composites is accurately measuring the thickness of adhesive bondlines in composite laminates. Precise bondline thickness control is essential for aerospace applications where adhesive layer thickness directly affects joint fracture properties and structural performance. This study focused on implementing machine learning techniques to determine the ultrasonic time of flight (directly correlated to thickness) in adhesive bondlines throughout autoclave cure cycles. A high-temperature (use up to 180°C) ultrasonic scanning system was deployed in an autoclave to provide time of flight data through composite panels. Three experiments were conducted on the curing of 305 mm × 305 mm unidirectional composite panels. In the first experiment, a piecewise function was fit for the temperature correction factor to account for changing autoclave temperatures. Due to deficiencies in the first calibration experiment, a second experiment was run, and the results were used to train a machine learning model. The revised experiment, in combination with the machine learning model, significantly increased the accuracy of the bondline time of flight predictions (~14% error reduced to <1%). Data was processed using the Regression Learner Application in MATLAB®, with a Support Vector Machine selected for the model. The result was a machine learning algorithm capable of reliably quantifying ultrasonic time of flight through adhesive bondlines. The third experiment provided independent test data for the machine learning model, demonstrating that the model produces accurate predictions from data beyond its training set.

Machine Learning↗
NASA NTRS1998

Shear-Sensitive Liquid Crystal Coating Method: Surface-Inclination Effects on Shear Vector Measurements

The shear-sensitive liquid crystal coating (SSLCC) method is an image-based technique for both visualizing dynamic surface-flow phenomena, such as transition and separation, and for measuring the continuous shear-stress vector distribution acting on an aerodynamic surface. Under proper lighting and viewing conditions (discussed below), the coating changes color in response to an applied aerodynamic shear. This color-change response is continuous and reversible, with a response time of milliseconds, and is a function of both the shear magnitude and the shear vector orientation relative to the observer. The liquid crystal phase of matter is a weakly-ordered, viscous, non-Newtonian fluid state that exists between the nonuniform liquid phase and the ordered solid phase of certain organic compounds. Cholesteric liquid crystal compounds possess a helical molecular arrangement that selectively scatters white light, incident along the helical axis, as a three-dimensional spectrum. This property is linked to the helical pitch length, which is within the range of wavelengths in the visible spectrum. The pitch length, and hence the wavelength of the scattered light, is influenced by shear stress normal to the helical axis. This unique optical property produces a measurable color change in response to an applied shearing force. The full-surface shear stress vector measurement method, developed at NASA-Ames, is schematically illustrated. As with the visualization method, the coated test surface is illuminated from the normal direction with white light and the camera is positioned at an above-plane view angle of approximately 30 deg. Experiments have been initiated at NASA Ames to begin the process of quantifying surface-inclination (surface-curvature) effects on shear vector measurement accuracy. In preliminary experiments, surface-inclination angles theta(sub x), theta(sub y) of 0, +/-5, +/-10, and +/-15 deg were employed. In this arrangement, white-light illumination was positioned normal to the untilted test surface, and the camera above-plane view angle was set at 30 deg relative to the untilted test surface. As can be seen, vector-aligned lambda(sub d) values showed no dependence on theta(sub x) or theta(sub y) for absolute values of these tilt angles is less than or equal to 15 deg. Acquisition and analyses of full-surface color images are presently underway to definitively document the insensitivity limits of the shear vector measurement methodology to surface-slope variations.

Reda, Daniel C.↗
DOE OSTI2021

Verification and validation of developed short-term forecasting models

Recent advancements in machine learning (ML) and artificial intelligence (AI) technologies provide an opportunity for leveraging data-driven algorithms to predict future nuclear power plant (NPP) operating conditions by using recorded plant process data. Successfully implementing these models can lead to cost-reducing, conditioned-based predictive maintenance through optimized maintenance schedules and a reduction of unnecessary maintenance activities. This report discusses the verification and validation of short-term forecasting processes (i.e., data cleaning, feature selection, model optimization, and forecasting) developed in previous reports. The verification and validation (V&V) process demonstrates the expected precision and accuracy when the ML model encounters new datasets from different systems. Shapley additive explanations were used as the primary means of feature selection across these different data set. Individual models were trained for each data set, then validated through a cross-validation procedure. In this report, two different ML models were tasked to predict variables from three different plant process data sets with varying prediction horizons. The results indicate that support vector regression (SVR) outperformed long short-term memory (LSTM) neural networks in regard to each data set and each prediction horizon in this study, but further tuning and optimization could improve long short-term memory results. However, each forecasting model showed reduced performance as the prediction horizon was extended from 1 hour to 1 day ahead. Research is ongoing to evaluate the optimal input variable space, which is based on a given set of process parameters, to further improve forecasting accuracy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Robust Online Sequential RVFLNs for Data Modeling of Dynamic Time-Varying Systems with Application of an Ironmaking Blast Furnace

In a world where the increasing complexity of modern industrial processes brings difficulties for accurate mathematical modeling, taking advantage of data has become an efficient solution to complex dynamic process modeling issue. In this paper, we develop a novel robust online sequential version of random vector functional-link networks (RVFLNs) for data-driven modeling of dynamic time-varying system and applied it in a blast furnace (BF) ironmaking process. First, to overcome the time-varying dynamics of process and to enable the RVFLNs to learn online with avoiding data saturation, an improved online sequential version of RVFLNs (OS-RFVLNs) is first presented by online sequential learning with forgetting factor. This improved OS-RVFLNs algorithm is not only suitable for the real-time and large data transfer situation, but also can adjust the sensitivity of the algorithm to different samples with the help of the introduced forgetting factor. Second, since the output weights of the improved OS-RVFLNs as well as other RVFLNs algorithms are obtained by the least squares approach, a robustness problem may occur when the training dataset is contaminated with various outliers. To solve this problem, a Cauchy distribution weighted M-estimator is introduced to improve the robustness of the improved OS- RVFLNs. For this proposed robust OS-RVFLNs (R-OS- RVFLNs), since the weights of different outlier data are properly determined by the Cauchy distribution function, their corresponding contribution on modeling can be properly distinguished. Thus robust and better modeling results can be achieved. Experiments using actual industrial data of BF ironmaking process and comparative studies have demonstrated that the proposed method produces a better estimation accuracy and stronger robustness than other methods.

Blast furnace (BF), Modelling, Dynamic systems↗
DOE OSTI2026

Search for Higgs boson production at high transverse momentum in the WW decay channel in proton-proton collisions at $\sqrt{s}=13$ TeV

A search for Higgs boson (H) production at high transverse momentum (p T ) in the WW decay channel is presented. The analysis uses proton-proton collisions at $\sqrt{s}=13$ TeV recorded by the CMS experiment in 2016–2018, corresponding to an integrated luminosity of 138 fb −1 . The visible decay products of the Higgs boson are reconstructed as a single large-radius jet with one isolated lepton or none (1ℓ and 0ℓ, respectively; ℓ = e, μ). The H-candidate jets are identified using an advanced transformer-based algorithm and are calibrated with the Lund jet plane reweighting technique. The 1ℓ channel is further split into gluon fusion, vector boson fusion, and associated production with hadronically decaying vector boson categories, while the 0ℓ channel considers all production processes inclusively. The measured cross section times the H → WW branching fraction relative to the standard model expectation is $\mu =-{0.19}_{-0.46}^{+0.48}$, indicating no evidence of a signal above the background. This measurement represents the first dedicated study of highly Lorentz-boosted H → WW decays, complementing earlier searches for high-p T Higgs boson in other decay channels.

Hadron-Hadron Scattering↗
NASA NTRS1980

Studies of high latitude current systems using Magsat vector data

Disturbance fields caused by global external current systems are analyzed in order to gain an improved understanding of the phydical processes which control high latitude current systems and to increase the confidence level in the identification of internal field levels. The basic approach is to: (1) categorize the vector data by those physical parameters important for investigation of external current systems; (2) map the disturbances for appropriate conditions; (3) model the currents which might cause the mapped disturbances; and (4) correlate results with data from other sources. It is concluded that the Magsat data set appears to have remarkably high precision and quality and should permit major advances to be made in modeling current distribution at high latitudes in the ionosphere and magnetosphere.

Burrows, J. R.↗
NASA NTRS2019

Creating Data-Driven Vector Visualizations of Satellite Orbit Tracks Using NASA GIBS and Worldview

NASA Earth Observing System (EOS) currently operates dozens of remote sensing satellites, many of which can be viewed directly in NASA’s open-source Worldview application. Much of this satellite imagery can be viewed in near-real time as it is processed and served by NASA’s Global Imagery Browse Service (GIBS). To better educate users on the time and location of imagery, GIBS serves orbit track specific layers for each satellite. Worldview has historically served these layers as raster images but recent updates have enabled the application to now serve these layers using vector tiles. With the release of Worldview v3.0, orbit track layers can be displayed using mapbox vector tiles (MVT). This visualization format allows users to not only view and change the color of orbit track layers, as they could do previously with rasters, but also inspect individual vector points and filter layers by specific parameters such as time. The data contained within a MVT is further enhanced in Worldview with the combination of a JSON description file served from GIBS used to describe the MVT data. This presentation will provide an overview of the process of consuming orbit track vector tiles and data files from GIBS using a pipeline to configure, build and ultimately display the orbit tracks in Worldview. Furthermore, the presentation aims to describe how others can leverage our open-source code to display and enhance vector layers in their own applications.

Rice, Zachary↗