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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 OSTI2026

Hidden-sectors search and probe of discrete symmetries at the REDTOP experiment

The $η$ and $η^{\prime}$ mesons are nearly unique in the particle universe since they are nearly Goldstone bosons, and their decay dynamics are strongly constrained. While earlier experiments collected samples of order $\sim 10^{9}η$, the proposed REDTOP (Rare Eta Decays To Observe Physics Beyond the Standard Model) facility targets $\mathcal{O}(10^{14})η$ and $\mathcal{O}(10^{12})η^\prime$, enabling broad searches for physics beyond the Standard Model. In this work, we present studies evaluating REDTOP sensitivity to processes that couple the Standard Model to New Physics through four portals: the Vector (dark photon), the Scalar (Higgs-mixing), the Axion-like, and the Heavy Lepton. In parallel, the proposed statistics allow precise tests of $CP$ and $T$ invariance and lepton universality and improve determinations of the $η/η'$ transition form factors, which are crucial inputs to the hadronic light-by-light contribution to the muon anomalous magnetic moment $(g-2)_μ$.

Gatto, C. [INFN, Naples; Northern Illinois U.]↗
DOE OSTI2023

Sub-m s−1 upper limits from a deep HARPS-N radial-velocity search for planets orbiting HD 166620 and HD 144579

ABSTRACT Minimizing the impact of stellar variability in radial velocity (RV) measurements is a critical challenge in achieving the 10 cm s−1 precision needed to hunt for Earth twins. Since 2012, a dedicated programme has been underway with HARPS-N, to conduct a blind RV rocky planets search (RPS) around bright stars in the Northern hemisphere. Here we describe the results of a comprehensive search for planetary systems in two RPS targets, HD 166620 and HD 144579. Using wavelength-domain line-profile decorrelation vectors to mitigate the stellar activity and performing a deep search for planetary reflex motions using a trans-dimensional nested sampler, we found no significant planetary signals in the data sets of either of the stars. We validated the results via data-splitting and injection recovery tests. Additionally, we obtained the 95th percentile detection limits on the HARPS-N RVs. We found that the likelihood of finding a low-mass planet increases noticeably across a wide period range when the inherent stellar variability is corrected for using scalpelsU-vectors. We are able to detect planet signals with Msin i ≤ 1 M⊕ for orbital periods shorter than 10 d. We demonstrate that with our decorrelation technique, we are able to detect signals as low as 54 cm s−1, which brings us closer to the calibration limit of 50 cm s−1 demonstrated by HARPS-N. Therefore, we show that we can push down towards the RV precision required to find Earth analogues using high-precision radial velocity data with novel data-analysis techniques.

Anna John, A. (ORCID:0000000217156939)↗
DOE OSTI2013

HUBBLE CONSTANT, LENSING, AND TIME DELAY IN RELATIVISTIC MODIFIED NEWTONIAN DYNAMICS

The time delay in galaxy gravitational lensing systems has been used to determine the value of the Hubble constant. As with other dynamical phenomena on the galaxy scale, dark matter is often invoked in gravitational lensing to account for the 'missing mass' (the apparent discrepancy between the dynamical mass and the luminous mass). Alternatively, modified gravity can be used to explain the discrepancy. In this paper, we adopt the tensor-vector-scalar gravity (TeVe S), a relativistic version of Modified Newtonian Dynamics, to study gravitational lensing phenomena and derive the formulae needed to evaluate the Hubble constant. We test our method on quasar lensing by elliptical galaxies in the literature. We focus on double-image systems with time delay measurement. Three candidates are suitable for our study: HE 2149-2745, FBQ J0951+2635, and SBS 0909+532. The Hubble constant obtained is consistent with the value used to fit the cosmic microwave background result in a neutrino cosmological model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Ensemble models for circuit topology estimation, fault detection and classification in distribution systems

This paper presents a methodology for simultaneous fault detection, classification, and topology estimation for adaptive protection of distribution systems. The methodology estimates the probability of the occurrence of each one of these events by using a hybrid structure that combines three sub-systems, a convolutional neural network for topology estimation, a fault detection based on predictive residual analysis, and a standard support vector machine with probabilistic output for fault classification. The input to all these sub-systems is the local voltage and current measurements. A convolutional neural network uses these local measurements in the form of sequential data to extract features and estimate the topology conditions. The fault detector is constructed with a Bayesian stage (a multitask Gaussian process) that computes a predictive distribution (assumed to be Gaussian) of the residuals using the input. Since the distribution is known, these residuals can be transformed into a Standard distribution, whose values are then introduced into a one-class support vector machine. The structure allows using a one-class support vector machine without parameter cross-validation, so the fault detector is fully unsupervised. Finally, a support vector machine uses the input to perform the classification of the fault types. All three sub-systems can work in a parallel setup for both performance and computation efficiency. In conclusion, we test all three sub-systems included in the structure on a modified IEEE123 bus system, and we compare and evaluate the results with standard approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Bayesian time-varying occupancy model for West Nile virus in Ontario, Canada

Occupancy models determine the true presence or absence of a species by adjusting for imperfect detection in surveys. They often assume that species presences can be detected only if sites are occupied during a sampling season. We extended these models to estimate occupancy rates that vary throughout a sampling season as well as account for spatial dependence among sites. For these methods, we constructed a fast Gibbs sampler with the Pólya-Gamma augmentation strategy to conduct inference on covariate effects. We applied these methods to evaluate how environmental conditions and surveillance practices are associated with the presence of West Nile virus in mosquito traps across Ontario, Canada from 2002 to 2017. We found that urban land cover and warm temperatures drove viral occupancy, whereas viral testing on pools with higher proportions of Culex mosquitoes was more likely to result in a positive test for West Nile virus. Models with time-varying occupancy effects achieved much lower Watanabe-Akaike information criteria than models without such effects. Our final model had strong predictive performance on test data that included some of the most extreme seasons, demonstrating the promise of these methods in the study of pathogens spread by mosquito vectors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Efficient Surrogate Model Development: Impact of Sample Size and Underlying Model Dimensions

This study compares eight surrogate-model construction approaches using computational experiments. Here, the construction approaches considered include: Artificial Neural Networks (ANNs), Automated Learning of Algebraic Models using Optimization (ALAMO), Radial Basis Networks (RBNs), Extreme Learning Machines (ELMs), Gaussian Progress Regression (GPR), Random Forests (RFs), Support Vector Regression (SVR), and Multivariate Adaptive Regression Splines. Each approach is used to construct surrogate models for predicting the outputs of thirty-four test functions. The data used to construct surrogate models were generated using Latin Hypercube Sampling (LHS), Halton and Sobol sampling methods. We studied the impact of sampling method, sample size, and challenge function characteristics on the accuracy of surrogate model predictions. Our results revealed that for smaller sample sizes, on average, surrogate models trained using the data points generated by Sobol sequences provided the best estimation for all surrogate model construction approaches. As the sample size increased, the impact of sampling methods diminished. The comparison of surrogate-modeling construction approaches at large sample sizes revealed that surrogate models trained using ANN, ALAMO, and ELM yielded smaller root mean squared error and higher adjusted R-squared values than the models trained using the rest of the approaches.

42 ENGINEERING↗
DOE OSTI2019

Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries

Machine-learning (ML) techniques have rapidly found applications in many domains of materials chemistry and physics where large data sets are available. Aiming to accelerate the discovery of materials for battery applications, in this work, we develop a tool based on ML models to predict voltages of electrode materials for metal-ion batteries. To this end, we use deep neural network, support vector machine, and kernel ridge regression as ML algorithms in combination with data taken from the Materials Project database, as well as feature vectors from properties of chemical compounds and elemental properties of their constituents. We show that our ML models have predictive capabilities for different reference test sets and, as an example, we utilize them to generate a voltage profile diagram and compare it to density functional theory calculations. In addition, using our models, we propose nearly 5000 candidate electrode materials for Na- and K-ion batteries. We also make available a web-accessible tool that, within a minute, can be used to estimate the voltage of any bulk electrode material for a number of metal ions. Here, these results show that ML is a promising alternative for computationally demanding calculations as a first screening tool of novel materials for battery applications.

25 ENERGY STORAGE↗
DOE OSTI2023

Classification of gaseous UF 6 assay by femtosecond LIBS in the 424.4 nm spectral region using numerical HOGSVD-DTW features

This technical note presents experimental results using numerical features of fs-LIBS data to classify the assay value of a gaseous UF 6 material. Here, the data-driven feature vectors are computed by Higher Order Generalized Singular Value Decomposition (HOGSVD) and Dynamic Time Warp (DTW). The method achieves 96.97% accuracy in spectral classification testing with fs-LIBS samples obtained from a UF 6 material with five known assay values ranging from 0.287% to 61.740%, with 100% accuracy for the four largest assay values ranging from 4.615% to 61.740%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

A Comparative Study of Distribution System Parameter Estimation Methods

In this paper, we compare two parameter estimation methods for distribution systems: residual sensitivity analysis and state-vector augmentation with a Kalman filter. These two methods were originally proposed for transmission systems, and are still the most commonly used methods for parameter estimation. Distribution systems have much lower measurement redundancy than transmission systems. Therefore, estimating parameters is much more difficult. To increase the robustness of parameter estimation, the two methods are applied with combined measurement snapshots (measurement sets taken at different points in time), so that the redundancy for computing the parameter values is increased. The advantages and disadvantages of both methods are discussed. The results of this paper show that state-vector augmentation is a better approach for parameter estimation in distribution systems. Simulation studies are done on a modified version of IEEE 13-Node Test Feeder with varying levels of measurement noise and non-zero error in the other system model parameters.

distribution system analysis↗
DOE OSTI2016

Searches for the Standard Model Higgs boson at the Tevatron collider

During Run II of the Tevatron collider, which took place from 2001 until 2011, the CDF and D0 detectors each collected approximately 10 fb -1 of collision data at a center-of-mass energy of . This dataset allowed for tests for the presence of the SM Higgs boson in the mass range 90-200 GeV in the production modes gg → H, W/ZH, vector-boson fusion, and H, with H decay modes H → , H → W + W - , H → τ + τ - , H → γγ, and H → ZZ. This chapter summarizes the search methods and the results of the Higgs boson search at the Tevatron. The increased sophistication of the analysis techniques as the collider run progressed is discussed, covering the strategies used over time to improve the sensitivity and breadth of the analyses. Using the full Tevatron data sample for both experiments, the combined Higgs search in all channels observes an excess consistent with the predicted SM Higgs boson signal with mass of 125 GeV, with a significance of 3.0 standard deviations above the background prediction.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

A Study of a Mini-Drift GEM Tracking Detector

In this paper, a GEM tracking detector with an extended drift region has been studied as part of an effort to develop new tracking detectors for future experiments at RHIC and for the Electron Ion Collider that is being planned for BNL or JLAB. The detector consists of a triple GEM stack with a 1.6 cm drift region that was operated in a mini TPC type configuration. Both the position and arrival time of the charge deposited in the drift region were measured on the readout plane which allowed the reconstruction of a short vector for the track traversing the chamber. The resulting position and angle information from the vector could then be used to improve the position resolution of the detector for larger angle tracks, which deteriorates rapidly with increasing angle for conventional GEM tracking detectors using only charge centroid information. Two types of readout planes were studied. One was a COMPASS style readout plane with 400 μm pitch XY strips and the other consisted of 2 × 10 mm 2 chevron pads. The detector was studied in test beams at Fermilab and CERN, along with additional measurements in the lab, in order to determine its position and angular resolution for incident track angles up to 45 degrees. Several algorithms were studied for reconstructing the vector using the position and timing information in order to optimize the position and angular resolution of the detector for the different readout planes. Finally, applications for large angle tracking detectors at RHIC and EIC are also discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Augmenting machine learning of energy landscapes with local structural information

We present a machine learning approach for accurately predicting formation energies of binary compounds in the context of crystal structure predictions. The success of any machine learning model depends significantly on the choice of representation used to encode the relevant physical information into machine-learnable data. We test different representation schemes based on partial radial and angular distribution functions (RDF+ADF) on Al–Ni and Cd–Te structures generated using our genetic algorithm for structure prediction. We observe a remarkable improvement in predictive accuracy upon transitioning from global to atom-centered representations, resulting in a threefold decrease in prediction errors. We show that a support vector regression model using a combination of atomic radial and angular distribution functions performs best at the formation energy prediction task, providing small root mean squared errors of 3.9 meV/atom and 10.9 meV/atom for Al–Ni and Cd–Te, respectively. We test the performance of our models against common traditional descriptors and find that RDF- and ADF-based representations significantly outperform many of those in the prediction of formation energies. The high accuracy of predictions makes our machine learning models great candidates for the exploration of energy landscapes.

Honrao, Shreyas J. (ORCID:0000000166155257)↗
DOE OSTI2025

Adiabatic quantum support vector machines

Adiabatic quantum computers can solve difficult optimization problems (e.g., the quadratic unconstrained binary optimization problem), and they seem well suited to train machine learning models. In this paper, we describe an adiabatic quantum approach for training support vector machines. We show that the time complexity of our quantum approach is an order of magnitude better than the classical approach. Next, we compare the test accuracy of our quantum approach against a classical approach that uses the Scikit-learn library in Python across five benchmark datasets (Iris, Wisconsin Breast Cancer (WBC), Wine, Digits, and Lambeq). We show that our quantum approach obtains accuracies on par with the classical approach. Finally, we perform a scalability study in which we compute the total training times of the quantum approach and the classical approach with an increasing number of features and an increasing number of data points in the training dataset. In conclusion, our scalability results show that the quantum approach obtains a 3.5–4.5x speedup over the classical approach on datasets with many (millions of) features.

Computational Complexity↗
DOE OSTI2016

Replication-competent human adenovirus 11p vectors can propagate in Vero cells

The use of continuous cell lines derived from the African green monkey kidney (AGMK) has led to major advances in virus vaccine development. However, to date, these cells have not been used to facilitate the creation of human adenoviruses because most human adenoviruses undergo abortive infections in them. Here, we report the susceptibility of AGMK-derived cells to adenovirus 11p (Ad11p) infection. First, we showed that CD46 molecules, which act as receptors for Ad11p, are expressed in AGMK cells. We then monitored Ad11p replication by measuring GFP expression as an indicator of viral transcription. We found that AGMK-derived cells were as capable as carcinoma cells at propagating full-length replication-competent Ad11p (RCAd11p) DNA. Of the AGMK cell lines tested, Vero cells had the greatest capacity for adenovirus production. Thus, AGMK cells can be used to evaluate RCAd11p-mediated gene delivery, and Vero cells can be used for the production of RCAd11pGFP vectors at relatively high yields. - Highlights: • Africa green monkey cell lines were monitored for human adenovirus 11p GFP vector infection. • Human CD46 molecules were detectable in these monkey cell lines. • Adenovirus 11p GFP vector can be propagated in Vero cells increases the safety of Ad11p-based vectors for clinical trials. • To use Vero cells for preparation of Ad11p vector avoids the potential inclusion of oncogenes from tumor cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Dark Energy Survey Year 3 Results: Cosmological Constraints from Cluster Abundances, Weak Lensing, and Galaxy Clustering

Galaxy clusters provide a unique probe of the late-time cosmic structure and serve as a powerful independent test of the $\Lambda$CDM model. This work presents the first set of cosmological constraints derived with ~16,000 optically selected redMaPPer clusters across nearly 5,000 $\rm{deg}^2$ using DES Year 3 data sets. Our analysis leverages a consistent modeling framework for galaxy cluster cosmology and DES-Y3 joint analyses of galaxy clustering and weak lensing (3x2pt), ensuring direct comparability with the DES-Y3 3x2pt analysis. We obtain constraints of $S_8 = 0.864 \pm 0.035$ and $\Omega_{\rm{m}} = 0.265^{+0.019}_{-0.031}$ from the cluster-based data vector. We find that cluster constraints and 3x2pt constraints are consistent under the $\Lambda$CDM model with a Posterior Predictive Distribution (PPD) value of $0.53$. The consistency between clusters and 3x2pt provides a stringent test of $\Lambda$CDM across different mass and spatial scales. Jointly analyzing clusters with 3x2pt further improves cosmological constraints, yielding $S_8 = 0.811^{+0.022}_{-0.020}$ and $\Omega_{\rm{m}} = 0.294^{+0.022}_{-0.033}$, a $24\%$ improvement in the $\Omega_{\rm{m}}-S_8$ figure-of-merit over 3x2pt alone. Moreover, we find no significant deviation from the Planck CMB constraints with a probability to exceed (PTE) value of $0.6$, significantly reducing previous $S_8$ tension claims. Finally, combining DES 3x2pt, DES clusters, and Planck CMB places an upper limit on the sum of neutrino masses of $\sum m_\nu < 0.26$ eV at 95% confidence under the $\Lambda$CDM model. These results establish optically selected clusters as a key cosmological probe and pave the way for cluster-based analyses in upcoming Stage-IV surveys such as LSST, Euclid, and Roman.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Synthesis of results for Brine Availability Test in Salt (BATS) DECOVALEX-2023 Task E

In the 2023 phase of the international collaborative DECOVALEX modeling project, Task E focused on understanding thermal, hydrological, and mechanical (THM) processes related to predicting brine migration in the excavation damaged zone around a heated excavation in salt. Salt is attractive as a disposal medium for radioactive waste because it is self-healing and is essentially impermeable and non-porous in the far field. Investigation of the short-term, near-field behavior is important for radioactive waste disposal because this early period strongly controls the amount of inflowing brine. Brine leads to corrosion of waste forms and waste packages, and possible dissolution of radionuclides with brine transport being a potential transport vector to the accessible environment.The Task was divided into steps. Step 0 included matching unheated brine inflow data from boreholes at the Waste Isolation Pilot Plant (WIPP) and matching temperature observations during a Brine Availability Test in Salt (BATS) heater test. Step 1 included validation of models against a thermo-poroelastic analytical solution, and two-phase flow around an excavation. Finally, Step 2 required all the individual components covered in steps 0 and 1 to come together to match observed brine inflow behavior during the same BATS heater test.There were a range of approaches from the teams, from mechanistic to prescriptive. Given the uncertainties in the problem, some teams used one- or two-dimensional models of the processes, while other teams included more geometrical complexity in three-dimensional models. Task E was a learning experience for the teams involved, and feedback from the modeling teams has led to changes in follow-on BATS experiments at WIPP. The primary Task E lessons learned were the impact of hydrologic initialization methods (wetting up vs. drying down), the difference between confined and unconfined thermal expansion, and the large changes in permeability associated with heating and cooling.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

An arbitrarily high-order three-dimensional Cartesian-grid method for reconstructing interfaces from volume fraction fields

Here Tthis work describes a newly developed, arbitrarily high-order Cartesian-grid method for reconstructing material interfaces from a volume fraction field. The method begins by identifying all of the grid cells in the volume fraction field that are intersected by the interface and need to be approximated by the reconstruction scheme. Finite-differences are used to calculate the gradient of the volume fraction field and provide an estimate of the surface normal in all of the interfacial grid cells. Groups of connected grid cells are then identified which all have the same dominant component of the normal vector. This grouping by orientation determines the proper dependent variable to use in the surface reconstruction (e.g. for a 2D curve, this step determines if the surface will be approximated by a function of x or y). A cumulative integral over the surface is constructed and fit using b-splines for two-dimensional problems or tensor-product b-splines for three-dimensional problems. This construction allows for the interface to be recovered through application of the second fundamental theorem of calculus. Fitting the cumulative integral with $\mathscr{N}$ th-order b-splines (or tensor-product b-splines) yields an ($\mathscr{N}$-1) th-order convergence rate of the interface shape. Differentiation of the b-spline interface function(s) allows for the high-order approximation of the normal vector and curvature to be obtained directly anywhere along b-spline. Together, the proposed reconstruction technique can achieve arbitrarily high mesh convergence rates. Validation tests are presented with mesh convergence rates ranging from fourth- to tenth-order.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

3D reconstruction of an inertial-confinement fusion implosion with neural networks using multiple heterogeneous data sources

3D asymmetries are major degradation mechanisms in inertial-confinement fusion implosions at the National Ignition Facility (NIF). These asymmetries can be diagnosed and reconstructed with the neutron imaging system (NIS) on three lines of sight around the NIF target chamber. Conventional tomographic reconstructions are used to reconstruct the 3D morphology of the implosion using NIS [Volegov et al., J. Appl. Phys. 127, 083301 (2020)], but the problem is ill-posed with only three imaging lines of sight. Asymmetries can also be diagnosed with the real-time neutron activation diagnostics (RTNAD) and the neutron time-of-flight (nToF) suite. Since the NIS, RTNAD, and nToF each sample a different part of the implosion using different physical principles, we propose that it is possible to overcome the limitations of too few imaging lines of sight by performing 3D reconstructions that combine information from all three heterogeneous data sources. This work presents a new machine learning-based reconstruction technique to do just this. By using a simple physics model and group of neural networks to map 3D morphologies to data, this technique can easily account for data of multiple different types. A simple proof-of-principle is presented, demonstrating that this technique can accurately reconstruct a hot-spot shape using synthetic primary neutron images and a hot-spot velocity vector. In particular, the hot-spot’s asymmetry, quantified as spherical harmonic coefficients, is reconstructed to within ±4% of the radius in 90% of test cases. In the future, this technique will be applied to actual NIS, RTNAD, and nToF data to better understand 3D asymmetries at the NIF.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗