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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Coulomb confinement in the Hamiltonian limit

The Gribov-Zwanziger scenario attributes the phenomenon of confinement to the instantaneous interaction term in the QCD Hamiltonian in the Coulomb gauge. For a static quark-antiquark pair, it leads to a potential energy that increases linearly with the distance between them. Lattice studies of the SU(2) Yang-Mills theory determined the corresponding (Coulomb) string tension for sources in the fundamental representation, 𝜎 𝐶 , to be about 3 times larger than the Wilson loop string tension, 𝜎 𝐹 . It is far above the Zwanziger variational bound, 𝜎 𝐶 ≥ 𝜎 𝐹 . We argue that the value often reported in the literature is artificially inflated. We examine the lattice definition of the instantaneous potential, find the source of the string tension’s enhancement, and perform its improved determination in SU(2) lattice gauge theory. We report our conservative estimate for the value of the Coulomb string tension as 𝜎 𝐶 /𝜎 𝐹 = 2.0 ± 0.4 and discuss its phenomenological implications.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Systematic study of projection biases in the weak lensing analysis of cosmic shear and the combination of galaxy clustering and galaxy-galaxy lensing

This paper presents the results of a systematic study of projection biases in the weak lensing analysis of cosmic shear and the combination of galaxy clustering and galaxy-galaxy lensing using data collected during the first year of running the Dark Energy Survey experiment. The study uses Lambda cold dark matter ( Λ CDM ) as the cosmological model and two-point correlation functions for the weak lensing (WL) analysis. The results in this paper show that, independent of the WL analysis, projection biases of more than 1 σ exist and are a function of the position of the true values of the parameters h , n s , Ω b , and Ω ν h 2 with respect to their prior probabilities. For cosmic shear, and the combination of galaxy clustering and galaxy-galaxy lensing, this study shows that the coverage probability of the 68.27% credible intervals ranges from as high as 93% to as low as 16% and that these credible intervals are inflated, on average, by 29% for cosmic shear and 20% for the combination of galaxy clustering and galaxy-galaxy lensing. The results of the study also show that, in six out of nine tested cases, the reduction in error bars obtained by transforming credible intervals into confidence intervals is equivalent to an increase in the amount of data by a factor of 3.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Warm and fuzzy dark matter: Free streaming of wave dark matter

Wave or fuzzy dark matter that is produced with relativistic wave numbers exhibits free-streaming effects analogous to warm or hot particle dark matter with relativistic momenta. Axions produced after inflation provide such a warm or mildly relativistic candidate, where the enhanced suppression and observational bounds are only moderately stronger than that from wave propagation of initially cold axions. More generally, the free-streaming damping also impacts isocurvature fluctuations from generation in causally disconnected patches. As coherent spatial fluctuations free stream away they leave incoherent and transient superpositions in their wakes. These multiple wave momentum streams are the wave analog of particle phase space fluctuations or directional collisionless damping of massive neutrinos or hot dark matter. The observable impact on both adiabatic and isocurvature fluctuations of fuzzy dark matter can differ from their cold dark matter counterparts due to free streaming depending on how warm or hot is their momentum distribution.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Refining localtype primordial non-Gaussianity: Sharpened bϕ constraints through bias expansion

Local-type primordial non-Gaussianity (PNG), predicted by many nonminimal models of inflation, creates a scale-dependent contribution to the power spectrum of large-scale structure tracers. Its amplitude is characterized by the product bϕfNLloc, where bϕ is an astrophysical parameter dependent on the properties of the tracer. However, bϕ exhibits significant secondary dependence on halo concentration and other astrophysical properties, which may bias and weaken the constraints on fNLloc. In this work, we demonstrate that incorporating knowledge of the relation between Lagrangian bias parameters and bϕ can significantly enhance PNG constraints. We employ the hybrid effective field theory approach at the field level and a linear regression model to seek a connection between the bias parameters and bϕ for halo and galaxy samples, constructed using the abacussummit simulation suite and mimicking the luminous red galaxies and quasistellar objects of the Dark Energy Spectroscopic Instrument survey. For the fixed-mass halo samples, our full bias model reduces the uncertainty by more than 70%, with most of that improvement coming from b∇, which we find to be an excellent proxy for concentration. For the galaxy samples, our model reduces the uncertainty on bϕ by 80% for all tracers. By adopting Lagrangian-bias informed priors on the parameter bϕ, future analyses can thus constrain fNLloc with less bias and smaller errors.

Hadzhiyska, Boryana↗
DOE OSTI2020

Early-Universe Simulations of the Cosmological Axion

Ultracompact dark matter (DM) minihalos at masses at and below 10 –12 $M_⊙$ arise in axion DM models where the Peccei-Quinn (PQ) symmetry is broken after inflation. The minihalos arise from density perturbations that are generated from the nontrivial axion self-interactions during and shortly after the collapse of the axion-string and domain-wall network. We perform high-resolution simulations of this scenario starting at the epoch before the PQ phase transition and ending at matter-radiation equality. We characterize the spectrum of primordial perturbations that are generated and comment on implications for efforts to detect axion DM. In this work, we also measure the DM density at different simulated masses and argue that the correct DM density is obtained for $m_a$ = 25.2 ± 11.0 μ eV .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Exploring Primordial Black Holes from the Multiverse with Optical Telescopes

Primordial black holes (PBHs) are a viable candidate for dark matter if the PBH masses are in the currently unconstrained “sublunar” mass range. We revisit the possibility that PBHs were produced by nucleation of false vacuum bubbles during inflation. We show that this scenario can produce a population of PBHs that simultaneously accounts for all dark matter, explains the candidate event in the Subaru Hyper Suprime-Cam (HSC) data, and contains both heavy black holes as observed by LIGO and very heavy seeds of supermassive black holes. Here, we demonstrate with numerical studies that future observations of HSC, as well as other optical surveys, such as LSST, will be able to provide a definitive test for this generic PBH formation mechanism if it is the dominant source of dark matter.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Limits to Gauge Coupling in the Dark Sector Set by the Nonobservation of Instanton-Induced Decay of Super-Heavy Dark Matter in the Pierre Auger Observatory Data

Instantons, which are nonperturbative solutions to Yang-Mills equations, provide a signal for the occurrence of quantum tunneling between distinct classes of vacua. They can give rise to decays of particles otherwise forbidden. Using data collected at the Pierre Auger Observatory, we search for signatures of such instanton-induced processes that would be suggestive of super-heavy particles decaying in the Galactic halo. These particles could have been produced during the post-inflationary epoch and match the relic abundance of dark matter inferred today. The nonobservation of the signatures searched for allows us to derive a bound on the reduced coupling constant of gauge interactions in the dark sector: α X ≲ 0.09, for 10 9 ≲ M X /GeV < 10 19 . Conversely, we obtain that, for instance, a reduced coupling constant α X = 0.09 excludes masses M X ≳ 3 x 10 13 GeV. In the context of dark matter production from gravitational interactions alone, we illustrate how these bounds are complementary to those obtained on the Hubble rate at the end of inflation from the nonobservation of tensor modes in the cosmological microwave background.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Simulated structure and thermodynamics of decagonal Al-Co-Cu quasicrystals

Atomic structures of Al-Co-Cu decagonal quasicrystals (dQCs) are investigated using empirical oscillating pair potentials (EOPP) in molecular dynamic (MD) simulations that we enhance by Monte Carlo (MC) swapping of chemical species and replica exchange. Predicted structures exhibit planar decagonal tiling patterns and are periodic along the perpendicular direction. We then recalculate the energies of promising structures using first-principles density functional theory (DFT), along with energies of competing phases. We find that our τ -inflated sequence of QC approximants (QCAs) are energetically unstable at low temperature by at least 3 meV/atom. Extending our study to finite temperatures by calculating harmonic vibrational entropy, as well as anharmonic contributions that include chemical species swaps and tile flips, our results suggest that the quasicrystal phase is entropically stabilized at temperatures in the range 600-800 K and above. It decomposes into ordinary (though complex) crystal phases at low temperatures, including a partially disordered B2-type phase. We discuss the influence of density and composition on QC phase stability; we compare the structural differences between Co-rich and Cu-rich quasicrystals; and we analyze the role of entropy in stabilizing the quasicrystal, concluding with a discussion of the possible existence of “high entropy” quasicrystals. Published by the American Physical Society 2024

Huang, Yang (ORCID:0009000045917347)↗
DOE OSTI2025

Fluctuations in Hill’s equation parameters and application to cosmic reheating

Cosmic inflation provides a compelling framework for explaining several observed features of our Universe, but its viability depends on an efficient reheating phase that converts the inflaton’s energy into Standard Model particles. This conversion often proceeds through nonperturbative mechanisms such as parametric resonance, which is described by Hill’s equation. In this work, we investigate how stochastic fluctuations in the parameters of Hill’s equation can influence particle production during reheating. We show that such fluctuations can arise from couplings to light scalar fields and can significantly alter the stability bands in the resonance structure, thereby enhancing the growth of fluctuations and broadening the region of efficient energy transfer. Using random matrix theory and stochastic differential equations, we decompose the particle growth rate into deterministic and noise-induced components and demonstrate analytically and numerically that even modest noise leads to substantial particle production in otherwise stable regimes. Furthermore, these results suggest that stochastic effects can robustly enhance the efficacy of reheating across a wide swath of parameter space, with implications for early Universe cosmology, UV completions involving multiple scalar fields, and the resolution of the cosmological moduli problem.

Cosmology↗
DOE OSTI2026

Feature Engineering and Ensemble Methods for Imbalanced ICS Intrusion Detection: Pipeline Audit and Constrained Evaluation

Industries are becoming increasingly connected and are more vulnerable to cyberattacks due to the widened attack surface. Industrial Control Systems (ICS) are among the most critical sectors that malicious actors can target, as such attacks can cause significant operational disruption and physical damage. It is imperative to detect such attacks as early as possible. This paper evaluates constraint-conditioned optimistic performance estimates for traditional ML models in ICS intrusion detection (i.e., estimates obtained under contiguous, non-shuffled temporal evaluation without test-set alteration, but with pre-split feature engineering that may introduce temporal leakage, due to dataset constraints). Our findings are threefold. First, we quantify how iterative feature engineering affects tree-based ensemble performance and examine how pipeline decisions (split strategy, sampling scope, and cleaning policy) can inflate or reduce reported IDS results under constraint-bound evaluation. Second, we compare intrinsic class-imbalance handling across ensemble models. Third, under our current pipeline constraints (including pre-split feature engineering), CatBoost achieves the best performance on Water Storage Tank (accuracy: 0.9831, class-1 F1: 0.9682), while Light- GBM achieves the best performance on Gas Pipeline (accuracy: 0.9618, class-1 F1: 0.9086).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

WinnowML: Stable feature selection for maximizing prediction accuracy of time-based system modeling

Online deep learning (ODL) has become an important methodology for modeling time-based performance of computer systems. An open problem is the intelligent selection of features from raw workload traces of computer systems. The best methods are overly sensitive to noisy data, causing frequent feature changes and re-training. Using all available features inflates training time and introduces model artifacts if some features should have been dropped. We present WinnowML, a method for automatically determining the most relevant feature subset for a predictive time-series model. WinnowML combines existing feature ranking algorithms and a history of each feature's ranking to iteratively rank a feature set to lower prediction error and maximize long term relevance. From this ranked feature set, the most relevant and stable subset is selected to train a model. Experimentally, we show how WinnowML can lower a model's mean absolute relative error up to 42% on average compared to the closest performing approach. Additionally, we lower the fluctuation in feature ranking and selection up to 65%. We also demonstrate how to combine WinnowML and a model search tool to provide improvements in performance of up to 14.5% when compared to using all the feature available.

Bel, Oceane MS↗
DOE OSTI2024

At Risk Population Estimates for Belarus, Poland and Slovakia with Machine Learning

High-resolution gridded population modeling is crucial for various applications, including disaster response planning, infectious disease spread modeling, climate change impact estimation, policy development, and more. Multiple gridded population datasets have been developed, each tailored to meet specific objectives. Among them, LandScan Global dataset is designed to represent ambient and unwarned population distributions. However, this dataset relies on a statistical approach that requires manual adjustments, making it time consuming and labour intensive. Existing machine learning (ML) methods often train and test at different spatial resolutions, potentially leading to inflated results, and they rely on Census population totals for disaggregation. To address these limitations, in this study we developed population estimates using ML models trained and tested at a consistent 30 arc-second resolution (≈1 square kilometer), specifically using Random Forest (RF) and XGBoost. These models were trained on 2020 datum to predict for 2021 for three countries: Belarus, Poland, and Slovakia. Our findings show that both RF (MAE varies from 5.75 to 13.25) and XGBoost (MAE varies from 8.15 to 23.44) model performance is close to LandScan Global estimates. Furthermore, neither of the models performed the best across all grid cells: the RF model was more effective in areas with lower populations, while XGBoost excelled in more densely populated regions. The proposed approach can be used for countries where the Census data is not available.

Lebakula, Viswadeep [ORNL] (ORCID:0000000152935914↗
DOE OSTI2026

The Impact of Time-Aware Design Choices in ICS Anomaly Detection

Industrial control systems (ICS) remain vulnerable to increasingly sophisticated cyberattacks, yet evaluating anomaly detection models in these environments is challenging due to temporal dependencies, missing-not-at-random patterns, and extremely imbalanced datasets. These factors make common practices—especially random data splits and na¨ıve imputation— prone to severe temporal leakage, which can inflate reported performance and obscure real-world limitations. In this work, we systematically examine classical machine learning models, temporal deep learning architecture, and tensordecomposition– based methods on a gas-pipeline dataset using a fully temporally separated evaluation pipeline designed to mimic realistic deployment conditions. Our findings show that proper temporal handling and MNAR-aware preprocessing significantly alter the relative performance of popular anomaly-detection methods, providing practical guidance for designing reliable, leakage-resistant ICS intrusion-detection systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The Effect of Clean Energy Generation Targets on the Portfolio of Electric Grid Generation Technologies

A least-cost generation and transmission expansion planning model is used to optimize the U.S. generation portfolio in 2030 under a range of Clean Energy Generation Target (CEGT) policy goals. As reference cases for comparison, the model is used to optimize the generation portfolio for the future electric grid with and without the Investment and Production Tax Credits (ITC and PTC). Next, the model is used to optimize the generation portfolio with a CEGT ranging from 10% to 70%. The results show that due to the ITC and PTC extensions by the 2022 Inflation Reduction Act, there is little additional investment in renewable energy generation assets at low CEGT levels. Significant additional installations of both renewable energy generation and energy storage systems – both batteries and pumped storage hydro – become important at CEGT levels above approximately 30%.

Aldeman, Matthew↗
DOE OSTI2025

Life‐cycle greenhouse gas emissions associated with nuclear power generation in the United States

Under the 2022 Inflation Reduction Act, tax credits of up to $3/kgH 2 are available to hydrogen producers if they generate emissions at levels below 0.45 kgCO 2 e/kgH 2 , spurring producers to explore how hydrogen production via electrolysis using electricity generated by nuclear power may qualify for such tax credits. With uranium as a primary fuel for nuclear power plants (NPPs) and no on-site emissions, the upstream emissions associated with nuclear fuel supply chains largely determine the carbon intensity of nuclear energy. Using the GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies) model, we evaluated the life-cycle greenhouse gas (GHG) emissions of uranium production and the use of uranium to generate electricity in light water reactor (LWR) NPPs. We evaluated the process chemicals and energy inputs throughout the nuclear fuel supply chain to identify the major contributors to nuclear fuel cycle GHG emissions. Such emissions are estimated at 3.0 gCO 2 e/kWh at NPPs in the United States. The greatest share of nuclear fuel cycle GHG emissions—comprising 53% of total emissions—are associated with electricity consumption throughout the fuel supply chain. We extended the analysis to include an evaluation of the carbon intensity of H 2 production via electrolysis using nuclear power from LWRs. Finally, we examined the impact of future (2035 and 2050) electricity supply chain scenarios on nuclear fuel cycle GHG emissions. Our analysis revealed a decrease of 33% (2035) and 46% (2050) in the carbon intensity of nuclear electricity relative to current nuclear fuel cycle GHG emissions.

greenhouse gas emissions↗
DOE OSTI2020

Shock compaction heating and collisional processes in the production of type 3 ordinary chondrites: Lessons from the (nearly) unique L3 chondrite melt breccia Northwest Africa 8709

Abstract Northwest Africa (NWA) 8709 is a rare example of a type 3 ordinary chondrite melt breccia and provides critical information for the shock compaction histories of chondrites. An L3 protolith for NWA 8709 is inferred on the basis of oxygen isotope composition, elemental composition, diverse mineral chemistry, and overall texture. NWA 8709 is among the most strongly shocked type 3 chondrites known, and experienced complete melting of the matrix and partial melting of chondrules. Unmelted phases underwent FeO reduction and partial homogenization, with reduction possibly occurring by reaction of olivine and low‐Ca pyroxene with an S‐bearing gas that was produced by vaporization. Chondrules and metal grains became foliated by uniaxial compaction, and during compression, chondrules and fragments became attached to form larger clumps. This process, and possibly also melt incorporation into chondrules to cause “inflation,” may have contributed to anomalously large chondrule sizes in NWA 8709. The melt breccia character is attributed to strong shock affecting a porous precursor. Data‐model comparisons suggest that a precursor with 23% porosity that was impacted by a 3 km/s projectile could have produced the meteorite. The rarity of other type 3 ordinary chondrite melt breccias implies that the immediate precursors to such chondrites were lower in porosity than the NWA 8709 precursor, or experienced weaker shocks. Altogether, the data imply a predominantly “quiet” dynamical environment to form most type 3 ordinary chondrites, with compaction occurring in a series of relatively weak shock events.

Ruzicka, Alex M.↗
DOE OSTI2024

Toward Polydisperse Flows With MFIX-EXA

In the presence of large size disparities, single-grid neighbor search algorithms lead to inflated neighbor lists that significantly degrade the performance of Lagrangian particle solvers. If Eulerian–Lagrangian (EL) frameworks are to remain performant when simulating realistic systems, improved neighbor detection approaches must be adopted. To this end, we consider the application of a multigrid neighbor search (MGNS) algorithm in the mfix-exa software package, an exascale EL solver built upon the AMReX library. Here, details regarding the implementation and verification of MGNS are provided along with speedup curves for a bidisperse mixing layer. MGNS is shown to yield up to 15$\times$ speedup on CPU and 6$\times$ speedup on GPU for the problems considered here. The mfix-exa software is then validated for a variety of polydisperse flows. Finally, a brief discussion is given for how dynamic MGNS may be completed, with application to spatially varying particle size distributions.

42 ENGINEERING↗
DOE OSTI2022

Support for climate policy researchers

In the past 2 years, the European Union and the United States announced plans to spend $573 and $391 billion, respectively, through 2030 on climate actions and passed landmark legislation such as the US Inflation Reduction Act. Although unprecedented in size and scope, these combined investments of $964 billion pale in comparison to the more than $4 trillion in global clean energy investment needed annually by 2030 to stay on track for net zero greenhouse gas emissions by 2050. Furthermore, to maximize the impact of this public money, efficient policies informed by independent, objective analysis will be needed. Yet scientists who commit to policy-relevant research face unique challenges that must be addressed.

54 ENVIRONMENTAL SCIENCES↗