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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Scoping study of lower hybrid current drive for CFETR

The paper assesses the applicability of lower hybrid current drive (LHCD) for two potential operating scenarios for the China Fusion Engineering Test Reactor (CFETR): the “hybrid” scenario in which some of the plasma current is sustained by the Ohmic transformer, and the fully non-inductive “steady state” scenario. Here, the πScope workflow engine was used to set up a large number of ray tracing/Fokker-Planck simulations (> 10 4 ) with parametric scans in the antenna poloidal position and launched parallel refractive index (n || ) for both the hybrid and steady state scenarios. Modeling predicts efficient off-axis current drive (1.3 MA for 20 MW launched power) with a peak near ρ of 0.6-0.65 for waves launched from the high field side (HFS). Waves launched from the low field side (LFS) damp at larger radius (ρ > 0.73) with similar efficiency to HFS launch. Stability analysis of the CFETR scenarios favors current drive profiles peaked near the mid-radius, suggesting that HFS launch is preferable due to the current drive location. The effect of wave scattering from density blobs in the edge/scrape-off-layer region was assessed through rotation of the perpendicular wavenumber at the ray origin. Simulations show that this effect can be quite large both in efficiency and damping location, however by adjusting the launched n|| much of the unperturbed performance can be recovered.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

An Application of a Modified Beta Factor Method for the Analysis of Software Common Cause Failures

This paper presents an approach for modeling software common cause failures (CCFs) within digital instrumentation and control (I&C) systems. CCFs consist of a concurrent failure between two or more components due to a shared failure cause and coupling mechanism. This work emphasizes the importance of identifying software-centric attributes related to the coupling mechanisms necessary for simultaneous failures of redundant software components. The groups of components that share coupling mechanisms are called common cause component groups (CCCGs). Most CCF models rely on operational data as the basis for establishing CCCG parameters and predicting CCFs. This work is motivated by two primary concerns: (1) a lack of operational and CCF data for estimating software CCF model parameters; and (2) the need to model single components as part of multiple CCCGs simultaneously. A hybrid approach was developed to account for these concerns by leveraging existing techniques: a modified beta factor model allows single components to be placed within multiple CCCGs, while a second technique provides software-specific model parameters for each CCCG. This hybrid approach provides a means to overcome the limitations of conventional methods while offering support for design decisions under the limited data scenario.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Confocal Raman Microscopy Investigation of Phospholipid Monolayers Deposited on Nitrile-Modified Surfaces in Porous Silica Particles

Phospholipid bilayers deposited on a variety of surfaces offer models for investigation of the lipid membrane structure and supports for biocompatible sensors. Hybrid-supported phospholipid bilayers (HSLBs) are stable membrane models for these investigations, typically prepared by self-assembly of a lipid monolayer over an n-alkane-modified surface. HSLBs have been prepared on n-alkyl chain-modified silica and used for lipophilicity-based chromatographic separations. The structure of these hybrid bilayers differs from vesicle membranes where the lipid head group spacing is greater due to interdigitation of the lipid acyl chains with the underlying n-alkyl chains bound to the silica surface. This interdigitated structure exhibits a broader melting transition at a higher temperature due to strong interactions between the lipid acyl chains and the immobile n-alkyl chains bound to silica. In the present work, we seek to reduce the interactions between a lipid monolayer and its supporting substrate by self-assembly of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) on porous silica functionalized with nitrile-terminated surface ligands. The frequency of Raman scattering of the surface -C≡N stretching mode at the lipid–nitrile interface is consistent with an n-alkane-like environment and insensitive to lipid head group charge, indicating that the lipid acyl chains are in contact with the surface nitrile groups. The head group area of this lipid monolayer was determined from the within-particle phospholipid concentration and silica specific surface area and found to be 54 ± 2 Å 2 , equivalent to the head group area of a DMPC vesicle bilayer. The structure of these nitrile-supported phospholipid monolayers was characterized below and above their melting transition by confocal Raman microscopy and found to be nearly identical to DMPC vesicle bilayers. Their narrow gel-to-fluid-phase melting transition is equivalent to dispersed DMPC vesicles, indicating that the acyl chain structure on the nitrile support mimics the outer leaflet structure of a vesicle membrane.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Noise Robustness and Experimental Demonstration of a Quantum Generative Adversarial Network for Continuous Distributions

Abstract The potential advantage of machine learning in quantum computers is a topic of intense discussion in the literature. Theoretical, numerical, and experimental explorations will most likely be required to understand its power. There have been different algorithms proposed to exploit the probabilistic nature of variational quantum circuits for generative modeling. In this paper, a hybrid architecture for quantum generative adversarial networks (QGANs) is employed and their robustness in the presence of noise is studied. A simple way of adding different types of noise to the quantum generator circuit is devised, and the noisy hybrid QGANs (HQGANs) are simulated numerically to learn continuous probability distributions, and to show that the performance of HQGANs remains unaffected. The effect of different parameters on the training time is also investigated to reduce the computational scaling of the algorithm and simplify its deployment on a quantum computer. The training on Rigetti's Aspen‐4‐2Q‐A quantum processing unit is then performed, and the results from the training are presented. The authors' results pave the way for experimental exploration of different quantum machine learning algorithms on noisy intermediate‐scale quantum devices.

Anand, Abhinav↗
DOE OSTI2025

Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building

With the recent demand for decarbonization and energy efficiency, advanced HVAC control using Deep Reinforcement Learning (DRL) becomes a promising solution. Due to its flexible structures, DRL has been successful in energy reduction for many HVAC systems. However, only a few researches applied DRL agents to manage the entire central HVAC system and control multiple components in both the water loop and the air loop, owing to its complex system structures. Moreover, those researches have not extended their applications by incorporating the indoor air quality, especially both CO2 and PM2.5concentrations, on top of energy saving and thermal comfort, as achieving those objectives simultaneously can cause multiple control conflicts. What's more, DRL agents are usually trained on the simulation environment before deployment, so another challenge is to develop an accurate but relatively simple simulator. Therefore, we propose a DRL algorithm for a central HVAC system to co-optimize energy consumption, thermal comfort, indoor CO2 level, and indoor PM2.5 level in an office building. To train the controller, we also developed a hybrid simulator that decoupled the complex system into multiple simulation models, which are calibrated separately using laboratory test data. The hybrid simulator combined the dynamics of the HVAC system, the building envelope, as well as moisture, CO2, and particulate matter transfer. Three control algorithms (rule-based, MPC, and DRL) are developed, and their performances are evaluated on the hybrid simulator environment with a realistic scenario (i.e., with stochastic noises). The test results showed that, the DRL controller can save 21.4 % of energy compared to a rule-based controller, and has improved thermal comfort, reduced indoor CO2 concentration. The MPC controller showed an 18.6 % energy saving compared to the DRL controller, mainly due to savings from comfort and indoor air quality boundary violations caused by unmeasured disturbances, and it also highlights computational challenges in real-time control due to non-linear optimization. Finally, we provide the practical considerations for designing and implementing the DRL and MPC controllers based on their respective pros and cons.

Guo, Fangzhou↗
DOE OSTI2026

Challenges and Vision for Standardization of Biopolymer Data Sets for Machine Learning

Machine learning (ML) is transforming materials research, yet potential for biopolymer discovery remains constrained by fragmented data and nonstandardized reporting. Biopolymers differ significantly from synthetic polymers, requiring specialized approaches to represent their biosynthetic origins, hierarchical structures, and application-specific metrics. In this Perspective, we identify three core challenges limiting biopolymer representation: information encoding, data quality, and data sharing. We describe the most pressing issues and propose commensurate approaches to address each key challenge. Recommendations include the design and adoption of biopolymer-specific fingerprinting and representation frameworks, development of hybrid human-large language model (LLM) data extraction strategies, and expanding Findable, Accessible, Interoperable, Reusable (FAIR)-compliant repositories. We propose a robust foundation to define interoperable, high-quality data sets that capture the full context of biopolymer materials. Standardized metadata, shared ontologies, and community-driven infrastructure would enable scalable, reproducible workflows and accelerate the ML-driven development of biopolymers.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effects of Selenium Doping in Zinc Telluride from First Principles

We report point defects and compensation in zinc telluride have been subjects of study for several decades due to the compound’s scientific/technological applications. Despite this, there remains some disagreement in the literature as to the fine details of its defect chemistry. Recently, it has also come under renewed interest due to the emerging Cd( 1–x )Zn x Te( 1–y )Se y material family, used in nuclear radiation detector applications. In this article, we use state-of-the-art computational tools to re-examine the native defect chemistry of ZnTe and explore how it changes upon addition of selenium using density functional theory with screened hybrid exchange–correlation functionals, thermochemical models of the equilibration environment, and statistical mechanics models of the point defect ensemble.

II-VI semiconductors↗
DOE OSTI2023

Electron Energization by Inertial Alfvén Waves in Density Depleted Flux Tubes at Jupiter

Juno satellite observations have illustrated substantial examples of broadband electron energization up to 10 5 –10 6 eV levels. In order to explain these observations, we use a hybrid gyrofluid kinetic-electron model in an untilted dipolar topology to illustrate energization to high levels in weak current conditions by inertial Alfvén waves close to the Jupiter ionosphere for ambient plasma densities and magnetic field perturbations inferred from Juno satellite observations. The key to the high energization is the extremely low densities evident in the observations which necessitates the acceleration of electrons to very high velocities in order to carry the field-aligned current.

58 GEOSCIENCES↗
DOE OSTI2020

Strong and fragile topological Dirac semimetals with higher-order Fermi arcs

Abstract Dirac and Weyl semimetals both exhibit arc-like surface states. However, whereas the surface Fermi arcs in Weyl semimetals are topological consequences of the Weyl points themselves, the surface Fermi arcs in Dirac semimetals are not directly related to the bulk Dirac points, raising the question of whether there exists a topological bulk-boundary correspondence for Dirac semimetals. In this work, we discover that strong and fragile topological Dirac semimetals exhibit one-dimensional (1D) higher-order hinge Fermi arcs (HOFAs) as universal, direct consequences of their bulk 3D Dirac points. To predict HOFAs coexisting with topological surface states in solid-state Dirac semimetals, we introduce and layer a spinful model of an s – d -hybridized quadrupole insulator (QI). We develop a rigorous nested Jackiw–Rebbi formulation of QIs and HOFA states. Employing ab initio calculations, we demonstrate HOFAs in both the room- ( α ) and intermediate-temperature ( α ″ ) phases of Cd 3 As 2 , KMgBi, and rutile-structure ( $$ \beta ^{\prime} $$ β ′ -) PtO 2 .

Science & Technology - Other Topics↗
DOE OSTI2025

A Grad-Shafranov model for compact quasisymmetric stellarators

A Grad-Shafranov equation (GSE) valid for compact quasisymmetric stellarators is derived by an asymptotic expansion around a vacuum field carried to first order. We obtain an equation for the existence of flux surfaces leading up to the GSE. The flux surface label must simultaneously satisfy the existence equation and the GSE, which generally leads to an overdetermined problem. We show how the overdetermined problem can be resolved within our model for a class of hybrid devices similar to that studied by Henneberg and Plunk [Phys. Rev. Res. 6, L022052 (2024)]. We are also able to solve the existence equation for flux surfaces analytically in the most general case by introducing a special coordinate system. This will enable us to carry out an optimization seeking to minimize the error in our GSE while obeying the flux surface existence equation, which will allow us to find solutions outside the class of hybrid devices. This will allow for a coarse-grained approximate search in the space of quasisymmetric equilibria that should be faster than a conventional stellarator optimization. Nevertheless, it would still be necessary to fine-tune the approximate solutions using conventional tools to obtain a more precise optimized equilibrium.

Nikulsin, N. (ORCID:0000000318611777)↗
DOE OSTI2024

The Atacama Cosmology Telescope DR6 and DESI: structure formation over cosmic time with a measurement of the cross-correlation of CMB lensing and luminous red galaxies

We present a high-significance cross-correlation of CMB lensing maps from the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6) with luminous red galaxies (LRGs) from the Dark Energy Spectroscopic Instrument (DESI) Legacy Survey spectroscopically calibrated by DESI. We detect this cross-correlation at a significance of 38σ; combining our measurement with the Planck Public Release 4 (PR4) lensing map, we detect the cross-correlation at 50σ. Fitting this jointly with the galaxy auto-correlation power spectrum to break the galaxy bias degeneracy with σ 8 , we perform a tomographic analysis in four LRG redshift bins spanning 0.4 ≤ z ≤ 1.0 to constrain the amplitude of matter density fluctuations through the parameter combination S 8 × = σ 8 (Ω m / 0.3) 0.4 . Prior to unblinding, we confirm with extragalactic simulations that foreground biases are negligible and carry out a comprehensive suite of null and consistency tests. Using a hybrid effective field theory (HEFT) model that allows scales as small as k max = 0.6 h/ Mpc, we obtain a 3.3% constraint on S 8 × = σ 8 (Ω m / 0.3) 0.4 = 0.792 +0.024 -0.028 from ACT data, as well as constraints on S 8 × (z) that probe structure formation over cosmic time. Our result is consistent with the early-universe extrapolation from primary CMB anisotropies measured by Planck PR4 within 1.2σ. Jointly fitting ACT and Planck lensing cross-correlations we obtain a 2.7% constraint of S 8 × = 0.776 +0.019 -0.021 , which is consistent with the Planck early-universe extrapolation within 2.1σ, with the lowest redshift bin showing the largest difference in mean. The latter may motivate further CMB lensing tomography analyses at z < 0.6 to assess the impact of potential systematics or the consistency of the ΛCDM model over cosmic time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Cosmological constraints from the cross-correlation of DESI Luminous Red Galaxies with CMB lensing from Planck PR4 and ACT DR6

Here, we infer the growth of large scale structure over the redshift range 0.4 ≲ z ≲ 1 from the cross-correlation of spectroscopically calibrated Luminous Red Galaxies (LRGs) selected from the Dark Energy Spectroscopic Instrument (DESI) legacy imaging survey with CMB lensing maps reconstructed from the latest Planck and ACT data. We adopt a hybrid effective field theory (HEFT) model that robustly regulates the cosmological information obtainable from smaller scales, such that our cosmological constraints are reliably derived from the (predominantly) linear regime. We perform an extensive set of bandpower- and parameter-level systematics checks to ensure the robustness of our results and to characterize the uniformity of the LRG sample. We demonstrate that our results are stable to a wide range of modeling assumptions, finding excellent agreement with a linear theory analysis performed on a restricted range of scales. From a tomographic analysis of the four LRG photometric redshift bins we find that the rate of structure growth is consistent with ΛCDM with an overall amplitude that is ≃ 5-7% lower than predicted by primary CMB measurements with modest (∼ 2σ) statistical significance. From the combined analysis of all four bins and their cross-correlations with Planck we obtain S 8 = 0.765 ± 0.023, which is less discrepant with primary CMB measurements than previous DESI LRG cross Planck CMB lensing results. From the cross-correlation with ACT we obtain S 8 = 0.790 +0.024 -0.027 , while when jointly analyzing Planck and ACT we find S 8 = 0.775 +0.019 -0.022 from our data alone and σ 8 = 0.772 +0.020 -0.023 with the addition of BAO data. These constraints are consistent with the latest Planck primary CMB analyses at the ≃ 1.6-2.2σ level, and are in excellent agreement with galaxy lensing surveys.

cosmological parameters from LSS↗
DOE OSTI2025

Novel challenges in tracking self-interacting dark matter subhalos

Cosmological N-body simulations are among the primary tools for studying structure formation in the Universe. Analyses of these simulations critically depend on accurately identifying and tracking dark matter subhalos over time. In recent years, several new algorithms have been developed to improve the accuracy and consistency of subhalo tracking in cold dark matter (CDM) simulations. These algorithms should be revisited in the context of new physics beyond gravity, which can modify the evolution and final properties of subhalo populations. In this work, we apply the particle-tracking-based subhalo finder Symfind to velocity-dependent self-interacting dark matter (SIDM) simulations with large cross section amplitudes to assess the performance of particle-tracking methods beyond the CDM paradigm. We find that the core-particle-tracking technique, which is key to the success of these algorithms in CDM, does not always yield accurate results in SIDM. In particular, the interplay between dark matter self-interactions and tidal stripping can cause the diffusion of core particles to larger radii, leading particle-tracking-based algorithms to prematurely lose track of SIDM subhalos. For massive core-expansion subhalos and core-collapse subhalos that experience close or repeated pericentric passages, a significant fraction of core particles can be lost, and particle-tracking-based finders such as Symfind offer no clear advantage over traditional methods that rely on identifying phase-space overdensities. On the other hand, for subhalos with large pericentric distances or fewer, more distant passages, Symfind tends to outperform. These differences depend sensitively on the cross section amplitude and turnover velocity of the underlying SIDM model. We therefore recommend a hybrid approach that leverages the strengths of both techniques to produce complete and robust catalogs of core-expansion and core-collapse SIDM subhalos.

cosmological simulations↗
DOE OSTI2020

Heavy flavor quenching and flow: the roles of initial condition, pre-equilibrium evolution, and in-medium interaction

Within an advanced Langevin-hydrodynamics framework coupled to a hybrid fragmentation-coalescence hadronization model, we study heavy flavor quenching and flow in relativistic heavy-ion collisions. We investigate how the initial heavy quark spectrum, the in-medium energy loss and hadronization mechanisms of heavy quarks, the evolution profile of the pre-equilibrium stage, the medium flow, and the temperature dependence of heavy quark diffusion coefficients influence the suppression and elliptic flow of heavy mesons at the RHIC and the LHC. Our results show that the different modeling of initial conditions, pre-equilibrium evolution, and in-medium interactions can individually yield uncertainties of approximately 10-40% in D meson suppression and flow at a low transverse momentum. Furthermore, we also find that proper combinations of collisional versus radiative energy loss, coalescence versus fragmentation in hadronization, and the inclusion of medium flow are the most important factors for describing the suppression and elliptic flow of heavy mesons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Evolution of structure growth during dark energy domination: Insights from the cross-correlation of DESI galaxies with CMB lensing and galaxy magnification

We use a hybrid effective field theory (HEFT) model to constrain the evolution of low-redshift (z≲0.4) matter fluctuations by cross-correlating DESI Bright Galaxy Survey (BGS) legacy imaging with the latest cosmic microwave background (CMB) lensing maps from Planck and Atacama cosmology telescope. Our tomographic BGS analysis finds that the evolution and amplitude of matter fluctuations align with CMB-conditioned Λ cold dark matter predictions. When including DESI baryon acoustic oscillation (BAO) measurements we obtain σ8=0.876-0.067+0.051 from BGS alone. Jointly analyzing BGS and luminous red galaxy (LRG) cross-correlations with the same CMB lensing maps yields σ8=0.791±0.021. As a complementary approach we isolate the galaxy magnification signal from the cross-correlation of nonoverlapping BGS and LRG photometric redshift bins, ruling out the null-magnification hypothesis at 11σ. For the first time, we constrain quasilinear structure growth from the (finite-difference calibrated) galaxy magnification signal and find σ8=0.720±0.047(stat)±0.050(sys) assuming a linear bias model and including BAO data.

Sailer, Noah↗
DOE OSTI2021

Shifted μ -hybrid inflation, gravitino dark matter, and observable gravity waves

We investigate supersymmetric hybrid inflation in a realistic model based on the gauge symmetry SU(4) c × SU(2) L × SU(2) R . The minimal supersymmetric standard model (MSSM) μ term arises, following Dvali, Lazarides, and Shafi, from the coupling of the MSSM electroweak doublets to a gauge singlet superfield which plays an essential role in inflation. The primordial monopoles are inflated away by arranging that the SU(4) c × SU(2) L × SU(2) R symmetry is broken along the inflationary trajectory. The interplay between the (above) μ coupling, the gravitino mass, and the reheating following inflation is discussed in detail. We explore regions of the parameter space that yield gravitino dark matter and observable gravity waves with the tensor-to-scalar ratio r ~ 10 –4 –10 –3 .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Uncertainty-Proof Hosting Capacity with Surrogate Affine Policy

Physical constraints must be enforced when dis-tributed energy resources, such as PV, are integrated into distribution network. Hosting capacity (HC) is thus introduced to define the maximum renewable that distribution system can accommodate. When the grid is further pushed towards low-carbon, many research efforts are devoted to increasing HC. Security and cost-efficiency become even more important in determining HC. This work proposes an uncertainty-proof HC with surrogate affine policy. Flexible resources are leveraged to increase HC. We propose a novel hybrid two-stage AC model with variable uncertainty set. An iterative algorithm is designed to solve the problem. The proposed model and solution approach are validated in modified 141-node feeders, and HC performance is also analyzed.

hosting capacity↗
DOE OSTI2025

Deep Factorization Machine Learning for Disaggregation of Transmission Load Profiles with High Penetration of Behind-The-Meter Solar

The ever-growing integration of distributed energy resources (DERs), especially behind-the-meter (BTM) solar generations, poses imperative operational challenges to system operators such as regional transmission organizations (RTOs). It is important for RTOs to effectively and accurately extract actual load profiles at the transmission level for a single node with significant BTM solar injection. This paper first illustrates the necessity of disaggregating the daily actual load profile of a single node. Furthermore, by segmenting nodes with selected timeseries features, nodes with significant BTM solar generation are identified. Lastly, a bi-level framework is proposed, comprising reference node disaggregation and DeepFM nodal disaggregation, aimed at disaggregating the nodal load profiles from which system operators require more information. By adopting a hybrid Deep Factorization Machine (DeepFM) model, the model achieve accurate results by extracting both linear and nonlinear relations between nodes in the same region and the zonal load and nodal load profile. To overcome the lack of ground truth, this paper segments the load profile into daytime, nighttime, and zero-crossing points and utilizes the latter two for evaluation purposes. The proposed disaggregation procedure is validated using real world, minute-level, normalized, and anonymized nodal data in the PJM service territory.

42 ENGINEERING↗