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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Effects of Surface and Top Wind Shear on the Spatial Organization of Marine Stratocumulus-Topped Boundary Layers

Typically, the convective nature of Stratocumulus topped boundary layers (STBL) involves the motion of updrafts and downdrafts, driven by surface fluxes and radiative cooling, respectively. The balance between shear and buoyant forcings at the surface can determine the organization of updrafts between cellular and roll structures. We investigate the effect of varying shear at the surface and top of the STBL using Large Eddy Simulations, taking DYCOMS II RF01 as a base case. We focus on spatial identification of the following features: coherent updrafts and downdrafts, and observe how they are affected by varying shear. Stronger surface shear organizes the updrafts in rolls, causes less well-mixed thermodynamic profiles, and decreases cloud fraction and liquid water path (LWP). Stronger top shear also decreases cloud fraction and LWP more than surface shear, by thinning the cloud from the top. Features with stronger top than surface shear are associated with a net downward momentum transport and show early signs of decoupling. Classifying updrafts and downdrafts based on their vertical span and horizontal size confirms the dominance of tall objects spanning the whole STBL. Tall objects occupy 30% of the volume in the STBL, while short ones occupy less than 1%. For updraft and downdraft fluxes, these tall objects explain 65% of the vertical velocity variance and 83% of the buoyancy flux, on average. Stronger top shear also weakens the contribution of downdrafts to the turbulent fluxes and tilts the otherwise vertical development of updrafts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Towards Pareto optimal high entropy hydrides via data-driven materials discovery

The ability to rapidly screen material performance in the vast space of high entropy alloys is of critical importance to efficiently identify optimal hydride candidates for various use cases. Given the prohibitive complexity of first principles simulations and large-scale sampling required to rigorously predict hydrogen equilibrium in these systems, we turn to compositional machine learning models as the most feasible approach to screen on the order of tens of thousands of candidate equimolar high entropy alloys (HEAs). Critically, we show that machine learning models can predict hydride thermodynamics and capacities with reasonable accuracy (e.g. a mean absolute error in desorption enthalpy prediction of ~5 kJ mol H 2 –1 ) and that explainability analyses capture the competing trade-offs that arise from feature interdependence. We can therefore elucidate the multi-dimensional Pareto optimal set of materials, i.e., where two or more competing objective properties can't be simultaneously improved by another material. This provides rapid and efficient down-selection of the highest priority candidates for more time-consuming density functional theory investigations and experimental validation. Various targets were selected from the predicted Pareto front (with saturation capacities approaching two hydrogen per metal and desorption enthalpy less than 60 kJ mol H 2 –1 ) and were experimentally synthesized, characterized, and tested amongst an international collaboration group to validate the proposed novel hydrides. Finally, additional top-predicted candidates are suggested to the community for future synthesis efforts, and we conclude with an outlook on improving the current approach for the next generation of computational HEA hydride discovery efforts.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Planck 2018 results. VII. Isotropy and Statistics of the CMB

Analysis of the Planck 2018 data set indicates that the statistical properties of the cosmic microwave background (CMB) temperature anisotropies are in excellent agreement with previous studies using the 2013 and 2015 data releases. In particular, they are consistent with the Gaussian predictions of the ΛCDM cosmological model, yet also confirm the presence of several so-called “anomalies” on large angular scales. The novelty of the current study, however, lies in being a first attempt at a comprehensive analysis of the statistics of the polarization signal over all angular scales, using either maps of the Stokes parameters, Q and U , or the E -mode signal derived from these using a new methodology (which we describe in an appendix). Although remarkable progress has been made in reducing the systematic effects that contaminated the 2015 polarization maps on large angular scales, it is still the case that residual systematics (and our ability to simulate them) can limit some tests of non-Gaussianity and isotropy. However, a detailed set of null tests applied to the maps indicates that these issues do not dominate the analysis on intermediate and large angular scales (i.e., ℓ ≲ 400). In this regime, no unambiguous detections of cosmological non-Gaussianity, or of anomalies corresponding to those seen in temperature, are claimed. Notably, the stacking of CMB polarization signals centred on the positions of temperature hot and cold spots exhibits excellent agreement with the ΛCDM cosmological model, and also gives a clear indication of how Planck provides state-of-the-art measurements of CMB temperature and polarization on degree scales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Structure and overstability of resistive modes with runaway electrons

In this paper, we investigate the effects of runaway electron current on the dispersion relation of resistive magnetohydrodynamic modes in tokamaks. We present a new theoretical model to derive the dispersion relation, which is based on the asymptotic analysis of the resistive layer structure of the modes. It is found that in addition to the conventional resistive layer, a new runaway current layer can emerge whose properties depend on the ratio of the Alfvén velocity to the runaway electron convection speed. Due to the contribution from this layer, both the tearing mode and kink mode will have a real frequency in addition to a growth rate. The derived dispersion relation has been compared with numerical results using both a simplified eigenvalue calculation and a M3D-C 1 linear simulation, and good agreement is found in both cases.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Guiding center and gyrokinetic orbit theory for large electric field gradients and strong shear flows

The guiding center and gyrokinetic theory of magnetized particle motion is extended to the regime of large electric field gradients perpendicular to the magnetic field. A gradient in the electric field directly modifies the oscillation frequency and causes the Larmor orbits to deform from circular to elliptical trajectories. In order to retain a good adiabatic invariant, there can only be strong dependence on a single coordinate at lowest order, so that resonances do not generate chaotic motion that destroys the invariant. When the gradient across magnetic flux surfaces is dominant, the guiding center drift velocity becomes anisotropic in response to external forces and additional curvature drifts must be included. The electric polarization density remains gyrotropic, but both the polarization and magnetization are modified by the change in gyrofrequency. The theory can be applied to shear flows that are even stronger than those observed in the edge transport barrier of a high-performance tokamak (H-mode) pedestal, even if the toroidal field is as small as or even smaller than the poloidal field. Yet, the theory retains a mathematical form that is similar to the standard case and can readily be implemented within existing simulation tools.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

The equation of state and shock-driven decomposition of polymethylmethacrylate (PMMA)

We present new equations of state (EOS) for polymethylmethacrylate and its shock-driven decomposition products, generated in both tabular form and as linear U S – u p fits to all previously available shock data. Different approaches are taken for materials shocked above and below the apparent threshold for chemical reactions at pressures P ≈ 30 GPa. Both EOSs are calibrated to and compared with a broad array of experimental results, as well as new ab initio molecular dynamics simulations presented here. Comparisons are good in most cases, and we focus our particular attention on those observables that test the distinction between reactants and products.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Hybridization and deconfinement in colloidal quantum dot molecules

The structural, electronic, and optical properties of CdSe/CdS core–shell colloidal quantum dot molecules, a new class of coupled quantum dot dimers, are explored using atomistic approaches. Unlike the case of dimers grown by molecular beam epitaxy, simulated strain profile maps of free-standing colloidal dimers show negligible additional strain resulting from the attachment. Further, the electronic properties of the relaxed dimers are described within a semiempirical pseudopotential model combined with the Bethe–Salpeter equation within the static screening approximation to account for electron–hole correlations. The interplay of strain, hybridization (tunneling splitting), quantum confinement, and electron–hole binding energies on the optical properties is analyzed and discussed. The effects of the dimensions of the neck connecting the two quantum dot building blocks, as well as the shell thickness, are studied.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Full-stack Quantification of Variability in Predicting Ion Transport Properties using Machine-learned Interatomic Potentials

Machine-learned interatomic potentials (MLIPs) have become the state-of-the-art for performing accurate, scalable molecular dynamics (MD) simulations. It is therefore crucial to understand and quantify the reliability of MLIPs for downstream property predictions. Uncertainty in predicted properties can arise from limitations in first-principles training data, intrinsic MLIP model errors in representing the data, and the statistical noise introduced during subsequent MD simulations. Using ion transport in Li7P3S11 as a case study, we systematically assess the impact of training set size and selection, neural network stochasticity, and MD sampling statistics on predicted diffusivity and activation energy. We find that when using equivariant MLIP architectures with standard MD protocols, uncertainty arising from MD sampling dominates over model-induced errors. In contrast, MLIP errors relative to the underlying first-principles data are consistently minor. Given this, there are two main routes to improving the accuracy of predictions based on MLIP potentials: adopting higher accuracy reference data generation methods, and improving the MD sampling statistics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Real-Time Inventory Change Detection for the Protactinium Decay Inventory of the Molten Salt Breeder Reactor

This work presents a novel monitoring method for detecting material loss from the decay inventory of the molten salt breeder reactor (MSBR) by monitoring for changes to the system dynamics using an isotopic ratio. Here the isotopic masses in the decay inventory of a MSBR were simulated under several material loss scenarios. In each case, the ratio of 231 Pa to 233 Pa served as a sensitive and lasting indicator of material loss. This isotope ratio quickly decreased outside the normal range after a material loss, and the ratio remained depressed for several years after the loss. The dynamics of this ratio were driven by the periodic batch discard from the decay inventory every 220 days, which was specified in the MSBR design to periodically remove fission product buildup. For this method, isotopic ratios were found to be rapid and enduring indicators of inventory change if they comprise a pair with a short half-life (e.g., 233 Pa) and a long half-life (e.g., 231 Pa) relative to the effective half-life induced by the driving system process (e.g., the batch discard cycle). Using such an isotope pair enabled a method to monitor for changes to the effective half-life of the system and by extension changes to the system inputs and outputs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Role of perturbed parallel magnetic field effects in predicting turbulent transport in NSTX

This study presents analysis of gyrokinetic simulations on the National Spherical Torus Experiment (NSTX) to investigate the effects of electromagnetic fields on plasma turbulence and transport. The simulations, performed with varying levels of fidelity using the gyrokinetic CGYRO code, include electrostatic (ES), single-field electromagnetic (EM1), and two-field electromagnetic (EM2) models. A detailed comparison across the simulation database reveals that electromagnetic effects increase both predicted growth rates and quasilinear fluxes, with EM2 simulations producing stronger turbulence than ES and EM1 cases. Quasilinear modeling using QLGYRO demonstrates that while the perturbed parallel magnetic field (δB ∥ ) does not drastically affect the total flux at experimental gradients, it leads to a shift in the dominant instability, altering mode structures from microtearing to kinetic ballooning modes (KBMs). The proximity of the plasma profiles to the KBM threshold is explored, with the experimental conditions being near the onset of KBM-driven transport. The KBM, with its large growth rates, is identified as a potential driver of electron temperature flattening, as it can rapidly transport heat across flux surfaces. Performing stability analysis shows core-localized unstable a low- mode that could contribute to the flattening at the early times of the discharge. TGYRO predictive modeling, incorporating both TGLF and QLGYRO, indicates that the inclusion of δB ∥ significantly improves the accuracy of temperature profile predictions in NSTX high-beta plasmas, although challenges remain in modeling the sharp flux discontinuities caused by KBM-driven instabilities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

The neutrino gravitational memory from a core collapse supernova: phenomenology and physics potential

General Relativity predicts that the passage of matter or radiation from an asymmetrically-emitting source should cause a permanent change in the local space-time metric. This phenomenon, called the gravitational memory effect, has never been observed, however supernova neutrinos have long been considered a promising avenue for its detection in the future. With the advent of deci-Hertz gravitational wave interferometers, observing the supernova neutrino memory will be possible, with important implications for multimessenger astronomy and for tests of gravity. In this work, we develop a phenomenological (analytical) toy model for the supernova neutrino memory effect, which is overall consistent with the results of numerical simulations. This description is then generalized to several case studies of interest. We find that, for a galactic supernova, the dimensionless strain, h(t), is of order ~ 10 -22 - 10 -21 , and develops over a typical time scale that varies between ~ 0.1 - 10 s, depending on the time-evolution of the anisotropy of the neutrino emission. The characteristic strain, h c (f), has a maximum at a frequency f max ~ Script $\mathcal{O}$(10 -1 ) - Script $\mathcal{O}$(1) Hz. The detailed features of the time- and frequency-structure of the memory strain will inform us of the matter dynamics near the collapsed core, and allow to distinguish between different stellar collapse scenarios. Next generation gravitational wave detectors like DECIGO and BBO will be sensitive to the neutrino memory effect for supernovae at typical galactic distances and beyond; with Ultimate DECIGO exceeding a detectability distance of 10 Mpc

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Stripe antiferromagnetism and disorder in the Mott insulator NaFe 1₋ x Cu x As ( x ≲0.5)

Neutron scattering measurements have demonstrated that the heavily Cu doped NaFe 1₋ x Cu x As compound behaves like a Mott insulator exhibiting both real-space Fe-Cu stripes and antiferromagnetism below a Néel temperature for x ≲ 0.5. In this work, we have investigated the evolution of structural and magnetic ordering using 23 Na and 75 As NMR for single crystals ( x = 0.39 and 0.48), confirming antiferromagnetism in the form of magnetic stripes. We show that end-chain defects in these stripes are the principal source of magnetic disorder and are responsible for cluster spin-glass transitions in both compounds, in the latter case coexistent with antiferromagnetism. Aided by our numerical simulation of the 75 As spectra, we show that a staggered magnetization at the Fe sites is induced by nonmagnetic Cu dopants.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Statistical and systematic uncertainties in extracting the source properties of neutron star-black hole binaries with gravitational waves

Gravitational waves emitted by neutron star black hole mergers encode key properties of neutron stars - such as their size, maximum mass and spins - and black holes. However, the presence of matter and the high mass ratio makes generating long and accurate waveforms from these systems hard to do with numerical relativity, and not much is known about systematic uncertainties due to waveform modeling. In this work, we simulate gravitational waves from neutron star black hole mergers by hybridizing numerical relativity waveforms produced with the SpEC code with a recent numerical relativity surrogate NRHybSur3dq8Tidal. These signals are analyzed using a range of available waveform families, and statistical and systematic errors are reported. We find that at a network signal-to-noise ratio (SNR) of 30, statistical uncertainties are usually larger than systematic offsets, while at an SNR of 70 the two become comparable. The individual black hole and neutron star masses, as well as the mass ratios, are typically measured very precisely, though not always accurately at high SNR. At a SNR of 30 the neutron star tidal deformability can only be bound from above, while for louder sources it can be measured and constrained away from zero. All neutron stars in our simulations are non-spinning, but in no case we can constrain the neutron star spin to be smaller than ~0.4 (90% credible interval). Waveform families whose late inspiral has been tuned specifically for neutron star black hole signals typically yield the most accurate characterization of the source parameters. Their measurements are in tension with those obtained using waveform families tuned against binary neutron stars, even for mass ratios that could be relevant for both binary neutron stars and neutron star black holes mergers.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

DriveSense: A Noise-Resilient Framework for Driving Mode Identification

Accurate drive mode classification is essential for enhancing the reliability and predictive maintenance of heavy-duty electric trucks. This study proposes a novel fuzzy logic-based framework, DriveSense, for real-time drive mode classification, addressing key challenges such as sensor noise, transitional behaviors, and computational efficiency. The proposed approach integrates a two-stage filtering pipeline, combining adaptive outlier removal and a dynamic Kalman filter to enhance data quality. A fuzzy inference system with smoothened trapezoidal membership functions is then applied to classify driving modes into standstill, constant speed, acceleration, and deceleration while mitigating the effects of noise and edge cases. Performance evaluation using real-world and simulated drive cycles demonstrates significant improvements in classification accuracy (up to 97.8%), F1-score (up to 0.97), and robustness against noise, while reducing false positives. Comparative analysis against baseline models, demonstrates DriveSense’s superior accuracy and generalizability across diverse driving patterns. The framework’s lightweight and interpretable fuzzy inference engine operates with low computational latency, ensuring compatibility with real-time embedded systems typical of heavy-duty electric trucks. Moreover, DriveSense models transitional behaviors through overlapping fuzzy sets and adaptive borderline classification logic, enabling smooth identification of subtle shifts such as rolling stops or gradual deceleration. These results highlight DriveSense’s potential to enhance predictive maintenance strategies, reduce downtime, and support scalable, fleet-wide diagnostics.

Kumar, Praveen [Oak Ridge National Laboratory (ORN↗
DOE OSTI2025

Describing Function Analysis of Transformer Magnetizing Inductance for Direct Power Control of Back-to-Back Modular Multilevel Converters with Advanced Grid Support

This paper provides a detailed investigation into the application of describing function-based analysis for assessing transformer magnetizing inductance and its impact on system performance. The focus is on a back-to-back modular multilevel converter architecture, designed to interconnect systems operating at different frequencies. The study explores the implementation of a Direct Power Control strategy, examining its effects on transformer magnetizing inductance saturation and offering effective mitigation techniques. Furthermore, the integration of advanced grid support functionalities is highlighted, demonstrating how these enhancements bolster the converter's ability to improve grid stability and power quality, positioning it as a robust solution for modern power systems. The proposed approach is validated through extensive computer simulations based MAT LAB/Simulink domain, supported by significant case study results, confirming its practical effectiveness.

back-to-back modular multilevel converters (B2B- M↗
DOE OSTI2026

PowerMappeR: Power-Optimized Mapping of SNNs onto ReRAM Crossbars coupled via Packet-Switched NoCs

Many recent efforts in developing hardware-accelerated spiking neural networks (SNNs) are characterized by deep co-design between algorithms, architectures, and devices. Architectural advances overcome device constraints by coupling together many small resistive-RAM (ReRAM) crossbars via a network-on-chip (NoC) for neuromorphic component operation. Concurrently, improved SNN training methods increase accuracy and structural sparsity in networks despite growing problem sizes. Finally, compilers leverage these attributes to minimize area and inter-crossbar communication while mapping large SNNs to sophisticated architectures. However, for compiler-driven co-design to realize increasingly complex and profitable optimizations, a compile-time view of power consumption is critical. We present PowerMappeR to express and optimize over mapping-, architecture-, and device-specific power consumption information. By modeling the dynamic power of well-established components, we develop an integer linear programming (ILP)-based, encoding-agnostic, parametric power estimation model. Using this model, we demonstrate practical improvements in area and inter-crossbar communication by 0%–9.5% and 1.4%–5.1%, respectively. We also limit hotspot formation during optimization, achieving comparable or better results in targeted metrics with up to 96.4%–97.1% restriction of hotspot magnitude. Finally, we introduce profile-guided formulations to reduce worst-case and expected-case hotspot magnitude by 40.7%–69.5% and 40.6%–56.3%, respectively. Optimizing worst-case hotspot magnitude incidentally improves expected-case magnitude by 10.85%–33.45%. Reciprocally, optimizing expected-case magnitude incidentally improves worst-case magnitude by 4.33%–39.87%. Validation against hardware simulators confirms that PowerMappeR can decrease dynamic power consumption by 12.6%–27.3%.

Pohl, Devin [ORNL] (ORCID:0009000040149027)↗
DOE OSTI2020

Grid-Forming Frequency Shaping Control for Low-Inertia Power Systems

As power systems transit to a state of high renewable penetration, little or no presence of synchronous generators makes the prerequisite of well-regulated frequency for grid-following inverters unrealistic. As such, there is a trend to resort to grid-forming inverters which set frequency directly. We propose a novel grid-forming frequency shaping control that is able to shape the aggregate system frequency dynamics into a first-order one with the desired steady-state frequency deviation and Rate of Change of Frequency (RoCoF) after a sudden power imbalance. The no overshoot property resulting from the first-order dynamics allows the system frequency to monotonically move towards its new steady-state without experiencing frequency Nadir, which largely improves frequency security. We prove that our grid-forming frequency-shaping control renders the system internally stable under mild assumptions. The performance of the proposed control is verified via numerical simulations on a modified Icelandic Power Network test case.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Spatial-Temporal PV Hosting Capacity Estimation and Evaluation

Evaluating Photovoltaic Hosting Capacity (PVHC) is an essential step in the process of integrating solar energy into power grids, particularly when focusing on the distribution network (DN) as the primary integration target. PVHC needs to be investigated, especially in cases where the grids are unbalanced, and their operational conditions vary spatially and temporally. This motivation prompted us to propose a scalable model tailored to this application. In this paper, we applied linearization to the alternating current optimal power flow (AC-OPF) and solar inverters, transforming the original problem into a mixed-integer linear programming (MILP) problem. Additionally, we accounted for the battery energy storage system (BESS) as a time-coupling factor for calculating PVHC. We then compared the PVHC results between the IEEE-13 bus and SMART-DS San Francisco (SFO) cases and discussed the extent to which BESS can enhance the PVHC of a DN. Furthermore, we designed a web-based graphical visualization for the SFO case, enabling user interaction with raw data and simulation results on a map through a graphical user interface (GUI). In summary, our results and findings provide valuable insights for future three-phase unbalanced AC-OPF PVHC practices and their visualization.

AC-optimal power flow↗