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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Identifying High Ionic Conductivity Compositions of Ionic Liquid Electrolytes Using Features of the Solvation Environment

Binary mixtures of ionic liquids with molecular solvents are gaining interest in electrochemical applications due to the improvement in their performance over neat ionic liquids. Dilution with suitable molecular solvents can reduce the viscosity and facilitate faster diffusion of ions, thereby yielding substantially higher ionic conductivity than that for a pure ionic liquid. Although viscosity and diffusion coefficients typically behave as monotonic functions of concentration, ionic conductivity often passes through a peak value at an optimum molar ratio of the molecular solvent to the ionic liquid. The ionic conductivity maximum is generally explained in terms of a balance between the ease of charge transport and the concentration of the charge carriers. In this work, fluctuation in the local environment surrounding an ion is invoked as a plausible explanation for the ionic conductivity mechanism with a binary mixture of 1-ethyl-3-methylimidazolium tetrafluoroborate and ethylene glycol as an example. The magnitude of the dynamism in the local environment is captured by measuring the spatial and temporal features of the solvation environment. Standard deviation in the number of ions in the solvation environment serves as a spatial feature, while the cage correlation lifetimes for oppositely charged ions within the first solvation shell serve as a temporal feature. Large standard deviations in the cluster ion population and short cage correlation lifetimes are indicators of highly dynamic ionic environment at the molecular level and consequently yield high ionic conductivity. Such compositions were found to be in good agreement with the optimum ionic liquid mole fractions obtained through experimental measurement. Short cage correlation lifetimes enable the identification of optimum mixture compositions using simulation trajectories significantly shorter than those required to implement the Nernst–Einstein or Einstein formalisms for calculating ionic conductivity. We validated the applicability of this approach across force fields and in six ionic liquid-molecular solvent electrolytes formed with combination of cations, anions, and solvents. We offer a computationally efficient approach of screening ionic liquid-molecular solvent binary mixture electrolytes to identify molar ratios that yield high ionic conductivity.

25 ENERGY STORAGE↗
DOE OSTI2025

All-Atom Modeling and Simulation of Biopolymer Interface: Dual Role of Antifouling Polymer Brushes

Antifouling polymer brushes are well-known for their exceptional resistance to unwanted protein adsorption. While experimental studies have extensively characterized protein–polymer brush interactions, computational investigations remain limited, largely due to the challenges in accurately modeling and integrating these complex, multicomponent systems without resorting to oversimplification. To address this challenge, this study presents one of the most comprehensive and realistic model systems to date, comprising the substrate, grafted polymers, and proteins. Our work interprets the interactions between polymer brush and protein based on realistic modeling without simplification. In particular, this study utilizes molecular modeling and simulation of polycationic and polyzwitterionic brushes─poly(dimethylaminoethyl methacrylate) (PDMAEMA), poly(2-(N-oxide-N,N-dimethylamino)ethyl methacrylate) (PNOMA), and poly(2-(N-3-sulfopropyl-N,N-dimethylammonium)ethyl methacrylate) (PSBMA)─grafted onto α-quartz substrates via polymerization initiator linkers. The brush models were developed to closely replicate experimentally synthesized samples and to provide detailed insights into structural and dynamical changes at the molecular level during protein adsorption. Using steered molecular dynamics simulations, we show that the PSBMA brush, due to its high local density, exhibits the greatest resistance to protein insertion. Cα root-mean-square deviation and interaction pattern analyses further reveal that the PSBMA brush also induces the most significant destabilization of lysozyme, while the PDMAEMA brush enhances protein stability through ion-mediated interactions. The PNOMA brush, while requiring the lowest force for protein adsorption, induces greater protein destabilization than the PDMAEMA brush, primarily due to electrostatic repulsion caused by a short carbon spacer length. Hydration analysis reveals that both the PSBMA brush and the lysozyme interacting with it exhibit the most rapid dehydration, attributed to the brush’s high local chain density, which results in the greatest lysozyme destabilization and the highest adsorption force. These findings highlight the dual role of antifouling polymer brushes: resisting protein adsorption and modulating protein structural dynamics. In conclusion, this study provides valuable insights for the rational design of next-generation antifouling materials and offers a framework for realistic model development in complex multicomponent systems.

Adsorption↗
DOE OSTI2023

How to identify different new neutrino oscillation physics scenarios at DUNE

Next generation neutrino oscillation experiments are expected to measure the remaining oscillation parameters with very good precision. They will have unprecedented capabilities to search for new physics that modify oscillations. DUNE, with its broad band beam, good particle identification, and relatively high energies will provide an excellent environment to search for new physics. If deviations from the standard three-flavor oscillation picture are seen however, it is crucial to know which new physics scenario is found so that it can be verified elsewhere and theoretically understood. We investigate several benchmark new physics scenarios by looking at existing long-baseline accelerator neutrino data from NOvA and T2K and determine at what sensitivity DUNE can differentiate among them. We consider sterile neutrinos and both vector and scalar non-standard neutrino interactions, all with new complex phases, the latter of which could conceivably provide absolute neutrino mass scale information. We find that, in many interesting cases, DUNE will have good model discrimination. We also perform a new fit to NOvA and T2K data with scalar NSI.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data

The design space for inertial confinement fusion (ICF) experiments is vast, and experiments are extremely expensive. Researchers rely heavily on computer simulations to explore the design space in search of high-performing implosions. However, ICF multiphysics codes must make simplifying assumptions, and thus deviate from experimental measurements for complex implosions. For more effective design and investigation, simulations require input from past experimental data to better predict future performance. In this work, we describe a cognitive simulation method for combining simulation and experimental data into a common, predictive model. This method leverages a machine learning technique called “transfer learning,” the process of taking a model trained to solve one task, and partially retraining it on a sparse dataset to solve a different, but related task. In the context of ICF design, neural network models are trained on large simulation databases and partially retrained on experimental data, producing models that are far more accurate than simulations alone. Here, we demonstrate improved model performance for a range of ICF experiments at the National Ignition Facility and predict the outcome of recent experiments with less than 10% error for several key observables. We discuss how the methods might be used to carry out a data-driven experimental campaign to optimize performance, illustrating the key product—models that become increasingly accurate as data are acquired.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Detailed study of quark-hadron duality in spin structure functions of the proton and neutron

Background: The response of hadrons, the bound states of the strong force (QCD), to external probes can be described in two different, complementary frameworks: as direct interactions with their fundamental constituents, quarks and gluons, or alternatively as elastic or inelastic coherent scattering that leaves the hadrons in their ground state or in one of their excited (resonance) states. The former picture emerges most clearly in hard processes with high momentum transfer, where the hadron response can be described by the perturbative expansion of QCD, while at lower energy and momentum transfers, the resonant excitations of the hadrons dominate the cross section. The overlap region between these two pictures, where both yield similar predictions, is referred to as quark-hadron duality and has been extensively studied in reactions involving unpolarized hadrons. Some limited information on this phenomenon also exists for polarized protons, deuterons, and 3 He nuclei, but not yet for neutrons. Purpose: In this paper, we present comprehensive and detailed results on the correspondence between the extrapolated deep inelastic structure function g 1 of both the proton and the neutron with the same quantity measured in the nucleon resonance region. Thanks to the fine binning and high precision of our data, and using a well-controlled perturbative QCD (pQCD) fit for the partonic prediction, we can make quantitative statements about the kinematic range of applicability of both local duality and global duality. Method: We use the most updated QCD global analysis results at high x from the Jefferson Lab Angular Momentum Collaboration to extrapolate the spin structure function g 1 into the nucleon resonance region and then integrate over various intervals in the scaling variable x. We compare the results with the large data set collected in the quark-hadron transition region by the CLAS Collaboration, including, for the first time, deconvoluted neutron data, integrated over the same intervals. We present this comparison as a function of the momentum transfer Q 2 . Results: We find that, depending on the integration interval and the minimum momentum transfer chosen, a clear transition to quark-hadron duality can be observed in both nucleon species. Furthermore, we show, for the first time, the approach to scaling behavior for g 1 measured in the resonance region at sufficiently high momentum transfer. Conclusions: Here, our results can be used to quantify the deviations from the applicability of pQCD for data taken at moderate energies and can help with extraction of quark distribution functions from such data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Heating of Cs2Te photocathode via field emission and radiofrequency pulsed heating: Implication toward breakdown

The occurrence of radiofrequency (RF) breakdown limits operational electromagnetic gradients in accelerator structures. Experimental evidence often suggests that breakdown events are associated with temperature and dark current spikes on the surface of RF devices. In the past decade, there has been increased interest in unveiling the mechanism behind breakdown initiation in metal copper and copper alloys; however, efforts regarding a breakdown phenomenon in photocathode-relevant semiconductors have been more limited. In this work, we explore field-emission-assisted heating via Nottingham and Joule processes, as a possible candidate for breakdown initiation. For this, field emission from an intrinsic Cs2Te ultrathin film coated on a copper substrate was modeled within the Stratton–Baskin–Lvov–Fursey formalism, describing the processes and effects in the bulk and on the surface of a photocathode exposed to high RF electromagnetic fields. It is shown that a field-emission characteristic deviates significantly from the classical Fowler–Nordheim (FN) theory, whereby predicting that dark current is orders of magnitude lower than the one expected by FN law. Conventional pulsed heating was also found to impose negligible heating to the photocathode. Both conclusions suggest that a Cs2Te photocathode coated on a metal substrate would be insensitive to catastrophic thermal-material runaway breakdown, unlike what is observed for metal surfaces. Finally, a few unconventional breakdown candidate scenarios are identified and discussed, including thermoelastic deformation and avalanche breakdown.

Shinohara, Ryo (ORCID:0000000276995732)↗
DOE OSTI2022

A method to predict texture effect on ion beam channeling analysis of polycrystals and the application to study the mosaic spreading effect in highly oriented pyrolytic graphite

We propose a method to convert the channeling Rutherford backscattering spectrum yield map of a single crystal to a polycrystal through a matrix rotation technique. The rotation matrix is determined by the deviation of the crystal axial direction from the original z axis. The final yield map is created after averaging the rotated yields using the texture function as the weight factor. For highly oriented pyrolytic graphite (HOPG) exhibiting mosaic spread, the method leads to a Gaussian kernel averaging of the map obtained from a single crystal. The yield map of a single crystal is obtained by a simulation of ion trajectories in a potential field described by Moliere screened Coulomb potentials. Yield maps are calculated under various σ values (standard deviations of mosaic spread). The simulated results are compared with experimental results obtained using 1.2 MeV alpha particle. σ is extracted through the best fitting, demonstrating that the method can be used to obtain texture details. The effects of mosaic spread on minimum yield χ min and the half-width at half maximum of angular scans ψ ½ are systematically modeled and compared with previous theoretical equations. The study also shows that previous theoretical equations are valid only at small σ values. The proposed method can be applied to any type of polycrystal and is not limited to HOPG. It provides near-surface mosaic spread and crystallography information with a longitudinal depth resolution of tens of nanometers and is not influenced by grain shapes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Combustion of metal powder with dinitrogen tetroxide

In this work, we present analysis of a novel reactive material system that employs dinitrogen tetroxide (N 2 O 4 ) as a liquid oxidizer with metal powder fuels. The oxidizer was added to micron scale aluminum and zirconium powders by a remote injection system. When ignited with a high voltage spark, the mixtures were observed to possess reactivity comparable to nanocomposite reactive materials, with open-tube flame expansion velocities from 500 to 1400 m/s depending on fuel/oxidizer ratio and tube diameter. Temperatures were observed to range from 3000 to 3500 K as measured with gray body fits to 16-channel time-resolved pyrometer and time-integrated spectrometer data. These values were significantly below calculated adiabatic flame temperatures, which we attribute to local deviations from stoichiometry and kinetic/energetic limitations similar to those observed in studies of particles burning in high pressures gaseous oxidizers. Al/N 2 O 4 reactivity was found to be most likely limited by the vaporization of the metal from the particle surface and Zr/N 2 O 4 was limited by slower burning and complex interactions involving the solubility of nitrogen and oxygen in the molten Zr. We also discuss the potential for these materials to be used to create an “on/off” reactive material, since N2O4 can be added remotely and driven to evaporate via vacuum or purge of inert gases to return it to a safe condition.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Scalability analysis of heavy-duty gas turbines using data-driven machine learning

With the increasing integration of variable renewable energy sources into power systems, the role of flexible power generation technologies like gas turbines (GT) in rapid grid balancing remains crucial. This sustained importance underscores the need for scaled and precise modeling of GT to ensure effective integration within evolving energy frameworks. While physics-driven GT models integrate thermodynamics, fluid dynamics, and combustion principles, they often rely on approximate mathematical representations to accommodate scaling that may not capture the actual complex dynamics for GTs and inertial effects associated to GTs with different ratings. In this study, a data-driven model is proposed using machine learning (ML) techniques to conduct GT scalability analysis and performance evaluation with high accuracy. The ML model, trained on data from various operating conditions and performance parameters, aims to uncover intricate relationships and patterns, resembling GT characteristics at different scales (ratings). The model is developed to capture complex system interaction and to adapt to changing operational scenarios at different capacities, providing valuable insights of power system dynamics. In this study, the real-time digital simulator platform was employed to generate training data for the ML model and assess its dynamic characteristics. The ultimate objective was to develop a detailed modeling framework based on governing equations and data-driven ML capable of predicting key performance indicators, in thermal systems such as GTs, including power output, speed, fuel consumption, and exhaust temperature under diverse operating conditions at different scales. The developed ML framework demonstrated high accuracy, with mean relative errors for GT power prediction, reference speed, exhaust temperature, and compressor pressure ratio (CPR) parameters consistently below 0.1% across typical load fluctuation scenarios. Maximum deviations were limited to approximately 0.5 K for exhaust temperature and 0.009 for CPR, underscoring the model’s ability to replicating dynamic GT behavior with high precision. The adaptability of the ML model enables its application across diverse operational conditions and its extension to other thermal systems. By leveraging advanced ML techniques, this study presents a robust and scalable modeling framework that enhances GT simulation precision, facilitating improved integration into evolving power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Recent Improvements in the Beam Capture at Fermilab Booster for High Intensity Operation

The Fermilab Booster uses multi-turn beam injection with all its cavities phased such that beam sees a net zero RF voltage even when each station is at the same maxi-mum voltage. During beam capture the RF voltage is increased slowly by using its paraphase system. At the end of the capture the feedback is turned on for beam acceleration. It is vital for present operations as well as during the PIP-II era that both the HLRF and LLRF systems provide the proper intended phase and RF voltage to preserve the longitudinal emittance from injection to extraction. In this paper, we describe the original architecture of the cavity phase distribution, our recent beam-based RF phase measurements, observed significant deviation in the relative phases between cavities and correction effort. Results from the improved capture for high intensity beam are also presented.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Modeling Monthly Nitrate Concentration in a Karst Spring with and without Discrete Conduit Flow

Understanding the coupled continuum pipe-flow framework for modeling contaminant transport in karst systems is critical for protecting water resources therein. This study simulated point and non-point source nitrate concentration in a karst spring and investigated the results generated from the flow and transport model with and without discrete conduit flow. CFPv2 and CMT3D models were integrated to address the changes in nitrate concentration at a monthly scale in a karst spring, and the results were compared with that from an equivalent porous media (EPM) model with high-hydraulic conductivity (K) zones set in the main karstified area to represent conduits. The results show that the CFPv2+CMT3D model is able to describe well the recession of nitrate concentration in spring discharge, and the relatively larger deviation (slower nitrate recession) from the observed trend for the EPM model is probably a result of the limitation of utilizing high hydraulic conductivity cells to represent conduit. Moreover, simulated hydraulic heads in poorly karstified areas from the two models both show slight differences from the observations (the head RMSE values of calibration/validation for CFPv2 and MODFLOW models are 0.16 m/0.25 m and 0.26 m/0.17 m, respectively), indicating the inclusion of conduits may not affect the simulation considerably, and the lower the proportion of karstic area, the slight effects brought from the inclusion of conduits in the model. For highly karstified areas, the CFPv2+CMT3D model could provide more accurate results (head RMSE of calibration/validation for CFPv2 and MODFLOW are 0.22 m/0.06 and 0.52 m/0.47 m, respectively), showing the coupled continuum pipe-flow framework may be more appropriate for applying to highly and maturely karstified areas where the variations in the behavior of flow and contaminant transport are more affected by turbulent flow regime.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Probing Top-Quark–Electron Interactions at Future Colliders

Top quark interactions offer a window into possible new high scale physics and many models of new physics predict that the top quark interactions will deviate significantly from those predicted by the standard model. We present an analysis of the experimental restrictions on anomalous 4-fermion 𝑒 + ⁢𝑒 − $⁢𝑡\bar{⁢𝑡}$ operators that is accurate to next-to-leading order (NLO) in both the electroweak and QCD interactions within the standard model effective field theory framework. At NLO, there is sensitivity to an extended set of anomalous interactions beyond those probed at leading order. A comparison of current limits from electroweak precision observables, along with expected future limits from Drell-Yan and $⁢𝑡\bar{⁢𝑡}$𝑒 +⁢ 𝑒 − production at the high luminosity LHC, from deep inelastic scattering at the EIC, and from projected sensitivities at the future FCC-ee and CEPC machines demonstrates that each of these programs extends the precision understanding of the interactions of top quarks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Search for a heavy neutral lepton with the MAGNETO-𝜈 experiment using 241 Pu 𝛽 − decays

The MAGNETO-𝜈 experiment searches for keV-scale heavy neutral leptons (HNLs) through precise measurements of the 𝛽 − -decay spectrum of 241 Pu. We present spectra comprising a total of 194 million 𝛽 − decays recorded using decay energy spectrometry with metallic magnetic calorimeters, representing the most statistically precise measurement of 241 Pu 𝛽 − decay to date. The 𝛽-endpoint energy was determined using 𝛾 rays and x-rays from an external 133 Ba calibration source, yielding 𝑄 𝛽 = 22.273⁢ (33) ⁢keV. The measured spectrum shows no statistically significant deviation from the allowed 𝛽-decay model. From a subset of the high-statistics data, we set an upper limit on the mixing of an 11.5-keV HNL with the electron neutrino, |𝑈 𝑒⁢4 | 2 < 1.31 × 10 −3 at the 95% confidence level.

A ≥ 220↗
DOE OSTI2024

Testing quantum gravity using pulsed optomechanical systems

An interesting idea, dating back to Feynman [Report from Chapel Hill Conference, edited by C. M. DeWitt and D. Rickles (1957)], argues that quantum mechanics may break down for large masses if one entertains the possibility that gravity can be “classical,” thereby leading to predictions different from conventional low-energy quantum gravity. Despite the technical difficulty in testing such deviations, a large number of experimental proposals have been put forward due to the high level of fundamental interest. Here, we consider the Schrödinger-Newton (SN) theory and the correlated worldline (CWL) theory, and show that they can be distinguished from conventional quantum mechanics, as well as each other, by performing pulsed optomechanics experiments. For CWL specifically we develop a framework resembling the commonly used “Heisenberg-picture” treatment of coupled oscillators, allowing one to perform simple calculations for such systems without delving into the deeper path-integral formalism. We find that discriminating between the theories will be very difficult until experimental control over low frequency quantum optomechanical systems is pushed much further. Furthermore, the predicted departures of SN and CWL from quantum mechanics occur at the same scale, so both alternative models could in principle be probed by a single experiment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Deep-Learning-Based Koopman Modeling for Online Control Synthesis of Nonlinear Power System Transient Dynamics

Power system stability and control have become more challenging due to the increasing uncertainty associated with renewable generation. Here, the performance of conventional control is highly driven by the physics-based offline-developed dynamic models that can deviate from the actual system characteristics under different operating conditions and/or configurations. Data-driven approaches based on online measurements can be a better solution to addressing these issues by capturing real-time operation conditions. This article describes a novel fully data-driven probabilistic framework to derive a linear representation of postcontingency grid dynamics and online prescribe control based on the derived model to enhance transient stability. The complex nonlinear power system dynamics is approximated by a linear model by using multiple neural network modules that infer distributions of the observations and introducing a Koopman layer to sample possible Koopman linear models from the inferred distributions. The trained model features linearity that can be easily incorporated into the existing linear control design paradigm and ease the controller design process. The effectiveness of Koopman-based control designs is validated through comparative case studies, which demonstrate increased prediction accuracy and control performance when applied to a power system with heterogeneous generator dynamics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Three-dimensional imaging of proton in basis light-front quantization

Here, we employ an effective Hamiltonian that includes the transverse and longitudinal confinement and the one-gluon exchange interaction with fixed coupling constant. By solving the eigenvalue equation in basis light-front quantization (BLFQ), we generate the light-front wavefunctions (LFWFs) for the nucleon in the valence quark Fock space. Fitting the model parameters, we obtain high quality descriptions of electromagnetic form factors and radius for proton while the results deviate somewhat from experimental data for neutron.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

MULTI-FIDELITY MODELING AND UNCERTAINTY QUANTIFICATION OF INVERTER BASED RESOURCES IN INTEGRATED T&D SYSTEMS

Uncertainty quantification plays a pivotal role in improving the accuracy and reliability of inverter operation within modern power systems that are increasingly dominated by inverter-based resources (IBRs). IBRs, especially those operating under grid forming (GFM) control, rely heavily on a complex set of control parameters and system measurements to maintain voltage, frequency, and power balance. Traditional deterministic modeling approaches often fail to capture these parameter deviations, potentially resulting in suboptimal control actions, reduced system stability, or even instability under high penetration of IBRs. In this paper, we demonstrate the application of model calibration and uncertainty quantification (UQ) principles to an integrated transmission and distribution (T&D) model involving a GFM converter and provide a framework for prioritizing control improvements, guiding robust design, and informing adaptive strategies that can accommodate real-time variability in system conditions. The proposed approach could be valuable in enhancing the robustness of current and future power systems under increased IBR penetrations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Thermodynamic stability of xenon-doped liquid argon detectors

Liquid argon detectors are employed in a wide variety of nuclear and particle physics experiments. The addition of small quantities of xenon to argon modifies its scintillation, ionization, and electroluminescence properties and can improve its performance as a detection medium. However, a liquid argon-xenon mixture can develop instabilities, especially in systems that require phase transitions or that utilize high xenon concentrations. In this work, we analyze the causes of these instabilities and describe a small (liter-scale) apparatus with a unique cryogenic circuit specifically designed to handle argon-xenon mixtures. The system is capable of condensing argon gas mixed with $\mathscr{O}$ (1%) xenon by volume and maintains a stable liquid mixture near the xenon saturation limit while actively circulating it in the gas phase. We also demonstrate control over instabilities that develop when the detector condition is allowed to deviate from optimized settings. This progress enables future liquid argon detectors to benefit from the effects of high concentrations of xenon doping, such as more efficient detection of low-energy ionization signals. In conclusion, this work also develops tools to study and mitigate instabilities in large argon detectors that use low concentration xenon doping.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗