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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Plasmonic Nonlinear Energy Transfer Enhanced Second Harmonic Generation Nanoscopy

Nonlinear optical response is a fingerprint of various physicochemical properties of materials related to symmetry, including crystallography, interfacial configuration, and carrier dynamics. However, the intrinsically weak nonlinear optical susceptibility and the diffraction limit of far-field optics restrict probing deep-subwavelength-scale nonlinear optics with measurable signal-to-noise ratio. Here, we propose an alternative approach toward efficient second harmonic generation (SHG) nanoscopy for SHG-active sample (zinc oxide nanowire; ZnO NW) using an SHG-active plasmonic nanotip. Our full-wave simulation suggests that the experimentally observed high near-field SHG contrast is possible when the nonlinear response of ZnO NW is enhanced and/or that of the tip is suppressed. Here, this result suggests possible evidence of quantum mechanical nonlinear energy transfer between the tip and the sample, modifying the nonlinear optical susceptibility. Further, this process probes the nanoscale corrosion of ZnO NW, demonstrating potential use in studying various physicochemical phenomena in nanoscale resolution.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Magnetic Energy Conversion in Magnetohydrodynamics: Curvature Relaxation and Perpendicular Expansion of Magnetic Fields

Abstract The mechanisms and pathways of magnetic energy conversion are an important subject for many laboratory, space, and astrophysical systems. Here, we present a perspective on magnetic energy conversion in magnetohydrodynamics through magnetic field curvature relaxation (CR) and perpendicular expansion (PE) due to magnetic pressure gradients, and quantify their relative importance in two representative cases, namely 3D magnetic reconnection and 3D kink-driven instability in an astrophysical jet. We find that the CR and PE processes have different temporal and spatial evolutions in these systems. The relative importance of the two processes tends to reverse as the system enters the nonlinear stage from the instability growth stage. Overall, the two processes make comparable contributions to magnetic energy conversion, with the PE process somewhat stronger than the CR process. We further explore how these energy conversion terms can be related to particle energization in these systems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Space-Charge Aberrations in Single-Shot Time-Resolved Transmission Electron Microscopy

In this paper, we discuss effects of space-charge fields on imaging performance in a single-shot time-resolved transmission electron microscope. Using a Green’s function perturbation method, we derive analytical estimates for the effects of space-charge nonlinearity on the image formation process and the associated aberration coefficients. The dependence of these coefficients on the initial beam phase space distribution is elucidated. The results are validated by particle tracking simulations and provide fundamental scaling laws for the trade-off between temporal and spatial resolutions in single-shot time-resolved transmission electron microscopy.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Combined selection of the dynamic model and modeling error in nonlinear aeroelastic systems using Bayesian Inference

Here, we report a Bayesian framework for concurrent selection of physics-based models and (modeling) error models. We investigate the use of colored noise to capture the mismatch between the predictions of calibrated models and observational data that cannot be explained by measurement error alone within the context of Bayesian estimation for stochastic ordinary differential equations. Proposed models are characterized by the average data-fit, a measure of how well a model fits the measurements, and the model complexity measured using the Kullback–Leibler divergence. The use of a more complex error models increases the average data-fit but also increases the complexity of the combined model, possibly over-fitting the data. Bayesian model selection is used to find the optimal physical model as well as the optimal error model. The optimal model is defined using the evidence, where the average data-fit is balanced by the complexity of the model. The effect of colored noise process is illustrated using a nonlinear aeroelastic oscillator representing a rigid NACA0012 airfoil undergoing limit cycle oscillations due to complex fluid–structure interactions. Several quasi-steady and unsteady aerodynamic models are proposed with colored noise or white noise for the model error. The use of colored noise improves the predictive capabilities of simpler models.

42 ENGINEERING↗
DOE OSTI2026

An Atom-Precise Approach to Damp First-Order Phase Transitions and Its Implications for Neuromorphic Signal Processing

Neuromorphic computing inspired by mammalian intelligence aims to emulate the nonlinear dynamics of biological neurons and synapses to achieve fast, low-energy, and highly efficient information processing. Brain-inspired computing relies on the design and discovery of materials exhibiting nonlinear current–voltage profiles, frequently underpinned by electronic state transitions, to achieve spiking neurons and dynamically tunable synapses. A signature challenge in the design of artificial neurons is controlling the steepness of first-order transitions in active elements, as abrupt transitions are at risk of driving unstable voltage and temperature oscillations, which result in catastrophic device failure. A critical knowledge gap is the lack of structure–function correlations mapping the composition and atomistic structure of crystalline solids to nonlinear dynamical response characteristics. Here, we address the key question of how modification of atomistic structure correlates with alteration of neuron-like functionality. Constructing oscillator circuits from millimeter-scale single crystals enables high-resolution atomic structure solutions, which we use to demonstrate that the selective positioning of Pb cations modifies charge ordering along a one-dimensional CuxV2O5 framework even at low insertion stoichiometries, thereby providing an atom-precise design parameter for damping first-order transitions. We use temperature-variant X-ray diffraction and X-ray spectroscopy to elucidate the suppression of Cu-ion shuttling based on the precise positioning of Pb ions in seven-coordinated tunnel interstitial sites as the mechanistic basis for transition broadening, thus bridging a critical gap between statistical mechanics and quantum chemical descriptions of phase transitions. Such mechanistic understanding thus paves the way to site-selective modification strategies for modulating the sharpness of first-order transitions, with an exemplary demonstration here in tuning neuronal signal processing.

Crystal structure↗
DOE OSTI2024

Generalization error guaranteed auto-encoder-based nonlinear model reduction for operator learning

Many physical processes in science and engineering are naturally represented by operators between infinite-dimensional function spaces. The problem of operator learning, in this context, seeks to extract these physical processes from empirical data, which is challenging due to the infinite or high dimensionality of data. An integral component in addressing this challenge is model reduction, which reduces both the data dimensionality and problem size. In this paper, we utilize low-dimensional nonlinear structures in model reduction by investigating Auto-Encoder-based Neural Network (AENet). AENet first learns the latent variables of the input data and then learns the transformation from these latent variables to corresponding output data. Our numerical experiments validate the ability of AENet to accurately learn the solution operator of nonlinear partial differential equations. Furthermore, we establish a mathematical and statistical estimation theory that analyzes the generalization error of AENet. Finally, our theoretical framework shows that the sample complexity of training AENet is intricately tied to the intrinsic dimension of the modeled process, while also demonstrating the robustness of AENet to noise.

Auto-encoder↗
DOE OSTI2024

Development of Modified Perturbation Solutions to the One-Phase Stefan Problems With a Convective Boundary

The classical Stefan problem is used to track the moving solid-liquid interface during the freezing process. Perturbation theory has often been applied to find an approximate analytical solution due to the nonlinearity of the moving interface. However, the Stefan number (i.e., the sensible over latent heat) must be small and usually less than 0.01 to assume the perturbation expansion, which in turn limits the thermal engineering applications. In this study, a modified perturbation solution is developed by adding a correction term after the leading-order solution to be valid for a much wider range of Stefan numbers (i.e., 0.01 less than or equal to Ste less than or equal to 1). Specifically, a one-phase Stefan problem is first formulated subjected to a convective boundary in the Cartesian, cylindrical, and spherical coordinate systems for inward solidification. The leading-order solution is calculated based on the regular perturbation theory, while the correction term is obtained using the Monte-Carlo method and a multi-variant regression. Results show that the correction term has a linear relationship with the Stefan number and is not significantly influenced by the Biot number. The proposed modified perturbation solution can accurately and rapidly predict the nonlinear moving interface motion for the freezing process.

DIRECT ENERGY CONVERSION,MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Development of a conduction-based model for analyzing frozen startup of alkali-metal heat pipes

One key area of interest in heat pipe modeling/simulation is to analyze the startup behavior of the liquid-metal heat pipes (LMHPs) from a frozen state. This so-called ‘frozen startup’ process involves a complex set of nonlinear mass and heat transport phenomena, including phase transitions from solid to liquid and vapor, multiphase interactions, microporous wick flow, and compressible vapor dynamics. The complexity of these processes makes it challenging to simulate LMHP’s frozen startup using conventional numerical methods or commercial computational fluid dynamics (CFD) software. This paper presents a simplified conduction-based modeling approach that can provide practical insights into the entire LMHP frozen startup process, while alleviating the challenges of modeling its complex physics. The theoretical foundation and physical assumptions of the proposed model are based solely on heat-conduction equation, allowing for a more tractable simulation without sacrificing essential physical accuracy. The proposed model was implemented in a commercial CFD software, and its prediction was compared with the experimental data obtained from sodium heat-pipe startup experiments. The comparison highlights the proposed model's ability to capture the transient thermal behavior of LMHP during frozen startup. This study not only validates the conduction-based frozen startup modeling method but also shows its potential as a practical and efficient tool for understanding the startup performance of the LMHP systems.

Microreactor↗
DOE OSTI2022

The principle of minimal power

This article is devoted to the memory of Yuri P Raizer, who passed away in 2021. He left a noticeable trace in gas discharge physics. The principle of minimal power (the state that requires minimal power is most probable) is thoroughly used in his books. Although the fundamental laws of physics do not imply this ad hoc principle, a detailed analysis of underlying phenomena can often reveal why nature prefers this path. Raizer illustrated this principle for plasma stratification, formation of electrode spots, discharge constriction, the shape of an arc channel, etc. We argue that the nonlinearity of equations describing gas discharges can often justify the realization of a plasma state maintained at minimal electric power. This nonlinearity appears because small groups of energetic electrons often control the ionization processes. The number of these electrons depends strongly on the ratio of the electric field to gas density, E/N. Under certain conditions, the ionization rate can also depend nonlinearly on electron density due to stepwise ionization and Coulomb collisions. Here, we use the principle of minimal power to illustrate some of Raizer's contributions to gas discharge physics from a single point of view. We demonstrate that nonlinearity of ionization processes in gas discharges can substantiate this principle for plasma stratification. However, striations of s, p, and r types in neon could exist with minimal or no ionization enhancement. This reminds us of Raizer's warning that applying the minimal power principle could lead to erroneous predictions, and a proper theory is required in each case to justify its use. 'The phenomenon of striations satisfies the principle of minimal power' – Yuri Raizer

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

All-optical phase conjugation using diffractive wavefront processing

Abstract Optical phase conjugation (OPC) is a nonlinear technique used for counteracting wavefront distortions, with applications ranging from imaging to beam focusing. Here, we present a diffractive wavefront processor to approximate all-optical phase conjugation. Leveraging deep learning, a set of diffractive layers was optimized to all-optically process an arbitrary phase-aberrated input field, producing an output field with a phase distribution that is the conjugate of the input wave. We experimentally validated this wavefront processor by 3D-fabricating diffractive layers and performing OPC on phase distortions never seen during training. Employing terahertz radiation, our diffractive processor successfully performed OPC through a shallow volume that axially spans tens of wavelengths. We also created a diffractive phase-conjugate mirror by combining deep learning-optimized diffractive layers with a standard mirror. Given its compact, passive and multi-wavelength nature, this diffractive wavefront processor can be used for various applications, e.g., turbidity suppression and aberration correction across different spectral bands.

42 ENGINEERING↗
DOE OSTI2020

High speed PLIF study of the Richtmyer–Meshkov instability upon re-shock

The Richtmyer–Meshkov instability (RMI) of a twice-shocked gas interface is researched using high-speed planar-laser induced fluorescence (PLIF) in the Wisconsin Shock Tube Laboratory’s vertical shock tube. The initial condition (IC) is a shear layer with broadband diffuse perturbations at the interface between a helium-acetone mixture and argon. This IC is accelerated by a shock of nominal strength M = 1.8, and then accelerated again by the transmitted shock that reflects off the end wall of the tube. An estimate of the light gas mole fraction is extracted from high-speed imaging using an iterative process that accounts for the nonlinear temperature dependence of the acetone’s fluorescence quantum yield (FQY) and absorption cross-section. A vorticity deposition model for the initial growth rate after reshock is compared with the Mikaelian model for re-shock. Previously used in literature, the number of generations is shown to naturally arise from a normalisation of the scalar transport equation. A self-similar analysis is then further performed using the mole fraction data to explore the evolution of the RMI after reshock and the higher order moments of the light gas mole fraction are compared with a proposed model.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Observation of the Interlayer Exciton Gases in WSe 2 -p:WSe 2 Heterostructures

Interlayer excitons (IXs) possess a much longer lifetime than intralayer excitons due to the spatial separation of the electrons and holes, and hence they have been pursued to create exciton condensates for decades. The recent emergence of 2D materials, such as transition-metal dichalcogenides (TMDs), and of their van der Waals heterostructures, in which two different 2D materials are layered together, has created new opportunities to study IXs. Here we present the observation of IX gases within two stacked structures consisting of hBN/WSe 2 /hBN/p:WSe 2 /hBN. The IX energies of the two different structures differed by 82 meV due to the different thicknesses of the hexagonal boron nitride spacer layer between the TMD layers. We demonstrate that the lifetime of the IXs is shortened when the temperature and the pump power increase. We attribute this nonlinear behavior to an Auger process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Autonomous Synthesis and Inverse Design of Electrochromic Polymers with High Efficiency and Accuracy

Here, the design and synthesis of functional polymers, aimed at targeted properties through specific structures, have long been challenged by their complex and often nonlinear structure–property relationships. Key processes, including knowledge accumulation for predictive design and experimental refinement and validation, are traditionally labor-insensitive and time-consuming, making it difficult to balance accuracy and efficiency. Here, we introduce an accelerated, autonomous system for the on-demand synthesis of electronic polymers that achieves the desired electrochromic functionality with high accuracy and efficiency. Our approach leverages large language model-assisted data mining, a physics-informed copolymer machine learning model, and an AI-driven autonomous robotic workflow in the Polybot lab. Within 72 h, Polybot autonomously synthesized electrochromic polymers (ECPs) with targeted, previously-unreported color values, including green polymers with specific absorption profiles, precisely fine-tuning copolymer structures with a 5% step size in comonomer composition within a three-monomer system. A publicly accessible ECP informatics database has also been created to foster knowledge exchange.

AI-driven Robotic Lab↗
DOE OSTI2026

Sedimentation and Nonlinear Trapping in Texas Reservoirs Identified Using Remote Sensing and Bathymetric Survey Records

Decreasing reservoir storage capacity due to sedimentation poses great challenges to aging U.S. reservoirs, as it reduces the efficacy and reliability of their socio‐economic services. However, systematic assessments of reservoir sedimentation rates and associated issues remain limited because of sparse and infrequent bathymetry survey data. In this study, we use remote sensing‐driven estimates of sediment concentrations to identify regions experiencing rapid reservoir capacity loss, as observed in repeated bathymetry surveys. Our analysis focuses on Texas, where one of the most reliable state‐level reservoir capacity loss data sets is available through a unique long‐term monitoring program by the Texas Water Development Board. We find that reservoirs with large storage capacities and high sedimentation rate are concentrated in Northeast Texas. We also show that reduced forest, increased barren land, and erosive soil properties are co‐varying with high reservoir sedimentation rates. Temporal changes in the longitudinal gradient of the remotely sensed sediment flux highlight the nonlinear nature of sediment trapping processes, and can be used to estimate the reservoir storage capacity loss over time. In combination with standard bathymetry surveys, our approach shows potential for more cost‐effective and frequent assessment of reservoir storage loss.

Lee, Jiyong [ORNL] (ORCID:0000000198957406)↗
DOE OSTI2022

Using kernel-based statistical distance to study the dynamics of charged particle beams in particle-based simulation codes

Measures of discrepancy between probability distributions (statistical distance) are widely used in the fields of artificial intelligence and machine learning. We describe how certain measures of statistical distance can be implemented as numerical diagnostics for simulations involving charged-particle beams. Related measures of statistical dependence are also described. The resulting diagnostics provide sensitive measures of dynamical processes important for beams in nonlinear or high-intensity systems, which are otherwise difficult to characterize. Here, the focus is on kernel-based methods such as maximum mean discrepancy, which have a well-developed mathematical foundation and reasonable computational complexity. Several benchmark problems and examples involving intense beams are discussed. While the focus is on charged-particle beams, these methods may also be applied to other many-body systems such as plasmas or gravitational systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Domain-wall-mediated polarization switching in ferroelectric AlScN: Strain relief and field-dependent dynamics

While scandium-doped aluminum nitride (AlScN) exhibits robust ferroelectricity and excellent thermal stability, its utility is limited by an exceptionally high coercive field (𝐸 𝑐 ) for polarization switching. Unraveling the atomistic switching dynamics is therefore critical for tailoring 𝐸 𝑐 . Here, in this study, we combine density functional theory and machine-learning molecular dynamics to elucidate the polarization switching mechanisms in AlScN over various Sc concentrations and applied electric fields. We find that excessive lattice strain strictly prohibits collective polarization switching, but the preexisting domain walls relieve strain and lead to a distinct switching dynamics—dictating a field-dependent switching mechanism. At low electric fields, switching occurs via gradual domain-wall propagation consistent with the Kolmogorov-Avrami-Ishibashi model. In contrast, high fields stimulate additional nucleation, driving a rapid, homogeneous reversal process described by the simultaneous nonlinear nucleation and growth model. These findings highlight the critical role of domain-wall dynamics and suggest domain engineering as a viable strategy to tailor coercive fields in AlScN and related ferroelectrics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Enhancement of terahertz fields in LiTaO 3 waveguides using a conical pulse front

The development of methods for the generation of strong ultrafast electromagnetic pulses in the terahertz (THz) spectral range has led to a surge of progress in nonlinear THz spectroscopy and THz control of molecular and collective responses. For spectroscopy in the 1-THz range, the submillimeter wavelengths and associated large spot sizes, large optical elements, and short distances between final focusing elements and samples can lead to cumbersome experimental setups that are incompatible with some sample environments. Here, we introduce a novel terahertz ring excitation (TREx) optical pumping geometry to generate superposing, focusing fields in planar THz waveguides made out of the electro-optic material lithium tantalate. High THz fields, >175 kV/cm, are generated and measured optically with no free-space THz propagation. The field level achieved by pumping with a sequence of concentric rings of excitation light exceeds by about 20× the result of a single cylindrically focused line of pump light that has been used routinely in previous work. The technique opens new prospects for compact waveguide-based linear and nonlinear THz spectroscopy and signal processing.

47 OTHER INSTRUMENTATION↗