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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Measurement and feedback-driven adaptive dynamics in the classical and quantum kicked top

In classical dynamical systems, stochastic feedback can stabilize otherwise unstable periodic orbits, giving rise to distinct controlled and uncontrolled phases as the rate of control application is varied. In this work, we apply these control protocols in classical, semiclassical, and quantum regimes to the kicked top, a paradigmatic model of quantum chaos. The quantum kicked top, modeled as the dynamics of a spin-S object, naturally interpolates between these regimes with the spin size S acting as an effective Planck constant. We show that the dynamics of the kicked top in classical, semiclassical, and fully quantum limits can all be controlled using stochastic feedback protocols. Comparing the full quantum dynamics to a truncated Wigner approximation that captures quantum noise but neglects interference beyond the Ehrenfest time, we find that low-moment observables are largely accounted for semiclassically, while the remaining discrepancy in higher moments is consistent with contributions from interference and possibly nonlinearities in rare trajectories that explore the compact phase space. We also find rapid purification in the numerics studied for all rates of control considered, suggesting that control quenches the top's ability to encode a qubit of quantum information even in the uncontrolled phase.

Prasad, Mahaveer [Tata Inst.; Singapore U. Tech. D↗
DOE OSTI2022

Simulating the non-monotonic strain response of nanoporous multiferroic composites under electric field control

In this work, we simulate and analyze the mechanical response of a class of multiferroic materials consisting of a templated porous nanostructure made out of cobalt ferrite (CFO) partially filled by atomic layer deposition (ALD) with a ferroelectric phase of lead zirconate titanate (PZT). The strain in the device is measured when an electric field is applied for varying ALD thicknesses, displaying a non-monotonic dependence with a maximum strain achieved for a coating thickness of 3 nm. To understand this behavior, we apply finite element modeling to the smallest repeatable unit of the nanoporous template and simulate the mechanical response as a function of PZT coating thickness. We find that this non-monotonic response is caused by the interplay between two driving forces opposing one another. First, increased porosity works toward increasing the strain due to a reduced system stiffness. Second, decreased porosity involves a larger mass fraction of PZT, which drives the electro-mechanical response of the structure, thus leading to a larger strain. The balance between these two driving forces is controlled by the shear coupling at the CFO/PZT interface and the effective PZT cross section along the direction of the applied electric field. Here, our numerical results show that considering a nonlinear piezoelectric response for PZT leads to an improved agreement with the experimental data, consistent with ex situ poling of the nanostructure prior to magnetic measurements.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Experimental observation of polarization-resolved nonlinear Thomson scattering of elliptically polarized light

We report experimental results from a study of nonlinear Thomson scattering of elliptically polarized light. Polarization-resolved radiation patterns of the scattered light are measured as a function of the elliptical polarization state of the incident laser light. The relativistic electron trajectory in intense elliptically polarized fields leads to the formation of unique radiated polarization states, which are observed by our measurements and predicted by a theoretical model. The polarization of Thomson scattered light depends strongly on the intensity of the incident light due to nonlinearity. The results are relevant to high-field electrodynamics and to research and development of light sources with novel capabilities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Fragmentation of faceted crystalline wires

The reduction of surface energy is a well-known driving force for the fragmentation of nanowires. Fragmentation can limit the service life of those small low-dimensional building blocks or can be controlled to induce beneficial shape changes. For isotropic surface energy, fragmentation is analogous to the classic Rayleigh-Plateau instability of liquid jets. However, commonly synthesized crystalline nanowires have strongly anisotropic surface energies and exhibit facets coinciding with cusps in the Wulff plot. Depending on growth orientation, different fragmentation behaviors have been seen in nanowires. Using phase-field simulations, we show that fragmentation of faceted nanowires with cubic crystal symmetry may occur by a finite-amplitude nonlinear instability, as opposed to a Rayleigh-Plateau-like linear instability, depending on nanowire growth orientation. Here, we carry out a weakly nonlinear analysis based on sharp interface theory to characterize the faceted nanowire shape corresponding to a surface-energy saddle point. The analysis predicts that the minimum amplitude of a periodic shape perturbation to trigger fragmentation increases with cusp strength but decreases inversely proportionally to the perturbation wavelength for long wavelengths, in good quantitative agreement with phase-field predictions. The results provide the theoretical foundation to predict nanowire stability as a function of length and surface energy in diverse applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Analysis of the nonlinear propagation of incoherent pulses

The nonlinear propagation of incoherent optical pulses is studied using a normalized nonlinear Schrödinger equation and statistical analysis, demonstrating various regimes that depend on the field’s coherence time and intensity. The quantification of the resulting intensity statistics using probability density functions shows that, in the absence of spatial effects, nonlinear propagation leads to an increase in the likelihood of high intensities in a medium with negative dispersion, and a decrease in a medium with positive dispersion. In the latter regime, nonlinear spatial self-focusing originating from a spatial perturbation can be mitigated, depending on the coherence time and amplitude of the perturbation. These results are benchmarked against the Bespalov–Talanov analysis applied to strictly monochromatic pulses.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

422 Million intrinsic quality factor planar integrated all-waveguide resonator with sub-MHz linewidth

High quality-factor (Q) optical resonators are a key component for ultra-narrow linewidth lasers, frequency stabilization, precision spectroscopy and quantum applications. Integration in a photonic waveguide platform is key to reducing cost, size, power and sensitivity to environmental disturbances. However, to date, the Q of all-waveguide resonators has been relegated to below 260 Million. Here, we report a Si 3 N 4 resonator with 422 Million intrinsic and 3.4 Billion absorption-limited Qs. The resonator has 453 kHz intrinsic, 906 kHz loaded, and 57 kHz absorption-limited linewidths and the corresponding 0.060 dB m -1 loss is the lowest reported to date for waveguides with deposited oxide upper cladding. These results are achieved through a careful reduction of scattering and absorption losses that we simulate, quantify and correlate to measurements. This advancement in waveguide resonator technology paves the way to all-waveguide Billion Q cavities for applications including nonlinear optics, atomic clocks, quantum photonics and high-capacity fiber communications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Uncertainty quantification of a physics-informed model based on sparse identification of a Thermal Energy Distribution System

Integrated energy systems (IES)s are crucial for enhancing the economy and efficiency of power generation sources (e.g., nuclear energy) necessary to unleash American energy dominance. These systems can be integrated with thermal energy storage (TES) and intermittent renewable energies to optimize overall energy use, peak-load regulation, and demand-side responses. However, the stabilization of energy generation, transport, and utilization introduces operational complexities that exceed the challenges of managing each sub-component individually. Currently, though IESs rely on human operators for efficiency and stability, reducing human error risk and enhancing performance through automation is highly desirable. Recent advances at Idaho National Laboratory have demonstrated successful control of the Thermal Energy Distributed System (TEDS). However, the automatic control system depends on a deterministic Sparse Identification of Nonlinear Dynamics with Control (SINDyC) model, which are trained based on simulation data from physics-based simulations. Because of uncertainties in physics-based simulation, SINDyC model results in large discrepancies against experimental data and cannot be reliably used in automatic control. In this paper, we present an innovative approach to address these discrepancies by quantifying uncertainties and developing a more robust model. We first generated trajectories by using first-principles physics codes to encapsulate the experiment. Next, we trained thousands of models by randomly sampling these trajectories. We then collapsed all those models into one probabilistic SINDyC by fitting a multivariate Gaussian distribution onto the resulting coefficient’s distribution. Despite its simplicity, our approach successfully produced 95% confidence intervals that captured the experimental trajectories. It even did so with a higher probability and better U-pooling score across six of the seven relevant quantities of interest (QoIs), as compared to other classical approaches. In conclusion, ongoing research is focusing on generating new experimental trajectories to validate this approach, and on employing Bayesian calibration to refine parametric uncertainties and guide future model development efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Time-series forecasting using manifold learning, radial basis function interpolation, and geometric harmonics

We address a three-tier numerical framework based on nonlinear manifold learning for the forecasting of high-dimensional time series, relaxing the “curse of dimensionality” related to the training phase of surrogate/machine learning models. At the first step, we embed the high-dimensional time series into a reduced low-dimensional space using nonlinear manifold learning (local linear embedding and parsimonious diffusion maps). Then, we construct reduced-order surrogate models on the manifold (here, for our illustrations, we used multivariate autoregressive and Gaussian process regression models) to forecast the embedded dynamics. Finally, we solve the pre-image problem, thus lifting the embedded time series back to the original high-dimensional space using radial basis function interpolation and geometric harmonics. The proposed numerical data-driven scheme can also be applied as a reduced-order model procedure for the numerical solution/propagation of the (transient) dynamics of partial differential equations (PDEs). In conclusion, we assess the performance of the proposed scheme via three different families of problems: (a) the forecasting of synthetic time series generated by three simplistic linear and weakly nonlinear stochastic models resembling electroencephalography signals, (b) the prediction/propagation of the solution profiles of a linear parabolic PDE and the Brusselator model (a set of two nonlinear parabolic PDEs), and (c) the forecasting of a real-world data set containing daily time series of ten key foreign exchange rates spanning the time period 3 September 2001–29 October 2020.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

A remapping scheme for history-dependent material state variables

Here, we present an interpolation scheme for use with material models involving history-dependent state variables. The interpolation scheme is based on the solution of a constrained optimization problem, to identify the smoothest monotone curve which produces an admissible solution. Unlike classical schemes used for transport equations, the proposed methodology accounts for the interdependence of the history variables and produces admissible interpolations of the material's state variables. After introducing a general solution scheme for the problem, a simplified and more computationally efficient method is derived. Finally, as a validation problem for the proposed methods, we consider the advection of internal state variables belonging to material models with highly nonlinear yield surfaces.

42 ENGINEERING↗
DOE OSTI2024

HHL algorithm with mapping function and enhanced sampling for model predictive control in microgrids

Here, this paper presents a refined quantum Harrow Hassidim Lloyd (HHL) algorithm for microgrid control. The first novelty of the developed method is that a mapping shift function enables the original HHL algorithm to handle general linear equations with non-singular and indefinite matrix. Second, a method of Matrix Extension for Amplifying Sampling Probabilities of Intended Solution (ME-ASPI) is proposed to design the reformulated linear algebraic equations, allowing for improved sampling efficiency of the quantum tomography in the refined HHL algorithm. Then, we applied the method to solve the model predictive control (MPC) problem in nonlinear dynamical microgrids. Specifically, with the ME-ASPI method, the refined HHL algorithm can effectively obtain the intended partial optimal control inputs for MPC. The optimization of quadratic programming problem in each time step of MPC is transformed into a linear system problem, which is addressed by the proposed quantum solver through using only partial information, with the time complexity improved from $\mathscr{O}(\mathscr{N}^{2.37286})$ classically to $\mathscr{O}(\mathscr{N}^{2} log \mathscr{N}$ x $p$ log $p)$ in quantum. Numerical examples have validated the effectiveness of the refined HHL algorithm with the proposed mapping function and the ME-ASPI method. By leveraging quantum properties, the proposed method provides a hybrid quantum–classical framework for microgrid control. This generic method can also potentially tackle many other challenges in analyzing and controlling general complex engineered systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Fascinating physics at the edge of magnetic fusion devices

Abstract The physics of the processes at the edge of magnetic fusion devices is multifaceted and exhibits complex, nonlinear synergistic effects. Even though this region occupies only a small portion of the whole device, it plays a crucial role in overall plasma confinement, heat exhaust, and plasma–wall interactions. The latter affects not only the performance but also the lifetime of plasma-facing components and therefore remains an outstanding challenge for future fusion reactors. At the edge of fusion devices, researchers are dealing with phenomena including classical and anomalous plasma transport, atomic physics effects, and physics of plasma-facing material under strong irradiation by particle and energy fluxes. The diversity of edge physics makes it particularly attractive for young scientists. Working in this field, they can find endless possibilities to demonstrate their talents and creativity. This short review describes just some of the basic scrape-off layer and divertor plasma phenomena including divertor plasma detachment, intermittent bursts of anomalous cross-field plasma transport, plasma–material interactions, and dust in fusion plasmas, which are of particular interest in relation to fusion reactors.

Physics↗
DOE OSTI2021

Growth of accretion driven scalar hair around Kerr black holes

We report that Scalar fields around compact objects are of interest for scalar-tensor theories of gravity and dark matter models consisting of a massive scalar, e.g., axions. We study the behavior of a scalar field around a Kerr black hole with nontrivial asymptotic boundary conditions - both nonzero density and nonzero angular momentum. Starting from an initial radially homogeneous configuration, a scalar cloud is accreted, which asymptotes to known stationary configurations over time. We study the cloud growth for different parameters including black hole spin, scalar field mass, and the scalar field density and angular momentum far from the black hole. We characterize the transient growth of the mass and angular momentum in the cloud, and the spatial profile of the scalar around the black hole, and relate the results of fully nonlinear simulations to an analytic perturbative expansion. We also highlight the potential for these accreted clouds to create monochromatic gravitational wave signals - similar to the signals from superradiant clouds, although significantly weaker in amplitude.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

A scalable multidimensional fully implicit solver for Hall magnetohydrodynamics

We propose an optimally performant fully implicit algorithm for the Hall magnetohydrodynamics (HMHD) equations based on multigrid-preconditioned Jacobian-free Newton-Krylov methods. HMHD is a challenging system to solve numerically because it supports stiff fast dispersive waves. The preconditioner is formulated using an operator-split approximate block factorization (Schur complement), informed by physics insight. We use a vector-potential formulation (instead of a magnetic field one) to allow a clean segregation of the problematic $\nabla$ x $\nabla$ x operator in the electron Ohm's law subsystem. This segregation allows the formulation of an effective damped block-Jacobi smoother for multigrid. We demonstrate by analysis that our proposed block-Jacobi iteration is convergent and has the smoothing property. The resulting HMHD solver is verified linearly with wave propagation examples, and nonlinearly with the GEM challenge reconnection problem by comparison against another HMHD code. We demonstrate the excellent algorithmic and parallel performance of the algorithm up to 16384 MPI tasks in two dimensions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Seven-core fibre Raman laser with intercore coupling

This paper reports results of a study aimed at producing a Raman fibre laser based on a multicore fibre with high-reflectivity fibre Bragg gratings inscribed in its peripheral cores using femtosecond laser pulses. We present spectral and power characteristics of a Raman laser whose output power at a wavelength of 1090 nm is 2.5 W. The laser emission linewidth has been shown to decrease as a result of the reduction in nonlinear effects owing to the larger effective mode area in the multicore fibre in comparison with a standard single-mode fibre Raman laser. (paper)

36 MATERIALS SCIENCE↗
DOE OSTI2023

Flux-based three-dimensional electrodynamic modeling approach to superconducting circuits and materials

Modeling the behavior of superconducting electronic circuits containing Josephson junctions is crucial for the design of superconducting information processors and devices. In this paper, we introduce DEC-QED, a computational approach for modeling the electrodynamics of superconducting electronic circuits containing Josephson junctions in arbitrary three-dimensional electromagnetic environments. DEC-QED captures the nonlinear response and induced currents in BCS superconductors and accurately captures phenomena such as the Meissner effect, flux quantization, and Josephson effects. Using a spatial coarse-graining formulation based on discrete exterior calculus (DEC), DEC-QED can accurately simulate transient and long-time dynamics in superconductors. Finally, the expression of the entire electrodynamic problem in terms of the gauge-invariant flux field and charges makes the resulting classical field theory suitable for second quantization.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Quantum-inspired method for solving the Vlasov-Poisson equations

Kinetic simulations of collisionless (or weakly collisional) plasmas using the Vlasov equation are often infeasible due to high-resolution requirements and the exponential scaling of computational cost with respect to dimension. Recently, it has been proposed that matrix product state (MPS) methods, a quantum-inspired but classical algorithm, can be used to solve partial differential equations with exponential speed-up, provided that the solution can be compressed and efficiently represented as a MPS within some tolerable error threshold. Here, in this work, we explore the practicality of MPS methods for solving the Vlasov-Poisson equations for systems with one coordinate in space and one coordinate in velocity, and find that important features of linear and nonlinear dynamics, such as damping or growth rates and saturation amplitudes, can be captured while compressing the solution significantly. Furthermore, by comparing the performance of different mappings of the distribution functions onto the MPS, we develop an intuition of the MPS representation and its behavior in the context of solving the Vlasov-Poisson equations, which will be useful for extending these methods to higher-dimensional problems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

MULTISTEP AND CONTINUOUS PHYSICS-INFORMED NEURAL NETWORK METHODS FOR LEARNING GOVERNING EQUATIONS AND CONSTITUTIVE RELATIONS

In this work, we investigate the applicability and relative merit of discrete and continuous versions of physics-informed neural network (PINN) methods for learning unknown governing equations or constitutive relations in a nonlinear dynamical system. In the case of unknown dynamics, entire right-hand-side (RHS) equations of the ordinary differential equations are unknown. In the case of unknown constitutive relations, however, the RHS equations are known up to the specification of constitutive relations (that may depend on the state of the system). We use a deep neural network to model unknown governing equations or constitutive relations. The discrete PINN approach combines classical multistep discretization methods for dynamical systems with neural-network-based machine learning methods. On the other hand, the continuous versions utilize deep neural networks to minimize the residual function for the continuous governing equations. We use the case of a fedbatch bioreactor system to study the effectiveness of these approaches and discuss conditions for their applicability. Our results indicate that the accuracy of the trained neural network models is much higher for the cases where we only have to learn a constitutive relation instead of all dynamics. This finding corroborates the well-known fact from scientific computing that building as much structural information as is available into an algorithm can enhance its efficiency and/or accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Intricate structure of the plasma Rayleigh–Taylor instability in shock tubes

Spikes and bubbles grow on unstable interfaces that are accelerated in high-energy-density conditions. If a shock propagates ahead of the interface, the plasma can be heated to extreme conditions where conduction and radiation fluxes influence the hydrodynamics. For example, a National Ignition Facility experiment found reduced single-mode nonlinear mixed-width growth in conditions scaled from a supernova explosion. We present high-resolution two-dimensional radiation hydrodynamic simulations with the Flash code that quantitatively reproduce the experiment. Radiative fluxes are primarily responsible for ablating the spike and removing the mushroom caps. The ablated plasma increases the mixed mass and forms a low-density halo with spikes forming in both directions. This is considerably more complex than classical instability. The halo is sensitive to ablative physics, so radiographing it may aid in the verification of energy transport modeling. Although ablation changes the spike shape, it has little effect on the overall mixed width for these parameters. This is because ablation enhances the bubble velocity but it has the opposite effect on the spike. The radiation transport instead suppresses the growth via increasing the shocked foam density, thus decreasing the Atwood number. Furthermore, a terminal velocity model including the rarefaction expansion agrees with the experimental mixed-width growth.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗