Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Linear dynamics”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Dynamic time-warping correction for shifts in ultrahigh resolving power ion mobility spectrometry and structures for lossless ion manipulations

Detection of arrival time shifts between ion mobility spectrometry (IMS) separations can limit achievable resolving power (Rp), particularly when multiple separations are summed or averaged, as commonly practiced. Such variations are more apparent in higher Rp measurements, and are particularly evident in long path length traveling wave structures for lossless ion manipulations (SLIM) IMS due to their typically much longer separation times. Here we explore the utility of a data processing approaches employing linear alignment (LA) and nonlinear dynamic time warping (DTW) of IMS separations to correct for variations between separations, such as due to pressure fluctuations. For multipass SLIM IMS separations, where narrow mobility range measurements have arrival times that can extend to several seconds, the LA approach effectively corrected for such variations, and significantly improvement Rp for summed separations. However, LA was much less effective for high Rp broad mobility range separations, such as obtained with multilevel SLIM IMS. Changes in IMS arrival times ions were observed to be correlated with small pressure changes, with approximately 0.6% relative arrival time shifts being common, sufficient to result in a loss of Rp for summed separations. Comparison of the approaches showed DTW alignment performed similarly to LA when used over a narrow mobility range, but was significantly better (providing narrower peaks and higher signal intensities) for wide mobility range data. We found the DTW approach increased Rp by as much as 115% for measurements in which 50 IMS separations over 2 seconds were summed, and leading to a large improvement in effective Rp. We conclude that DTW is superior to LA for ultrahigh resolution broad mobility range SLIM IMS separations, correcting for ion arrival time shifts regardless of the cause. Our tool is publicly available for use with universal ion mobility format (.UIMF) and text (.txt) files.

Data alignment, dynamic time warping, ion mobility↗
DOE OSTI2021

Data-driven learning of Mori–Zwanzig operators for isotropic turbulence

Developing reduced-order models for turbulent flows, which contain dynamics over a wide range of scales, is an extremely challenging problem. In statistical mechanics, the Mori–Zwanzig (MZ) formalism provides a mathematically exact procedure for constructing reduced-order representations of high-dimensional dynamical systems, where the effects due to the unresolved dynamics are captured in the memory kernel and orthogonal dynamics. Turbulence models based on MZ formalism have been scarce due to the limited knowledge of the MZ operators, which originates from the difficulty in deriving MZ kernels for complex nonlinear dynamical systems. In this work, we apply a recently developed data-driven learning algorithm, which is based on Koopman's description of dynamical systems and Mori's linear projection operator, on a set of fully resolved isotropic turbulence datasets to extract the Mori–Zwanzig operators. With data augmentation using known turbulence symmetries, the extracted Markov term, memory kernel, and orthogonal dynamics are statistically converged and the generalized fluctuation–dissipation relation can be verified. The properties of the memory kernel and orthogonal dynamics, and their dependence on the choices of observables are investigated to address the modeling assumptions that are commonly used in MZ-based models. A series of numerical experiments are then constructed using the extracted kernels to evaluate the memory effects on prediction. The results show that the prediction errors are strongly affected by the choice of observables and can be further reduced by including the past history of the observables in the memory kernel.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Barkas effect in strongly magnetized plasmas

Strongly magnetized plasmas, which are characterized by the particle gyrofrequency exceeding the plasma frequency, exhibit novel transport properties. For example, recent work showed that the friction force on a test charge moving through a strongly magnetized plasma not only consists of the typical stopping power component but also includes components perpendicular to the test charge's velocity. However, these studies only considered test charges that have the same sign as the charge of the plasma particles. Here, we extend these calculations to the case of charges with opposite signs (such as an ion interacting with strongly magnetized electrons). This is done with both a novel generalized Boltzmann kinetic theory and molecular dynamics simulations. It is found that the friction force changes dramatically depending on the sign of the interacting charges. Likewise, the stopping power component for oppositely charged particles decreases in magnitude compared with like-charged particles, and the perpendicular components increase in magnitude. Moreover, the difference between the two cases increases as the gyrofrequency becomes larger compared with the plasma frequency. Finally, the electrical resistivity is calculated from the friction force, where it is found that strong magnetization in conjunction with oppositely charged interactions significantly decreases the parallel resistivity and increases the perpendicular resistivity.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Augmenting subspace optimization methods with linear bandits

In this work, we consider the framework of methods for unconstrained minimization that are, in each iteration, restricted to a model that is only a valid approximation to the objective function on some affine subspace containing an incumbent point. These methods are of practical interest in computational settings where derivative information is either expensive or impossible to obtain. Recent attention has been paid in the literature to employing randomized matrix sketching for generating the affine subspaces within this framework. We consider a relatively straightforward, deterministic augmentation of such a generic subspace optimization method. In particular, we consider a sequential optimization framework where actions consist of one-dimensional linear subspaces and rewards consist of (approximations to) the magnitudes of directional derivatives computed in the direction of the action subspace. Reward maximization in this context is consistent with maximizing lower bounds on descent guaranteed by first-order Taylor models. This sequential optimization problem can be analysed through the lens of dynamic regret. We modify an existing linear upper confidence bound (UCB) bandit method and prove sublinear dynamic regret in the subspace optimization setting. We demonstrate the efficacy of employing this linear UCB method in a setting where forward-mode algorithmic differentiation can provide directional derivatives in arbitrary directions and in a derivative-free setting. For the derivative-free setting, we propose SS-POUNDers, an extension of the derivative-free optimization method POUNDers that employs the linear UCB mechanism to identify promising subspaces. Our numerical experiments suggest a preference, in either computational setting, for employing a linear UCB mechanism within a subspace optimization method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Full-field imaging learning machine (FILM)

A method of determining dynamic properties of a structure (linear or nonlinear) includes receiving spatio-temporal inputs, generating mode shapes and modal components corresponding to the spatio-temporal inputs using a trained deep complexity coding artificial neural network, and subsequently generating the dynamic properties by analyzing each modal component using a trained learning machine. A computing system for non-contact determination of dynamic properties of a structure includes a camera, a processor, and a memory including computer-executable instructions. When the instructions are executed, the system is caused to receive spatio-temporal image data, decompose the spatio-temporal image data into constituent manifold components using an autoencoder, and analyze the constituent manifold components using a trained learning machine to determine the dynamic properties.

Yang, Yongchao↗
DOE OSTI2026

Photon phase-space dynamics in a plasma wakefield accelerator

Frequency up-shifting of laser light in a beam-driven plasma wakefield has the potential to provide high-intensity sources of short wavelength radiation. Simulations have demonstrated that a laser pulse can undergo large frequency shifts, limited only by the drive beam energy, when the plasma density is tailored to match the accelerating phase of the wake to the group velocity of the pulse. Here, we study the dynamical evolution of photons in the 1D1P phase-space vicinity of the plasma wake-phase matching condition. Numerical calculations using a photon kinetic model are validated by direct comparison with 1D and quasi-3D particle-in-cell simulations. These calculations form the basis of a linear theory of the photon dynamics which reveals several important results, including scalings for the properties of the witness pulse and a self-similar solution for the photon phase-space dynamics. One prediction of the analytic theory is that the pulse can be compressed indefinitely, though the amount of compression would ultimately be limited by practical constraints. These results suggest that photon acceleration can provide a novel source of sub-femtosecond, short wavelength radiation.

XUV generation↗
DOE OSTI2021

Optimal Linear Water Density for Proton Transport in Tunnel Oxides

Proton intercalation and transport is key to pseudocapacitive energy storage of oxide electrodes in acidic electrolytes. Although proton transport in water confined in layered or 2D materials is attracting great interest, much less is known about how the proton is transported in the 1D channels of tunnel oxides such as hexagonal WO 3 (h-WO 3 ). Here, we use first-principles molecular dynamics to reveal an optimal linear density of four water molecules per nanometer that yields the highest proton diffusivity. The volcano shape of proton diffusivity versus linear water density is a result of balancing the linear hydrogen-bond chain and the rotation of the water molecules to enable the Grotthuss mechanism. Furthermore, this insight provides a unifying view of proton transport along a single file of water molecules confined in hydrophilic 1D channels.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Quasi-Lindblad pseudomode theory for open quantum systems

Here, we introduce a new framework to study the dynamics of open quantum systems with linearly coupled Gaussian baths. Our approach replaces the continuous bath with an auxiliary discrete set of pseudomodes with dissipative dynamics, but we further relax the complete positivity requirement in the Lindblad master equation and formulate a quasi-Lindblad pseudomode theory. We show that this quasi-Lindblad pseudomode formulation directly leads to a representation of the bath correlation function in terms of a complex weighted sum of complex exponentials, an expansion that is known to be rapidly convergent in practice and thus leads to a compact set of pseudomodes. The pseudomode representation is not unique and can differ by a gauge choice. When the global dynamics can be simulated exactly, the system dynamics is unique and independent of the specific pseudomode representation. However, the gauge choice may affect the stability of the global dynamics, and we provide an analysis of why and when the global dynamics can retain stability despite losing positivity. We showcase the performance of this formulation across various spectral densities in both bosonic and fermionic problems, finding significant improvements over conventional pseudomode formulations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Ultrafast vibrational dynamics of a trigonal planar anionic probe in ionic liquids (ILs): A two-dimensional infrared (2DIR) spectroscopic investigation

A major impediment limiting the widespread application of ionic liquids (ILs) is their high shear viscosity. Incorporation of a tricyanomethanide (TCM - ) anion in ILs leads to low shear viscosity and improvement of several characteristics suitable for large scale applications. However, properties including interactions of TCM - with the local environment and dynamics of TCM - have not been thoroughly investigated. Herein, we have studied the ultrafast dynamics of TCM - in several imidazolium ILs using linear IR and two-dimensional infrared spectroscopy techniques. The spectral diffusion dynamics of the CN stretching modes of TCM - in all ILs exhibit a nonexponential behavior with a short time component of ~2 ps and a long time component spanning ~9 ps to 14 ps. The TCM - vibrational probe reports a significantly faster relaxation of ILs compared to those observed previously using linear vibrational probes, such as thiocyanate and selenocyanate. Our results indicate a rapid relaxation of the local ion-cage structure embedding the vibrational probe in the ILs. The faster relaxation suggests that the lifetime of the local ion-cage structure decreases in the presence of TCM - in the ILs. Linear IR spectroscopic results show that the hydrogen-bonding interaction between TCM - and imidazolium cations in ILs is much weaker. Shorter ion-cage lifetimes together with weaker hydrogen-bonding interactions account for the low shear viscosity of TCM - based ILs compared to commonly used ILs. In addition, this work demonstrates that TCM - can be used as a potential vibrational reporter to study the structure and dynamics of ILs and other molecular systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Predicting September Arctic Sea Ice: A Multimodel Seasonal Skill Comparison

This study quantifies the state of the art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multimodel dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–20 for predictions of pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on 1 June, 1 July, 1 August, and 1 September. This diverse set of statistical and dynamical models can individually predict linearly detrended pan-Arctic SIE anomalies with skill, and a multimodel median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and central Arctic sectors. The skill of dynamical and statistical models is generally comparable for pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least 3 months in advance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Dynamic Control of Sodium Cold Trap Purification Temperature Using LSTM System Identification

This study investigates the dynamic regulation of the sodium cold trap purification temperature at Argonne National Laboratory’s liquid sodium test facility, employing long short-term memory (LSTM) system identification techniques. The investigation introduces an innovative hybrid approach by integrating model predictive control (MPC) based on first principles dynamic models with a multi-step time–frequency LSTM model in predicting the temperature profiles of a sodium cold trap purification system. The long short-term memory–model predictive controller (LSTM-MPC) model employs a sliding window scheme to gather training samples for multi-step prediction, leveraging historical data to construct predictive models that capture the non-linearities of the complex system dynamics without explicitly modeling the underlying physical processes. The performance of the LSTM-MPC and MPC were evaluated through simulation experiments, where both models were assessed on their capacity to maintain the cold trap temperature within predefined set-points while minimizing deviations and overshoots. Results obtained show how the data-driven LSTM-MPC model demonstrates stability and adaptability. In contrast, the traditional MPC model exhibits irregularities, particularly evident as overshoots around set-point limits, which can potentially compromise its effectiveness over long prediction time intervals. The findings obtained offer valuable insights into integrating data-driven techniques for enhancing real-time monitoring systems.

LSTM-MPC↗
DOE OSTI2022

Resonant three-wave interactions with linear source/sink terms: Exploring the parameter space

Here, it is shown that the resonance three wave interactions in the presence of linear source/sink terms exhibit a wide variety of time-evolution dynamics, such as exponential growth, limit cycles, and strange attractors. The particular features of the dynamics are determined primarily by the values of linear growth rates, forming a 2D parameter space. An exploration of this parameter space is the main focus of this present work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

ELM-free enhanced D α H-mode with near zero NBI torque injection in DIII-D tokamak

Enhanced D α H-mode (EDA H-mode), an ELM-free H-mode regime, and the concomitant quasi-coherent mode (QCM) are explored in neutral beam heated, lower single null plasmas with near zero external torque injection. This regime exhibits good energy confinement (H 98y2 ~ 1) with β N ~ 2, high density, regime access at low input power, and no ELMs. New time–resolved measurements of electron and ion density, temperature, plasma rotation, and radial electric field as well as turbulence, flows, thermal and particle transport, and linear instability calculations examining the dynamics of the QCM are presented. Measurements using multiple fluctuation diagnostics reveal the QCM to be a separatrix spanning mode, peaking just inside the separatrix, existing in a wide range of k ⊥ ρ s ~ 0.1–1.2 with multiple harmonics, and propagating with a very small phase velocity in the plasma frame, where k ⊥ is the binormal wavenumber and ρ s is the ion sound radius. Linear gyrokinetic simulations of this EDA H-mode discharge with CGYRO indicate that the trapped electron mode (TEM) and electron temperature gradient mode are the dominant instabilities in the radial region where QCM is also unstable. Qualitative analysis indicates that the properties of TEM are consistent with the experimentally observed characteristics of the QCM. These similarities suggest that the QCM is consistent with a TEM instability existing in the edge region of the EDA H-mode plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Floquet stability analyses of stratified oscillating boundary layers on adiabatic slopes

The presence of a no-slip, impermeable, adiabatic, sloped boundary in an otherwise quiescent, stably stratified, Boussinesq flow generates baroclinic vorticity within a diffusive boundary layer. Such conditions are typical of the oscillating boundary layers on adiabatic abyssal slopes, sloped lake bathymetry and sloped coastal bathymetry in the absence of high-wavenumber internal waves, mean flows, far-field turbulence on larger scales, and resonant tidal–bathymetric interaction. We investigate the linear stability of the oscillating flow within non-dimensional parameter space typical of the $M_2$ tide and hydraulically smooth, middle-latitude abyssal slopes through Floquet linear stability analysis. The flow dynamics depends on three non-dimensional variables: the Reynolds number for Stokes’ second problem, the Prandtl number, and a frequency ratio that accounts for the resonance conditions ($C$, criticality) of the buoyant restoring force and the tidal forcing. The Floquet analysis results suggest that oscillating laminar boundary layers on adiabatic abyssal slopes are increasingly unstable as Reynolds number, criticality parameter and/or spanwise disturbance wavenumber are increased. We also show that the two-dimensional Floquet linear instability necessarily generates three-dimensional baroclinic vorticity, which suggests that the evolution of the gravitational instabilities may be nonlinear as $t→∞$.

42 ENGINEERING↗
DOE OSTI2025

Assessing VQLS for Fluid Dynamics on a Hybrid Quantum-HPC Stack

Recent advances in quantum linear solvers offer a promising direction for accelerating extreme scientific computations such as fluid dynamics. However, the deep and complex circuits required by many quantum algorithms limit their practical use on current quantum hardware. The Variational Quantum Linear Solver (VQLS) presents a viable alternative for near-term quantum devices (NISQ), and initial efforts have explored its application to select fluid dynamics problems. In this work, we evaluate the use of VQLS for canonical fluid dynamics problems, aiming to identify pathways for generalizing its implementation across a broader class of systems. We analyze the impact of various circuit ansatz and classical optimizers on solution quality and convergence behavior. Furthermore, we assess the algorithm's feasibility within a hybrid quantum–high-performance computing (HPC) framework by porting it to QFw, a state-of-the-art quantum-HPC software stack. 11This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the US DOE under Contract No. DE-AC05-00OR22725.

Gopalakrishnan Meena, Murali [ORNL] (ORCID:0000000↗
DOE OSTI2025

Nonlinear Scaling of Water–Ion Interactions and Dynamics in Alkaline Solutions

Water-ion interactions govern many solvent properties critical to solution-phase chemistry and the behavior of liquid water. The water-ion interactions in alkaline conditions were probed using two-dimensional infrared spectroscopy (2D IR), small-angle x-ray scattering (SAXS), and nuclear magnetic resonance spectroscopy (NMR). Energy transfer between the donor molecule KSeCN, used as a 2D IR probe, and the acceptor molecule NaOD was used to track the average separation distance of ions in a D 2 O solution, while SAXS measurements showed effects in the bulk D 2 O solvent. Here, we observe consistent nonlinear scaling in the SeCN - and OD - average separation distance as a function of NaOD concentration while bulk solution D 2 O-to-D 2 O average separation distance remained highly linear. Ultrafast measurements of solution dynamics via 2D IR and polarization-selective pump-probe spectroscopy show consistent scaling in correlation times as a function of concentration. These results suggests that the SeCN - and OD - anions participate in a water-ion network that significantly reduces the degrees of freedom for the distribution of ions in solution below the standard stochastic for ion distribution.

2D IR Spectroscopy↗
DOE OSTI2020

Implementing Inertial Control for PMSG-WTG in Region 2 Using Virtual Synchronous Generator with Multiple Virtual Rotating Masses

With the increasing integration of renewable energy, the problems associated with a deteriorating grid frequency profile and potential power system instability have become more significant. In this paper, the inertial control algorithm using a virtual synchronous generator (VSG) is implemented on a Type 4 permanent magnet synchronous generator (PMSG) wind turbine generator (WTG). The overall nonlinear dynamic model and its small-signal linearization of the PMSG-WTG using a VSG is established and comprehensively analyzed. Inevitably, the direct application of the VSG introduces a large inertia, which causes conflict between the fast variance of available wind power and inverter control with slow dynamics, particularly in Region 2 of the wind turbine. Aiming to address such issues, a VSG with multiple virtual rotating masses is proposed to improve the active power tracking performance as well as to boost the inertial control of a VSG. The inertial responses are verified in a modified 10-MVA IEEE 14-bus microgrid system. The assessment of the simulation results demonstrates the applicability of the VSG on renewable energy generation units.

17 WIND ENERGY↗