Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “nonlinear analysis”

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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Chirality-Dependent Second Harmonic Generation of MoS 2 Nanoscroll with Enhanced Efficiency

Materials with high second harmonic generation (SHG) efficiency and reduced dimensions are favorable for integrated photonics and nonlinear optical applications. Here, we fabricate MoS 2 nanoscrolls with different chiralities and study their SHG performances. As a 1D material, MoS 2 nanoscroll shows reduced symmetry and strong chirality dependency in the polarization-resolved SHG characterizations. This SHG performance can be well explained by the superposition theory of second harmonic field of the nanoscroll walls. MoS 2 nanoscrolls with certain chiralities and diameters in our experiment can have SHG intensity up to 95 times stronger than that of monolayer MoS 2 , and the full potential can still be further exploited. The same chirality-dependent SHG can be expected for nanoscrolls or nanotubes composed of other noncentrosymmetric 2D materials, such as WS 2 , WSe 2 , and hBN. Here, the characterization and analysis results presented here can also be exploited as a nondestructive technique to determine the chiralities of these nanoscrolls and nanotubes.

42 ENGINEERING↗
DOE OSTI2025

Periodic Korteweg–de Vries soliton potentials generate quasisymmetric magnetic field strength in a finite plasma- β equilibrium

Quasisymmetry (QS) is a hidden symmetry of the magnetic field strength, B , that enables effective confinement of charged particles in a fully three-dimensional (3D) toroidal plasma equilibrium. Such equilibria are typically modeled by the ideal magnetohydrostatic (MHS) equations. The nonlinear, overdetermined nature of the QS MHS equations severely complicates our understanding of the interplay between 3D shaping, equilibrium properties such as pressure and rotational transform, and B . Progress has been made through expansions near the magnetic axis; however, a more comprehensive theory is desirable. Using a combination of analysis and regression on a large dataset of numerically optimized quasisymmetric stellarators, we demonstrate that there is a hidden lower dimensionality of B on a magnetic flux surface with connections to the theory of periodic solitons. We show that B on a flux surface is determined by three or at most four flux functions, each of which determines a critical value of the derivative of B along the field line. While being consistent with the near-axis models, our results are global and hold even on the last closed flux surface.

Differential geometry↗
DOE OSTI2020

Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation

Classical molecular dynamics simulates the time evolution of molecular systems through the phase space spanned by the positions and velocities of the constituent atoms. Molecular-level thermodynamic, kinetic, and structural data extracted from the resulting trajectories provide valuable information for the understanding, engineering, and design of biological and molecular materials. The cost of simulating many-body atomic systems makes simulations of large molecules prohibitively expensive, and the high-dimensionality of the resulting trajectories presents a challenge for analysis. Driven by advances in algorithms, hardware, and data availability, there has been a flare of interest in recent years in the applications of machine learning – especially deep learning – to molecular simulation. Furthermore, these techniques have demonstrated great power and flexibility in both extracting mechanistic understanding of the important nonlinear collective variables governing the dynamics of a molecular system, and in furnishing good low-dimensional system representations with which to perform enhanced sampling or develop long-timescale dynamical models. It is the purpose of this article to introduce the key machine learning approaches, describe how they are married with statistical mechanical theory into domain-specific tools, and detail applications of these approaches in understanding and accelerating biomolecular simulation.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Probing coherent electronic superpositions of singly and doubly excited states of krypton with extreme-ultraviolet four-wave-mixing spectroscopy

Radiative nonlinear four-wave mixing can monitor the evolution of electronic wave packets, providing access to lifetimes and quantifying the light-induced couplings between excited states. In this article, we report the observation of quantum beats in an autoionizing electronic wave packet in krypton, probed using this technique. Analysis of the signal reveals that these beats originate from the contribution of previously unassigned, doubly excited states interacting with singly excited ones. We introduce a minimal theoretical model, based on multichannel quantum-defect theory, which quantitatively reproduces both the wave-packet dynamics and the static spectrum. This work combines a versatile, noncommensurate XUV-IR-based experimental scheme with a tractable model, establishing a powerful approach for the metrology and control of complex, correlated electronic states.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Atacama Cosmology Telescope DR6 and DESI: Structure growth measurements from the cross-correlation of DESI legacy imaging galaxies and CMB lensing from ACT DR6 and 𝑃⁢𝑙⁢𝑎⁢𝑛⁢𝑐⁢𝑘 PR4

We measure the growth of cosmic density fluctuations on large scales and across the redshift range 0.3 < 𝑧 < 0.8 through galaxy clustering and the cross-correlation of the ACT data release 6 cosmic microwave background (CMB) lensing map and galaxies from the Dark Energy Spectroscopic Instrument Legacy Survey, using three galaxy samples spanning the redshifts of 0.3 ≲ 𝑧 ≲ 0.45, 0.45 ≲ 𝑧 ≲ 0.6, 0.6 ≲ 𝑧 ≲ 0.8. We adopt a scale cut where nonlinear effects are negligible, so that the cosmological constraints are derived from the linear regime. We determine the amplitude of matter fluctuations over all three redshift bins using Atacama Cosmology Telescope (ACT) data alone to be 𝑆 8 ≡ 𝜎 8 ⁢(Ω 𝑚 /0.3) 0.5 =0.772 ± 0.040 in a joint analysis combining the three redshift bins and ACT lensing alone. Using a combination of ACT and Planck data we obtain 𝑆 8 = 0.765 ± 0.032. The lowest redshift bin used is the least constraining and exhibits a ∼2⁢𝜎 tension with the other redshift bins; thus we also report constraints excluding the first redshift bin, giving 𝑆 8 = 0.785 ± 0.033 for the combination of ACT and Planck. This result is in excellent agreement at the 0.3⁢𝜎 level with measurements from galaxy lensing, but is 1.8⁢𝜎 lower than predictions based on Planck primary CMB data. Understanding whether this hint of discrepancy in the growth of structure at low redshifts arises from a fluctuation, from systematics in data, or from new physics is a high priority for forthcoming CMB lensing and galaxy cross-correlation analyses.

cosmic microwave background↗
DOE OSTI2022

Grassmannian Diffusion Maps--Based Dimension Reduction and Classification for High-Dimensional Data

This work introduces the Grassmannian diffusion maps (GDMaps), a novel nonlinear dimensionality reduction technique that defines the affinity between points through their representation as low-dimensional subspaces corresponding to points on the Grassmann manifold. Here, the method is designed for applications, such as image recognition and data-based classification of constrained high-dimensional data where each data point itself is a high-dimensional object (i.e., a large matrix) that can be compactly represented in a lower-dimensional subspace. The GDMaps is composed of two stages. The first is a pointwise linear dimensionality reduction wherein each high-dimensional object is mapped onto the Grassmann manifold representing the low-dimensional subspace on which it resides. The second stage is a multipoint nonlinear kernel-based dimension reduction using diffusion maps to identify the subspace structure of the points on the Grassmann manifold. To this end, an appropriate Grassmannian kernel is used to construct the transition matrix of a random walk on a graph connecting points on the Grassmann manifold. Spectral analysis of the transition matrix yields low-dimensional Grassmannian diffusion coordinates embedding the data into a low-dimensional reproducing kernel Hilbert space. Further, a novel data classification/recognition technique is developed based on the construction of an overcomplete dictionary of reduced dimension whose atoms are given by the Grassmannian diffusion coordinates. Three examples are considered. First, a "toy" example shows that the GDMaps can identify an appropriate parametrization of structured points on the unit sphere. The second example demonstrates the ability of the GDMaps to revealing the intrinsic subspace structure of high-dimensional random field data. In the last ex- ample, a face recognition problem is solved considering face images subject to varying illumination conditions, changes in face expressions, and occurrence of occlusions. The technique presented high recognition rates (i.e., 95% in the best case) using a fraction of the data required by conventional methods.

42 ENGINEERING↗
DOE OSTI2024

A tutorial review of machine learning-based model predictive control methods

Abstract This tutorial review provides a comprehensive overview of machine learning (ML)-based model predictive control (MPC) methods, covering both theoretical and practical aspects. It provides a theoretical analysis of closed-loop stability based on the generalization error of ML models and addresses practical challenges such as data scarcity, data quality, the curse of dimensionality, model uncertainty, computational efficiency, and safety from both modeling and control perspectives. The application of these methods is demonstrated using a nonlinear chemical process example, with open-source code available on GitHub. The paper concludes with a discussion on future research directions in ML-based MPC.

Wu, Zhe [Department of Chemical and Biomolecular E↗
DOE OSTI2025

Atacama Cosmology Telescope DR6 and DESI: Structure growth measurements from the cross-correlation of DESI legacy imaging galaxies and CMB lensing from ACT DR6 and P l a n c k PR4

We measure the growth of cosmic density fluctuations on large scales and across the redshift range 0.3 < z < 0.8 through galaxy clustering and the cross-correlation of the ACT data release 6 cosmic microwave background (CMB) lensing map and galaxies from the Dark Energy Spectroscopic Instrument Legacy Survey, using three galaxy samples spanning the redshifts of 0.3 ≲ z ≲ 0.45 , 0.45 ≲ z ≲ 0.6 , 0.6 ≲ z ≲ 0.8 . We adopt a scale cut where nonlinear effects are negligible, so that the cosmological constraints are derived from the linear regime. We determine the amplitude of matter fluctuations over all three redshift bins using Atacama Cosmology Telescope (ACT) data alone to be S 8 ≡ σ 8 ( Ω m / 0.3 ) 0.5 = 0.772 ± 0.040 in a joint analysis combining the three redshift bins and ACT lensing alone. Using a combination of ACT and Planck data we obtain S 8 = 0.765 ± 0.032 . The lowest redshift bin used is the least constraining and exhibits a ∼ 2 σ tension with the other redshift bins; thus we also report constraints excluding the first redshift bin, giving S 8 = 0.785 ± 0.033 for the combination of ACT and Planck. This result is in excellent agreement at the 0.3 σ level with measurements from galaxy lensing, but is 1.8 σ lower than predictions based on Planck primary CMB data. Understanding whether this hint of discrepancy in the growth of structure at low redshifts arises from a fluctuation, from systematics in data, or from new physics is a high priority for forthcoming CMB lensing and galaxy cross-correlation analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

On resistive interchange, double tearing, and resonant and non-resonant infernal modes in spherical tokamak negative central shear discharges

We examine the linear and nonlinear stability of a sequence of reversed shear NSTX equilibria with the same toroidal current density and pressure profile but with different toroidal field strengths. All equilibria have two q = 2 surfaces and have q > 1 everywhere. For this sequence, which all have β P ∼ 0.5, as the minimum value of q decreases below about 1.4, the localized resistive interchange criterion is strongly violated, and the unstable mode with toroidal mode number n=1 changes its dominant poloidal mode number m from a (m,n)=(2,1) double tearing mode to a non-resonant (1,1) infernal mode. This (1,1) mode nonlinearly flattens both the current density and the pressure near the magnetic axis. The higher-n unstable modes are all resistive infernal modes localized around the surface where q has a minimum and the magnetic shear vanishes. They generally saturate nonlinearly at low amplitude but can cause magnetic surface breakup near the axis.

Spitzer resistivity↗
DOE OSTI2022

Comparing Calibration Algorithms for the Rapid Characterization of Pretreated Corn Stover Using Near-Infrared Spectroscopy

Rapid characterization of biomass composition is a key enabling technology for biorefineries—the ability to measure the chemical composition of biomass materials entering the biorefinery as well as the composition of key process intermediate streams would allow real-time process control and the development of robust models to predict process performance. The utility of near-infrared (NIR) spectroscopy for rapid characterization requires multivariate algorithms for building calibration models. The most prevalent algorithm used for building calibration models using NIR spectra is the linear modeling algorithm Partial Least Squares Regression (PLS). Nonlinear regression algorithms (which are typically more computationally intensive than linear modeling approaches) have gained popularity in recent years due to their ability to solve a wide variety of classification and regression problems and the dramatic increase in available computational resources. In this work, we demonstrate that a calibration model can predict the composition of corn stover process intermediate samples pretreated with three different treatments—hot water (HW), dilute acid (DA), and deacetylation followed by dilute acid (DDA). We quantitatively compare three different algorithms for building prediction models based on near-infrared spectroscopy—partial least squares (PLS), support vector machines (SVM), and random forests (RF). We demonstrate the utility of improving model performance by accounting for instrument performance variability using repeated measurements of standard materials (e.g., the “repeatability file” strategy) and investigate its performance with nonlinear regression techniques, and we discuss methods for quantifying the uncertainties of specific predictions among the three methods.

09 BIOMASS FUELS↗
DOE OSTI2022

Stabilization of the Polar Structure and Giant Second-Order Nonlinear Response of Single Crystal γ-NaAs 0.95 Sb 0.05 Se 2

The dearth of suitable materials significantly restricts the practical development of infrared (IR) laser systems with highly efficient and broadband tuning. Recently, γ-NaAsSe 2 is reported, and it exhibits a large nonlinear second-harmonic generation (SHG) coefficient of 590 pm V —1 at 2 µm. However, the crystal growth of γ-NaAsSe 2 is challenging because it undergoes a phase transition to centrosymmetric δ-NaAsSe 2 . Herein, the stabilization of non-centrosymmetric γ-NaAsSe 2 by doping the As site with Sb, which results in γ-NaAs 0.95 Sb 0.05 Se 2 is reported. The congruent melting behavior is confirmed by differential thermal analysis with a melting temperature of 450 °C and crystallization temperature of 415 °C. Single crystals with dimensions of 3 mm × 2 mm are successfully obtained via zone refining and the Bridgman method. The purification of the material plays a significant role in crystal growth and results in a bandgap of 1.78 eV and thermal conductivity of 0.79 Wm —1 K —1 . The single-crystal SHG coefficient of γ-NaAs 0.95 Sb 0.05 Se 2 exhibits an enormous value of |d 11 | = 648 ± 74 pm V —1 , which is comparable to that of γ-NaAsSe 2 and ≈20× larger than that of AgGaSe 2 . Importantly, the bandgap of γ-NaAs 0.95 Sb 0.05 Se 2 (1.78 eV) is similar to that of AgGaSe 2 , thus rendering it highly attractive as a high-performing nonlinear optical material.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Broken symmetries associated with a Kagome chiral charge order

Chirality, or handedness, is ubiquitous in science, from cell biology to physics, and in condensed matter can underlie exotic phases such as chiral charge density waves and chiral superconductivity. However, detecting subtle broken symmetries that define such states is challenging, leading to debate and controversy. Here, using second-order optical response, we reveal the broken symmetries of a chiral charge density wave in the Kagome lattice KV 3 Sb 5 . Polarization-dependent mid-infrared photocurrent microscopy uncovers a longitudinal, helicity-dependent photocurrent associated with the charge order, indicating broken inversion and mirror symmetries. These findings, supported by theoretical analysis, directly establish the intrinsic chiral nature of the ordered state. Moreover, the absence of a circular photogalvanic effect perpendicular to the incident light imposes stringent constraints on the point-group symmetries. Our study not only visualizes the chiral nature of the Kagome charge order, but also highlights the nonlinear photogalvanic effect as a sensitive probe for detecting subtle symmetry breakings.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Tracking the topology of neural manifolds across populations

Neural manifolds summarize the intrinsic structure of the information encoded by a population of neurons. Advances in experimental techniques have made simultaneous recordings from multiple brain regions increasingly commonplace, raising the possibility of studying how these manifolds relate across populations. However, when the manifolds are nonlinear and possibly code for multiple unknown variables, it is challenging to extract robust and falsifiable information about their relationships. We introduce a framework, called the method of analogous cycles, for matching topological features of neural manifolds using only observed dissimilarity matrices within and between neural populations. We demonstrate via analysis of simulations and in vivo experimental data that this method can be used to correctly identify multiple shared circular coordinate systems across both stimuli and inferred neural manifolds. Conversely, the method rejects matching features that are not intrinsic to one of the systems. Further, as this method is deterministic and does not rely on dimensionality reduction or optimization methods, it is amenable to direct mathematical investigation and interpretation in terms of the underlying neural activity. We thus propose the method of analogous cycles as a suitable foundation for a theory of cross-population analysis via neural manifolds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Causal and stable first-order chiral hydrodynamics

We derive the set of inequalities that is necessary and sufficient for nonlinear causality and linear stability of first-order relativistic hydrodynamics with either a U ( 1 ) V conserved current or a U ( 1 ) A current with a chiral anomaly or both. Our results apply to generic hydrodynamic frames in which no relations among the transport parameters are imposed. Furthermore, our analysis yields, to the best of our knowledge, the first theory of viscous chiral hydrodynamics proven to be causal and stable. We find that causality demands the absence of vorticity-induced heat flux, forcing a departure from the thermodynamic frame in the chiral case. The inequalities for causality and stability define a hypervolume in the space of transport parameters, wherein each point corresponds to a consistent formulation. Notably, causality is determined by just three combinations of transport parameters. We present our results in a form amenable to numerical hydrodynamic simulations. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2024

CI-MOR Final Report: Analysis and Validation of Critical Infrastructure Models using Model Order Reduction

This report summarizes the research and capabilities developed as part of the project “Analysis and Validation of Critical Infrastructure Models using Model Order Reduction” (CI-MOR) LDRD project. CI-MOR research enables the solution of large, complex optimization models that naturally arise in national security challenges involving critical infrastructures. Specifically, CI-MOR researchers developed methods to (1) rigorously approximate complex, nonlinear optimization formulations, (2) identify alternative near-optimal solutions, (3) accelerate optimization workflows used for complex applications, and (4) rigorously integrate domain knowledge in stochastic-process models. This report provides an overview of the research done in CI-MOR, and we describe application exemplars used to illustrate CI-MOR capabilities. Furthermore, we describe the software developed by CI-MOR that researchers can leverage to analyze new applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Higrad/Firetec Wildfire Simulations

Wildfire behavior is a result of complex nonlinear interactions between a variety of physical processes and chemical reactions. Understanding the coupling between these components and the ways in which their interactions are influenced by their surrounding environment (e.g. winds, fuels, and topography) is a challenging undertaking. The IEEE SciVis 2022 Contest is focused on the visualization and analysis of simulations of wildfires.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

DROP DURABILITY ASSESSMENT OF ELECTRONIC ASSEMBLIES UNDER OFF-AXIS LOADING WITH SKEWED FIXTURES

This thesis studies drop durability of electronic assemblies when the acceleration vector is oriented at 45° to the out-of-plane direction of the circuit card. The off-axis drop tests are accomplished with a skewed fixture and are conducted as a proxy for multiaxial drop testing. Advanced shock testing and vibration test methods have been developed over the last few decades to better represent real-world field environments during ground-based laboratory testing. However, many of these test methods require expensive and specialized equipment not available in most laboratories. An alternative approach for approximating simultaneous loading along multiple axes on conventional equipment utilizes skewed fixtures which have seen use in off-axis random vibration and drop impact testing. These methods generally rely on the conversion of a uniaxial input load from the test equipment (using a uniaxial drop tower or shaker) into a multiaxial load when resolved in the reference frame of the test article (mounted on a skewed fixture). Skewed fixture design is presented and recommendations for conducting skewed angle drop testing are introduced based on local measurements along the skewed face of the fixture to accurately monitor the impact event. Characterization tests were performed with a skewed fixture, at simultaneous acceleration loads from 500 to 3,000 g in two (in-plane and out-of-plane) directions, while meeting standard time domain tolerances. Upon experimental characterization, drop shock durability tests were conducted on a printed circuit assembly (PCA). Mean drops-to-failure were measured and quantified with Weibull statistics. Dominant solder joint failure modes were identified via failure analysis. Prior work on inclined angle impact testing is limited, and the majority of solder joint interconnect level fatigue studies are conducted considering perpendicular loading normal the circuit card. Low-cycle fatigue curves are generated based on plastic strain and plastic work density within the solder joint. A multiscale nonlinear finite element model is used to relate board-level flexure to solder joint interconnect level plastic strain. A high strain rate solder constitutive model allows for accurate modeling of solder plasticity resulting from high-impact drop shock. Fatigue parameters are computed from the Coffin-Manson relation and Palmgren-Miner damage accumulation. This work serves to apply established low-cycle fatigue methods for conventional drop shock loading (impact normal to circuit card) to non-perpendicular loading with a skewed fixture.

Hower, Jonathan [Kansas City National Security Cam↗
DOE OSTI2020

A generalized power-law criterion for rocks based on Mohr failure theory

Nonlinearity of Mohr envelopes confirmed by past laboratory test data can be of considerable importance in theoretical interpretation of rock strength behaviors and many practical applications. Current nonlinear failure criteria are introduced with parameters to empirically fit test data, and the physical and mechanical meanings of these fitting parameters are often not known. Predicted strengths by theoretical criteria (e.g., Griffith criterion) generally do not agree well with test data. Considering the nonlinear properties of a Mohr envelope, a nonlinear failure criterion in a generalized power-law form is derived in this paper based on strength magnitude coefficient (a) and failure envelope curvature coefficient (n) which are expressed in terms of uniaxial tensile strength, uniaxial compressive strength, and angle of internal friction. Test data on granite, marble, dolomite, and andesite from published literature were chosen to test the performance of the proposed criterion along with Griffith and Hoek-Brown criteria. Statistical analysis was performed to evaluate the application of different criteria in predicting the strength of rocks. In addition, some Griffith-typed criteria are mathematically shown to be the special cases of the proposed criterion. The proposed and Hoek-Brown criteria have similar power-law forms with a significant difference in the coefficient functions that govern the shear strength magnitude of rock. The different tensile and compressive failure mechanisms are also investigated from the perspective of the relationship between the uniaxial tension Mohr circle and the proposed failure envelope.

58 GEOSCIENCES↗