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Results for “nonlinear structural 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.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Morphology and Transport of Multivalent Cation-Exchanged Ionomer Membranes Using Perfluorosulfonic Acid–Ce Z+ as a Model System

Perfluorosulfonic acids (PFSAs) are commonly used as solid polymer electrolyte membranes (PEMs) in electrochemical energy devices, where they are vulnerable to attack by radical species during operation. A popular strategy to combat this problem is to introduce radical scavengers like cerium (Ce) ions that neutralize these species before they attack the PFSA. Such cation doping creates a multi-ion system, in which understanding the mechanisms of cation solvation and transport becomes important for the effective design and utilization of PFSA–cation systems. Ce ions also provide a representative model system for multication-exchanged ionomers in electrochemical systems. In this study, hydration and conductivity measurements, along with X-ray fluorescence and scattering, are employed to elucidate how Ce ion exchange alters PFSA’s ionic solvation, as well as nano- and mesoscale morphologies, which ultimately control its ion transport properties. A molecular transport model is used to deconvolute the impact of Ce ions on the local solvation structure of water in the membrane from mesoscale changes of the transport pathways. The combined experimental and theoretical analysis reveals a nonlinear decrease in conductivity driven by cation solvation at the molecular level and morphological changes at longer length scales. Migration–diffusion coupling, its nonlinear dependence on ion exchange and hydration, and its overall implications for ionomer performance are also discussed. Finally, these findings have the potential to be translated into other mixed cation–ionomer systems for a wide range of energy and environmental devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Disentangling Memristive and Memcapacitive Effects in Droplet Interface Bilayers Using Dynamic Impedance Spectroscopy

The underlying principles for generating intelligent behavior in living organisms are fundamentally different from those in traditional solid-state circuits. Biomimetic neuromorphic equivalents based on biological membranes offer novel implementation of tunable plasticity and diverse mechanisms to control functionality. Here, dynamic electrochemical impedance spectroscopy (dEIS) to probe diphytanoylphosphatidylcholine (DPhPC) droplet interface bilayers (DIBs) to better understand the differences in molecular level structure/dynamics that give rise to hysteretic loops and neuromorphic, memelement behaviors in lipid bilayers in response to electrical biasing is used. Importantly, this system does not have ion-conducting channels and is, therefore, not expected to show memristive behavior. Surprisingly, both memristive and memcapacitive behaviors by measuring the time-dependent complex impedance of DPhPC DIBs are detected. Furthermore, it is shown that nonlinear memristance can originate from structural changes in the bilayer, affecting its dielectric properties. This novel dEIS application allows for the simultaneous analysis of the system's changing memristive and memcapacitive properties, which originate from different molecular restructuring processes. Moreover, and importantly, access to this type of information increases the number of neuromorphic processes supported simultaneously in a single two-terminal device.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multiscale design of nonlinear materials using a Eulerian shape optimization scheme

Motivated by recent advances in manufacturing, the design of materials is the focal point of interest in the material research community. One of the critical challenges in this field is finding optimal material microstructure for a desired macroscopic response. This work presents a computational method for the mesoscale-level design of particulate composites for an optimal macroscale-level response. The method relies on a custom shape optimization scheme to find the extrema of a nonlinear cost function subject to a set of constraints. Three key “modules” constitute the method: multiscale modeling, sensitivity analysis, and optimization. Multiscale modeling relies on a classical homogenization method and a nonlinear NURBS-based generalized finite element scheme to efficiently and accurately compute the structural response of particulate composites using a nonconformal discretization. A three-parameter isotropic damage law is used to model microstructure-level failure. An analytical sensitivity method is developed to compute the derivatives of the cost/constraint functions with respect to the design variables that control the microstructure's geometry. The derivation uncovers subtle but essential new terms contributing to the sensitivity of finite element shape functions and their spatial derivatives. Several structural problems are solved to demonstrate the applicability, performance, and accuracy of the method for the design of particulate composites with a desired macroscopic nonlinear stress-strain response.

42 ENGINEERING↗
DOE OSTI2021

Measurement and control of optical nonlinearities in dispersive dielectric multilayers

Dispersive dielectric multilayer mirrors, high-dispersion chirped mirrors in particular, are widely used in modern ultrafast optics to manipulate spectral chirps of ultrashort laser pulses. Dispersive mirrors are routinely designed for dispersion compensation in ultrafast lasers and are assumed to be linear optical components. In this work, we report the experimental characterization of an unexpectedly strong nonlinear response in these chirped mirrors. At modest peak intensities <2 TW/cm 2 —well below the known laser-induced damage threshold of these dielectric structures—we observed a strong reflectivity decrease, local heating, transient spectral modifications, and time-dependent absorption of the incident pulse. Through computational analysis, we found that the incident laser field can be enhanced by an order of magnitude in the dielectric layers of the structure. The field enhancement leads to a wavelength-dependent nonlinear absorption, that shows no signs of cumulative damage before catastrophic failure. The nonlinear absorption is not a simply two-photon process but instead is likely mediated by defects that facilitate two-photon absorption. To mitigate this issue, we designed and fabricated a dispersive multilayer design that strategically suppresses the field enhancement in the high-index layers, shifting the high-field regions to the larger-bandgap, low-index layers. This strategy significantly increases the maximum peak intensity that the mirror can sustain. However, our finding of an onset of nonlinear absorption even at ‘modest’ fluence and peak intensity has significant implications for numerous past published experimental works employing dispersive mirrors. Additionally, our results will guide future ultrafast experimental work and ultrafast laser design.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Exploration of superconducting multi-mode cavity architectures for quantum computing

Superconducting radio-frequency (SRF) cavities coupled to transmon circuits have proven to be a promising platform for building high-coherence quantum information processors. An essential aspect of this realization involves designing high quality factor three-dimensional superconducting cavities to extend the lifetime of quantum systems. To increase the computational capability of this architecture, we are exploring a multimode approach. This paper presents the design optimization process of a multi-cell SRF cavity to perform quantum computation based on an existing design developed in the scope of particle accelerator technology. We perform parametric electromagnetic simulations to evaluate and optimize the design. In particular, we focus on the analysis of the interaction between a nonlinear superconducting circuit known as the transmon and the cavity. This parametric design optimization is structured to serve as a blueprint for future studies on similar systems.

Reineri, Alessandro↗
DOE OSTI2021

Deep Learning Segmentation of Complex Features in Atomic-Resolution Phase-Contrast Transmission Electron Microscopy Images

Phase-contrast transmission electron microscopy (TEM) is a powerful tool for imaging the local atomic structure of materials. TEM has been used heavily in studies of defect structures of two-dimensional materials such as monolayer graphene due to its high dose efficiency. However, phase-contrast imaging can produce complex nonlinear contrast, even for weakly scattering samples. It is, therefore, difficult to develop fully automated analysis routines for phase-contrast TEM studies using conventional image processing tools. For automated analysis of large sample regions of graphene, one of the key problems is segmentation between the structure of interest and unwanted structures such as surface contaminant layers. In this study, we compare the performance of a conventional Bragg filtering method with a deep learning routine based on the U-Net architecture. We show that the deep learning method is more general, simpler to apply in practice, and produces more accurate and robust results than the conventional algorithm. We provide easily adaptable source code for all results in this paper and discuss potential applications for deep learning in fully automated TEM image analysis.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Nonlinear elasticity and damping govern ultrafast dynamics in click beetles

Many small animals use springs and latches to overcome the mechanical power output limitations of their muscles. Click beetles use springs and latches to bend their bodies at the thoracic hinge and then unbend extremely quickly, resulting in a clicking motion. When unconstrained, this quick clicking motion results in a jump. While the jumping motion has been studied in depth, the physical mechanisms enabling fast unbending have not. Here, we first identify and quantify the phases of the clicking motion: latching, loading, and energy release. We detail the motion kinematics and investigate the governing dynamics (forces) of the energy release. We use high-speed synchrotron X-ray imaging to observe and analyze the motion of the hinge’s internal structures of four Elater abruptus specimens. We show evidence that soft cuticle in the hinge contributes to the spring mechanism through rapid recoil. Using spectral analysis and nonlinear system identification, we determine the equation of motion and model the beetle as a nonlinear single-degree-of-freedom oscillator. Quadratic damping and snap-through buckling are identified to be the dominant damping and elastic forces, respectively, driving the angular position during the energy release phase. The methods used in this study provide experimental and analytical guidelines for the analysis of extreme motion, starting from motion observation to identifying the forces causing the movement. Here, the tools demonstrated here can be applied to other organisms to enhance our understanding of the energy storage and release strategies small animals use to achieve extreme accelerations repeatedly.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Multiple Degradation Mechanisms in Reinforced Concrete Structures, Modeling and Risk Analysis

The overarching goal of this project is to complement ongoing Department of Energy (DOE) Light Water Reactor Sustainability (LWRS)-funded Grizzly concrete modeling development efforts by providing improved multi-physics models for incorporation into the Grizzly and BlackBear codes. Objectives of this work identified at the outset of this project include: 1. Coupling between mechanical damage and transport processes. The mechanical damage will be characterized by nonlinear mechanical constitutive models appropriate for concrete used in nuclear power plants (NPPs). 2. Improved representation of the coupling among the transport process, building on the current moisture and thermal transport models in Grizzly. The parameters for coupling among heat and mass transport in the multi-physics model will be experimentally determined. 3. Coupling among different scale levels of concrete constituents, fine and coarse aggregates, cement paste, hydration products, and pore structure. 4. Probabilistic analysis of the random nature of the heterogeneous concrete structure and the environmental factors (ambient temperature and humidity) and their multiple effects on concrete deterioration. 5. Benchmark verification, validation and uncertainty quantification of the thermo-hygro- chemo-mechanical (THCM) concrete formulation. The end goal of this work was to provide a comprehensive and robust simulation capability that can be applied at the engineering scale for analysis of realistic deterioration scenarios in NPP concrete structures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Accelerating uncertainty quantification in incremental dynamic analysis using dimension reduction-based surrogate modeling

We propose a surrogate modeling framework based on dimension reduction to facilitate the quantification of seismic risk of structural systems in performance-based earthquake engineering. The framework adopts incremental dynamic analysis (IDA) for addressing hazard variability, and promotes significant computational efficiency improvement for propagating epistemic uncertainties associated with the structural models. It utilizes both linear and nonlinear dimension reduction approaches, equipped with inverse mappings, to learn a functional between the input parameter space (e.g., the epistemic uncertainties of the structure) to the high-dimensional output space created through the IDA implementation across different ground motions and seismic intensity levels. Polynomial chaos expansion is adopted as the surrogate model to learn this functional in the reduced space. A nine-story steel moment-resisting frame with uncertain structural properties is used as a testbed. Furthermore, we select the seismic fragility curves as a measure of the structure’s seismic performance, since it provides an estimate of the probability of entering specified damage states for given levels of ground shaking.

42 ENGINEERING↗
DOE OSTI2021

Use of computer vision for analysis of image datasets from high temperature plasma experiments

Great strides have been made in improving the quality of x-ray radiographs in high energy density plasma experiments, enabled in part by innovations in engineering and manufacturing of integrated circuits and materials. As a consequence, the radiographs of today are filled with a great deal of detail, but few of these features are extracted in a systematic way. Analysis techniques familiar to plasma physicists tend toward brittle 1D lineout or Fourier transform type analyses. The techniques applied to process our data have not kept pace with improvements in the quality of our data. Fortunately, the field of computer vision has a wealth of tools to offer, which have been widely used in industrial imaging and, more recently, adopted in biological imaging. We demonstrate the application of computer vision techniques to the analysis of x-ray radiographs from high energy density plasma experiments, as well as give a brief tutorial on the computer vision techniques themselves. These tools robustly extract 2D contours of shocks, boundaries of inhomogeneities, and secondary flows, thereby allowing for increased automation of analysis, as well as direct and quantitative comparisons with simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

A quantitative assessment of the model form error of friction models across different interface representations for jointed structures

Hysteretic models are widely used to model frictional interactions in joints to recreate experimental behavior. However, it is unclear which models are best suited for fitting or predicting the responses of structures. Here, the present study evaluates 26 friction model/interface representation combinations to quantify the model form error. A Quasi-Static Modal Analysis approach (termed Rayleigh Quotient Nonlinear Modal Analysis) is adopted to calculate the nonlinear system response, and a Multi-Objective Optimization is solved to fit experimental data of the first mode of the Brake-Reuß Beam. Optimized parameters from the first mode are applied to the second and third bending modes to quantify the predictive ability of the models. Formulations for both tracing full hysteresis loops and recreating hysteresis loops from a single loading curve (Masing assumptions) are considered. Smoothly varying models applied to a five patch representation showed the highest flexibility (for fitting mode 1) and good predictive potential (for modes 2 and 3). For a second formulation, which uses 152 frictional elements to represent the interface, the physically motivated spring in series with a Coulomb slip model (elastic dry friction) has high error for fitting mode 1 and performs near the middle for predicting higher modes. For both interface representation, the best fit models are not the most physical, but rather the ones with the most parameters (as expected); however, the more physical models perform somewhat better for predicting the higher modes.

42 ENGINEERING↗
DOE OSTI2023

Applications of the Dulmage–Mendelsohn decomposition for debugging nonlinear optimization problems

Nonlinear modeling and optimization is a valuable tool for aiding decisions by engineering practitioners, but programming an optimization problem based on a complex electrical, mechanical, or chemical process is a time-consuming and error-prone activity. Therefore, there is a need for model analysis and debugging tools that can detect and diagnose modeling errors. One such tool is the Dulmage–Mendelsohn decomposition, which identifies structurally under- and over-determined subsets in systems of equations and variables by partitioning the bipartite graph of the system. This work provides the necessary background to understand the Dulmage–Mendelsohn decomposition and its application to the analysis of nonlinear optimization problems, demonstrates its use in diagnosing a variety of modeling errors, and introduces software implementations for analyzing nonlinear optimization problems in the Pyomo and JuMP algebraic modeling languages.

42 ENGINEERING↗
DOE OSTI2020

Dependence of emittance on the length of an electron bunch during laser-plasma acceleration in guiding structures

We report a theoretical analysis and numerical simulation of the dynamics of transverse emittance of an electron bunch during its acceleration in wake fields generated by a laser pulse in a weakly nonlinear mode. Analytical expressions are obtained for the main factors affecting the emittance growth during acceleration and the case is considered when the characteristic transverse size of the injected bunch exceeds the matched radius determined by the focusing force at the injection point, the initial emittance and the electron bunch energy, and the resulting value of emittance is much larger than the initial one. The dynamics of the emittance growth during acceleration as a function of the length of the electron bunch is described, and the length of the bunch is found at which there occurs a complete phase mixing of betatron oscillations of electrons and the emittance increases to its maximum value determined by the bunch parameters and the focusing force at the injection point. Analytical expressions are in good agreement with the results of numerical simulation. (paper)

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Accelerating particle-in-cell kinetic plasma simulations via reduced-order modeling of space-charge dynamics using dynamic mode decomposition

We present a data-driven reduced-order modeling of the space-charge dynamics for electromagnetic particle-in-cell (EMPIC) plasma simulations based on dynamic mode decomposition (DMD). The dynamics of the charged particles in kinetic plasma simulations such as EMPIC is manifested through the plasma current density defined along the edges of the spatial mesh. We showcase the efficacy of DMD in modeling the time evolution of current density through a low-dimensional feature space. Not only do such DMD based predictive reduced-order models help accelerate EMPIC simulations, they also have the potential to facilitate investigative analysis and control applications. Here, we demonstrate the proposed DMD-EMPIC scheme for reduced-order modeling of current density and speedup in EMPIC simulations involving electron beam under the influence of magnetic field, virtual cathode oscillations, and backward wave oscillator.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

The Relationship between Precipitation and Precipitable Water in CMIP6 Simulations and Implications for Tropical Climatology and Change

It is well documented that over the tropical oceans, column-integrated precipitable water (pw) and precipitation (P) have a nonlinear relationship. In this study moisture budget analysis is used to examine this P–pw relationship in a normalized precipitable water framework. It is shown that the parameters of the nonlinear relationship depend on the vertical structure of moisture convergence. Specifically, the precipitable water values at which precipitation is balanced independently by evaporation versus by moisture convergence define a critical normalized precipitable water, pw nc . This is a measure of convective inhibition that separates tropical precipitation into two regimes: a local evaporation-controlled regime with widespread drizzle and a precipitable water–controlled regime. Most of the 17 CMIP6 historical simulations examined here have higher pw nc compared to ERA5, and more frequently they operate in the drizzle regime. When compared to observations, they overestimate precipitation over the high-evaporation oceanic regions off the equator, thereby producing a ‘‘double ITCZ’’ feature, while underestimating precipitation over the large tropical landmasses and over the climatologically moist oceanic regions near the equator. The responses to warming under the SSP585 scenario are also examined using the normalized precipitable water framework. It is shown that the critical normalized precipitable water value at which evaporation versus moisture convergence balance precipitation decreases as a result of the competing dynamic and thermodynamic responses to warming, resulting in an increase in drizzle and total precipitation. Statistically significant historical trends corresponding to the thermodynamic and dynamic changes are detected in ERA5 and in lowintensity drizzle precipitation in the PERSIANN precipitation dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Confidentiality-preserving machine learning algorithms for soft-failure detection in optical communication networks

Automated fault management is at the forefront of next-generation optical communication networks. The increase in complexity of modern networks has triggered the need for programmable and software-driven architectures to support the operation of agile and self-managed systems. In these scenarios, the European Telecommunications Standards Institute zero-touch network and service management approach is imperative. The need for machine learning algorithms to process the large volume of telemetry data brings safety concerns as distributed cloud-computing solutions become the preferred approach for deploying reliable communication network automation. This paper’s contribution is twofold. First, we propose a simple yet effective method to guarantee the confidentiality of the telemetry data based on feature scrambling. The method allows the operation of third-party computational services without direct access to the full content of the collected data. Additionally, the effectiveness of four unsupervised machine learning algorithms for soft-failure detection is evaluated when applied to the scrambled telemetry data. The methods are based on factor analysis, principal component analysis, nonlinear principal component analysis, and singular value decomposition. Most dimensionality reduction algorithms have the common property that they can maintain similar levels of fault classification performance while hiding the data structure from unauthorized access. Evaluations of the proposed algorithms demonstrate this capability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Quantum kinetics of anomalous and nonlinear Hall effects in topological semimetals

Highlights: • Linear and photogalvanic anomalous Hall responses are systematically derived. • Extrinsic mechanisms of AHE include Gaussian, diffractive, hybrid skew scattering. • Diagrammatic calculations are matched to semiclassical picture of AHE. • The Pancharatnam phase of multifold fermions determines the skew scattering amplitude. • Photon-induced interband scattering are accompanied by coordinate shifts. We present a systematic microscopic derivation of the semiclassical Boltzmann equation for band structures with the finite Berry curvature based on Keldysh technique of nonequilibrium systems. In the analysis, an AC electrical driving field is kept up to quadratic order, and both cases of small and large frequencies corresponding to intra- and interband transitions are considered. In particular, this formulation is suitable for the study of nonlinear Hall effect and photogalvanic phenomena. The role of impurity scattering is carefully addressed. Specifically, in addition to previously studied side-jump and skew-scattering processes, quantum interference diffractive contributions are now explicitly incorporated within the developed framework. This theory is applied to multifold fermions in topological semimetals, for which the generic formula for the skew scattering rate from the Pancharatnam phase is obtained along with the corresponding anomalous Hall conductivity.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Characteristics and selection of near-fault simulated earthquake ground motions for nonlinear analysis of buildings

Earthquake-induced ground shaking near rupturing faults is highly sensitive to the rupture characteristics, seismic wave propagation patterns and site conditions, and field recordings of near-fault shaking are relatively sparse. These challenges complicate the assessment of the seismic performance of near-fault structures. A common approach to representing near-fault ground motion in engineering analysis is to explicitly consider and select records with strong directivity pulses (pulse records). We use three-dimensional high-resolution physics-based earthquake simulations to test this approach in the context of scenario-based ground motion record selection, and to study the important characteristics of near-fault ground shaking. We highlight the deficiencies associated with classifying near-fault simulated records as “pulse” or “non-pulse,” based on the presence of a single dominating pulse in the velocity time history. We show that this approach is inadequate for characterizing near-fault shaking on soft soils which can be dominated by both forward rupture directivity and basin amplification effects. We conduct ground motion selection experiments for the analysis of near-fault structures with and without explicit classification of the pulse features in the records, and evaluate the bias in the predicted structural demands. We find that the maximum interstory drift demands on building structures imposed by unscaled site-specific simulated ground motion records selected based on relevant spectral shape features are not sensitive to the classification of records as pulse/non-pulse. Therefore, with regard to predicting the maximum interstory drifts in near-fault buildings, we do not find justification for the binary pulse classification of near-fault records.

42 ENGINEERING↗