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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 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Multifractality in human heartbeat dynamics

There is evidence that physiological signals under healthy conditions may have a fractal temporal structure. Here we investigate the possibility that time series generated by certain physiological control systems may be members of a special class of complex processes, termed multifractal, which require a large number of exponents to characterize their scaling properties. We report on evidence for multifractality in a biological dynamical system, the healthy human heartbeat, and show that the multifractal character and nonlinear properties of the healthy heart rate are encoded in the Fourier phases. We uncover a loss of multifractality for a life-threatening condition, congestive heart failure.

Non-NASA Center↗
NASA NTRS2006

Momentum Transfer in a Spinning Fuel Tank Filled with Xenon

Transient spin-up and spin-down flows inside of spacecraft fuel tanks need to be analyzed in order to properly design spacecraft control systems. Knowledge of the characteristics of angular momentum transfer to and from the fuel is used to size the de-spin mechanism that places the spacecraft in a controllable in-orbit state. In previous studies, several analytical models of the spin-up process were developed. However, none have accurately predicted all of the flow dynamics. Several studies have also been conducted using Navier-Stokes based methods. These approaches have been much more successful at simulating the dynamic processes in a cylindrical container, but have not addressed the issue of momentum transfer. In the current study, the spin-up and spin-down of a fuel tank filled with gaseous xenon has been investigated using a three-dimensional unsteady Navier-Stokes code. Primary interests have been concentrated on the spin-up/spin-down time constants and the initial torque imparted on the system. Additional focus was given to the relationship between the dominant flow dynamics and the trends in momentum transfer. Through the simulation of both a cylindrical and a spherical tank, it was revealed that the transfer of angular momentum is nonlinear at early times and tends toward a linear pattern at later times. Further investigation suggests that the nonlinear spin up is controlled by the turbulent transport of momentum, while the linear phase is controlled by a Coriolis driven (Ekman) flow along the outer wall. These results indicate that the spinup and spin-down processes occur more quickly in tanks with curved surfaces than those with defined top, bottom, and side walls. The results also provide insights for the design of spacecraft de-spin mechanisms.

Peugeot, John W.↗
DOE OSTI2020

Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold

This paper introduces a surrogate modeling scheme based on Grassmannian manifold learning to be used for cost-efficient predictions of high-dimensional stochastic systems. The method exploits subspace-structured features of each solution by projecting it onto a Grassmann manifold. This point-wise linear dimensionality reduction harnesses the structural information to assess the similarity between solutions at different points in the input parameter space. The method utilizes a solution clustering approach in order to identify regions of the parameter space over which solutions are sufficiently similarly such that they can be interpolated on the Grassmannian. In this clustering, the reduced-order solutions are partitioned into disjoint clusters on the Grassmann manifold using the eigen-structure of properly defined Grassmannian kernels and, the Karcher mean of each cluster is estimated. Then, the points in each cluster are projected onto the tangent space with origin at the corresponding Karcher mean using the exponential mapping. For each cluster, a Gaussian process regression model is trained that maps the input parameters of the system to the reduced solution points of the corresponding cluster projected onto the tangent space. Using this Gaussian process model, the full-field solution can be efficiently predicted at any new point in the parameter space. In certain cases, the solution clusters will span disjoint regions of the parameter space. In such cases, for each of the solution clusters we utilize a second, density-based spatial clustering to group their corresponding input parameter points in the Euclidean space. The proposed method is applied to two numerical examples. Here, the first is a nonlinear stochastic ordinary differential equation with uncertain initial conditions where the surrogate is used to predict the time history solution. The second involves modeling of plastic deformation in a model amorphous solid using the Shear Transformation Zone theory of plasticity, where the proposed surrogate is used to predict the full strain field of a material specimen under large shear strains.

42 ENGINEERING↗
DOE OSTI2023

Foundations of machine learning for low-temperature plasmas: methods and case studies

Abstract Machine learning (ML) and artificial intelligence have proven to be an invaluable tool in tackling a vast array of scientific, engineering, and societal problems. The main drivers behind the recent proliferation of ML in practically all aspects of science and technology can be attributed to: (a) improved data acquisition and inexpensive data storage; (b) exponential growth in computing power; and (c) availability of open-source software and resources that have made the use of state-of-the-art ML algorithms widely accessible. The impact of ML on the field of low-temperature plasmas (LTPs) could be particularly significant in the emerging applications that involve plasma treatment of complex interfaces in areas ranging from the manufacture of microelectronics and processing of quantum materials, to the LTP-driven electrification of the chemical industry, and to medicine and biotechnology. This is primarily due to the complex and poorly-understood nature of the plasma-surface interactions in these applications that pose unique challenges to the modeling, diagnostics, and predictive control of LTPs. As the use of ML is becoming more prevalent, it is increasingly paramount for the LTP community to be able to critically analyze and assess the concepts and techniques behind data-driven approaches. To this end, the goal of this paper is to provide a tutorial overview of some of the widely-used ML methods that can be useful, amongst others, for discovering and correlating patterns in the data that may be otherwise impractical to decipher by human intuition alone, for learning multivariable nonlinear data-driven prediction models that are capable of describing the complex behavior of plasma interacting with interfaces, and for guiding the design of experiments to explore the parameter space of plasma-assisted processes in a systematic and resource-efficient manner. We illustrate the utility of various supervised, unsupervised and active learning methods using LTP datasets consisting of commonly-available, information-rich measurements (e.g. optical emission spectra, current–voltage characteristics, scanning electron microscope images, infrared surface temperature measurements, Fourier transform infrared spectra). All the ML demonstrations presented in this paper are carried out using open-source software; the datasets and codes are made publicly available. The FAIR guiding principles for scientific data management and stewardship can accelerate the adoption and development of ML in the LTP community.

Physics↗
DOE OSTI2021

Automatically adaptive stabilized finite elements and continuation analysis for compaction banding in geomaterials

Under compressive creep, viscoplastic solids experiencing internal mass transfer processes can accommodate singular cnoidal wave solutions as material instabilities at the stationary wave limit. These instabilities appear when the loading rate is significantly faster than the material's capacity to diffusive internal perturbations, leading to localized failure features (e.g., cracks and compaction bands). These cnoidal waves, generally found in fluids, have strong nonlinearities that produce periodic patterns. Due to the singular nature of the solutions, the applicability of the theory is currently limited. Additionally, practical simulation tools require proper regularization to overcome the challenges that singularity induces. We focus on the numerical treatment of the governing equation using a nonlinear approach building on a recent adaptive stabilized finite element method. This automatic refinement method provides an error estimate that drives mesh adaptivity, a crucial feature for the problem at hand. We compare the performance of this adaptive strategy against analytical and standard finite element solutions. We then investigate the sensitivity of the diffusivity ratio, the parameter controlling the process, and identify multiple possible solutions with several stress peaks. We also show the evolution of the spacing between peaks for all solutions as a function of that parameter.

42 ENGINEERING↗
DOE OSTI2026

Regional surrogates for predictive control of digital twins

Digital twins of complex systems must involve a model that is fast, generalizable, and usable for real-time control. For example, high-fidelity nonlinear multiphysics simulations can capture laser-material interactions, but are too slow for optimization or model predictive control (MPC). Reduced-order models, used to accelerate such computation, frequently fail to generalize to unseen inputs or control states. We show theoretically that this failure is intrinsic, i.e., that a learned model is non-unique outside the sampled subspace when its low-rank structure arises from limited excitation and clustered eigenvalues, rather than from a user-imposed truncation alone. Motivated by this result, we propose a control-ready regional surrogate-construction framework for both autonomous and nonautonomous dynamics; it employs Koopman lifting to represent nonlinearities, while preserving spatial locality. We illustrate our approach by constructing a control-ready surrogate for the digital twin of a thermal component of additive-manufacturing process. Our surrogate, localized in space through a von Neumann stencil, is learned from noisy high-fidelity simulations that emulate thermal-camera images collected during the manufacturing. It is linear in thermo-physically augmented states so that MPC reduces to a convex quadratic program. The surrogate requires no online correction, generalizes to unseen scan paths and power profiles of the laser, and is more than three orders of magnitude faster than a finite-difference solver. Furthermore, when the MPC sequence computed on the digital twin is applied to this solver, closed-loop temperature regulation is recovered, showing that the surrogate preserves control-relevant input-output behavior.

Data-driven model↗
DOE OSTI2020

Data-driven recovery of hidden physics in reduced order modeling of fluid flows

In this article, we introduce a modular hybrid analysis and modeling (HAM) approach to account for hidden physics in reduced order modeling (ROM) of parameterized systems relevant to fluid dynamics. The hybrid ROM framework is based on using first principles to model the known physics in conjunction with utilizing the data-driven machine learning tools to model the remaining residual that is hidden in data. This framework employs proper orthogonal decomposition as a compression tool to construct orthonormal bases and Galerkin projection (GP) as a model to build the dynamical core of the system. Our proposed methodology hence compensates structural or epistemic uncertainties in models and utilizes the observed data snapshots to compute true modal coefficients spanned by these bases. The GP model is then corrected at every time step with a data-driven rectification using a long short-term memory (LSTM) neural network architecture to incorporate hidden physics. A Grassmannian manifold approach is also adopted for interpolating basis functions to unseen parametric conditions. The control parameter governing the system's behavior is thus implicitly considered through true modal coefficients as input features to the LSTM network. The effectiveness of the HAM approach is then discussed through illustrative examples that are generated synthetically to take hidden physics into account. Furthermore, our approach thus provides insights addressing a fundamental limitation of the physics-based models when the governing equations are incomplete to represent underlying physical processes.

42 ENGINEERING↗
DOE OSTI2025

Genetic algorithm-based geometry calibration for dynamic compression x-ray diffraction experiments

An important component of dynamic compression x-ray diffraction (XRD) experiment analysis is geometry calibration: proper data interpretation requires knowledge of the precise detector position and orientation and, if the experiment involves a single-crystal sample, knowledge of the lattice orientation. The determination of these parameters in the arbitrary three-dimensional (3D) scattering geometries often present in dynamic compression facilities is challenging, as the associated optimization problem can be highly nonlinear, nonsmooth, and discontinuous. We present a genetic algorithm-based approach for performing dynamic compression XRD calibrations that overcomes these obstacles. We provide details regarding the image processing, algorithm implementation, and open-source software deployment and demonstrate the capability of the approach to calibrate the detector and crystal parameters in 3D geometries. Notably, we demonstrate the solver’s capacity to find the crystal orientation without a priori rotation constraints.

Brown, Nathan P. [Sandia National Laboratories (SN↗
DOE OSTI2024

Code for the manuscript "Mori-Zwanzig Modal Decomposition"

We would like to create an open source repository in LANL's github on code written in Julia, in which we implement and extend the data-driven Mori-Zwanzig method for extracting large-scale spatio-temporal structures from data, which we call MZMD. This method is an extension of Dynamic Mode Decomposition (DMD) in which Mori-Zwanzig memory kernels are included into the associated companion matrix. In the code we would like to release, we apply MZMD to a flow over a cylinder with Reynolds number 100 rather than the much larger data set used in the associated manuscript. DMD is used extensively in the fluid dynamics community mainly for extracting large scale spatio-temporal structures (patters) from flow data. This is useful for understanding the key mechanisms that generate certain complex dynamical process relevant in engineering design. In MZMD, we improve upon DMD by adding the Mori-Zwanzig memory kernels, and show this improvement is especially important in strongly nonlinear regions of the flow.

Woodward, Michael↗
NASA NTRS1973

On optimal nonlinear estimation. I - Continuous observation.

A generalization of Bucy's (1965) representation theorem is obtained under very weak hypotheses. The generalized theorem is shown to play the same role in the case of general optimal estimation for an arbitrary random process as does the Bucy theorem in the case of optimal filtering for a diffusion process. At least for the models considered, the possibility is pointed out to reduce all sequential estimation problems to the problem of filtering. Hence, filtering theory is seen to represent the core of estimation theory, and is believed to define the direction in which future research should be focused.

Lo, J. T.↗
NASA NTRS1978

The development of structure in the expanding universe

A model for clustering in an expanding universe is developed based on an application of the coagulation equation to the collision and aggregation of bound condensations. While the growth rate of clustering is determined by the rate at which density fluctuations reach the nonlinear regime and therefore depends on the initial fluctuation spectrum, the mass spectrum rapidly approaches a self-similar limiting form. This form is determined by the tidal processes which lead to the merging of condensations, and is not dependent on initial conditions.

Silk, J.↗
NASA NTRS1988

A review of melt and vapor growth techniques for polydiacetylene thin films for nonlinear optical applications

Methods for the growth of polydiacetylene thin films by melt and vapor growth and their subsequent polymerization are summarized. Films with random orientations were obtained when glass or quartz were used as substrates in the vapor growth process. Oriented polydiacetylene films were fabricated by the vapor deposition of diacetylene monomer onto oriented polydiacetylene on a glass substrate and its subsequent polymerization by UV light. A method for the growth of oriented thin films by a melt-shear growth process as well as a method of film growth by seeded recrstallization from the melt between glass plates, that may be applied to the growth of polydiacetylene films, are described. Moreover, a method is presented for the fabrication of single crystal thin films of polyacetylenes by irradiation of the surface of diacetylene single crystals to a depth between 100 and 2000 angstroms.

Penn, B. G.↗
NASA NTRS1992

An evaluation of corrosion protection by two epoxy primers on 2219-T87 and 7075-T73 aluminum

A comparison of the corrosion protection provided by two amine epoxy primers was made using salt fog, alternate immersion, and total immersion as exposure media. The study is the result of a request to use an unqualified low volatile organic carbon (VOC) primer (AKZO 463-6-78) in place of the current primer (AKZO 463-6-3) because environmental regulations have eliminated use of the current primer in many states. Primed, scribed samples of 2219-T87 and 7075-T73 aluminum were exposed to 5-percent NaCl salt fog and 3.5-percent NaCl alternate immersion for a period of 90 days. In addition, electrode samples immersed in 3.5-percent NaCl were tested using electrochemical impedance spectroscopy (EIS). The EG&G model 368 ac impedance measurement system was used to monitor changing properties of AKZO 463-6-78 and AKZO 463-6-3 primed 2219-T87 aluminum for a period of 30 days. The response of the corroding system of a frequency scan can be modeled in terms of an equivalent circuit consisting of resistors and capacitors in a specific arrangement. Each resistor/capacitor combination represents physical processes taking place within the electrolyte, at the electrolyte/primer surface, within the coating, and at the coating/substrate surface. Values for the resistors and capacitors are assigned following a nonlinear least squares fit of the data to the equivalent circuit. Changes in the values of equivalent circuit parameters during the 30-day exposure allow assessment of the time to and mechanism of coating breakdown.

Mendrek, M. J.↗
NASA NTRS1993

Optoelectronic associative memory

An associative optical memory including an input spatial light modulator (SLM) in the form of an edge enhanced liquid crystal light valve (LCLV) and a pair of memory SLM's in the form of liquid crystal televisions (LCTV's) forms a matrix array of an input image which is cross correlated with a matrix array of stored images. The correlation product is detected and nonlinearly amplified to illuminate a replica of the stored image array to select the stored image correlating with the input image. The LCLV is edge enhanced by reducing the bias frequency and voltage and rotating its orientation. The edge enhancement and nonlinearity of the photodetection improves the orthogonality of the stored image. The illumination of the replicate stored image provides a clean stored image, uncontaminated by the image comparison process.

Chao, Tien-Hsin↗
NASA NTRS1994

Method for ambiguity resolution in range-Doppler measurements

A method for resolving range and Doppler target ambiguities when the target has substantial range or has a high relative velocity in which a first signal is generated and a second signal is also generated which is coherent with the first signal but at a slightly different frequency such that there exists a difference in frequency between these two signals of Delta f(sub t). The first and second signals are converted into a dual-frequency pulsed signal, amplified, and the dual-frequency pulsed signal is transmitted towards a target. A reflected dual-frequency signal is received from the target, amplified, and changed to an intermediate dual-frequency signal. The intermediate dual-frequency signal is amplified, with extracting of a shifted difference frequency Delta f(sub r) from the amplified intermediate dual-frequency signal done by a nonlinear detector. The final step is generating two quadrature signals from the difference frequency Delta f(sub t) and the shifted difference frequency Delta f(sub r) and processing the two quadrature signals to determine range and Doppler information of the target.

Heymsfield, Gerald M.↗
NASA NTRS1994

Variations in the modal characteristics of a telescopically deploying beam

The equations of motion for a two-segment deploying telescopic beam are derived through application of Lagrange's equation. The outer tube of the beam is fixed at one end and the inner tube slides freely relative to the fixed segment. The resulting nonlinear, non-autonomous set of equations is linearized and simplified to the standard Euler-Bernoulli partial differential equations for an elastic beam by freezing the deployment process at various stages of deployment, and examining the small amplitude and natural modes of vibration of the resulting configuration. Application of the natural boundary conditions and compatibility of motion relations for the two segments in their common region of overlap leads to a transcendental characteristic equation in the frequency parameter Beta(L). Numerical solution of the equation for the characteristic roots determines the modal frequencies, and the corresponding mode shapes are obtained from the general solution of the Euler-Bernoulli equation tailored to the natural boundary conditions. Sample results of modal frequencies and shapes are presented for various stages of deployment and discussed. It is shown that for all intermediate stages of deployment (between 0 and 100 percent) the spectral distribution is drastically altered by the appearance of regions of very closely spaced modal frequencies. The sources of this modal agglomeration are explored.

Amos, Anthony K.↗
NASA NTRS1996

Free-Surface and Contact Line Motion of Liquid in Microgravity

This project involves fundamental studies of the role of nonlinearity in determining the motion of liquid masses under the principal influences of surface tension, viscosity and inertia. Issues to be explored are relevant to aspects of terrestrial processes, as well as being immediately applicable to fluid management in a low-gravity environment. Specific issues include: (1) the mechanic's of liquid masses in large-amplitude motions, (2) the influence of bounding surfaces on the motion, and (3) the ability of such surfaces to control liquid motion by wetting forces, especially when they are augmented by various surface treatments. Mathematical techniques include asymptotic analysis of the governing equations, for problem simplification, and numerical simulation, using both boundary-element and finite-difference methods. The flow problem is divided into an 'outer' or inviscid potential-flow region and one or more inner, or viscous dominated, regions. Relevant to one inner region, the vicinity of the contact line, we discuss time-dependent simulation of slow droplet motion, on a surface of variable wettability, using the lubrication approximation. The simulation uses a disjoining pressure model and reproduces realistic wetting-dewetting behavior.

Schwartz, Leonard W.↗