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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Processing at the Speed of Light

Spatial Light Modulators (SLMs) are critical elements in optical processing systems used for imaging, displaying, data storage, communications, and other applications. By taking advantage of the natural properties of light beams, the devices process information at speeds unattainable by human operators and most machines, with high-resolution results.Boulder Nonlinear Systems, Inc., is one of the world s foremost SLM manufacturers. Applications of this technology are briefly described.

Source record↗
NASA NTRS2004

Shell Buckling Design Criteria Based on Manufacturing Imperfection Signatures

An analysis-based approach .for developing shell-buckling design criteria for laminated-composite cylindrical shells that accurately accounts for the effects of initial geometric imperfections is presented. With this approach, measured initial geometric imperfection data from six graphite-epoxy shells are used to determine a manufacturing-process-specific imperfection signature for these shells. This imperfection signature is then used as input into nonlinear finite-element analyses. The imperfection signature represents a "first-approximation" mean imperfection shape that is suitable for developing preliminary-design data. Comparisons of test data and analytical results obtained by using several different imperfection shapes are presented for selected shells. Overall, the results indicate that the analysis-based approach presented for developing reliable preliminary-design criteria has the potential to provide improved, less conservative buckling-load estimates, and to reduce the weight and cost of developing buckling-resistant shell structures.

Hilburger, Mark W.↗
NASA NTRS2007

The Many Worlds of Transition Research.

The transition from laminar to turbulent flow in a boundary layer is a complex phenomenon that may take different routes, each involving distinct stages governed by different, often not-yet unraveled dynamical principles. There are, surprisingly, questions concerning virtually every stage in the process, beginning with receptivity to external disturbances, the linear stability of spatially developing flows, different possible nonlinear end games, the formation and propagation of turbulent spots and the emergence of fully developed turbulent flow. There seems no doubt that the flow has to be seen as a forced, nonlinear spatio-temporal system, but the system is so complex that to extract simple insights is still very difficult.

Narasimha, R.↗
NASA NTRS2010

Optimized FPGA Implementation of Multi-Rate FIR Filters Through Thread Decomposition

Multirate (decimation/interpolation) filters are among the essential signal processing components in spaceborne instruments where Finite Impulse Response (FIR) filters are often used to minimize nonlinear group delay and finite-precision effects. Cascaded (multi-stage) designs of Multi-Rate FIR (MRFIR) filters are further used for large rate change ratio, in order to lower the required throughput while simultaneously achieving comparable or better performance than single-stage designs. Traditional representation and implementation of MRFIR employ polyphase decomposition of the original filter structure, whose main purpose is to compute only the needed output at the lowest possible sampling rate. In this paper, an alternative representation and implementation technique, called TD-MRFIR (Thread Decomposition MRFIR), is presented. The basic idea is to decompose MRFIR into output computational threads, in contrast to a structural decomposition of the original filter as done in the polyphase decomposition. Each thread represents an instance of the finite convolution required to produce a single output of the MRFIR. The filter is thus viewed as a finite collection of concurrent threads. The technical details of TD-MRFIR will be explained, first showing its applicability to the implementation of downsampling, upsampling, and resampling FIR filters, and then describing a general strategy to optimally allocate the number of filter taps. A particular FPGA design of multi-stage TD-MRFIR for the L-band radar of NASA's SMAP (Soil Moisture Active Passive) instrument is demonstrated; and its implementation results in several targeted FPGA devices are summarized in terms of the functional (bit width, fixed-point error) and performance (time closure, resource usage, and power estimation) parameters.

DSP↗
DOE OSTI2023

Code for multi-shape Gaussian process (GP) fitting with uncertainty quantification (UQ)

This is code associated with the publication “Nonparametric Multi-shape Modeling with Uncertainty Quantification,” authored by Hengrui Luo (Lawrence Berkeley National Laboratory) and Justin Strait (Los Alamos National Laboratory). The code is used to fit multiple-output Gaussian process (GP) models of planar closed curves to collections of ordered point sets, allowing for flexible nonlinear prediction of the underlying curve under dense or sparse point set samplings and with or without noise, as well as tractable uncertainty quantification. To do this, we employ use of a periodic kernel to account for the nonlinear input space of closed curves, and combine with coregionalization models to account for dependence both (i) between curve coordinates, and (ii) between pairs of curves. Functions in the code are capable of fitting these models, as well as performing additional tasks with the fitted curves such as (a) shape registration and alignment, (b) shape averaging, and (c) fitting for curve sub-populations / clusters.

Strait, Justin↗
NASA NTRS1976

Spectral and spatial energetics of the GISS model atmosphere

A calculation of the spectral and spatial energetics of the Goddard Institute for Space Studies (GISS) 9-level model of the global atmosphere is given. Results are presented from a model simulation of January 1973 and compared with previous model studies and an observational analysis for the Northern Hemisphere. The spectral analysis permits detailed study of those physical processes of scale larger than the grid spacing and allows indirect conclusions concerning unresolved subgrid-scale processes. Major conclusions for larger-than-grid-scale processes are that (1) the GISS model with a relatively coarse resolution of 4 deg latitude by 5 deg longitude produces significant nonlinear energy transfers; and (2) baroclinic conversion is large near planetary wave-number 6, but both the GISS model and another orographic model show a less peaked structure than nonorographic models and observations. A linear fit to the kinetic energy spectrum yields a power law of -2.6 in the wavenumber region 8 to 15. For subgrid-scale processes, the turbulence theory result of very small energy flux cascade is obtained.

Tenenbaum, J.↗
NASA NTRS2010

Loop Shaping Control Design for a Supersonic Propulsion System Model Using Quantitative Feedback Theory (QFT) Specifications and Bounds

This paper covers the propulsion system component modeling and controls development of an integrated mixed compression inlet and turbojet engine that will be used for an overall vehicle Aero-Propulso-Servo-Elastic (APSE) model. Using previously created nonlinear component-level propulsion system models, a linear integrated propulsion system model and loop shaping control design have been developed. The design includes both inlet normal shock position control and jet engine rotor speed control for a potential supersonic commercial transport. A preliminary investigation of the impacts of the aero-elastic effects on the incoming flow field to the propulsion system are discussed, however, the focus here is on developing a methodology for the propulsion controls design that prevents unstart in the inlet and minimizes the thrust oscillation experienced by the vehicle. Quantitative Feedback Theory (QFT) specifications and bounds, and aspects of classical loop shaping are used in the control design process. Model uncertainty is incorporated in the design to address possible error in the system identification mapping of the nonlinear component models into the integrated linear model.

Connolly, Joseph W.↗
NASA NTRS1992

An IIR median hybrid filter

A new class of nonlinear filters, the so-called class of multidirectional infinite impulse response median hybrid filters, is presented and analyzed. The input signal is processed twice using a linear shift-invariant infinite impulse response filtering module: once with normal causality and a second time with inverted causality. The final output of the MIMH filter is the median of the two-directional outputs and the original input signal. Thus, the MIMH filter is a concatenation of linear filtering and nonlinear filtering (a median filtering module). Because of this unique scheme, the MIMH filter possesses many desirable properties which are both proven and analyzed (including impulse removal, step preservation, and noise suppression). A comparison to other existing median type filters is also provided.

Bauer, Peter H.↗
DOE OSTI2022

A gradient-based deep neural network model for simulating multiphase flow in porous media

We report simulation of multiphase flow in porous media is crucial for the effective management of subsurface energy and environment-related activities. The numerical simulators used for modeling such processes rely on spatial and temporal discretization of the governing mass and energy balance partial-differential equations (PDEs) into algebraic systems via finite-difference/volume/element methods. These simulators usually require dedicated software development and maintenance, and suffer low efficiency from a runtime and memory standpoint for problems with multi-scale heterogeneity, coupled-physics processes or fluids with complex phase behavior. Therefore, developing cost-effective, data-driven models can become a practical choice, and in this work, we choose deep learning approaches as they can handle high dimensional data and accurately predict state variables with strong nonlinearity. In this paper, we describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We tackle the nonlinearity of flow in porous media induced by rock heterogeneity, fluid properties, and fluid-rock interactions by decomposing the nonlinear PDEs into a dictionary of elementary differential operators. We use a combination of operators to handle rock spatial heterogeneity and fluid flow by advection. Since the augmented differential operators are inherently related to the physics of fluid flow, we treat them as first principles prior knowledge to regularize the GDNN training. We use the example of pressure management at geologic CO 2 storage sites, where CO 2 is injected in saline aquifers and brine is produced, and apply GDNN to construct a predictive model that is trained with physics-based simulation data and emulates the physics process. We demonstrate that GDNN can effectively predict the nonlinear patterns of subsurface responses, including the temporal and spatial evolution of the pressure and saturation plumes. We also successfully extend the GDNN to convolutional neural network (CNN), namely gradient-based CNN (GCNN), and validate its capability to improve the prediction accuracy. GDNN has great potential to tackle challenging problems that are governed by highly nonlinear physics and enable the development of data-driven models with higher fidelity.

42 ENGINEERING↗
NASA NTRS2003

Unified Models of Turbulence and Nonlinear Wave Evolution in the Extended Solar Corona and Solar Wind

The PI (Cranmer) and Co-I (A. van Ballegooijen) made significant progress toward the goal of building a "unified model" of the dominant physical processes responsible for the acceleration of the solar wind. The approach outlined in the original proposal comprised two complementary pieces: (1) to further investigate individual physical processes under realistic coronal and solar wind conditions, and (2) to extract the dominant physical effects from simulations and apply them to a one-dimensional and time-independent model of plasma heating and acceleration. The accomplishments in the report period are thus divided into these two categories: 1a. Focused Study of Kinetic MHD Turbulence. We have developed a model of magnetohydrodynamic (MHD) turbulence in the extended solar corona that contains the effects of collisionless dissipation and anisotropic particle heating. A turbulent cascade is one possible way of generating small-scale fluctuations (easy to dissipate/heat) from a pre-existing population of low-frequency Alfven waves (difficult to dissipate/heat). We modeled the cascade as a combination of advection and diffusion in wavenumber space. The dominant spectral transfer occurs in the direction perpendicular to the background magnetic field. As expected from earlier models, this leads to a highly anisotropic fluctuation spectrum with a rapidly decaying tail in the parallel wavenumber direction. The wave power that decays to high enough frequencies to become ion cyclotron resonant depends on the relative strengths of advection and diffusion in the cascade. For the most realistic values of these parameters, though, there is insufficient power to heat protons and heavy ions. The dominant oblique waves undergo Landau damping, which implies strong parallel electron heating. We thus investigated the nonlinear evolution of the electron velocity distributions (VDFs) into parallel beams and discrete phase-space holes (similar to those seen in the terrestrial magnetosphere) which are an alternate means of heating protons via stochastic interactions similar to particle-particle collisions. 1b. Focused Study of the Multi-Mode Detailed Balance Formalism. The PI began to explore the feasibility of using the "weak turbulence," or detailed-balance theory of Tsytovich, Melrose, and others to encompass the relevant physics of the solar wind. This study did not go far, however, because if the "strong" MHD turbulence discussed above is a dominant player in the wind's acceleration region, this formalism is inherently not applicable to the corona. We will continue to study the various published approaches to the weak turbulence formalism, especially with an eye on ways to parameterize nonlinear wave reflection rates. 2. Building the Unified Model Code Architecture. We have begun developing the computational model of a time-steady open flux tube in the extended corona. The model will be "unified" in the sense that it will include (simultaneously for the first time) as many of the various proposed physical processes as possible, all on equal footing. To retain this generality, we have formulated the problem in two interconnected parts: a completely kinetic model for the particles, using the Monte Carlo approach, and a finite-difference approach for the self-consistent fluctuation spectra. The two codes are run sequentially and iteratively until complete consistency is achieved. The current version of the Monte Carlo code incorporates gravity, the zero-current electric field, magnetic mirroring, and collisions. The fluctuation code incorporates WKJ3 wave action conservation and the cascade/dissipation processes discussed above. The codes are being run for various test problems with known solutions. Planned additions to the codes include prescriptions for nonlinear wave steepening, kinetic velocity-space diffusion, and multi-mode coupling (including reflection and refraction).

Cranmer, Steven R.↗
NASA NTRS2021

Direct Multipoint Observations Capturing the Reformation of a Supercritical Fast Magnetosonic Shock

Using multipoint Magnetospheric Multiscale (MMS) observations in an unusual string-of-pearls configuration, we examine in detail observations of the reformation of a fast magnetosonic shock observed on the upstream edge of a foreshock transient structure upstream of Earth’s bow shock. The four MMS spacecraft were separated by several hundred km, comparable to suprathermal ion gyro-radius scales or several ion inertial lengths. At least half of the shock reformation cycle was observed, with a new shock ramp rising up out of the “foot” region of the original shock ramp. Using the multipoint observations, we convert the observed time-series data into distance along the shock normal in the shock’s rest frame. That conversion allows for a unique study of the relative spatial scales of the shock’s various features, including the shock’s growth rate, and how they evolve during the reformation cycle. Analysis indicates that: the growth rate increases during reformation, electron-scale physics play an important role in the shock reformation, and energy conversion processes also undergo the same cyclical periodicity as reformation. Strong, thin electron-kinetic-scale current sheets and large-amplitude electrostatic and electromagnetic waves are reported. Evidence is also presented of nonlinear wave decay from electromagnetic whistler-mode “lion roars” to electrostatic solitary waves in the downstream plasma regime. Results highlight the critical cross-scale coupling between electron-kinetic-and ion-kinetic-scale processes and details of the nature of nonstationarity, shock-front reformation at collisionless, fast magnetosonic shocks.

D L Turner↗
DOE OSTI2022

Unsupervised discovery of nonlinear plasma physics using differentiable kinetic simulations

Plasma supports collective modes and particle–wave interactions that lead to complex behaviour in, for example, inertial fusion energy applications. While plasma can sometimes be modelled as a charged fluid, a kinetic description is often crucial for studying nonlinear effects in the higher-dimensional momentum–position phase space that describes the full complexity of the plasma dynamics. We create a differentiable solver for the three-dimensional partial-differential equation describing the plasma kinetics and introduce a domain-specific objective function. Using this framework, we perform gradient-based optimization of neural networks that provide forcing function parameters to the differentiable solver given a set of initial conditions. We apply this to an inertial-fusion-relevant configuration and find that the optimization process exploits a novel physical effect.

plasma nonlinear phenomena↗
DOE OSTI2025

Grid-Assisted Direct-Current Magnetron Sputtering of Titanium: Plasma Discharge Diagnostics

Grid-assisted magnetron sputtering is attractive for the deposition of ultrathick Be coatings in inertial confinement fusion (ICF) applications because it offers improved control over the plasma. Here, we investigate the plasma discharge characteristics of grid-assisted direct current magnetron sputtering with Ti as a surrogate sputter target material instead of Be. We map the magnetic field of our magnetron source assembly, study the current-voltage characteristics of both the source and the substrate, and measure the energy distributions of ions bombarding the film surface during growth. The results reveal a slightly asymmetric magnetic field of the magnetron source array and strong dependencies of the plasma discharge characteristics on both the grid and the substrate voltages. Positive grid voltage increases both target and substrate currents, increases the plasma potential, and broadens the energy distributions of ions bombarding the substrate. Grid biasing effects on the plasma discharge characteristics and substrate current can be attributed to corresponding changes to the plasma potential, while substrate biasing effects are more complex and nonlinear. Furthermore, these results demonstrate that grid-assisted deposition offers additional flexibility in controlling the deposition process for tuning properties of ultrathick films for ICF applications.

ICF targets↗
DOE OSTI2025

Interpretable machine learning-guided design of Fe-based soft magnetic alloys

Here, we present a machine learning (ML) guided approach to predict saturation magnetization (𝑀 S ) and coercivity (𝐻 C ) in Fe-rich soft magnetic alloys, particularly Fe-Si-B systems. ML models trained on experimental data reveal that increasing Si and B content reduces 𝑀 S from 1.81 T (DFT ≈ 2.04 T) to ≈1.54 T (DFT ≈ 1.56T) in Fe-Si-B, which is attributed to decreased magnetic density and structural modifications. Experimental validation of ML predicted magnetic saturation on Fe-1Si-1B (2.09 T), Fe-5Si-5B (2.01 T), and Fe-10Si-10B (1.54 T) alloy compositions further supports our findings. These trends are consistent with density functional theory predictions, which link increased electronic disorder and band broadening to lower 𝑀 S values. Experimental validation on selected alloys confirms the predictive accuracy of the ML model, with good agreement across compositions. Beyond predictive accuracy, detailed uncertainty quantification and model interpretability including through feature importance and partial dependence analysis reveal that 𝑀 S is governed by a nonlinear interplay between Fe content and early transition metal ratios, while 𝐻 C is more sensitive to processing conditions such as ribbon thickness and thermal treatment windows. The ML framework was further applied to Fe-Si-B/Cr/Cu/Zr/Nb alloys in a pseudoquaternary compositional space, which shows comparable magnetic properties to NANOMET (Fe 84.8 ⁢Si 0.5 ⁢B 9.4 ⁢Cu 0.8⁢ P 3.5 ⁢C 1 ), FINEMET (Fe 73.5 ⁢Si 13.5 ⁢B 9 Cu 1 ⁢Nb 3 ), NANOPERM (Fe 88 ⁢Zr 7⁢ B 4 ⁢Cu 1 ), and HITPERM (Fe 44 ⁢Co 44 ⁢Zr 7⁢ B 4 ⁢Cu 1 . Our findings demonstrate the potential of the ML framework for accelerated search of high-performance soft magnetic materials.

density functional theory↗
DOE OSTI2022

PETSc TSAdjoint: A Discrete Adjoint ODE Solver for First-Order and Second-Order Sensitivity Analysis

Here, we present a new software system PETSc TSAdjoint for first-order and second order adjoint sensitivity analysis of time-dependent nonlinear differential equations. The derivative calculation in PETSc TSAdjoint is essentially a high-level algorithmic differentiation process. The adjoint models are derived by differentiating the timestepping algorithms and implementing them based on the parallel infrastructure in PETSc. Full differentiation of the library code, including MPI routines, is avoided, and users do not need to derive their own adjoint models for their specific applications. PETSc TSAdjoint can compute the first-order derivative, that is, the gradient of a scalar functional, and the Hessian-vector product, which carries second-order derivative information, while requiring minimal input (a few callbacks) from the users. The adjoint model employs optimal checkpointing schemes in a manner that is transparent to users. Finally, usability, efficiency, and scalability are demonstrated through examples from a variety of applications.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1971

Differential geometric methods in system theory.

Discussion of certain problems in system theory which have been or might be solved using some basic concepts from differential geometry. The problems considered involve differential equations, controllability, optimal control, qualitative behavior, stochastic processes, and bilinear systems. The main goal is to extend the essentials of linear theory to some nonlinear classes of problems.

Brockett, R. W.↗
NASA NTRS1979

Digital processing of ionospheric electron content data

Ionospheric electron content data contain periodicities that are produced by a diversity of sources including hydromagnetic waves, gravity waves, and lunar tides. Often these periodicities are masked by the strong daily variation in the data. Digital filtering can be used to isolate the weaker components. The filtered data can then be further processed to provide estimates of the source properties. In addition, homomorphic filtering may be used to identify nonlinear interactions in the ionosphere.

Bernhardt, P. A.↗