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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 523 records · Page 29

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NASA NTRS1998

Graphite Fiber Textile Preform/Cooper Matrix Composites

The purpose of this research was to produce a finned tube constructed of a highly conductive braided graphite fiber preform infiltrated with a copper matrix. In addition, the tube was to be fabricated with an integral geometry. The preform was integral in the sense that the tube and the fin could be braided to yield one continuous part. This composite component is a candidate for situations with high heat transmitting and radiation requirements. A proof-of-concept finned tube was braided and infiltrated with a copper matrix proving that a viable process was developed to fabricate the desired component. Braiding of high conductivity carbon fibers required much trial-and-error and development of special procedures. There are many tradeoffs between braidability and fiber conductivity. To understand the properties and structure of the braided finned tube, an geometric model of the braid structure was derived. This derivation set the basis for the research because knowing the tow orientations helped decipher the thermal as well as the mechanical and conduction tendencies. Infiltration of the fibers into a copper matrix was a complex procedure, and was performed by TRA, of Salt Lake City, Utah, using a proprietary process. Several batches were fabricated with a final, high quality batch serving as a confirming proof-of-concept.

Filatovs, George J.↗

NASA NTRSDate not supplied

Simulation of Deployable Composite Structures Based On Mechanics of Structure Genome

In this paper, a simulation method for analyzing deployable composite structures is presented. With a proper material model, effective plate/shell properties of the composites is obtained based on Mechanics of Structure Genome (MSG), and then implemented into a user subroutine UGENS for global structure simulation in ABAQUS. Column bending test (CBT) and composite boom and hub structure are studied for demonstration. A viscoelastic material model with direct integration implementation is adopted in this paper. CBT simulation shows good agreement with experiments during relaxation, while errors are observed when comparing residual deformation. This simulation can be potentially used as a calibration tool for material properties. After CBT simulation, a demonstrative model with a lenticular boom and the hub is created in ABAQUS. Complete process of flattening, coiling, stowage, deploying and recovery is simulated with the viscoelastic material model. Residual deformation of the boom is analyzed.

Multi-scale modeling↗

NASA NTRS2015

Composition, Structure and Formation of Low-Density Planets Within 0.5 AU of Their Star

Kepler has discovered that tens of percent of sunlike stars possess one or more planets larger than Earth within 0.5 AU. Masses of dozens of small and mid-sized exoplanets have been measured using radial velocity or transit timing variations. Few transiting planets larger than 1.6 Earth radii are rocky. Rather, the planetary mass-radius diagram for planetary radius between 1.6 and 8 times that of Earth shows a tendency for mass to increase slowly with size. The scatter is larger than can be explained by observational errors and correlations with temperature, implying that the population is heterogeneous. Many Neptune-size planets have very low densities and must have a substantial fraction of their volumes occupied by hydrogen and/or helium. Models of the structure, growth and evolution of planets made of mixtures of rock and light gases will be presented and compared with observations of both individual bodies and of the characteristics of the population as a whole.

radial velocity↗

NASA NTRS1989

Bandwidth-limited robust nonlinear sliding control of pointing and tracking maneuvers

It is shown how multiaxial spacecraft tracking and pointing maneuvers with known control bandwidth and given tracking error bounds can be implemented by variable-structure control in the presence of uncertain vehicle and target dynamics. It is shown how to select a nonlinear sliding surface relating attitude and rate variables, as well as a Lyapunov function in the surface variables that absorbs multiplicative model undertainties, thereby simplifying the computation of control corrections. It is then shown how a boundary layer envelope can be designed, within which the components of the surface error dynamics can be modeled as the outputs of designer-selected decoupled low-pass filters. Closed-loop stability conditions accounting for the coupling between the attitude error dynamics and the surface error dynamics are then obtained.

Dwyer, Thomas A. W., III↗

NASA NTRS2006

Inspection and Verification of Domain Models with PlanWorks and Aver

When developing a domain model, it seems natural to bring the traditional informal tools of inspection and verification, debuggers and automated test suites, to bear upon the problems that will inevitably arise. Debuggers that allow inspection of registers and memory and stepwise execution have been a staple of software development of all sorts from the very beginning. Automated testing has repeatedly proven its considerable worth, to the extent that an entire design philosophy (Test Driven Development) has been developed around the writing of tests. Unfortunately, while not entirely without their uses, the limitations of these tools and the nature of the complexity of models and the underlying planning systems make the diagnosis of certain classes of problems and the verification of their solutions difficult or impossible. Debuggers provide a good local view of executing code, allowing a fine-grained look at algorithms and data. This view is, however, usually only at the level of the current scope in the implementation language, and the data-inspection capabilities of most debuggers usually consist of on-line print statements. More modem graphical debuggers offer a sort of tree view of data structures, but even this is too low-level and is often inappropriate for the kinds of structures created by planning systems. For instance, god or constraint networks are at best awkward when visualized as trees. Any any non-structural link between data structures, as through a lookup table, isn't captured at all. Further, while debuggers have powerful breakpointing facilities that are suitable for finding specific algorithmic errors, they have little use in the diagnosis of modeling errors.

Bedrax-Weiss, Tania↗

DOE OSTI2023

Multifidelity Neural Network Formulations for Prediction of Reactive Molecular Potential Energy Surfaces

Here, this paper focuses on the development of multifidelity modeling approaches using neural network surrogates, where training data arising from multiple model forms and resolutions are integrated to predict high-fidelity response quantities of interest at lower cost. We focus on the context of quantum chemistry and the integration of information from multiple levels of theory. Important foundations include the use of symmetry function-based atomic energy vector constructions as feature vectors for representing structures across families of molecules and single-fidelity neural network training capabilities that learn the relationships needed to map feature vectors to potential energy predictions. These foundations are embedded within several multifidelity topologies that decompose the high-fidelity mapping into model-based components, including sequential formulations that admit a general nonlinear mapping across fidelities and discrepancy-based formulations that presume an additive decomposition. Methodologies are first explored and demonstrated on a pair of simple analytical test problems and then deployed for potential energy prediction for C 5 H 5 using B2PLYP-D3/6-311++G(d,p) for high-fidelity simulation data and Hartree–Fock 6-31G for low-fidelity data. For the common case of limited access to high-fidelity data, our computational results demonstrate that multifidelity neural network potential energy surface constructions achieve roughly an order of magnitude improvement, either in terms of test error reduction for equivalent total simulation cost or reduction in total cost for equivalent error.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1982

Dynamic modeling of structures from measured complex modes

A technique is presented to use a set of identified complex modes together with an analytical mathematical model of a structure under test to compute improved mass, stiffness and damping matrices. A set of identified normal modes, computed from the measured complex modes, is used in the mass orthogonality equation to compute an improved mass matrix. This eliminates possible errors that may result from using approximated complex modes as normal modes. The improved mass matrix, the measured complex modes and the higher analytical modes are then used to compute the improved stiffness and damping matrices. The number of degrees-of-freedom of the improved model is limited to equal the number of elements in the measured modal vectors. A simulated experiment shows considerable improvements, in the system's analytical dynamic model, over the frequency range of the given measured modal information.

Ibrahim, s. R.↗

NASA NTRS1985

Dynamic Modeling of Structures from Measured Complex Modes

Ibrahim, S. R.↗

NASA NTRS1991

Jitter model and signal processing techniques for pulse width modulation optical recording

A jitter model and signal processing techniques are discussed for data recovery in Pulse Width Modulation (PWM) optical recording. In PWM, information is stored through modulating sizes of sequential marks alternating in magnetic polarization or in material structure. Jitter, defined as the deviation from the original mark size in the time domain, will result in error detection if it is excessively large. A new approach is taken in data recovery by first using a high speed counter clock to convert time marks to amplitude marks, and signal processing techniques are used to minimize jitter according to the jitter model. The signal processing techniques include motor speed and intersymbol interference equalization, differential and additive detection, and differential and additive modulation.

Liu, Max M.-K.↗

DOE OSTI2026

Observational Data for Next-Generation Climate Model Evaluation: Requirements, Considerations, and Best Practices

Climate model simulations are an important source of information about our planet’s climate system and also enable informed decision-making under different future scenarios. As a new archive of results from the next generation of climate models is anticipated to become available with the Coupled Model Intercomparison Project phase 7 (CMIP7), the need to develop efficient and robust methods to evaluate models is paramount. Observations are an integral part of model evaluation, providing a means to quantify and understand the degree to which climate models can faithfully reproduce Earth system processes. Such analysis is critical for constraining climate projections, identifying areas of focus for model development, and assisting analysts in deciphering the utility of models for specific applications. Observations of Earth system come from a diversity of sources, span different space–time domains, and are produced by different communities, and each dataset features different data structures and formats, metadata standards, and its own unique uncertainties. Uncertainties in an observational dataset may stem from gaps in temporal and spatial coverage, instrumentation errors, or assumptions in retrieval and processing methods. How then does one ensure that observational data are ready for use and utilized in the most appropriate way for robust, rapid, and routine climate model evaluation? The CMIP7 Model Benchmarking Task Team with input from the broader climate modeling, model evaluation, and observational data communities present a vision and considerations for best practices toward the optimal and appropriate use of observational data to support next-generation climate model evaluation.

Climate models↗

DOE OSTI2022

Fast and Accurate Predictions of Total Energy for Solid Solution Alloys with Graph Convolutional Neural Networks

We use graph convolutional neural networks (GCNNs) to produce fast and accurate predictions of the total energy of solid solution binary alloys. GCNNs allow us to abstract the lattice structure of a solid material as a graph, whereby atoms are modeled as nodes and metallic bonds as edges. This representation naturally incorporates information about the structure of the material, thereby eliminating the need for computationally expensive data pre-processing which would be required with standard neural network (NN) approaches. We train GCNNs on ab-initio density functional theory (DFT) for copper-gold (CuAu) and iron-platinum (FePt) data that has been generated by running the LSMS-3 code, which implements a locally self-consistent multiple scattering method, on OLCF supercomputers Titan and Summit. GCNN outperforms the ab-initio DFT simulation by orders of magnitude in terms of computational time to produce the estimate of the total energy for a given atomic configuration of the lattice structure. We compare the predictive performance of GCNN models against a standard NN such as dense feedforward multi-layer perceptron (MLP) by using the root-mean-squared errors to quantify the predictive quality of the deep learning (DL) models. We find that the attainable accuracy of GCNNs is at least an order of magnitude better than that of the MLP.

Lupo Pasini, Massimiliano↗

DOE OSTI2024

Respecting causality for training physics-informed neural networks

While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this date PINNs have not been successful in simulating dynamical systems whose solution exhibits multi-scale, chaotic or turbulent behavior. In this work we attribute this shortcoming to the inability of existing PINNs formulations to respect the spatio-temporal causal structure that is inherent to the evolution of physical systems. We argue that this is a fundamental limitation and a key source of error that can ultimately steer PINN models to converge towards erroneous solutions. We address this pathology by proposing a simple re-formulation of PINNs loss functions that can explicitly account for physical causality during model training. We demonstrate that this simple modification alone is enough to introduce significant accuracy improvements, as well as a practical quantitative mechanism for assessing the convergence of a PINNs model. Here we provide state-of-the-art numerical results across a series of benchmarks for which existing PINNs formulations fail, including the chaotic Lorenz system, the Kuramoto–Sivashinsky equation in the chaotic regime, and the Navier–Stokes equations. To the best of our knowledge, this is the first time that PINNs have been successful in simulating such systems, introducing new opportunities for their applicability to problems of industrial complexity.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

OC6 Phase Ib: Validation of the CFD predictions of difference-frequency wave excitation on a FOWT semisubmersible

During the previous OC5 11 project, state-of-the-art mid-fidelity engineering tools for floating wind systems were found to consistently underpredict the nonlinear, low-frequency responses of semisubmersible offshore wind platforms, leading to substantial errors in the structural loads because of low-frequency surge and pitch resonance. To examine this underprediction, a coordinated investigation with computational fluid dynamics (CFD) simulations and model-basin experiments was carried out. Both investigations involved a fixed and simplified OC5-DeepCwind semisubmersible in bichromatic waves. The wave excitations - especially the nonlinear, difference-frequency excitation - on the structure from the CFD simulations were compared to the experimental measurements for validation, with uncertainty analyses for both the experimental and the CFD results. Furthermore, the wave excitations on each column of the semisubmersible were measured separately in the experiment, allowing the validation of the CFD results to be done on a per-column basis. Overall, the CFD predictions of the difference-frequency excitations agree with the experimental measurements, suggesting the CFD solutions can be used as a reference for tuning and improving the engineering-level tools and can provide a means to better understand the underprediction at low frequencies.

16 TIDAL AND WAVE POWER↗

NASA NTRS1985

Selecting step sizes in sensitivity analysis by finite differences

This paper deals with methods for obtaining near-optimum step sizes for finite difference approximations to first derivatives with particular application to sensitivity analysis. A technique denoted the finite difference (FD) algorithm, previously described in the literature and applicable to one derivative at a time, is extended to the calculation of several simultaneously. Both the original and extended FD algorithms are applied to sensitivity analysis for a data-fitting problem in which derivatives of the coefficients of an interpolation polynomial are calculated with respect to uncertainties in the data. The methods are also applied to sensitivity analysis of the structural response of a finite-element-modeled swept wing. In a previous study, this sensitivity analysis of the swept wing required a time-consuming trial-and-error effort to obtain a suitable step size, but it proved to be a routine application for the extended FD algorithm herein.

Iott, J.↗

NASA NTRS1987

DYCAST: A finite element program for the crash analysis of structures

DYCAST is a nonlinear structural dynamic finite element computer code developed for crash simulation. The element library contains stringers, beams, membrane skin triangles, plate bending triangles and spring elements. Changing stiffnesses in the structure are accounted for by plasticity and very large deflections. Material nonlinearities are accommodated by one of three options: elastic-perfectly plastic, elastic-linear hardening plastic, or elastic-nonlinear hardening plastic of the Ramberg-Osgood type. Geometric nonlinearities are handled in an updated Lagrangian formulation by reforming the structure into its deformed shape after small time increments while accumulating deformations, strains, and forces. The nonlinearities due to combined loadings are maintained, and stiffness variation due to structural failures are computed. Numerical time integrators available are fixed-step central difference, modified Adams, Newmark-beta, and Wilson-theta. The last three have a variable time step capability, which is controlled internally by a solution convergence error measure. Other features include: multiple time-load history tables to subject the structure to time dependent loading; gravity loading; initial pitch, roll, yaw, and translation of the structural model with respect to the global system; a bandwidth optimizer as a pre-processor; and deformed plots and graphics as post-processors.

Pifko, A. B.↗

NASA NTRS1986

The galaxy distribution and the large-scale structure of the universe

Data related to the large-scale galaxy distribution are discussed. The galaxy counts of Shane-Wirtanen (1967) are analyzed; the effects of residual systematic errors on the galaxy distribution measurements are considered. The analysis reveals that the Shane-Wirtanen data are not applicable to the study of large-scale structure. A model which is capable of measuring galaxy correlation functions on scales greater than about 10 Mpc is evaluated.

Geller, M. J.↗

NASA NTRS1988

Identifiability Of Systems With Modeling Errors

Advances in theory of modeling errors reported. Recent paper on errors in mathematical models of deterministic linear or weakly nonlinear systems. Extends theoretical work described in NPO-16661 and NPO-16785. Presents concrete way of accounting for difference in structure between mathematical model and physical process or system that it represents.

Hadaegh, Yadolah "fred"↗

NASA NTRS1988

On the design of the dissipative LQG-type controllers

The design of dissipative linear-quadratic-Gaussian-type compensators for positive real plants is considered. It is shown that if the noise covariance matrices (used as weighting matrices) satisfy certain conditions, the compensator has a strictly positive real transfer function matrix. The stability of the resulting closed-loop system is guaranteed regardless of modeling errors as long as the plant remains positive real. In view of this property, the controller is expected to be useful for vibration suppression in large, flexible space structures.

Lozano-Leal, R.↗