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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Finite element analysis using NASTRAN applied to helicopter transmission vibration/noise reduction

A finite element NASTRAN model of the complete forward rotor transmission housing for the Boeing Vertol CH-47 helicopter was developed and applied to reduce transmission vibration/noise at its source. In addition to a description of the model, a technique for vibration/noise prediction and reduction is outlined. Also included are the dynamic response as predicted by NASTRAN, test data, the use of strain energy methods to optimize the housing for minimum vibration/noise, and determination of design modifications which will be manufactured and tested. The techniques presented are not restricted to helicopters but are applicable to any power transmission system. The transmission housing model developed can be used further to evaluate static and dynamic stresses, thermal distortions, deflections and load paths, fail-safety/vulnerability, and composite materials.

Howells, R. W.↗
NASA NTRS1975

Radar Derived Spatial Statistics of Summer Rain: Data Reduction and Analysis - Volume 2

Data reduction and analysis procedures are discussed along with the physical and statistical descriptors used. The statistical modeling techniques are outlined and examples of the derived statistical characterization of rain cells in terms of the several physical descriptors are presented. Recommendations concerning analyses which can be pursued using the data base collected during the experiment are included.

Konrad, T. G.↗
NASA NTRS1993

A consistent model reduction of measured modal parameters for reduced-order active control

The problem of synthesizing reduced-order linear models of vibrating structures for the design of fixed-order dynamic feedback control is investigated. The present technique builds on a recently developed procedure for constructing an objective set of mass and stiffness matrices from measured modal parameters that are akin to the Craig-Bampton synthesized ones obtained from finite element models. The constructed mass and stiffness matrices are determined directly from the identification of experimental data, however, rather than through correlation or reconciliation of a finite element model. A model truncation criterion is then applied to the identified minimum-order mass and stiffness model to satisfy certain observability/controllability requirements for the reduced model. Numerical examples illustrate the effectiveness of the proposed technique for synthesizing reduced-order controllers from system realizations of experimental data. The dynamic performance of the resulting closed-loop models is assessed using the known full-order structural dynamics and compared with existing model reduction techniques.

Alvin, K. F.↗
DOE OSTI2025

Generative learning of densities on manifolds

A generative modeling framework is proposed that combines diffusion models and manifold learning to efficiently sample data densities on manifolds. The approach utilizes Diffusion Maps to uncover possible low-dimensional underlying (latent) spaces in the high-dimensional data (ambient) space. Two approaches for sampling from the latent data density are described. The first is a score-based diffusion model, which is trained to map a standard normal distribution to the latent data distribution using a neural network. The second one involves solving an Itô stochastic differential equation in the latent space. Additional realizations of the data are generated by lifting the samples back to the ambient space using Double Diffusion Maps , a recently introduced technique typically employed in studying dynamical system reduction; here the focus lies in sampling densities rather than system dynamics. The proposed approaches enable sampling high dimensional data densities restricted to low-dimensional, a priori unknown manifolds. The efficacy of the proposed framework is demonstrated through a benchmark problem and a material with multiscale structure.

Double diffusion maps↗
NASA NTRS1977

Orbit determination accuracies using satellite-to-satellite tracking

The uncertainty in relay satellite sate is a significant error source which cannot be ignored in the reduction of satellite-to-satellite tracking data. Based on simulations and real data reductions, it is numerically impractical to use simultaneous unconstrained solutions to determine both relay and user satellite epoch states. A Bayesian or least squares estimation technique with an a priori procedure is presented which permits the adjustment of relay satellite epoch state in the reduction of satellite-to-satellite tracking data without the numerical difficulties introduced by an ill-conditioned normal matrix.

Vonbun, F. O.↗
NASA NTRS1992

Database computing in HEP

The major SSC experiments are expected to produce up to 1 Petabyte of data per year each. Once the primary reconstruction is completed by farms of inexpensive processors, I/O becomes a major factor in further analysis of the data. We believe that the application of database techniques can significantly reduce the I/O performed in these analyses. We present examples of such I/O reductions in prototypes based on relational and object-oriented databases of CDF data samples.

Day, C. T.↗
NASA NTRS1973

Application of phase-change technique to thin sections with heating on both surfaces

A numerical digital computer program was developed to calculate the heat-transfer coefficients for both sides of a one-dimensional finite slab subject to the boundary conditions ascribed to the phase-change coating heat-transfer technique. In a typical tunnel test situation where a thin wing was exposed to heating on both sides, the data reduction procedures for a semi-infinite slab gave heat-transfer coefficients as much as 375 percent too high on the side with the lowest heating. The results from the one-dimensional finite-slab procedure are presented in the form of correction factors to the solution for a semi-infinite slab in terms of parameters normally used with the phase-change heat-transfer technique. These correlations are not restricted to slab thickness or thermophysical properties and are easily used to obtain accurate data on thin model sections.

Hunt, J. L.↗
NASA NTRS1974

A fluctuating surface pressure test technique utilizing Mach number sweeps at transonic speeds

A multichannel on-line RMS data acquisition and reduction system has been developed using commercial RMS computing modules and a programmable calculator. Details of this system, which has the capability of acquiring 96 channels of RMS data and computing and printing desired parameters in near real-time, are presented. In addition, raw data can be recorded at a much higher rate for computation and printing later. Results are presented showing the benefits of this system in 'sweep' tests where one parameter such as Mach number or angle of attack is slowly varied with time.

Hanly, R. D.↗
DOE OSTI2021

A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder

Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, e.g., any advection-dominated flow phenomena such as in traffic flow, atmospheric flows, and air flow over vehicles, a low-dimensional linear subspace poorly approximates the solution. To address cases such as these, we have developed a fast and accurate physics-informed neural network ROM, namely nonlinear manifold ROM (NM-ROM), which can better approximate high-fidelity model solutions with a smaller latent space dimension than the LS-ROMs. Our method takes advantage of the existing numerical methods that are used to solve the corresponding full order models. The efficiency is achieved by developing a hyper-reduction technique in the context of the NM-ROM. Numerical results show that neural networks can learn a more efficient latent space representation on advection-dominated data from 1D and 2D Burgers' equations. A speedup of up to 2.6 for 1D Burgers' and a speedup of 11.7 for 2D Burgers' equations are achieved with an appropriate treatment of the nonlinear terms through a hyper-reduction technique. Lastly, a posteriori error bounds for the NM-ROMs are derived that take account of the hyper-reduced operators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Nonlinear manifold reduced order model

Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, e.g., any advection-dominated flow phenomena such as in traffic flow, atmospheric flows, and air flow over vehicles, a lowdimensional linear subspace poorly approximates the solution. To address cases such as these, we have developed a fast and accurate physics-informed neural network ROM, namely nonlinear manifold ROM (NM-ROM), which can better approximate high-fidelity model solutions with a smaller latent space dimension than the LS-ROMs. Our software takes advantage of the existing numerical methods that are used to solve the corresponding full order models. The efficiency is achieved by developing a hyper-reduction technique in the context of the NM-ROM. Numerical results show that neural networks can learn a more efficient latent space representation on advection-dominated data from 1D and 2D Burgers' equations. A speedup of up to 2.6 for 1D Burgers' and a speedup of 11.7 for 2D Burgers' equations are achieved with an appropriate treatment of the nonlinear terms through a hyper-reduction technique.

Choi, Youngsoo↗
DOE OSTI2020

Nonlinear manifold-based reduced order model

Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations, in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, e.g., any advection-dominated flow phenomena such as in traffic flow, atmospheric flows, and air flow over vehicles, a low-dimensional linear subspace poorly approximates the solution. To address cases such as these, we have developed a fast and accurate physicsinformed neural network ROM, namely nonlinear manifold ROM (NM-ROM), which can better approximate high-fidelity model solutions with a smaller latent space dimension than the LSROMs. Our method takes advantage of the existing numerical methods that are used to solve the corresponding full order models. The efficiency is achieved by developing a hyper-reduction technique in the context of the NM-ROM. Numerical results show that neural networks can learn a more efficient latent space representation on advection-dominated data from 1D and 2D Burgers’ equations. A speedup of up to 2.6 for 1D Burgers’ and a speedup of 11.7 for 2D Burgers’ equations are achieved with an appropriate treatment of the nonlinear terms through a hyper-reduction technique. Finally, a posteriori error bounds for the NM-ROMs are derived that take account of the hyper-reduced operators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Investigating Quantum Materials with Half-Polarized Diffraction and magnetic PDF analysis at the HB-2A Neutron Powder Diffractometer

Local magnetic ordering and anisotropy is often central to the emergent behavior and subsequent functional properties in quantum materials and beyond. Neutron powder diffraction provides a straightforward yet extremely powerful technique for quantitative measurements of microscopic magnetic properties. The HB-2A powder diffractometer located at the High Flux Isotope Reactor in ORNL is traditionally utilized for long-range magnetic structure determination. Recently these capabilities have been extended to include methods aimed at accessing local magnetism: Half- polarized neutron powder diffraction (pNPD) and magnetic pair distribution function (mPDF) analysis. These two distinct techniques are possible on HB-2A due to the versatility of the instrument’s reciprocal space coverage, resolution and novel ultra-low temperature multi-sample changers that operate down to dilution refrigerator temperatures. This provides unique capabilities not found on any powder diffraction instrument and is particularly well suited to investigations of magnetic quantum materials. The development and implementation of these techniques will be discussed with a series of science case examples ranging from geometric frustrated magnets to magnetic metal-organic frameworks. Data reduction and analysis tools will be presented that enable the extraction of the local site susceptibility tensor and local spin-spin correlations in real space. Finally, potential combinations of these techniques in the form of half-polarized magnetic pair distribution function (pmPDF) analysis will be considered. Looking forward, HB-2A is undergoing a detector upgrade that will be in the user program by 2026. This will offer an order of magnitude increase in count rates to further aid the development of these often low signal measurements and provide new scientific capabilities.

Neutron Scattering↗
NASA NTRS1976

Filament wound rocket motor chambers

The design, analysis, fabrication and testing of a Kevlar-49/HBRF-55A filament wound chamber is reported. The chamber was fabricated and successfully tested to 80% of the design burst pressure. Results of the data reduction and analysis from the hydrotest indicate that the chamber design and fabrication techniques used for the chamber were adequate and the chamber should perform adequately in a static test.

Source record↗
NASA NTRS1982

Development of test methodology for dynamic mechanical analysis instrumentation

Dynamic mechanical analysis instrumentation was used for the development of specific test methodology in the determination of engineering parameters of selected materials, esp. plastics and elastomers, over a broad range of temperature with selected environment. The methodology for routine procedures was established with specific attention given to sample geometry, sample size, and mounting techniques. The basic software of the duPont 1090 thermal analyzer was used for data reduction which simplify the theoretical interpretation. Clamps were developed which allowed 'relative' damping during the cure cycle to be measured for the fiber-glass supported resin. The correlation of fracture energy 'toughness' (or impact strength) with the low temperature (glassy) relaxation responses for a 'rubber-modified' epoxy system was negative in result because the low-temperature dispersion mode (-80 C) of the modifier coincided with that of the epoxy matrix, making quantitative comparison unrealistic.

Allen, V. R.↗
NASA NTRS1986

Data inversion algorithm development for the hologen occultation experiment

The successful retrieval of atmospheric parameters from radiometric measurement requires not only the ability to do ideal radiometric calculations, but also a detailed understanding of instrument characteristics. Therefore a considerable amount of time was spent in instrument characterization in the form of test data analysis and mathematical formulation. Analyses of solar-to-reference interference (electrical cross-talk), detector nonuniformity, instrument balance error, electronic filter time-constants and noise character were conducted. A second area of effort was the development of techniques for the ideal radiometric calculations required for the Halogen Occultation Experiment (HALOE) data reduction. The computer code for these calculations must be extremely complex and fast. A scheme for meeting these requirements was defined and the algorithms needed form implementation are currently under development. A third area of work included consulting on the implementation of the Emissivity Growth Approximation (EGA) method of absorption calculation into a HALOE broadband radiometer channel retrieval algorithm.

Gordley, Larry L.↗
NASA NTRS1990

A new accelerometer recording system for shuttle use

Microgravity investigators are interested in enhancing the capabilities and improving the information return from accelerometers used in microgravity research. In addition to improving the accelerometer sensor, efforts should be directed towards using recent advances in microprocessor technology and system design techniques to improve sensor calibration and temperature compensation, online data display and analysis, and data reduction and information storage. Results from the above areas of investigation should be combined in an integrated design for a spaceflight microgravity accelerometer package.

Lichtenberg, Byron↗
NASA NTRS2016

Summary of Full-Scale Blade Displacement Measurements of the UH- 60A Airloads Rotor

Blade displacement measurements using multi-camera photogrammetry techniques were acquired for a full-scale UH-60A rotor, tested in the National Full-Scale Aerodynamic Complex 40-Foot by 80-Foot Wind Tunnel. The measurements, acquired over the full rotor azimuth, encompass a range of test conditions that include advance ratios from 0.15 to 1.0, thrust coefficient to rotor solidity ratios from 0.01 to 0.13, and rotor shaft angles from -10.0 to 8.0 degrees. The objective was to measure the blade displacements and deformations of the four rotor blades and provide a benchmark blade displacement database to be utilized in the development and validation of rotorcraft prediction techniques. An overview of the blade displacement measurement methodology, system development, and data analysis techniques are presented. Sample results based on the final set of camera calibrations, data reduction procedures and estimated corrections that account for registration errors due to blade elasticity are shown. Differences in blade root pitch, flap and lag between the previously reported results and the current results are small. However, even small changes in estimated root flap and pitch can lead to significant differences in the blade elasticity values.

Displacement↗
DOE OSTI2025

FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration

Federated learning (FL) is a distributed machine learning approach that enables multiple local clients and a central server to collaboratively train a model while keeping the data on their own devices. First-order methods, particularly those incorporating variance reduction techniques, are the most widely used FL algorithms due to their simple implementation and stable performance. However, these methods tend to be slow and require a large number of communication rounds to reach the global minimizer. We propose FedOSAA, a novel approach that preserves the simplicity of first-order methods while achieving the rapid convergence typically associated with second-order methods. Our approach applies one Anderson acceleration (AA) step following classical local updates based on first-order methods with variance reduction, such as FedSVRG and SCAFFOLD, during local training. This AA step is able to leverage curvature information from the history points and gives a new update that approximates the Newton-GMRES direction, thereby significantly improving the convergence. We establish a local linear convergence rate to the global minimizer of FedOSAA for smooth and strongly convex loss functions. Numerical comparisons show that FedOSAA substantially improves the communication and computation efficiency of the original first-order methods, achieving performance comparable to second-order methods like GIANT.

Feng, Xue [University of California, Davis]↗