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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Dynamic Stiffness and Damping Characteristics of a High-Temperature Air Foil Journal Bearing

Using a high-temperature optically based displacement measurement system, a foil air bearing's stiffness and damping characteristics were experimentally determined. Results were obtained over a range of modified Sommerfeld Number from 1.5E6 to 1.5E7, and at temperatures from 25 to 538 C. An Experimental procedure was developed comparing the error in two curve fitting functions to reveal different modes of physical behavior throughout the operating domain. The maximum change in dimensionless stiffness was 3.0E-2 to 6.5E-2 over the Sommerfeld Number range tested. Stiffness decreased with temperature by as much as a factor of two from 25 to 538 C. Dimensionless damping was a stronger function of Sommerfeld Number ranging from 20 to 300. The temperature effect on damping being more qualitative, showed the damping mechanism shifted from viscous type damping to frictional type as temperature increased.

Howard, Samuel A.↗
NASA NTRS2002

Independent Confirmation of the Pioneer 10 Anomalous Acceleration

I perform an independent analysis of radio Doppler tracking data from the Pioneer 10 spacecraft for the time period 1987-1994. All of the tracking data were taken from public archive sources, and the analysis tools were developed independently by myself. I confirm that an apparent anomalous acceleration is acting on the Pioneer 10 spacecraft, which is not accounted for by present physical models of spacecraft navigation. My best fit value for the acceleration, including corrections for systematic biases and uncertainties, is (8.60 plus or minus 1.34) x 10(exp -8) centimeters per second, directed towards the Sun. This value compares favorably to previous results. I examine the robustness of my result to various perturbations of the analysis method, and find agreement to within plus or minus 5%. The anomalous acceleration is reasonably constant with time, with a characteristic variation time scale of greater than 70 yr. Such a variation timescale is still too short to rule out on-board thermal radiation effects, based on this particular Pioneer 10 data set.

Markwardt, Craig B.↗
NASA NTRS2007

Implementation of the NASA AG-Bed Rest Pilot

To examine the efficacy of artificial gravity (AG) as a countermeasure to spaceflight deconditioning, intermittent AG produced by a horizontal short-radius centrifuge (SRC) was utilized on human test subjects deconditioned by bed rest. This poster will present the subject screening, study design, logistics, and implementation of the 41 day pilot study conducted at the University of Texas Medical Branch, Galveston, TX bed rest facility. An extensive screening process was employed to exclude subjects that were dissimilar to the U.S. astronaut population. Candidates underwent a modified U.S. Air Force Class III physical and tests of bone density, cardiovascular fitness, vestibular system function, psychological fitness and centrifuge tolerance. 15 subjects completed the study; 7 control and 8 AG treatment. All provided written consent to volunteer after the nature of the study and its hazards were clearly explained to them. Standard conditions were strictly regulated; Ta = 72 +/- 2 F, humidity = 70 +/- 5%, light/dark cycle 16h:8h. All fluid intake (minimum 28.5 ml/kg body weight/day) and urine output was monitored. Caloric intake was adjusted as necessary to maintain body weight. Carbohydrate, fat and protein were provided in a ratio of 55:30:15. Phosphorus intake was 1400 mg/d, sodium intake was 2 mmol/kg/d, potassium intake was 1.3 mmol/kg/d, and dietary calcium intake was 1000 mg/d. A physician examined each subject daily. During the first 11 days of the study protocol, subjects were ambulatory, but confined to the facility. Subjects participated in multiple baseline tests of bone, muscle, cardiovascular, sensory-motor, immunological, and psychological function. On the 12th day, subjects entered the bed rest phase of the study, during which they were confined to strict 6? head down tilt bed rest for 21 days. Beginning 24 hrs into this period, treatment subjects received 1 hour daily exposures to artificial gravity which was produced by spinning the subjects on a 3.0 m radius SRC. They were oriented radially in the supine position so that the centrifugal force was aligned with their long body axis, and while spinning, they #stood# on a force plate, supporting the centrifugal loading (2.5 g at the feet, 1.0 g at the heart). The subject station allowed free translation over approximately 10 cm to ensure full loading of the lower extremities and to allow for anti-orthostatic muscle contractions. Control subjects were positioned on the centrifuge but did not spin. Following the bed rest phase, subjects were allowed to ambulate again, but remained within the facility for an additional 9 days and participated in multiple follow-up tests of physiological function.

Warren, L. E.↗
NASA NTRSDate not supplied

How Can We Harness the Power of Machine Learning With TEMPO Data?

There are many potential applications of machine learning for TEMPO data that include - Improve retrievals by reducing the effect of random instrument noise - Expand coverage by producing data in moderately cloudy conditions (see also Fasnacht et al. poster) - Help diagnose impacts of instrumental artifacts - Produce value-added products quickly by training on existing products from other sensors (land and ocean) - Speed up processing by training on products produced with full-physics algorithms (e.g., NO 2 slant column fitting may take ~1 hour/orbit but with a neural net it may take only minutes)

NO2↗
DOE OSTI2021

Quantitative analysis of Raman spectral parameters for carbon fibers: practical considerations and connection to mechanical properties

Although the literature on the Raman spectra of carbon fibers is vast, no consistent, robust predictive relationship between mechanical properties of carbon fibers and spectral parameters exists. This shortcoming is due to the use of numerous fitting functions to evaluate Raman spectra of carbon fibers and the inconsistencies in establishing the best fitting models in a statistically robust fashion. To address this gap, we present a comprehensive work on the Raman spectra of carbon fibers that combines a vast library of experimental data with a robust numerical analysis and a statistical evaluation of a wide range of suggested fitting models. This manuscript begins with a brief review of the commonly applied fitting models. Then, the Raman spectra of 32 commercially available polyacrylonitrile-based carbon fibers collected at excitation wavelengths 532, 633, and 785 nm are presented and the best fit for all fibers is evaluated based on several statistical criteria in conjunction with numerical calculations and physical arguments. The results suggest that high-performance fibers must be fit with at least five peaks, whereas high-tensile modulus fibers are best fit with at least six distinct peaks. Finally, we employ simultaneous fitting of the Raman spectra of specific fibers and wavelengths and demonstrate that strong correlations exist between mechanical properties and the D1 peak position and shape across the range of evaluated mechanical properties. Finally, we suggest straightforward improvements in fitting analysis procedures that can be implemented to increase coherency in the understanding of the underlying carbon fiber microstructure intuited from Raman spectroscopy.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Charged particle track reconstruction with SπRIT Time Projection Chamber

In this work, we present a software framework, SπRITROOT, which is capable of track reconstruction and analysis of heavy-ion collision events recorded with the SRIT time projection chamber. The track-fitting toolkit GENFIT and the vertex reconstruction toolkit RAVE are applied to a box-type detector system. A pattern recognition algorithm which performs helix track finding and handles overlapping pulses is described. The performance of the software is investigated using experimental data obtained at the Radioactive Isotope Beam Facility (RIBF) at RIKEN. This work focuses on data from 132 Sn + 124 Sn collision events with beam energy of 270 AMeV. Particle identification is established using < dE/dx >and magnetic rigidity, with pions, hydrogen isotopes, and helium isotopes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2019

Retrieval of Snow Freeboard of Antarctic Sea Ice Using Waveform Fitting of CryoSat-2 Returns

In this paper we develop a CryoSat-2 algorithm to retrieve the surface elevation of the air–snow interface over Antarctic sea ice. This algorithm utilizes a two-layer physical model that accounts for scattering from a snow layer atop sea ice as well as scattering from below the snow surface. The model produces waveforms that are fit to CryoSat-2 level 1B data through a bounded trust region least-squares fitting process. These fit waveforms are then used to track the air–snow interface and retrieve the surface elevation at each point along the CryoSat-2 ground track, from which the snow freeboard is computed. To validate this algorithm, we compare retrieved surface elevation measurements and snow surface radar return power levels with those from Operation IceBridge, which flew along a contemporaneous CryoSat-2 orbit in October 2011 and November 2012. Average elevation differences (standard deviations) along the flight lines (IceBridge Airborne Topographic Mapper, ATM – CryoSat-2) are found to be 0.016 cm (29.24 cm) in 2011 and 2.58 cm (26.65 cm) in 2012. The spatial distribution of monthly average pan-Antarctic snow freeboard found using this method is similar to what was observed from NASA's Ice, Cloud, and land Elevation Satellite (ICESat), where the difference (standard deviation) between October 2011–2017 CryoSat-2 mean snow freeboard and spring 2003–2007 mean freeboard from ICESat is 1.92 cm (9.23 cm). While our results suggest that this physical model and waveform fitting method can be used to retrieve snow freeboard from CryoSat-2, allowing for the potential to join laser and radar altimetry data records in the Antarctic, larger (∼30 cm) regional differences from ICESat and along-track differences from ATM do exist, suggesting the need for future improvements to the method. Snow–ice interface elevation retrieval is also explored as a potential to obtain snow depth measurements. However, it is found that this retrieval method often tracks a strong scattering layer within the snow layer instead of the actual snow–ice interface, leading to an overestimation of ice freeboard and an underestimation of snow depth in much of the Southern Ocean but with promising results in areas such as the East Antarctic sector.

Fons, Steven W.↗
DOE OSTI2021

Physics-Informed Neural Networks for Heat Transfer Problems

Abstract Physics-informed neural networks (PINNs) have gained popularity across different engineering fields due to their effectiveness in solving realistic problems with noisy data and often partially missing physics. In PINNs, automatic differentiation is leveraged to evaluate differential operators without discretization errors, and a multitask learning problem is defined in order to simultaneously fit observed data while respecting the underlying governing laws of physics. Here, we present applications of PINNs to various prototype heat transfer problems, targeting in particular realistic conditions not readily tackled with traditional computational methods. To this end, we first consider forced and mixed convection with unknown thermal boundary conditions on the heated surfaces and aim to obtain the temperature and velocity fields everywhere in the domain, including the boundaries, given some sparse temperature measurements. We also consider the prototype Stefan problem for two-phase flow, aiming to infer the moving interface, the velocity and temperature fields everywhere as well as the different conductivities of a solid and a liquid phase, given a few temperature measurements inside the domain. Finally, we present some realistic industrial applications related to power electronics to highlight the practicality of PINNs as well as the effective use of neural networks in solving general heat transfer problems of industrial complexity. Taken together, the results presented herein demonstrate that PINNs not only can solve ill-posed problems, which are beyond the reach of traditional computational methods, but they can also bridge the gap between computational and experimental heat transfer.

Engineering↗
DOE OSTI2024

Advanced Method Optimization for Sampling and Analysis Instrumentation

This work presents a generalized approach for analytical method optimization that branches the gap between techniques historically employed and accurate modern optimization techniques suitable for various applications. The novelty of the described strategy is the utilization of multivariate, multiobjective optimization with Karush-Kuhn-Tucker conditions to bound the optimization space to solutions within the physical limitations of instrumentation. Briefly, the basic steps outlined in this paper are to (1) determine the objective(s) that should be maximized or minimized based on the goals of the analytical application, (2) conduct a screening experiment, (3) perform ANOVA to determine the parameters which have a statistically significant effect on the objective, (4) conduct an experiment (e.g., Box-Behnken design) to collect data for fitting the objective equation, and (5) determine the physical constraints of the parameters and solve the Lagrangian to determine the optimal method parameters. A broad approach to optimization target selection allows for robust method tuning to develop improved data sets amenable for chemometrics and machine learning algorithm development. Gas chromatography-mass spectrometry was selected as a use case due to its broad use across scientific fields and time-consuming method development involving numerous parameters. In conclusion, this strategy can reduce the cost of research, improve data quality, and enable the rapid development of new analytical technique.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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↗
NASA NTRS2014

Evaluating Observation Influence on Regional Water Budgets in Reanalyses

The assimilation of observations in reanalyses incurs the potential for the physical terms of budgets to be balanced by a term relating the fit of the observations relative to a forecast first guess analysis. This may indicate a limitation in the physical processes of the background model, or perhaps inconsistencies in the observing system and its assimilation. In the MERRA reanalysis, an area of long term moisture flux divergence over land has been identified over the Central United States. Here, we evaluate the water vapor budget in this region, taking advantage of two unique features of the MERRA diagnostic output; 1) a closed water budget that includes the analysis increment and 2) a gridded diagnostic output data set of the assimilated observations and their innovations (e.g. forecast departures). In the Central United States, an anomaly occurs where the analysis adds water to the region, while precipitation decreases and moisture flux divergence increases. This is related more to a change in the observing system than to a deficiency in the model physical processes. MERRAs Gridded Innovations and Observations (GIO) data narrow the observations that influence this feature to the ATOVS and Aqua satellites during the 06Z and 18Z analysis cycles. Observing system experiments further narrow the instruments that affect the anomalous feature to AMSUA (mainly window channels) and AIRS. This effort also shows the complexities of the observing system, and the reactions of the regional water budgets in reanalyses to the assimilated observations.

Atmospheric Reanalysis↗
DOE OSTI2024

Decoding the B → K ν ν excess at Belle II: Kinematics, operators, and masses

An excess in the branching fraction for B + → K + ν ν recently measured at Belle II may be a hint of new physics. We perform thorough likelihood analyses for different new physics scenarios such as B → K X with a new invisible particle X , or B → K χ χ through a scalar, vector, or tensor current with χ being a new invisible particle or a neutrino. We find that vector-current three-body decay with m X ≃ 0.6 GeV—which may be dark matter—is most favored, while two-body decay with m X ≃ 2 GeV is also competitive. The best-fit branching fractions for the scalar and tensor cases are a few times larger than for the two-body and vector cases. Past measurements provide further discrimination, although the best-fit parameters stay similar. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Differentiable vertex fitting for jet flavor tagging

This work explores the use of differentiable programming to integrate domain knowledge, in the form of domain specific software, into neural networks to develop scientific machine learning systems. We propose a differentiable vertex fitting algorithm that estimates the crossing point of multiple curves. In the high energy physics setting, these curves are defined by particle equations of motion and the crossing point represents the origin of particle production. This differentiable vertex fitting algorithm can be seamlessly integrated into neural networks, and we show its utility and efficacy in the high energy physics application of the classification of jets, i.e., collimated streams of particles in particle detectors whose originating parent particle we aim to classify. We demonstrate how differentiable vertex fitting can be integrated into larger transformer-based models for jet flavor tagging and show improvements in heavy flavor jet classification when compared to baseline models. Published by the American Physical Society 2024

Smith, Rachel E. C. (ORCID:0000000335851262)↗
NASA NTRS2012

The Fast Evolution of SN 2010bh Associated with XRF 100316D

The first observational evidence of a connection between supernovae (SNe) and gamma-ray bursts (GRBs) was found about a decade ago. Since then, only half a dozen spectroscopically confirmed associations have been discovered and XRF 1003160 associated with the type-Ic SN 20lObh is among the latest. Aims. We constrain the progenitor radius, the host-galaxy extinction, and the physical parameters of the explosion of XRF l00316D/SN 20lObh at z 0.059. We study the SN brightness and colours in the context of GRB-SNe. Methods. We began observations with the Gamma-Ray burst Optical and Near-infrared Detector (GROND) 12 hours after the GRB trigger and continued until 80 days after the burst. GROND provided excellent photometric data in six filter bands covering a wavelength range from approximately 350 to 1800 nm, significantly expanding the pre-existing data set for this event. Combining GROND and Swift data, the early broad-band spectral energy distribution (SED) is modelled with a blackbody and afterglow component attenuated by dust and gas absorption. The temperature and radius evolution of the thermal component are analysed and combined with earlier measurements available from the literature. Templates of SN 1998bw are fitted to the SN itself to directly compare the lightcurve properties. Finally, a two-component parametrized model is fitted to the quasi-bolometric light curve. which delivers physical parameters of the explosion. Results. The best-fit models to the broad-band SEDs imply moderate reddening along the line of sight through the host galaxy (A(sub v.host = 1.2 +/- 0.1 mag). Furthermore, the parameters of the blackbody component reveal a cooling envelope at an apparent initial radius of 7 x 10(exp 11) cm, which is compatible with a dense wind surrounding a Wolf-Rayet star. A multicolor comparison shows that SN 2010bh is 60 - 70% as bright as SN 1998bw. Reaching maximum brightness at 8 - 9 days after the burst in the blue bands, SN 20lObh proves to be the most rapidly evolving GRB-SNe to date. Modelling of the quasi-bolometric light curve yields M(sub Ni) = 0.21 +/- 0.03 solar M and M(sub ej) = 2.6 +/- 0.2 solar M, typical of values within the GRB-SN population. The kinetic energy is E(sub k) = (2.4 +/- 0.7) x 10(exp 52) erg, which is making this SN the second most energetic GRB-SN after SN 1998bw. Conclusions. This supernova has one of the earliest peaks ever recorded and thereafter fades more rapidly than other GRB-SNe, hypernovae, or typical type-Ic SNe. This could be explained by a thin envelope expanding at very high velocities, which is therefore unable to retain the gamma-rays that would prolong the duration of the SN event.

Olivares E., F.↗
NASA NTRS2000

On The Computational Capabilities of Physical Systems: Relationship With Conventional Computer Science - Part 2

In the first of this pair of papers, it was proven that there cannot be a physical computer to which one can properly pose any and all computational tasks concerning the physical universe. It was then further proven that no physical computer C can correctly carry out all computational tasks that can be posed to C. As a particular example, this result means that no physical computer that can, for any physical system external to that computer, take the specification of that external system's state as input and then correctly predict its future state before that future state actually occurs; one cannot build a physical computer that can be assured of correctly "processing information faster than the universe does". These results do not rely on systems that are infinite, and/or non-classical, and/or obey chaotic dynamics. They also hold even if one uses an infinitely fast, infinitely dense computer, with computational powers greater than that of a Turing Machine. This generality is a direct consequence of the fact that a novel definition of computation - "physical computation" - is needed to address the issues considered in these papers, which concern real physical computers. While this novel definition does not fit into the traditional Chomsky hierarchy, the mathematical structure and impossibility results associated with it have parallels in the mathematics of the Chomsky hierarchy. This second paper of the pair presents a preliminary exploration of some of this mathematical structure. Analogues of Chomskian results concerning universal Turing Machines and the Halting theorem are derived, as are results concerning the (im)possibility of certain kinds of error-correcting codes. In addition, an analogue of algorithmic information complexity, "prediction complexity", is elaborated. A task-independent bound is derived on how much the prediction complexity of a computational task can differ for two different reference universal physical computers used to solve that task, a bound similar to the "encoding" bound governing how much the algorithm information complexity of a Turing machine calculation can differ for two reference universal Turing machines. Finally, it is proven that either the Hamiltonian of our universe proscribes a certain type of computation, or prediction complexity is unique (unlike algorithmic information complexity), in that there is one and only version of it that can be applicable throughout our universe.

Wolpert, David H.↗
DOE OSTI2026

A Perspective on Traditional and Data Driven Electrochemical Modeling and Analysis

To understand the behavior of electrochemical systems, we need to reduce the dimensionality of the measured current-voltage-time (I-V-t) data by fitting models, thus enabling us to analyze and compare the governing physics. Traditionally, the process for this is an 'expert first' approach: defining the model and its explicit assumptions based on inductive reasoning or empirical observation, fitting small portions of the I-V-t data where assumptions are most valid or carefully designing experiments to enforce key assumptions, and then interpreting the model parameters. However, modern data-driven methods enable a new paradigm: a 'data first' approach, where the latent behaviors governing the system's measured response are identified directly using machine-learning models that optimize both model structure and parameters from the I-V-t data, guaranteeing that the learned model explains as much of the observed system response as possible. After model identification, the model can then be interrogated by an expert to connect observed behaviors with underlying physics. This talk will review several different types of electrochemical analysis (electrochemical impedance, differential voltage-capacity, electrochemical kinetics) and compare the traditional and data-driven methods for analyzing the data.

42 ENGINEERING↗
NASA NTRS1994

An improved solution to the 'Rankine-Hugoniot' problem

This paper presents an extension of the nonlinear least squares fitting technique of Vinas and Scudder (1986) (VS), which finds the physical and geometrical properties of nondissipational magnetohydrodynamic (MHD) shocks. The new method incorporates plasma temperature observations in the form of normal momentum flux and energy density flux conservation as well as plasma density, velocity, and magnetic field data. The new technique is capable of using known standard deviations in the individual measurement points to properly weight the fitting procedure. The new fitting code is validated through the analysis of synthetic shocks with known physical and geometrical properties. Finally, it is compared to the original VS method and the preaveraged velocity coplanarity technique.

Szabo, A.↗