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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Examination of Annular-Electrode Spark Discharges in Flowing Oxygen – Experimental Nuances

Microsecond sparks and the resulting plume of hot gas/plasma were examined against a parametric pressure-distance matrix. Schlieren imaging is used to capture the spatial and temporal location of spark discharge exhaust for two milliseconds. Low pressure and larger gap widths created the largest size and intensity signal for the spark-affected plumes. Experimental exit-plume velocities trend well with analytic predictions using a mean pressure between the chamber and atmospheric conditions. Due to the quadratic relation of the annulus area and gap width, larger gap width velocities are more accurately represented by analytic predictions using atmospheric pressure as the larger exit area restricts the flow less. The same pressure adjustment, when applied to breakdown voltages, improves data alignment with Paschen’s Curve.

Darren C Tinker↗
NASA NTRS1981

On the OLP prediction of the unstable modes of the flat plate turbulent boundary layer

The Orr et al. (OLP) stability analysis is reformulated to yield profiles from hot-wire velocity covariance data in the boundary layer of a flow over a flat plate. The proper orthogonal decomposition theorem (PODT) is used to maximize the mean square of the inner product of the velocity vector with a deterministic candidate vector function in Hilbert space. The OLP formulation couples the PODT for the eddy viscosity with a global extremum principle for the disturbance kinetic energy. Derivation of the governing equations is described analytically, as are the extraction of the mean velocity profile and the eddy viscosity. Finally, techniques for characterizing the dominant mode of the velocity field are introduced and applied to three-dimensional flow over a flat plate. Good agreement is found between the predictions, using laboratory data, and the measured energy and three-dimensional structures of the dominant eddies.

Wadia, A. R.↗
NASA NTRS1983

Thermochemical response of honeycomb sandwich panels

A simple study aimed at predicting the thermochemical response of honeycomb sandwich panels is presented. The overall thermal conductivity coefficient for the panel is obtained through a consideration of the convective gas movement within the cell spaces. The earlier correlations of Catton and Edwards are used. The analytical solution for the one-dimensional approximation is quoted from an earlier study. In comparison with three data points obtained at JPL at heat fluxes of 2.5, 5 and 8 W/sq cm, the char penetration depth is well predicted, especially when the debonding of the face sheet is considered. Re-radiation of incident heat flux from the debonded face sheet plays an important role in this model.

Ramohalli, K.↗
DOE OSTI2023

Storm-DEPART (Damage Estimate Prediction and Recovery Tool)

Storm-DEPART (Damage Estimate Prediction and Restoration Tool): Each year hurricanes and tropical storms in the United States damage critical infrastructure assets, disrupt the services they provide, and cause millions to billions of dollars in economic impacts due to extended recovery times. The Storm-DEPART tool and analytical output enable more impactful data-driven decision-making capabilities and strengthen national-level disaster preparedness, response, and recovery. Storm-DEPART, built through multi-month collaboration between Entergy and INL, combines Entergy’s critical infrastructure inventory data with weather forecasts to predict damages to Electric utility’s assets due to natural disasters and the estimated recovery support needed, including time, materials, and resource allocation. In the event of an approaching hurricane, this innovative solution can assess potential damage to power generation capacity, transmission grids, distribution networks, and communications assets from wind bands, storm surge, and flooding. With more effective predictions, Entergy can more efficiently allocate resources to mitigate impacts and optimize recovery for customers. Storm-DEPART also allows Electric utilities the ability to apply a planning scenario and model expected damage to better inform infrastructure restoration needs leading to enhance system resiliency. The technology is fully transferrable to other electric utilities with the same damage estimating challenges. The INL team is working on the evolution of Storm-DEPART to include ice event damage prediction framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Storm-DEPART (Damage Estimate Prediction and Recovery Tool)

Storm-DEPART (Damage Estimate Prediction and Restoration Tool): Each year hurricanes and tropical storms in the United States damage critical infrastructure assets, disrupt the services they provide, and cause millions to billions of dollars in economic impacts due to extended recovery times. The Storm-DEPART tool and analytical output enable more impactful data-driven decision-making capabilities and strengthen national-level disaster preparedness, response, and recovery. Storm-DEPART, built through multi-month collaboration between Entergy and INL, combines Entergy’s critical infrastructure inventory data with weather forecasts to predict damages to Electric utility’s assets due to natural disasters and the estimated recovery support needed, including time, materials, and resource allocation. In the event of an approaching hurricane, this innovative solution can assess potential damage to power generation capacity, transmission grids, distribution networks, and communications assets from wind bands, storm surge, and flooding. With more effective predictions, Entergy can more efficiently allocate resources to mitigate impacts and optimize recovery for customers. Storm-DEPART also allows Electric utilities the ability to apply a planning scenario and model expected damage to better inform infrastructure restoration needs leading to enhance system resiliency. The technology is fully transferrable to other electric utilities with the same damage estimating challenges. The INL team is working on the evolution of Storm-DEPART to include ice event damage prediction framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Online System ID for Predicting Power Plant Performance Throughout Cycling Operations

This presentation represents a review of the background research conducted by NETL to apply artificial intelligence, i.e. auto-recursive algorithms and data analytics to detect leaks in utility scale boilers and laboratory power systems. The new project being funded by the Advanced Sensors and Controls Program is part of the Field Work Proposal funded in EY21 as Task 53 to demonstrate the application of these techniques on a utility scale power system.

Shadle, Lawrence↗
NASA NTRSDate not supplied

Inlet Radiated Noise Predictions for the NASA Source Diagnostic Test Fan Using Physics-Based Simulations

Commercial supersonic aircraft are expected to produce considerably more noise during landing and take-off operations than existing subsonic aircraft. Establishing noise regulation is challenging because no examples of this aircraft type are currently flying. Existing aircraft noise prediction tools can be used, but the noise source models for these empirical tools are typically based on data from subsonic aircraft components. The present work documents a procedure for using physics-based computational tools to generate the propagating sound field radiating out the nacelle inlet. Although no analytical source modeling is demonstrated, these tools utilize measured data to compare the complex pressure for various propagating modes. The discrepancies found in applying the propagation tools to the subsonic inlet will be ascribed to calculations of supersonic inlets being used to update current empirical noise prediction tools.

Noise Predictions↗
NASA NTRSDate not supplied

How the Open Data Policy of the Landsat Program Has Advanced Our Understanding of Environmental Change

A time series is a sequence of observations of a phenomenon taken sequentially in time. A crucial characteristic of a time series is the dependence among adjacent observations – techniques for analyzing this dependence are referred to as time series analysis. This analytical approach enables us to predict or forecast future values of a time series, study the impact of various inputs on the observed phenomenon, and examine interrelationships among related time series variables. Within the geographical sciences, time series analysis has historically been limited to coarse-resolution satellite data, as constructing time series of data suitable for studying land cover and land use dynamics, such as Landsat data, were prohibitively costly. A transformative shift occurred in 2008 when the U.S. Government decided to make free and open all past and future data collected by the Landsat satellite program. The decision brought about a paradigm shift away from analyzing individual images or observations to continuous monitoring in time. Of particular relevance to environmental remote sensing is the ability to forecast observations – if we can predict how future observations should behave, we can infer information about how the land surface is changing. In this presentation, we will examine literature examples that showcase scientific gains enabled by time series analysis of satellite data. We will delve into how the analysis of dense time series of satellite data revealed that overall rate of forest disturbance in the Amazon has increased despite a reduction in deforestation; how different types of forest degradation, previously unquantified, are now being accurately assessed in the Caucasus region; and how we now can study the highly dynamic and intricate patterns of shifting cultivation in Southeast Asia.

Pontus Olofsson↗
DOE OSTI2026

Using Best Basis Inventory Data to Direct Strategies for Real Time Monitoring of Hanford High Level Waste – 26226

The potential to accelerate the processing of low- and high-level tank waste by applying real-time monitoring (RTM) of chemical and physical properties has prompted research into the suitability of multiple analytical methods for that purpose. The broad variety of waste stream properties and the large number of analytes of interest (as evidenced by Waste Acceptance Criteria (WAC) and Process Control Limit (PCL) lists) lead to an overwhelming set of possible analytical scenarios. This report describes the use of Best Basis Inventory (BBI) data to find the most relevant analytical targets for the specific case of monitoring the blending of High Level Waste from multiple tanks prior to introduction into a vitrification facility. Campaigns for blending this waste to minimize the risk of exceeding WACs and PCLs have been proposed. However, the predicted compositions of the blended materials do not incorporate any uncertainties that may be associated with the representativeness of the waste layer samples or the laboratory analyses that generated the BBI data. Also, they do not include any uncertainty associated with the precision of collecting highly specific fractions of the layers during a blending campaign or any inhomogeneities that may exist in those layers. Monte Carlo methods are used to apply uncertainties to the compositions of the individual layers specified in the campaign recipes. The resulting variations in the compositions of the blended materials allow estimation of the risks of exceeding WACs and PCLs for each campaign. A critical subset of WACs/PCLs – NOx, NaK, AlFeZr, and S – are especially at risk of being exceeded in multiple campaigns. These analytes should be the focus of instrument development. We also have extracted the expected solid/supernate distribution for these analytes, which establishes important performance criteria for individual analytical methods. The BBI data also permits an understanding of the different chemical forms in which the analytes appear. Thus, the need to establish instrumental sensitivity to these forms can be gainfully addressed. Although concentrating on one specific application – the blending of tank waste - this approach should be generalizable for the analysis of other possible RTM applications for waste processing.

Lascola, Robert [Savannah River National Laborator↗
NASA NTRS1988

Failure of compression-loaded multi-directional composite laminates

An analytical and experimental investigation of the failure of selected compression-loaded composite laminates is described. A general nonlinear theory is presented for predicting a laminate's compressive strength and failure mode. The theory includes the effects of out-of-plane ply waviness, inplane fiber waviness, and fiber scissoring. A simple compressive test technique is used to obtain the experimental data. The analytical and experimental results show good agreement for theta less than 45 deg and show excellent agreement for theta not less than 45 deg. The dominant compression failure modes for the laminates in this study were found to be interlaminar shearing, inplane matrix shearing, and matrix compression.

Shuart, Mark J.↗
NASA NTRS2020

Sub-Mesoscales at Ocean Fronts: Dissipation of Eddy Kinetic Energy

D’Asaro et al. measured the dissipation of eddy kinetic energy at the Kuroshio ocean front, found it to be 1-2 orders of magnitude larger than the value predicted by the law of the wall and suggested that the enhancement is due to sub-mesoscales, SM. To assess their suggestion, we employ two SM models. In the first model the buoyancy flux is based on baroclinic instabilities only and there is no momentum flux (Reynolds stresses). The model is unable to reproduce the Kuroshio data. The second model includes baroclinic instabilities, wind stress and Reynolds stresses. It reproduces the data satisfactorily. The analytic formula for the dissipation that we present can be used in OGCMs to parameterize dissipation at ocean fronts other than Kuroshio, e.g., Gulf Stream and ACC, an extension that could improve the predictions of climate studies.

sub-mesoscale models↗
DOE OSTI2021

Trust in Artificial Intelligence: Meta-Analytic Findings

Objective The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. Background There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. Method Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. Results Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. Conclusion Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. Application Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

Behavioral Sciences↗
NASA NTRS1985

Implementation and verification of a comprehensive helicopter coupled rotor - Fuselage analysis

The analytical basis and the application of a Rotor/Airframe Comprehensive Aeroelastic Program (RACAP) are described in detail. The rationale behind each analytical choice is outlined and the modular procedure is described. The program is verified by application to the AH-1G helicopter. The applicability of various airload prediction models is examined, and both the steady and vibratory responses of the blade are compared with flight test data. Reasonable correlation is found between measured and calculated blade response, with excellent correlation for vibration amplitudes at various locations on the fuselage such as engine, pilot seat, and gunner. Within the analytical model, comparisons are drawn between an isolated blade analysis and a coupled rotor/fuselage model. The deficiency of the former in the context of the AH-1G is highlighted.

Wood, E. R.↗
NASA NTRS1982

Fatigue and fracture research in metals

Fatigue and fracture research on monolithic and laminated metals is discussed. The research concentrated on three areas: stress analyses of two and three dimensional cracked bodies, fatigue crack growth, and fracture toughness. Analytical methods were developed to predict fatigue crack growth and fracture strengths of cracked specimens. Such specimens represent typical aircraft structural details (such as cracks from holes). These specimens were subjected to simple constant amplitude loading and to more complex flight load histories. Test data from both in house tests and from the literature are used to substantiate the analytical methods. These analyses extended the theory of fracture mechanics to deal with fatigue crack growth and fracture of complex crack configurations that are typical of aircraft materials and structural details.

J. C. Newman, Jr.↗
NASA NTRS1978

Numerical design of streamlined tunnel walls for a two-dimensional transonic test

An analytical procedure is discussed for designing wall shapes for streamlined, nonporous, two-dimensional, transonic wind tunnels. It is based upon currently available 2-D inviscid transonic and boundary layer analysis computer programs. Predicted wall shapes are compared with experimental data obtained from the NASA Langley 6 by 19 inch Transonic Tunnel where the slotted walls were replaced by flexible nonporous walls. Comparisons are presented for the empty tunnel operating at a Mach number of 0.9 and for a supercritical test of an NACA 0012 airfoil at zero lift. Satisfactory agreement is obtained between the analytically and experimentally determined wall shapes.

Newman, P. A.↗
NASA NTRS1994

Idealized textile composites for experimental/analytical correlation

Textile composites are fiber reinforced materials produced by weaving, braiding, knitting, or stitching. These materials offer possible reductions in manufacturing costs compared to conventional laminated composites. Thus, they are attractive candidate materials for aircraft structures. To date, numerous experimental studies have been performed to characterize the mechanical performance of specific textile architectures. Since many materials and architectures are of interest, there is a need for analytical models to predict the mechanical properties of a specific textile composite material. Models of varying sophistication have been proposed based on mechanics of materials, classical laminated plate theory, and the finite element method. These modeling approaches assume an idealized textile architecture and generally consider a single unit cell. Due to randomness of the textile architectures produced using conventional processing techniques, experimental data obtained has been of limited use for verifying the accuracy of these analytical approaches. This research is focused on fabricating woven textile composites with highly aligned and accurately placed fiber tows that closely represent the idealized architectures assumed in analytical models. These idealized textile composites have been fabricated with three types of layer nesting configurations: stacked, diagonal, and split-span. Compression testing results have identified strength variations as a function of nesting. Moire interferometry experiments are being used to determine localized deformations for detailed correlation with model predictions.

Adams, Daniel O.↗
NASA NTRS2019

Acoustic Analysis of Sensor Ports

Acquiring data in rocket engines that is representative of the actual dynamic environment can often be difficult due to a multitude of influences. One source of contamination that is often not considered entirely is the response associated with the acoustic cavity created by a sensor offset. It is common to offset a sensor due to various reasons such as for mounting accessibility, thermal isolation, shock reduction, or prevention of debris impingement. While estimating the natural frequency of the acoustic cavity is straightforward, limited analysis has been described on the determination of the overall frequency response. The sensor port design approach usually attempts to ensure the port is short enough such that the acoustic response is negligible near the frequency-of-interest, but this requires knowledge of the frequency response and simple rules-of-thumb are not always guaranteed. Data correction and/or data interpretation is also often desired for an unsatisfactory response. The limited response analysis in the literature only offers approximations or neglects important contributions. A new approach is devised theoretically and computationally that captures the true acoustic response of a sensor port. This paper summarizes the acoustics background, the port response theoretical development, and provides comparisons of a port acoustic response using an analytical model and computational acoustics. The effects of nonlinear acoustics are also examined. Additionally, the paper summarizes the design of a specialized filter using the predicted sensor port response that can be applied to data for correction.

Casiano, M. J.↗