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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Automated Classification of ROSAT Sources Using Heterogeneous Multiwavelength Source Catalogs

We describe an on-line system for automated classification of X-ray sources, ClassX, and present preliminary results of classification of the three major catalogs of ROSAT sources, RASS BSC, RASS FSC, and WGACAT, into six class categories: stars, white dwarfs, X-ray binaries, galaxies, AGNs, and clusters of galaxies. ClassX is based on a machine learning technology. It represents a system of classifiers, each classifier consisting of a considerable number of oblique decision trees. These trees are built as the classifier is 'trained' to recognize various classes of objects using a training sample of sources of known object types. Each source is characterized by a preselected set of parameters, or attributes; the same set is then used as the classifier conducts classification of sources of unknown identity. The ClassX pipeline features an automatic search for X-ray source counterparts among heterogeneous data sets in on-line data archives using Virtual Observatory protocols; it retrieves from those archives all the attributes required by the selected classifier and inputs them to the classifier. The user input to ClassX is typically a file with target coordinates, optionally complemented with target IDs. The output contains the class name, attributes, and class probabilities for all classified targets. We discuss ways to characterize and assess the classifier quality and performance and present the respective validation procedures. Based on both internal and external validation, we conclude that the ClassX classifiers yield reasonable and reliable classifications for ROSAT sources and have the potential to broaden class representation significantly for rare object types.

McGlynn, Thomas↗
NASA NTRS2006

The Low-Noise Potential of Distributed Propulsion on a Catamaran Aircraft

The noise shielding potential of an inboard-wing catamaran aircraft when coupled with distributed propulsion is examined. Here, only low-frequency jet noise from mid-wing-mounted engines is considered. Because low frequencies are the most difficult to shield, these calculations put a lower bound on the potential shielding benefit. In this proof-of-concept study, simple physical models are used to describe the 3-D scattering of jet noise by conceptualized catamaran aircraft. The Fast Scattering Code is used to predict noise levels on and about the aircraft. Shielding results are presented for several catamaran type geometries and simple noise source configurations representative of distributed propulsion radiation. Computational analyses are presented that demonstrate the shielding benefits of distributed propulsion and of increasing the width of the inboard wing. Also, sample calculations using the FSC are presented that demonstrate additional noise reduction on the aircraft fuselage by the use of acoustic liners on the inboard wing trailing edge. A full conceptual aircraft design would have to be analyzed over a complete mission to more accurately quantify community noise levels and aircraft performance, but the present shielding calculations show that a large acoustic benefit could be achieved by combining distributed propulsion and liner technology with a twin-fuselage planform.

Posey, Joe W.↗
NASA NTRS2007

Curved Duct Noise Prediction Using the Fast Scattering Code

Results of a study to validate the Fast Scattering Code (FSC) as a duct noise predictor, including the effects of curvature, finite impedance on the walls, and uniform background flow, are presented in this paper. Infinite duct theory was used to generate the modal content of the sound propagating within the duct. Liner effects were incorporated via a sound absorbing boundary condition on the scattering surfaces. Simulations for a rectangular duct of constant cross-sectional area have been compared to analytical solutions and experimental data. Comparisons with analytical results indicate that the code can properly calculate a given dominant mode for hardwall surfaces. Simulated acoustic behavior in the presence of lined walls (using hardwall duct modes as incident sound) is consistent with expected trends. Duct curvature was found to enhance weaker modes and reduce pressure amplitude. Agreement between simulated and experimental results for a straight duct with hard walls (no flow) was excellent.

Dunn, M. H.↗
NASA NTRS2009

Open Rotor Noise Prediction Methods at NASA Langley- A Technology Review

Open rotors are once again under consideration for propulsion of the future airliners because of their high efficiency. The noise generated by these propulsion systems must meet the stringent noise standards of today to reduce community impact. In this paper we review the open rotor noise prediction methods available at NASA Langley. We discuss three codes called ASSPIN (Advanced Subsonic-Supersonic Propeller Induced Noise), FW - Hpds (Ffowcs Williams-Hawkings with penetrable data surface) and the FSC (Fast Scattering Code). The first two codes are in the time domain and the third code is a frequency domain code. The capabilities of these codes and the input data requirements as well as the output data are presented. Plans for further improvements of these codes are discussed. In particular, a method based on equivalent sources is outlined to get rid of spurious signals in the FW - Hpds code.

Farassat, F.↗
NASA NTRS2010

Open Rotor Noise Prediction at NASA Langley - Capabilities, Research and Development

The high fuel prices of recent years have caused the operating cost of the airlines to soar. In an effort to bring down the fuel consumption, the major aircraft engine manufacturers are now taking a fresh look at open rotors for the propulsion of future airliners. Open rotors, also known as propfans or unducted fans, can offer up to 30 per cent improvement in efficiency compared to high bypass engines of 1980 vintage currently in use in most civilian aircraft. NASA Langley researchers have contributed significantly to the development of aeroacoustic technology of open rotors. This report discusses the current noise prediction technology at Langley and reviews the input data requirements, strengths and limitations of each method as well as the associated problems in need of attention by the researchers. We present a brief history of research on the aeroacoustics of rotating blade machinery at Langley Research Center. We then discuss the available noise prediction codes for open rotors developed at NASA Langley and their capabilities. In particular, we present the two useful formulations used for the computation of noise from subsonic and supersonic surfaces. Here we discuss the open rotor noise prediction codes ASSPIN and one based on Ffowcs Williams-Hawkings equation with penetrable data surface (FW - Hpds). The scattering of sound from surfaces near the rotor are calculated using the fast scattering code (FSC) which is also discussed in this report. Plans for further improvements of these codes are given.

Farassat, Fereidoun↗
NASA NTRS2012

Broadband Noise Prediction When Turbulence Simulation Is Available - Derivation of Formulation 2B and Its Statistical Analysis

We show that a simple modification of Formulation 1 of Farassat results in a new analytic expression that is highly suitable for broadband noise prediction when extensive turbulence simulation is available. This result satisfies all the stringent requirements, such as permitting the use of the exact geometry and kinematics of the moving body, that we have set as our goal in the derivation of useful acoustic formulas for the prediction of rotating blade and airframe noise. We also derive a simple analytic expression for the autocorrelation of the acoustic pressure that is valid in the near and far fields. Our analysis is based on the time integral of the acoustic pressure that can easily be obtained at any resolution for any observer time interval and digitally analyzed for broadband noise prediction. We have named this result as Formulation 2B of Farassat. One significant consequence of Formulation 2B is the derivation of the acoustic velocity potential for the thickness and loading terms of the Ffowcs Williams-Hawkings (FW-H) equation. This will greatly enhance the usefulness of the Fast Scattering Code (FSC) by providing a high fidelity boundary condition input for scattering predictions.

Farassat, Fereidoun↗
NASA NTRS2012

Improved Snow Mapping Accuracy with Revised MODIS Snow Algorithm

The MODIS snow cover products have been used in over 225 published studies. From those reports, and our ongoing analysis, we have learned about the accuracy and errors in the snow products. Revisions have been made in the algorithms to improve the accuracy of snow cover detection in Collection 6 (C6), the next processing/reprocessing of the MODIS data archive planned to start in September 2012. Our objective in the C6 revision of the MODIS snow-cover algorithms and products is to maximize the capability to detect snow cover while minimizing snow detection errors of commission and omission. While the basic snow detection algorithm will not change, new screens will be applied to alleviate snow detection commission and omission errors, and only the fractional snow cover (FSC) will be output (the binary snow cover area (SCA) map will no longer be included).

Riggs, George↗
NASA NTRS2018

NASA Feedback to DLA Land & Maritime Moratorium on Wafer Fab Audits

On September 29, 2017 the Defense Logistics Agency (DLA), Land and Maritime, announced that on September 1, 2017 they had begun a moratorium on the auditing of wafer fabrication facilities (fabs) used in the production of semiconductor die for military (MIL) qualified integrated circuits and discrete semiconductors (transistors and diodes). The announcement said the moratorium would last for six months initially and would apply to all wafer fabs qualified to MIL-PRF-19500 (discretes) and MIL-PRF-38535 (ICs) located, onshore or offshore. The stated rationale behind this action was to give DLA time to evaluate the risk of modifying current surveillance practices across 70+ Federal Stock Classes (FSC) under the jurisdiction of the DLA Qualifying Activity (VQ). The objective was to free up the resources used in these audits so they could be redirected towards audits of other commodities which were under resourced, notably connectors. In the announcement letter, DLA-VQ requested data from the community to assist with decisions on wafer fab re-audit cycle modifications. NASA used available information on current and historical audit schedules to gather such data and analyzed it to provide an in-depth understanding of the impact of locations, part types, in-house or out-of-house fabs on those schedules over time. This presentation provides the results of NASA's data analysis and suggest some changes to auditing practices that could increase efficient use of resources without significant increases in risk.

fab audits↗
DOE OSTI2026

Optimizing resource allocation in Miscanthus breeding via sparse testing designs for genomic prediction

Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in conventional perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse bio-based products. Increasing biomass yield will increase profitability and environmental benefits, so it is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed implementing sparse testing designs. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; G×E interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for G×E interaction consistently presented the highest PA and the lowest MSE: for CNN (PA: ~0.77, MSE: ~0.5) and YDY (PA: ~0.70, MSE: ~1.3) while for TCM and AIL these ranged from ~0.28 to 0.41 and ~1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus.

Miscanthus sacchariflorus (MSA)↗
DOE OSTI2026

Multi-trait multi-environment genomic prediction strategies for Miscanthus sacchariflorus

Genomic selection holds the potential to serve as a strategic tool to enhance the genetic gain of complex traits in Miscanthus breeding programs. The development of improved cultivars requires their assessment for various traits across diverse environments to ensure suitable overall performance. Hence, the multi-trait multi-environment (MTME) genomic prediction (GP) models offer an opportunity to improve selection accuracy. This study aims to evaluate the potential of five GP models: (1) three MTME models including genotype-by-trait-by-environment interaction (G×E×T) and (2) two single-trait multi-environment (STME) models (with and without G×E interaction). A Miscanthus sacchariflorus population comprising 336 genotypes evaluated in three environments and scored for four traits (biomass yield YDY, total culm number TCM, average internode length AIL, and culm node number CNN) was analyzed. The predictive ability of the models was evaluated considering three cross-validation schemes resembling realistic scenarios (CV1: predicting new genotypes, CVP: predicting missing traits in a given environment, and CV2: predicting partially observed genotypes). On average, in all cross-validation schemes compared to the STME the predictive ability of the MTME models was 10% to 70% higher for TCM and AIL. On the other hand, for YDY and CNN, both STME models performed similarly or slightly better (between 5 to 64%) than the MTME models in most environments. While the MTME models were not successful for all traits when compared to their STME counterparts, MTME models improved the prediction of the performance of genotypes that were untested across environments or lacked trait information in a specific environment. Overall, our study suggests that MTME GP models can be implemented in Miscanthus breeding programs to improve the predictive ability of the complex traits, shorten breeding cycles, and accelerate selection decisions.

genomic prediction (GP)↗