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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Aircraft noise prediction program theoretical manual: Rotorcraft System Noise Prediction System (ROTONET), part 4

This document describes the theoretical methods used in the rotorcraft noise prediction system (ROTONET), which is a part of the NASA Aircraft Noise Prediction Program (ANOPP). The ANOPP code consists of an executive, database manager, and prediction modules for jet engine, propeller, and rotor noise. The ROTONET subsystem contains modules for the prediction of rotor airloads and performance with momentum theory and prescribed wake aerodynamics, rotor tone noise with compact chordwise and full-surface solutions to the Ffowcs-Williams-Hawkings equations, semiempirical airfoil broadband noise, and turbulence ingestion broadband noise. Flight dynamics, atmosphere propagation, and noise metric calculations are covered in NASA TM-83199, Parts 1, 2, and 3.

Weir, Donald S.↗
NASA NTRS1996

Aircraft Noise Prediction Program (ANOPP) Fan Noise Prediction for Small Engines

The Fan Noise Module of ANOPP is used to predict the broadband noise and pure tones for axial flow compressors or fans. The module, based on the method developed by M. F. Heidmann, uses empirical functions to predict fan noise spectra as a function of frequency and polar directivity. Previous studies have determined the need to modify the module to better correlate measurements of fan noise from engines in the 3000- to 6000-pound thrust class. Additional measurements made by AlliedSignal have confirmed the need to revise the ANOPP fan noise method for smaller engines. This report describes the revisions to the fan noise method which have been verified with measured data from three separate AlliedSignal fan engines. Comparisons of the revised prediction show a significant improvement in overall and spectral noise predictions.

Hough, Joe W.↗
NASA NTRS2008

Comparison of NTF Experimental Data with CFD Predictions from the Third AIAA CFD Drag Prediction Workshop

Recently acquired experimental data for the DLR-F6 wing-body transonic transport con figuration from the National Transonic Facility (NTF) are compared with the database of computational fluid dynamics (CFD) predictions generated for the Third AIAA CFD Drag Prediction Workshop (DPW-III). The NTF data were collected after the DPW-III, which was conducted with blind test cases. These data include both absolute drag levels and increments associated with this wing-body geometry. The baseline DLR-F6 wing-body geometry is also augmented with a side-of-body fairing which eliminates the flow separation in this juncture region. A comparison between computed and experimentally observed sizes of the side-of-body flow-separation bubble is included. The CFD results for the drag polars and separation bubble sizes are computed on grids which represent current engineering best practices for drag predictions. In addition to these data, a more rigorous attempt to predict absolute drag at the design point is provided. Here, a series of three grid densities are utilized to establish an asymptotic trend of computed drag with respect to grid convergence. This trend is then extrapolated to estimate a grid-converged absolute drag level.

Vassberg, John C.↗
DOE OSTI2024

Predicting Drug Effects from High-dimensional Asymmetric Drug Data Sets using Graph Neural Networks: A Comprehensive Analysis of Multi-target Drug Effect Prediction

Graph neural networks (GNNs) have emerged as one of the most effective Machine learning (ML) techniques for drug effect prediction from drug molecular graphs. Despite having immense potential, GNN models lack performance when using data sets that contain high dimensional asymmetrically co-occurrent drug effects as targets with complex correlations between them. Training individual learning models for each drug effect and incorporating every prediction result for a wide spectrum of drug effects is beyond practicality. Such an implication provides a testbed to address this challenge as multi-target prediction problems, aiming to predict all drug effects at a time. We develop standard and hybrid graph neural networks (GNNs)to perform two separate tasks that are multi-regression for continuous values and multi-label classification for categorical values contained in our data sets. Since this step makes the target data even more sparse and introduces asymmetric label co-occurrence, the learning of multi-label classification models becomes difficult and heavily impacts the GNN's performance. To address these challenges, we propose a new data oversampling technique to improve multi-label classification performances on all the given imbalanced molecular graph data sets. Using the technique, we improve the data imbalance ratio of the drug effects better than before while protecting the data set's integrity. Finally, we evaluate multi-label classification performance using the best-performant hybrid GNN model on all the oversampled data sets obtained from the proposed oversampling technique. These results outperform those of other ML models including GNN models when they are trained on the original data sets or oversampled data sets using MLSMOTE (a well-known oversampling technique) in all evaluation metrics precision, recall, and F1 score by a significant margin.

Bose, Avishek [ORNL]↗
DOE OSTI2025

AI-Enabled Operations at Fermi Complex: Multivariate Time Series Prediction for Outage Prediction and Diagnosis

The Main Control Room of the Fermilab accelerator complex continuously gathers extensive time-series data from thousands of sensors monitoring the beam. However, unplanned events such as trips or voltage fluctuations often result in beam outages, causing operational downtime. This downtime not only consumes operator effort in diagnosing and addressing the issue but also leads to unnecessary energy consumption by idle machines awaiting beam restoration. The current threshold-based alarm system is reactive and faces challenges including frequent false alarms and inconsistent outage-cause labeling. To address these limitations, we propose an AI-enabled framework that leverages predictive analytics and automated labeling. Using data from $2,703$ Linac devices and $80$ operator-labeled outages, we evaluate state-of-the-art deep learning architectures, including recurrent, attention-based, and linear models, for beam outage prediction. Additionally, we assess a Random Forest-based labeling system for providing consistent, confidence-scored outage annotations. Our findings highlight the strengths and weaknesses of these architectures for beam outage prediction and identify critical gaps that must be addressed to fully harness AI for transitioning downtime handling from reactive to predictive, ultimately reducing downtime and improving decision-making in accelerator management.

Jain, Milan [PNL, Richland] (ORCID:000000021676111↗
NASA NTRS1972

Application of Avco data analysis and prediction techniques (ADAPT) to prediction of sunspot activity

The results are presented of the application of Avco Data Analysis and Prediction Techniques (ADAPT) to derivation of new algorithms for the prediction of future sunspot activity. The ADAPT derived algorithms show a factor of 2 to 3 reduction in the expected 2-sigma errors in the estimates of the 81-day running average of the Zurich sunspot numbers. The report presents: (1) the best estimates for sunspot cycles 20 and 21, (2) a comparison of the ADAPT performance with conventional techniques, and (3) specific approaches to further reduction in the errors of estimated sunspot activity and to recovery of earlier sunspot historical data. The ADAPT programs are used both to derive regression algorithm for prediction of the entire 11-year sunspot cycle from the preceding two cycles and to derive extrapolation algorithms for extrapolating a given sunspot cycle based on any available portion of the cycle.

Hunter, H. E.↗
NASA NTRS1980

Solar-terrestrial predictions proceedings. Volume 4: Prediction of terrestrial effects of solar activity

Papers about prediction of ionospheric and radio propagation conditions based primarily on empirical or statistical relations is discussed. Predictions of sporadic E, spread F, and scintillations generally involve statistical or empirical predictions. The correlation between solar-activity and terrestrial seismic activity and the possible relation between solar activity and biological effects is discussed.

Donnelly, R. E.↗
NASA NTRS1986

Noise predictions of a high bypass turbofan engine using the Lockheed Near-Field Noise Prediction Program

The prediction of engine noise during cruise using the Near-Field Noise Prediction Program developed by Lockheed is examined. Test conditions were established which simulate the operation of a high bypass turbofan engine under a wide range of operating conditions. These test conditions include variations in altitude, flight Mach number and thrust setting. Based on the results of noise prediction made using the Lockheed program, an evaluation of the impact of these test conditions on the overall sound pressure level(OASPL)and the one-third octave band spectra is made. An evaluation of the sensitivity of flight condition parameters is also made. The primary noise source from a high bypass turbofan was determined to be fan broadband shock noise. This noise source can be expected to be present during normal cruising conditions. When present, fan broadband shock noise usually dominates at all frequencies and all directivity angles. Other noise sources of importance are broadband shock noise from the primary jet, fan noise, fan mixing noise and turbine noise.

Rawls, J. W., Jr.↗
NASA NTRS1987

Comparison of two transonic noise prediction formulations using the aircraft noise prediction program

This paper addresses recently completed work on using Farassat's Formulation 3 noise prediction code with the Aircraft Noise Prediction Program (ANOPP). Software was written to link aerodynamic loading generated by the Propeller Loading (PLD) module within ANOPP with formulation 3. Included are results of comparisons between Formulation 3 with ANOPP's existing noise prediction modules, Subsonic Propeller Noise (SPN) and Transonic Propeller Noise (TPN). Four case studies are investigated. Results of the comparison studies show excellent agreement for the subsonic cases. Differences found in the comparisons made under transonic conditions are strictly numerical and can be explained by the way in which the time derivative is calculated in Formulation 3. Also included is a section on how to execute Formulation 3 with ANOPP.

Spence, Peter L.↗
NASA NTRS1989

Noise produced by turbulent flow into a rotor: Users manual for atmospheric turbulence prediction and mean flow and turbulence contraction prediction

A users manual for a computer program for predicting atmospheric turbulence and mean flow and turbulence contraction as part of a noise prediction scheme for nonisotropic turbulence ingestion noise in helicopters is described. Included are descriptions of the various program modules and subroutines, their function, programming structure, and the required input and output variables. This routine is incorporated as one module of NASA's ROTONET helicopter noise prediction program.

Simonich, J. C.↗
NASA NTRS2000

Small Engine Technology (SET) - Task 13 ANOPP Noise Prediction for Small Engines: Jet Noise Prediction Module, Wing Shielding Module, and System Studies Results

This Final Report has been prepared by AlliedSignal Engines and Systems, Phoenix, Arizona, documenting work performed during the period May 1997 through June 1999, under the Small Engines Technology Program, Contract No. NAS3-27483, Task Order 13, ANOPP Noise Prediction for Small Engines. The report specifically covers the work performed under Subtasks 4, 5 and 6. Subtask 4 describes the application of a semi-empirical procedure for jet noise prediction, subtask 5 describes the development of a procedure to predict the effects of wing shielding, and subtask 6 describes the results of system studies of the benefits of the new noise technology on business and regional aircraft.

Lieber, Lysbeth↗
NASA NTRS2003

A Predictive Safety Management System Software Package Based on the Continuous Hazard Tracking and Failure Prediction Methodology

The goal of this research was to integrate a previously validated and reliable safety model, called Continuous Hazard Tracking and Failure Prediction Methodology (CHTFPM), into a software application. This led to the development of a safety management information system (PSMIS). This means that the theory or principles of the CHTFPM were incorporated in a software package; hence, the PSMIS is referred to as CHTFPM management information system (CHTFPM MIS). The purpose of the PSMIS is to reduce the time and manpower required to perform predictive studies as well as to facilitate the handling of enormous quantities of information in this type of studies. The CHTFPM theory encompasses the philosophy of looking at the concept of safety engineering from a new perspective: from a proactive, than a reactive, viewpoint. That is, corrective measures are taken before a problem instead of after it happened. That is why the CHTFPM is a predictive safety because it foresees or anticipates accidents, system failures and unacceptable risks; therefore, corrective action can be taken in order to prevent all these unwanted issues. Consequently, safety and reliability of systems or processes can be further improved by taking proactive and timely corrective actions.

Quintana, Rolando↗
NASA NTRS2010

Predicting Airspace Capacity Impacts Using the Consolidated Storm Prediction for Aviation

Convective weather is currently the largest contributor to air traffic delays in the United States. In order to make effective traffic flow management decisions to mitigate these delays, weather forecasts must be made as early and as accurately as possible. A forecast product that could be used to mitigate convective weather impacts is the Consolidated Storm Prediction for Aviation. This product provides forecasts of cloud water content and convective top heights at 0- to 8-hour look-ahead times. The objective of this study was to examine a method of predicting the impact of convective weather on air traffic sector capacities using these forecasts. Polygons representing forecast convective weather were overlaid at multiple flight levels on a sector map to calculate the fraction of each sector covered by weather. The fractional volume coverage was used as the primary metric to determine convection s impact on sectors. Results reveal that the forecasts can be used to predict the probability and magnitude of weather impacts on sector capacity up to eight hours in advance.

Russell, Carl↗
NASA NTRS2014

Prediction of Adverse Events in Time Series Data Using ACCEPT (Adverse Condition and Critical Event Prediction Toolbox)

Abstract:An open-source toolbox called ACCEPT (Adverse Condition and Critical Event Prediction Toolbox) that operates from within the Matlab programming environment is introduced. The toolbox provides an open source, special-purpose functionality that can be used specifically for the prediction or forecasting of adverse events in time series data. It also provides a single, unifying framework in which to compare a variety of combinations of algorithmic approaches addressing this problem. The architectural framework also offers the flexibility for expansion to accommodate the newest, latest popular regression and detection methods for this particular problem, enabling the development of an infrastructure that can act as a proving ground platform for comparing new techniques to the state of the art. As such, its intention is to act as a catalyst in advancing the state of the art in technologies related to this problem. There are a variety of other toolboxes that offer similar capabilities and features, however they are either more suitable as general purpose machine learning toolboxes or are only available commercially or through agreements. ACCEPT fills that niche by offering visibility into a specific approach for addressing the adverse event prediction problem.

programming environments↗
DOE OSTI2024

Characterization of Precipitation-Strengthening Heat-Resistant Austenitic Stainless Steels for Life-Prediction Modeling

In this study, the role of minor alloying additions in 347H stainless steels (UNS34709, ASTM A240/240M) on creep-rupture properties at 650-750°C and microstructure evolution during isothermal exposure at 750°C has been investigated, aiming to provide the experimental dataset as boundary conditions of physics-based modeling for material/component life prediction. Four different 347H heats containing various amounts of boron and nitrogen additions were prepared and evaluated. The combined additions of B and N are found to stabilize the strengthening secondary M23C6 carbides and retarding the transition from M23C6 to sigma phase precipitates during thermal exposure. The observed kinetics of microstructure evolution reasonably explains the improvement of creep-rupture properties of 347H stainless steels with the B and N additions.

Yamamoto, Yukinori↗
NASA NTRS1975

Prediction and verification of creep behavior in metallic materials and components for the space shuttle thermal protection system. Volume 2: Phase 2 subsize panel cyclic creep predictions

A method for predicting permanent cyclic creep deflections in stiffened panel structures was developed. The resulting computer program may be applied to either the time-hardening or strain-hardening theories of creep accumulation. Iterative techniques were used to determine structural rotations, creep strains, and stresses as a function of time. Deflections were determined by numerical integration of structural rotations along the panel length. The analytical approach was developed for analyzing thin-gage entry vehicle metallic-thermal-protection system panels subjected to cyclic bending loads at high temperatures, but may be applied to any panel subjected to bending loads. Predicted panel creep deflections were compared with results from cyclic tests of subsize corrugation and rib-stiffened panels. Empirical equations were developed for each material based on correlation with tensile cyclic creep data and both the subsize panels and tensile specimens were fabricated from the same sheet material. For Vol. 1, see N75-21431.

Cramer, B. A.↗
NASA NTRS1975

Observations and theory of Mg II lines in early type stars. II - Theory and predicted profiles. III - The observations and a comparison with the predictions

Profiles of the UV Mg II lines in the spectra of early type stars are computed in a grid of model atmospheres with effective temperatures between 8000 and 35,000 K and log g values of 2.5 to 4.0 using the LTE and non-LTE theories of line formation. The theoretical results indicate that the line cores are strengthened by non-LTE effects over the entire temperature range, resonance-line wings are weaker in the cooler models than in the LTE case, and very large deviations from LTE occur in the hot low-gravity models. These predictions are compared with the equivalent widths of the UV Mg II lines in 106 stars and of the visual lines in 48 stars (spectral types O4 to A3). The observed equivalent widths of normal stars in luminosity classes II through V are found to agree with the predictions over the entire range from O8 to A2 if a certain Mg/H abundance is adopted. The line intensities observed in supergiants, Be, Bp, and Ap stars are discussed.

Snijders, M. A. J.↗
NASA NTRS1979

Solar-terrestrial Predictions Proceedings. Volume 1: Prediction Group Reports

The current practice in solar terrestrial predictions is reviewed with emphasis of prediction, warning, and monitoring services. Topics covered include: ionosphere-reflected HF radio propagation; radiation hazards for manned space flights and high altitude and high latitude aircraft flights; and geomagnetic activity.

Donnelly, R. F.↗