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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 145 records · Page 8

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Predicting Sintering Window of Binder Jet Additively Manufactured Parts Using a Coupled Data Analytics and CALPHAD Approach

Batch-to-batch variation in powder compositions for binder jet additive manufacturing (BJAM) can significantly deter defining an “ideal” sintering window for a given alloy. One way to overcome the problem is by running sintering experiments at various temperatures for each batch of the powder. However, such an approach increases the time required to achieve large-scale production of parts. The predictive capabilities of computational thermodynamic tools like CALPHAD can be leveraged to overcome the challenge, especially for binder jet additive manufacturing, since the process occurs under near-equilibrium conditions. However, calculating the sintering window using CALPHAD can be computationally expensive, considering many possible feedstock compositions within “specification”. Here, we generate high throughput CALPHAD data for nickel-based superalloys to develop machine learning models to predict the sintering window rapidly. The predictive capability of the models has been validated using published results on BJAM of Inconel 718 and 625. Further, validated models are lightweight and can be deployed in an industrial setting to get sintering window in an accelerated manner.

36 MATERIALS SCIENCE↗

NASA NTRS1985

Subsonic pressure distributions near a wing-fin juncture on a supersonic arrow-wing configuration with wing-mounted vertical fins

A generic supersonic arrow-wing configuration has been extensively studied to develop a consistent experimental data base to aid in verifying analytical prediction methods. One of the wing configurations studied was a twisted and cambered wing with wing-mounted vertical fins and trailing-edge flaps. The pressure data obtained on that configuration was limited in the wing-fin juncture region, and no pressures were obtained on the fin itself. However, detailed data in the juncture region and on the fin are essential to check out the analytical prediction methods. Therefore, the model was modified to provide details of the juncture pressure field as well as pressures on the fin. The results of this experimental investigation showed that the addition of a fin helped improve the wing pressure distribution outboard of the fin. Deflection of the trailing-edge flap did not adversely affect this trend. The pressure distributions on the vertical fin were generally affected more by change in angle of attack than by flap deflection. The spanwise pressure distribution on the fin itself indicated a load reversal with angle of attack.

Huffman, J. K.↗

NASA NTRS1982

Determination of ball bearing dynamic stiffness

The dynamic radial stiffness characteristics of rolling element bearings are currently determined by analytical methods that have not been experimentally verified. These bearing data are vital to rotating machinery design integrity because accurate critical speeds and rotor stability predictions are highly dependent on the bearing stiffness. A tester was designed capable of controlling the bearing axial preload, speed, and rotor unbalance. The rotor and support structures were constructed to permit critical speeds that are predominantly determined by a 57 mm test bearing. A curve of calculated critical speed versus stiffness was used to determine the actual bearing stiffness from the empirical data. The results of extensive testing are used to verify analytical predictions, increase confidence in existing bearing computer programs, and to serve as a data base for efforts to correct these programs.

Beatty, R. F.↗

DOE OSTI2023

Accelerated Materials Design for Molten Salt Technologies Using Innovative High-Throughput Methods

The focus of the project is on building an innovative accelerated materials design platform for molten salt technologies using novel high-throughput methods coupled to data analytics. The main objectives is to predict a FeCrMnNi alloy compositional space with better corrosion resistance than stainless steel 316 and identify new molten salt corrosion mechanisms. The project demonstrates the feasibility to use high-throughput methods coupled to data analytics to accelerate alloy design for extreme environments applications. Using a trained and tested machine learning (ML) model, 2000 FeCrMnNi alloy corrosion rate in molten chloride salts were predicted and a compositional field with corrosion rate lower than 316 stainless steel was identified. The ML model interpretability unveiled multiple features of importance in the model prediction. Some features were expected to be of relative significance, such as work function, surface energy and alloy electronegativity, and the ML model interpretability analysis confirmed those. On the other hand, the most important feature is the diffusion coefficient of Ni in the bulk alloy which indicates that a surface diffusion mechanism plays an important role in the overall corrosion mechanism in molten salts.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Battery Life Prediction Using Reduced-Order Physics Models and Machine Learning (CRADA Final Report)

Phase 1 (Original CRADA, plus no-cost extension modifications #1-3, 6/1/2017 to 3/13/2021): The Australian Department of Defence (AUDoD) is performing accelerated aging tests of Li-ion batteries to benchmark their reliability and degradation characteristics. Using its previously developed battery lifetime predictive model framework, the National Laboratory of the Rockies (NLR) will develop analytical models based the AUDoD data to predict lifetime of the multiple Li-ion battery chemistries under real-world use scenarios of interest to AUDoD. The NLR model is based on physical degradation mechanisms encountered by Li-ion batteries and has been previously validated. Phase 2 (CRADA modification #4, plus no-cost extension modification #5, 2/22/2021 to 3/30/2025): Train and support Australian Department of Defence personnel to use NLR software for model-based estimation of Li-ion battery lifetime using accelerated battery aging data collected by the Australian Department of Defence. Under separate DOE funding from 2019 to 2021, NLR enhanced its battery life-prediction software using machine learning algorithms to automate portions of the model-fitting process, requiring significantly less labor and expert judgment and also adding uncertainty quantification, increasing statistical rigor. Under Phase 2, NLR will customize NLR Software and provide it to AuDoD. NLR will enhance its NLR Model to capture aging modes of AuDoD's multi-cell modules, including cell-balancing effects. NLR will develop example single-cell and multi-cell models based on one AuDoD battery aging dataset. NLR will train AuDoD personnel on NLR Software. By the conclusion of the project, NLR will have provided AuDoD the training materials, a user manual and software needed to perform their own analysis of additional and/or future battery aging datasets.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2015

Experimental Evaluation of Tuned Chamber Core Panels for Payload Fairing Noise Control

Analytical models have been developed to predict the sound absorption and sound transmission loss of tuned chamber core panels. The panels are constructed of two facesheets sandwiching a corrugated core. When ports are introduced through one facesheet, the long chambers within the core can be used as an array of low-frequency acoustic resonators. To evaluate the accuracy of the analytical models, absorption and sound transmission loss tests were performed on flat panels. Measurements show that the acoustic resonators embedded in the panels improve both the absorption and transmission loss of the sandwich structure at frequencies near the natural frequency of the resonators. Analytical predictions for absorption closely match measured data. However, transmission loss predictions miss important features observed in the measurements. This suggests that higher-fidelity analytical or numerical models will be needed to supplement transmission loss predictions in the future.

Schiller, Noah H.↗

NASA NTRS1992

The influence of fiber waviness on the compressive behavior of unidirectional continuous fiber composites

The influence of fiber waviness and matrix nonlinearity on the compressive behavior of continuous fiber composites is studied. A micromechanics model based on the kinematics of the fibers has been developed to predict the behavior of unidirectional composites with initially wavy fibers under compressive loads. The initial waviness has been idealized as sinusoidal. Nonlinear shear behavior of the matrix has been included. Because the shear strain in the matrix is a function of position, the deformed shape of the fibers differs from the initial shape. Therefore, the deformed fiber shape is represented by a sine series. Fiber waviness and matrix shear properties have been determined experimentally for T650-42/Radel C under several conditions. These experimental data have been used as input into the analytical model. Predicted compressive response is in reasonable agreement with experimental data.

Highsmith, Alton L.↗

NASA NTRS1998

The Application of Reflected GPS Signals to Ocean Remote Sensing

The L-band broadcast signal from the Global Positioning System (GPS) which has reflected off of the sea surface is under study for use as a ocean, coastal and wetlands remote sensing tool. The reflected signal from a given GPS satellite is cross-correlated with the pseudorandom noise code uniquely identifying that satellite. The shape of this cross-correlation, ordinarily a very sharp triangle when tracking a direct line of sight signal, becomes wider and smoother as the mean square slope of the reflecting surface increases. It is proposed that the surface wind speed can be determined by matching the recorded shape of this cross-correlation to that predicted by theoretical models as a function of wind speed and direction. The significance of these effects increases with altitude of the receiver. Experimental data have been collected using a specially modified GPS receiver on aircraft and on a balloon at altitudes of up to 25 km. These data compare favorably with predictions of analytical models and demonstrate the dependence of the waveform shape on surface wind speed and receiver altitude. The advantages that this measurement technique has over conventional scatterometers is the small size, low cost and simplicity of the receiver hardware, no requirement for a transmitter, and the ability to simultaneously collect data from usually 10 or more points (from a low earth orbiting satellite). This number could if the Russian GLONASS (Global Navigation Satellite System) satellites are also considered as additional sources of radiation. Furthermore, the bistatic scattering geometry is complementary to the backscatter used by conventional scatterometers.

Garrison, James L.↗

NASA NTRS1997

Rotating Shake Test and Modal Analysis of a Model Helicopter Rotor Blade

Rotating blade frequencies for a model generic helicopter rotor blade mounted on an articulated hub were experimentally determined. Testing was conducted using the Aeroelastic Rotor Experimental System (ARES) testbed in the Helicopter Hover Facility (HBF) at Langley Research Center. The measured data were compared to pretest analytical predictions of the rotating blade frequencies made using the MSC/NASTRAN finite-element computer code. The MSC/NASTRAN solution sequences used to analyze the model were modified to account for differential stiffening effects caused by the centrifugal force acting on the blade and rotating system dynamic effects. The correlation of the MSC/NASTRAN-derived frequencies with the experimental data is, in general, very good although discrepancies in the blade torsional frequency trends and magnitudes were observed. The procedures necessary to perform a rotating system modal analysis of a helicopter rotor blade with MSC/NASTRAN are outlined, and complete sample data deck listings are provided.

Wilkie, W. Keats↗

NASA NTRS1979

Design of space payloads for transient environments

Current practices used to design space payload structures for transient dynamic loads are described. Included in the discussion of the design process are the estimation of preliminary loads, development of dynamic models, calculation of transient loads, verification of analytical models, establishment of development tests, definition of the qualification tests, and acquisition of flight data. An indication of the accuracy of the analytical predictions is provided by comparing them with actual test and flight data. Many illustrations and much data obtained during the Viking Project are used to clarify the current practices.

Wada, B. K.↗

NASA NTRS2015

Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

Accurate taxi time prediction can be used for more efficient runway scheduling to increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. This paper describes two different approaches to predicting taxi times, which are a data-driven analytical method using machine learning techniques and a fast-time simulation-based approach. These two taxi time prediction methods are applied to realistic flight data at Charlotte Douglas International Airport (CLT) and assessed with actual taxi time data from the human-in-the-loop simulation for CLT airport operations using various performance measurement metrics. Based on the preliminary results, we discuss how the taxi time prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast-time simulation model for implementing it with an airport scheduling algorithm in real-time operational environment.

Lee, Hanbong↗

DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗

DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data (Final Report)

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.

20 FOSSIL-FUELED POWER PLANTS↗

NASA NTRS1996

Effects of Material Degradation on the Structural Integrity of Composite Materials: Experimental Investigation and Modeling of High Temperature Degradation Mechanisms

It has previously been demonstrated that simple coupled reaction-diffusion models can approximate the aging behavior of PMR-15 resin subjected to different oxidative environments. Based on empirically observed phenomena, a model coupling chemical reactions, both thermal and oxidative, with diffusion of oxygen into the material bulk should allow simulation of the aging process. Through preliminary modeling techniques such as this it has become apparent that accurate analytical models cannot be created until the phenomena which cause the aging of these materials are quantified. An experimental program is currently underway to quantify all of the reaction/diffusion related mechanisms involved. The following contains a summary of the experimental data which has been collected through thermogravimetric analyses of neat PMR-15 resin, along with analytical predictions from models based on the empirical data. Thermogravimetric analyses were carried out in a number of different environments - nitrogen, air and oxygen. The nitrogen provides data for the purely thermal degradation mechanisms while those in air provide data for the coupled oxidative-thermal process. The intent here is to effectively subtract the nitrogen atmosphere data (assumed to represent only thermal reactions) from the air and oxygen atmosphere data to back-figure the purely oxidative reactions. Once purely oxidative (concentration dependent) reactions have been quantified it should then be possible to quantify the diffusion of oxygen into the material bulk.

Cunningham, Ronan A.↗

NASA NTRS1982

In-flight lift and drag measurements on a first generation jet transport equipped with winglets

A KC-135A aircraft equipped with wing tip winglets was flight tested to demonstrate and validate the potential performance gain of the winglet concept as predicted from analytical and wind tunnel data. Flight data were obtained at cruise conditions for Mach numbers of 0.70, 0.75, and 0.80 at a nominal altitude of 36,000 ft. and winglet configurations of 15 deg cant/-4 deg incidence, 0 deg cant/-4 deg incidence, and baseline. For the Mach numbers tested the data show that the addition of winglets did not affect the lifting characteristics of the wing. However, both winglet configurations showed a drag reduction over the baseline configuration, with the best winglet configuration being the 15 deg cant/-4 deg incidence configuration. This drag reduction due to winglets also increased with increasing lift coefficient. It was also shown that a small difference exists between the 15 deg cant/-4 deg incidence flight and wind tunnel predicted data. This difference was attributed to the pillowing of the winglet skins in flight which would decrease the winglet performance.

Lux, D. P.↗

NASA NTRS1986

Turbine airfoil film cooling

Emphasis is placed on developing more accurate analytical models for predicting turbine airfoil external heat transfer rates. Performance goals of new engines require highly refined, accurate design tools to meet durability requirements. In order to obtain improvements in analytical capabilities, programs are required which focus on enhancing analytical techniques through verification of new models by comparison with relevant experimental data. The objectives of the current program are to develop an analytical approach, based on boundary layer theory, for predicting the effects of airfoil film cooling on downstream heat transfer rates and to verify the resulting analytical method by comparison of predictions with hot cascade data obtained under this program.

Hylton, Larry D.↗

NASA NTRS2014

Constructing the "Best" Reliability Data for the Job

Modern business and technical decisions are based on the results of analyses. When considering assessments using "reliability data", the concern is how long a system will continue to operate as designed. Generally, the results are only as good as the data used. Ideally, a large set of pass/fail tests or observations to estimate the probability of failure of the item under test would produce the best data. However, this is a costly endeavor if used for every analysis and design. Developing specific data is costly and time consuming. Instead, analysts rely on available data to assess reliability. Finding data relevant to the specific use and environment for any project is difficult, if not impossible. Instead, we attempt to develop the "best" or composite analog data to support our assessments. One method used incorporates processes for reviewing existing data sources and identifying the available information based on similar equipment, then using that generic data to derive an analog composite. Dissimilarities in equipment descriptions, environment of intended use, quality and even failure modes impact the "best" data incorporated in an analog composite. Once developed, this composite analog data provides a "better" representation of the reliability of the equipment or component can be used to support early risk or reliability trade studies, or analytical models to establish the predicted reliability data points. Data that is more representative of reality and more project specific would provide more accurate analysis, and hopefully a better final decision.

DeMott, D. L.↗

NASA NTRS2014

Constructing the Best Reliability Data for the Job

Kleinhammer, R. K.↗