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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 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Collagen Turnover in Relation to Risk Factors and Hemodynamics in Human Intracranial Aneurysms

Determinants for molecular and structural instability, that is, impending growth or rupture, of intracranial aneurysms (IAs) remain uncertain. To elucidate this, we endeavored to estimate the actual turnover rates of the main molecular constituent in human IA (collagen) on the basis of radiocarbon ( 14 C) birth dating in relation to IA hemodynamics. Collagen turnover rates in excised human IA samples were calculated using mathematical modeling of 14 C birth dating data of collagen in relation to risk factors and histological markers for collagen maturity/turnover in selected IA. Hemodynamics were simulated using image-based computational fluid dynamics. Correlation, logistic regression, and receiver operating characteristic analyses were performed. Collagen turnover rates were estimated in 46 IA (43 patients); computational fluid dynamics could be performed in 20 IA (20 patients). The mean collagen turnover rate (γ) constituted 126% (±1% error) per year. For patients with arterial hypertension, γ was greater than 2600% annually, whereas γ was distinctly lower with 32% (±1% error) per year for patients without risk factors, such as smoking and hypertension. There was a distinct association between histological presence of rather immature collagen in human IA and the presence of modifiable risk factors. Spatial-temporal averaged wall shear stress predicted rapid collagen turnover (odds ratio, 1.6 [95% CI, 1.0–2.7]). Receiver operating characteristic analysis demonstrated a good test accuracy (area under the curve, 0.798 [95% CI, 0.598–0.998]) for average wall shear stress with a threshold ≥4.9 Pa for rapid collagen turnover. Our data indicate that turnover rates and stability of collagen in human IA are strongly associated with the presence of modifiable risk factors and aneurysmal hemodynamics. Finally, these findings underline the importance of strict risk factor modification in patients with unruptured IA. Future should include more detailed risk factor data to establish a more causal understanding of hemodynamics and the rupture risk of individual IA.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2005

Pressure-Sensitive Paint Investigation of Double-Delta Wing Vortex Flow Manipulation

A pressure-sensitive paint (PSP) technique was applied in a wind tunnel experiment in the NASA Langley Research Center 8-Foot Transonic Pressure Tunnel to quantify the effect of wing fillets on the global vortex-induced surface static pressure field about a sharp leading-edge 76 deg/40 deg double delta wing, or strake-wing, model at subsonic and transonic speeds. Global calibrations of the PSP were obtained at M = 0.50, 0.70, 0.85, 0.95, and 1.20, a Reynolds number per unit length of 2.0 million, and angles of attack from 10 degrees to 30 degrees using an in-situ method featuring the simultaneous acquisition of electronically-scanned pressures (ESP) at discrete locations on the model. The mean error in the PSP measurements relative to the ESP data was approximately 2 percent or less at M = 0.50 to 0.85 but increased to several percent at M = 0.95 and 1.20. The PSP pressure distributions and pseudo-colored planform view pressure maps clearly revealed the vortex-induced pressure signatures at all Mach numbers and angles of attack. Small fillets having a parabolic or diamond planform situated at the strake-wing intersection were designed to manipulate the vortical flows by, respectively, removing the leading-edge discontinuity or introducing additional discontinuities. The fillets caused global changes in the vortex-dominated surface pressure field that were effectively captured in the PSP measurements. The vortex surface pressure signatures were compared to available off-surface vortex cross-flow structures obtained using a laser vapor screen (LVS) flow visualization technique. The fillet effects on the PSP pressure distributions and the observed leading-edge vortex flow characteristics were consistent with the trends in the measured lift, drag, and pitching moment coefficients.

Erickson, Gary E.↗
NASA NTRS2004

Pressure-Sensitive Paint Investigation of Double-Delta Wing Vortex Flow Manipulation

A pressure-sensitive paint (PSP) technique was applied in a wind tunnel experiment in the NASA Langley Research Center 8-Foot Transonic Pressure Tunnel to quantify the effect of wing fillets on the global vortex-induced surface static pressure field about a sharp leading-edge 76o/40o double delta wing, or strake-wing, model at subsonic and transonic speeds. Global calibrations of the PSP were obtained at M = 0.50, 0.70, 0.85, 0.95, and 1.20, a Reynolds number per unit length of 2.0 million, and angles of attack from 10 degrees to 20 degrees using an in-situ method featuring the simultaneous acquisition of electronically-scanned pressures (ESP) at discrete locations on the model. The mean error in the PSP measurements relative to the ESP data was approximately 2 percent or less at M = 0.50 to 0.85 but increased to several percent at M =0.95 and 1.20. The PSP pressure distributions and pseudo-colored planform view pressure maps clearly revealed the vortex-induced pressure signatures at all Mach numbers and angles of attack. Small fillets having a parabolic or diamond planform situated at the strake-wing intersection were designed to manipulate the vortical flows by, respectively, removing the leading-edge discontinuity or introducing additional discontinuities. The fillets caused global changes in the vortex-dominated surface pressure field that were effectively captured in the PSP measurements. The vortex surface pressure signatures were compared to available off-surface vortex cross-flow structures obtained using a laser vapor screen (LVS) flow visualization technique. The fillet effects on the PSP pressure distributions and the observed leading-edge vortex flow characteristics were consistent with the trends in the measured lift, drag, and pitching moment coefficients.

Erickson, Gary E.↗
DOE OSTI2019

A Decision Tree Based Machine Learning Algorithm for Rate Estimation

A subgraph isomorphic decision tree algorithm was developed for estimating chemical reaction rates. The algorithm essentially associates differences in rate estimation with differences in sub-structures on the reactant or reactants, and the tree splits reactions into groups with similar rates by identifying the substructural differences that affect rate estimation the most. The algorithm was trained on rates from the Reaction Mechanism Generator (RMG) database and compared with a much more traditional rate rule estimator based on the same data. Compared to the previous RMG rate rules, the prediction accuracy of the new algorithm, measured by median absolute error (MAE) factor, root mean squared error (RMSE) factor, and 2-sigma error factor, is consistently improved by a factor of two. Uncertainty estimation was also developed for the decision trees based on fitting a lognormal distribution to the leave-one-out errors at each node. Improvements in accuracy introduced by the algorithm should significantly reduce the amount of effort required to refine models and the need for additional quantum calculations and thus reduce the amount of time and cost required to create and refine biofuel models.

Johnson, Matthew S.↗
NASA NTRS1985

Near infrared leaf reflectance modeling

Near infrared leaf reflectance modeling using Fresnel's equation (Kumar and Silva, 1973) and Snell's Law successfully approximated the spectral curve for a 0.25-mm turgid oak leaf lying on a Halon background. Calculations were made for ten interfaces, air-wax, wax-cellulose, cellulose-water, cellulose-air, air-water, and their inverses. A water path of 0.5 mm yielded acceptable results, and it was found that assignment of more weight to those interfaces involving air versus water or cellulose, and less to those involving wax, decreased the standard deviation of the error for all wavelengths. Data suggest that the air-cell interface is not the only important contributor to the overall reflectance of a leaf. Results also argue against the assertion that the near infrared plateau is a function of cell structure within the leaf.

Parrish, J. B.↗
NASA NTRS2025

Improving Adhesive Bondline Time of Flight Predictions During Autoclave Cure Utilizing Machine Learning

Composite materials are increasingly being used in aerospace applications due to their superior strength-to-weight ratio compared to commonly used metals. A current limitation to widespread adoption is the certification of adhesively bonded joints. One approach to improving adhesive bonding in composites is accurately measuring the thickness of adhesive bondlines in composite laminates. Precise bondline thickness control is essential for aerospace applications where adhesive layer thickness directly affects joint fracture properties and structural performance. This study focused on implementing machine learning techniques to determine the ultrasonic time of flight (directly correlated to thickness) in adhesive bondlines throughout autoclave cure cycles. A high-temperature (use up to 180°C) ultrasonic scanning system was deployed in an autoclave to provide time of flight data through composite panels. Three experiments were conducted on the curing of 305 mm × 305 mm unidirectional composite panels. In the first experiment, a piecewise function was fit for the temperature correction factor to account for changing autoclave temperatures. Due to deficiencies in the first calibration experiment, a second experiment was run, and the results were used to train a machine learning model. The revised experiment, in combination with the machine learning model, significantly increased the accuracy of the bondline time of flight predictions (~14% error reduced to <1%). Data was processed using the Regression Learner Application in MATLAB®, with a Support Vector Machine selected for the model. The result was a machine learning algorithm capable of reliably quantifying ultrasonic time of flight through adhesive bondlines. The third experiment provided independent test data for the machine learning model, demonstrating that the model produces accurate predictions from data beyond its training set.

Machine Learning↗
DOE OSTI2024

Improving gas adsorption modeling for MOFs by local calibration of Hubbard U parameters

While computational screening with density functional theory (DFT) is frequently employed for the screening of metal–organic frameworks (MOFs) for gas separation and storage, commonly applied generalized gradient approximations (GGAs) exhibit self-interaction errors, which hinder the predictions of adsorption energies. We investigate the Hubbard U parameter to augment DFT calculations for full periodic MOFs, targeting a more precise modeling of gas molecule–MOF interactions, specifically for N2, CO2, and O2. We introduce a calibration scheme for the U parameter, which is tailored for each MOF, by leveraging higher-level calculations on the secondary building unit (SBU) of the MOF. When applied to the full periodic MOF, the U parameter calibrated against hybrid HSE06 calculations of SBUs successfully reproduces hybrid-quality calculations of the adsorption energy of the periodic MOF. The mean absolute deviation of adsorption energies reduces from 0.13 eV for a standard GGA treatment to 0.06 eV with the calibrated U, demonstrating the utility of the calibration procedure when applied to the full MOF structure. Furthermore, attempting to use coupled cluster singles and doubles with perturbative triples calculations of isolated SBUs for this calibration procedure shows varying degrees of success in predicting the experimental heat of adsorption. It improves accuracy for N2 adsorption for cases of overbinding, whereas its impact on CO2 is minimal, and ambiguities in spin state assignment hinder consistent improvements of O2 adsorption. Our findings emphasize the limitations of cluster models and advocate the use of full periodic MOF systems with a calibrated U parameter, providing a more comprehensive understanding of gas adsorption in MOFs.

Chemistry↗
DOE OSTI2024

Material Model Parameters Optimization in Liquid Mercury Target Dynamics Simulation With Machine Learning Surrogates

A pulsed spallation target is subjected to very short (∼0.7μs) but intense loads (23.3 kJ) from repeated proton pulses, which knock away neutrons from the mercury atoms’ nuclei for a wide range application in physics, engineering, medicine, petroleum exploration, biology, chemistry, etc. The effect of this pulsed loading on the stainless-steel target module which contains the flowing mercury target material is difficult to predict not only due to its short but intense explosive-like physical reaction, but also the nonlinear material behavior of the liquid mercury in the structure. Injecting small helium bubbles in the mercury has been an efficient method of mitigating the pressure wave at high power level stage. However, prediction of the resultant loading on the target is more difficult when helium gas is intentionally injected into the mercury. A 2-phase material model that incorporates the Rayleigh-Plesset (R-P) model is expected to address this complex multi-physics dynamics problem by including the bubble dynamics in the liquid mercury. A parameter sensitivity study was firstly employed to understand their impact on the simulation strains. The investigated parameters included E, μ, γ, σ, n, VFgas, and gas cumulative volume curve control parameters a and b. Verification and validation results from sparse polynomial expansions (SPE) method and directional Gaussian smoothing (DGS) optimization show that the surrogate model had training error of ∼7% and validation error of ∼15%, indicating that machine learning methods and surrogate models can help optimize the uncertain parameters in the complex 2-phase material model. This approach is expected to fill the knowledge gap between unknown liquid-gas mixture material model and measured vessel strain responses.

Lin, Lianshan↗
DOE OSTI2024

SSTDR and FDR Detection of Un-Energized and Energized Cable Anomalies Including Thermal Degradation Using Machine Learning

Historically, cables are initially qualified for nuclear power plant use for 40 years. As plants extend their operating license to 60 and 80 years, continued use of these cables must shift to a performance-based approach since it is cost prohibitive to completely replace cables that are likely still capable of performing their design function. A variety of cable tests are available and are commonly applied during outages when the cables can be taken out of service. Frequency domain reflectometry (FDR) is one of these test methods that is being more broadly accepted and used because it not only detects anomalies along the cable with a low-voltage signal that does not stress the cable insulation, but the technique also locates the anomalies. This supports follow-up local inspection and local repair or partial replacement of a damaged cable segment. Currently, FDR testing is only applied to cables that are taken out of service since the test instrument would be damaged by operational voltages. A related technology that has found some acceptance in the aircraft and rail industry is spread spectrum time domain reflectometry (SSTDR). This technology has been implemented with a custom commercial instrument by LiveWire Innovation that is designed to operate on live cables up to 1000 volts and with a bandwidth of 48 MHz. Initial evaluation by the Pacific Northwest National Laboratory (PNNL) of the Live Wire system indicated that a broader bandwidth (BW) SSTDR may be better for many kinds of flaws. This led PNNL to develop an SSTDR laboratory instrument suitable for tests up to 500 MHz bandwidth. Testing on energized cables is also desirable for online monitoring systems so an inductive clamshell coupler was developed that allows energized cables to be tested up to at least 5 kV and likely higher voltage levels. Dielectric spectroscopy and tan delta testing plus various laboratory destructive tests were included in this data acquisition campaign directed to feed a machine learning (ML) study. With these kinds of developments, online energized cable tests may be possible with industrial adoption of such hardware advances but it will be completely impractical to have highly skilled data analysts continually examine these complex signals for indications of damage or compromised conditions. If online testing is to be implemented in new test hardware, it must be accompanied by software that can interpret the signals and alert plant operators of changing or degraded conditions. The thermally aged, shielded cable investigated here was separately treated for ML analysis. Visual analysis of electrical data showed generally increasing peaks where the cable entered and exited the oven. These peaks were not exactly aligned with expected locations, but these differences were attributed to velocity of propagation calibration errors. Only supervised ML was applied to the thermally aged data as this data was only available shortly before the committed publication date of this report. The supervised ML was structured to divide the 0 to 70-day responses as ‘normal’ from 0 to 35 days or ‘anomalous’ from 36 to 70 days, based on cable tensile elongation at break (EAB) insulation characterization. Using 80% of the data for training and 20% for testing, the supervised ML predicted normal versus anomalous was 70% accurate. Important conclusions include: • Accuracy to predict the presence of cable damage is improved from the 2023 effort by more training data. Weighted accuracies for comparisons among the instruments ranged from 67 to 89 % for unsupervised ML and 71 to 99% for supervised ML. • Based on the synthetic data tests, the unsupervised models are more generalizable to unseen anomalies. The Multi-Layer Perceptron classifier (MLP) model reported as high as 99.7% accuracy on the test data, but this dropped to 58.3% when tested on the synthetic data. In contrast, the unsupervised Pointwise model only achieved 89.7% accuracy on the experimental data but reported 78.3% accuracy on the synthetic data. • The best anomaly indicators are higher frequency (400 MHz BW) FDR data. Other tests may be interesting but for this study, this was the best predicter.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Using Deep Machine Learning to Conduct Object-Based Identification and Motion Detection on Safeguards Video Surveillance

Video surveillance is one of the core monitoring technologies used by the International Atomic Energy Agency (IAEA) Department of Safeguards at safeguarded nuclear facilities worldwide. Current IAEA image-review software has functions for scene-change detection, black image detection and missing scene analysis, but their capabilities are not optimum. The current workflow for the detection of safeguards relevant events heavily depends on inspectors’ laborious visual examination of surveillance videos, which is a time-consuming process and prone to errors. To improve the accuracy of the process and reduce inspectors’ burden, the paper proposes using deep machine learning to detect objects of interest in video streams and to conduct object-based motion detection. The hypothesis of this work is that deep machine learning will reduce the burden on inspectors and reduce errors by automatically locating and identifying objects of interest in video streams. Objects of interest include casks and fuel assemblies that are typically monitored by inspectors. The algorithm being developed in this work is based on a computationally efficient deep machine learning algorithm – You Only Look Once (YOLO) – but is further devised to address specific challenges related to the operation of nuclear facilities. The developed model (which is called YOLO-NSG – YOLO for Nuclear Safeguards) is evaluated with data sets collected at the test facilities at Brookhaven National Laboratory (BNL) and Sandia National Laboratories (SNL). The initial focus of the research is for application at safeguarded nuclear reactors, such as pressurized heavy-water reactors, where video surveillance is broadly deployed, but can be extended to other use cases of nuclear safeguards. The detailed structure of YOLO-NSG model is introduced and the test results are reported in the paper.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Assessing Electronic Structure Methods for Long-Range Three-Body Dispersion Interactions: Analysis and Calculations on Well-Separated Metal Atom Trimers

Three-body dispersion interactions are much weaker than their two-body counterpart. However, their importance grows quickly as the number of interacting monomers rises. To explore the numerical performance of correlation methods for long-range three-body dispersion, we performed calculations on eight very simple dispersion-dominated model metal trimers: Na 3 , Mg 3 , Zn 3 , Cd 3 , Hg 3 , Cu 3 , Ag 3 , and Au 3 . One encouraging aspect is that relatively small basis sets of augmented triple-ζ size appear to be adequate for three-body dispersion in the long-range. Coupled cluster calculations were performed at high levels to assess MP3, CCSD, CCSD(T), empirical density functional theory dispersion (D3), and the many-body dispersion (MBD) approach. We found that the accuracy of CCSD(T) was generally significantly lower than for two-body interactions, with errors sometimes reaching 20% in the investigated systems, while CCSD and particularly MP3 were generally more erratic. MBD is found to perform better than D3 at large distances, whereas the opposite is true at shorter distances. When computing reference numbers for three-body dispersion, care should be taken to appropriately represent the effect of the connected triple excitations, which are significant in most cases and incompletely approximated by CCSD(T).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Surrogate model evaluation and building energy benchmarking for commercial buildings

Building energy consumption benchmarking involves challenges associated with various energy patterns for different building types; heating, ventilating, and air-conditioning (HVAC) system types; and climates. Given significant variation in energy use patterns, accurate prediction of long-term energy use using surrogate models remains challenging. Multiple linear regression (MLR) is commonly used for building energy benchmarking because of its simple structure; however, it lacks accuracy compared to other black-box models. Although many studies have compared surrogate models and offer guidance on model selection based on metrics, they do not provide detailed analysis on improving the surrogate model accuracy. In this paper, we implement a surrogate model using polynomial ridge regression (i.e., MLR with interaction terms combined with ridge regularization) for small office and retail strip mall buildings across six HVAC system types and all climate zones, for electricity and natural gas in baseline and proposed scenarios. A simulation workflow is developed using OpenStudio TM /EnergyPlus TM to generate simulation data using measures over a wide range of efficiency inputs. Enhancements based on statistical insights are used for improving the model accuracy using filters, input transformations, and change points. Surrogate models achieved average coefficient of variation of the root mean squared error (CVRMSE) values of 2.17, 1.06, 2.05, and 3.26 for proposed electricity, proposed natural gas, baseline electricity, and baseline natural gas, respectively, with enhancements reducing CVRMSE by an average of 14.9% across all combinations. We provide model interpretation via Shapley additive explanations to determine which input variables most influence energy consumption and provide supportive arguments for enhancements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

GraphAlign: Graph-Enabled Machine Learning for Seismic Event Filtering

This report summarizes results from a 2 year effort to improve the current automated seismic event processing system by leveraging machine learning models that can operated over the inherent graph data structure of a seismic sensor network. Specifically, the GraphAlign project seeks to utilize prior information on which stations are more likely to detect signals originating from particular geographic regions to inform event filtering. To date, the GraphAlign team has developed a Graphical Neural Network (GNN) model to filter out false events generated by the Global Associator (GA) algorithm. The algorithm operates directly on waveform data that has been associated to an event by building a variable sized graph of station waveforms nodes with edge relations to an event location node. This builds off of previous work where random forest models were used to do the same task using hand crafted features. The GNN model performance was analyzed using an 8 week IMS/IDC dataset, and it was demonstrated that the GNN outperforms the random forest baseline. We provide additional error analysis of which events the GNN model performs well and poorly against concluded by future directions for improvements.

58 GEOSCIENCES↗
NASA NTRS1976

Super high compression of line drawing data

Models which can be used to accurately represent the type of line drawings which occur in teleconferencing and transmission for remote classrooms and which permit considerable data compression were described. The objective was to encode these pictures in binary sequences of shortest length but such that the pictures can be reconstructed without loss of important structure. It was shown that exploitation of reasonably simple structure permits compressions in the range of 30-100 to 1. When dealing with highly stylized material such as electronic or logic circuit schematics, it is unnecessary to reproduce configurations exactly. Rather, the symbols and configurations must be understood and be reproduced, but one can use fixed font symbols for resistors, diodes, capacitors, etc. Compression of pictures of natural phenomena such as can be realized by taking a similar approach, or essentially zero error reproducibility can be achieved but at a lower level of compression.

Cooper, D. B.↗
NASA NTRS1996

Numerical Analysis of the Sea State Bias for Satellite Altimetry

Theoretical understanding of the dependence of sea state bias (SSB) on wind wave conditions has been achieved only for the case of a unidirectional wind-driven sea. Recent analysis of Geosat and TOPEX altimeter data showed that additional factors, such as swell, ocean currents, and complex directional properties of realistic wave fields, may influence SSB behavior. Here we investigate effects of two-dimensional multimodal wave spectra using a numerical model of radar reflection from a random, non-Gaussian surface. A recently proposed ocean wave spectrum is employed to describe sea surface statistics. The following findings appear to be of particular interest: (1) Sea swell has an appreciable effect in reducing the SSB coefficient compared with the pure wind sea case but has less effect on the actual SSB owing to the corresponding increase in significant wave height. (2) Hidden multimodal structure (the two-dimensional wavenumber spectrum contains separate peaks, for swell and wind seas, while the frequency spectrum looks unimodal) results in an appreciable change of SSB. (3) For unimodal, purely wind-driven seas, the influence of the angular spectral width is relatively unimportant; that is, a unidirectional sea provides a good qualitative model for SSB if the swell is absent. (4) The pseudo wave age is generally much better fo parametrization the SSB coefficient than the actual wave age (which is ill-defined for a multimodal sea) or wind speed. (5) SSB can be as high as 5% of the significant wave height, which is significantly greater than predicted by present empirical model functions tuned on global data sets. (6) Parameterization of SSB in terms of wind speed is likely to lead to errors due to the dependence on the (in practice, unknown) fetch.

Glazman, R. E.↗
NASA NTRS1995

Vertical Scales of Turbulence at the Mount Wilson Observatory

The vertical scales of turbulence at the Mount Wilson Observatory are inferred from data from the University of California at Berkeley Infrared Spatial Interferometer (ISI), by modeling path length fluctuations observed in the interferometric paths to celestial objects and those in instrumental ground-based paths. The correlations between the stellar and ground-based path length fluctuations and the temporal statistics of those fluctuations are modeled on various timescales to constrain the vertical scales. A Kolmogorov-Taylor turbulence model with a finite outer scale was used to simulate ISI data. The simulation also included the white instrumental noise of the interferometer, aperture-filtering effects, and the data analysis algorithms. The simulations suggest that the path delay fluctuations observed in the 1992-1993 ISI data are largely consistent with being generated by refractivity fluctuations at two characteristic vertical scales: one extending to a height of 45 m above the ground, with a wind speed of about 1 m/ s, and another at a much higher altitude, with a wind speed of about 10 m/ s. The height of the lower layer is of the order of the dimensions of trees and other structures near the interferometer, which suggests that these objects, including elements of the interferometer, may play a role in generating the lower layer of turbulence. The modeling indicates that the high- attitude component contributes primarily to short-period (less than 10 s) fluctuations, while the lower component dominates the long-period (up to a few minutes) fluctuations. The lower component turbulent height, along with outer scales of the order of 10 m, suggest that the baseline dependence of long-term interferometric, atmospheric fluctuations should weaken for baselines greater than a few tens of meters. Simulations further show that there is the potential for improving the seeing or astrometric accuracy by about 30%-50% on average, if the path length fluctuations in the lower component are directly calibrated. Statistical and systematic effects induce an error of about 15 m in the estimate of the lower component turbulent altitude.

Treuhaft, Robert N.↗
NASA NTRS2006

Wind Tunnel Application of a Pressure-Sensitive Paint Technique to a Double Delta Wing Model at Subsonic and Transonic Speeds

A pressure-sensitive paint (PSP) technique was applied in a wind tunnel experiment in the NASA Langley Research Center 8-Foot Transonic Pressure Tunnel to study the effect of wing fillets on the global vortex induced surface static pressure field about a sharp leading-edge 76 deg./40 deg. double delta wing, or strake-wing, model at subsonic and transonic speeds. Global calibrations of the PSP were obtained at M(sub infinity) = 0.50, 0.70, 0.85, 0.95, and 1.20, a Reynolds number per unit length of 2.0 million, and angles of attack from 10 degrees to 20 degrees using an insitu method featuring the simultaneous acquisition of electronically scanned pressures (ESP) at discrete locations on the model. The mean error in the PSP measurements relative to the ESP data was approximately 2 percent or less at M(sub infinity) = 0.50 to 0.85 but increased to several percent at M(sub infinity) =0.95 and 1.20. The PSP pressure distributions and pseudo-colored, planform-view pressure maps clearly revealed the vortex-induced pressure signatures at all Mach numbers and angles of attack. Small fillets having parabolic or diamond planforms situated at the strake-wing intersection were respectively designed to manipulate the vortical flows by removing the leading-edge discontinuity or introducing additional discontinuities. The fillets caused global changes in the vortex-dominated surface pressure field that were effectively captured in the PSP measurements. The vortex surface pressure signatures were compared to available off-surface vortex cross-flow structures obtained using a laser vapor screen (LVS) flow visualization technique. The fillet effects on the PSP pressure distributions and the observed leading-edge vortex flow characteristics were consistent with the trends in the measured lift, drag, and pitching moment coefficients.

Erickson, Gary E.↗
DOE OSTI2025

CALPHAD-based ICME design of single-step aging to enhance mechanical strength of WAAM Haynes 282

To match the strength of wire-arc additive manufactured Haynes 282 to its wrought counterpart via a single-step aging heat treatment, the CALPHAD (Calculation of Phase Diagrams) method is integrated with physics-based process-structure-property models and experimental validation. The integrated computational materials engineering (ICME) framework simulates the effects of aging on γ′ and M 23 C 6 precipitation and the resulting yield strength. To improve simulation reliability, the interfacial energies between γ/γ′ and γ/M 23 C 6 carbides were estimated by comparison with precipitation kinetic modeling and measured precipitate sizes. γ′ and M23C6 were found to precipitate simultaneously between 640 and 860 °C, producing microstructures similar to those produced by two-step aging. The optimal γ′ size for peak yield stress was calculated to be 20–23 nm. WAAM Haynes 282 aged at 780 °C for 50 h exceeded the mechanical performance of its wrought counterpart subjected to two-step aging, though desired properties can also be achieved at 800 °C for 16 h or less. The error in yield strength is less than 20 MPa, demonstrating good agreement between the modeling framework and experiments. Creep studies showed that WAAM Haynes 282 exceeded the calculated rupture time, reaching 481 h. This proposed methodology can accelerate the design of aging heat treatments for any γ′-strengthened nickel-base alloy, minimizing the resources required for trial-and-error experiments.

CALPHAD↗