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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Extraction of mechanical properties of materials through deep learning from instrumented indentation

Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress–strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals an d alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulation and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys.

3D printed materials, stress-strain behavior, mult↗
DOE OSTI2022

Investigation of the thermal conductivity of molten LiF-NaF-KF with experiments, theory, and equilibrium molecular dynamics

Molten salts are being proposed for numerous advanced energy applications, including advanced nuclear reactors, concentrating solar power plants, thermal energy storage, and fusion reactors. Accurate knowledge of the thermophysical properties of molten salts directly impact the performance of these energy systems and are essential for design and safety analyses. Thermal conductivity data for fluoride molten salts and mixtures are especially lacking. In this work, experimental measurements of thermal conductivity using the steady-state variable gap technique were performed on eutectic LiF-NaF-KF from 834 to 1195 K. The experiment accounts for radiative, convective, and conductive heat losses. In addition, theoretical and molecular dynamics models are used, from 750 K up to 1300 K, to estimate the thermal conductivity for comparison with the experimental results. The results of experiments show a weak negative deviation of thermal conductivity with temperature, unlike previous experimental results in the literature. The measured thermal conductivity magnitudes agree with the theoretical and molecular dynamics predictions, aside from the data above 1100 K, where heat losses and radiative errors are the most significant, having a 16% maximum deviation from theory. These experimental results provide new thermal conductivity data for the LiF-NaF-KF system and further validation of the predictive models. The theoretical model was used to map the composition and temperature dependent thermal conductivity of LiF-NaF-KF and the mapping’s deviation from a linear additivity estimation of thermal conductivity. Additionally, this mapping showed the highest deviations from linearity for KF-LiF rich mixtures and increasing deviation with temperature. Notably, the deviation from linearity near the LiF-NaF-KF eutectic composition was around 25%.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2011

Error Estimate of the Ares I Vehicle Longitudinal Aerodynamic Characteristics Based on Turbulent Navier-Stokes Analysis

Numerical predictions of the longitudinal aerodynamic characteristics for the Ares I class of vehicles, along with the associated error estimate derived from an iterative convergence grid refinement, are presented. Computational results are based on the unstructured grid, Reynolds-averaged Navier-Stokes flow solver USM3D, with an assumption that the flow is fully turbulent over the entire vehicle. This effort was designed to complement the prior computational activities conducted over the past five years in support of the Ares I Project with the emphasis on the vehicle s last design cycle designated as the A106 configuration. Due to a lack of flight data for this particular design s outer mold line, the initial vehicle s aerodynamic predictions and the associated error estimates were first assessed and validated against the available experimental data at representative wind tunnel flow conditions pertinent to the ascent phase of the trajectory without including any propulsion effects. Subsequently, the established procedures were then applied to obtain the longitudinal aerodynamic predictions at the selected flight flow conditions. Sample computed results and the correlations with the experimental measurements are presented. In addition, the present analysis includes the relevant data to highlight the balance between the prediction accuracy against the grid size and, thus, the corresponding computer resource requirements for the computations at both wind tunnel and flight flow conditions. NOTE: Some details have been removed from selected plots and figures in compliance with the sensitive but unclassified (SBU) restrictions. However, the content still conveys the merits of the technical approach and the relevant results.

Aerodynamic characteristics↗
NASA NTRS2003

Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters

Near-infrared spectroscopy (NIRS) can be used to noninvasively measure changes in the concentrations of oxy- and deoxyhemoglobin in tissue. We have previously shown that while global changes can be reliably measured, focal changes can produce erroneous estimates of concentration changes (NeuroImage 13 (2001), 76). Here, we describe four separate sources for systematic error in the calculation of focal hemoglobin changes from NIRS data and use experimental methods and Monte Carlo simulations to examine the importance and mitigation methods of each. The sources of error are: (1). the absolute magnitudes and relative differences in pathlength factors as a function of wavelength, (2). the location and spatial extent of the absorption change with respect to the optical probe, (3). possible differences in the spatial distribution of hemoglobin species, and (4). the potential for simultaneous monitoring of multiple regions of activation. We found wavelength selection and optode placement to be important variables in minimizing such errors, and our findings indicate that appropriate experimental procedures could reduce each of these errors to a small fraction (<10%) of the observed concentration changes.

Non-NASA Center↗
DOE OSTI2022

Human Performance Analysis Depending on Operator Expertise (Student vs. Operator) and Simulator Complexity (Rancor Microworld vs. Compact Nuclear Simulator)

Human reliability analysis (HRA) evaluates human errors and provides human error probabilities (HEPs) for application in probabilistic safety assessment (PSA), which is a comprehensive safety assessment method for nuclear power plants (NPPs). Generally, HRA methods estimate HEPs based on human reliability data collected from actual historical measurement, simulator experiments, or expert judgement. Most recent HRA data collection studies focus on collecting data via full-scope main control room (MCR) simulators with actual licensed reactor operators. Contrary to this, Idaho National Laboratory (INL) has adopted a different approach, which attempts to collect HRA data based on experiment using simplified simulators and student participants by following the Simplified Human Error Experimental Program (SHEEP). This approach has a couple of advantages compared to full-scope data collection. Representatively, it has relatively low entry point for collecting HRA data, and secures large sample sizes with reasonable cost and labor. In the previous studies, we developed the SHEEP framework, then verified whether the data collected through the framework could support a representative full-scope data collection study, i.e., the Human Reliability Data Extraction (HuREX) study. Also, we analyzed human performance measurements depending on participant type (i.e., student vs. operator). In this paper, we analyze human performance data collected from an experiment comparing operator expertise and simulator complexity when using the more simplified simulator developed by INL, i.e., Rancor Microworld and the less simplified simulator, i.e., Compact Nuclear Simulator (CNS) developed by Korea Atomic Energy Research Institute (KAERI). Analysis of variance (ANOVA) tests and correlation analysis are used for analyzing the experimental data.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Processing Aleatory and Epistemic Uncertainties in Experimental Data From Sparse Replicate Tests of Stochastic Systems for Real-Space Model Validation

This paper presents a practical methodology for propagating and processing uncertainties associated with random measurement and estimation errors (that vary from test-to-test) and systematic measurement and estimation errors (uncertain but similar from test-to-test) in inputs and outputs of replicate tests to characterize response variability of stochastically varying test units. Also treated are test condition control variability from test-to-test and sampling uncertainty due to limited numbers of replicate tests. These aleatory variabilities and epistemic uncertainties result in uncertainty on computed statistics of output response quantities. The methodology was developed in the context of processing experimental data for “real-space” (RS) model validation comparisons against model-predicted statistics and uncertainty thereof. The methodology is flexible and sufficient for many types of experimental and data uncertainty, offering the most extensive data uncertainty quantification (UQ) treatment of any model validation method the authors are aware of. It handles both interval and probabilistic uncertainty descriptions and can be performed with relatively little computational cost through use of simple and effective dimension- and order-adaptive polynomial response surfaces in a Monte Carlo (MC) uncertainty propagation approach. A key feature of the progressively upgraded response surfaces is that they enable estimation of propagation error contributed by the surrogate model. Sensitivity analysis of the relative contributions of the various uncertainty sources to the total uncertainty of statistical estimates is also presented. Finally, the methodologies are demonstrated on real experimental validation data involving all the mentioned sources and types of error and uncertainty in five replicate tests of pressure vessels heated and pressurized to failure. Simple spreadsheet procedures are used for all processing operations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Prediction of the Cu oxidation state from EELS and XAS spectra using supervised machine learning

Abstract Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed information about bonding, distributions and locations of atoms, and their coordination numbers and oxidation states. However, analysis of XAS/EELS data often relies on matching an unknown experimental sample to a series of simulated or experimental standard samples. This limits analysis throughput and the ability to extract quantitative information from a sample. In this work, we have trained a random forest model capable of predicting the oxidation state of copper based on its L-edge spectrum. Our model attains an R 2 score of 0.85 and a root mean square error of 0.24 on simulated data. It has also successfully predicted experimental L-edge EELS spectra taken in this work and XAS spectra extracted from the literature. We further demonstrate the utility of this model by predicting simulated and experimental spectra of mixed valence samples generated by this work. This model can be integrated into a real-time EELS/XAS analysis pipeline on mixtures of copper-containing materials of unknown composition and oxidation state. By expanding the training data, this methodology can be extended to data-driven spectral analysis of a broad range of materials.

36 MATERIALS SCIENCE↗
NASA NTRS1991

Multiple single event upsets in CMOS static rams

The occurrence of multiple upset errors during ground tests can contaminate the data and lead to error cross sections which are too high. In space, multiple errors may produce higher upsets than predicted and if they occur in single words they can defeat error detection and correction hardware. This investigation involves data which were taken during an experimental study of dose imprint effects in static memories. The results show that multiple errors occurred mainly for heavy ions with high linear energy transfers and with the majority of these in the soft upset sections of the dose-imprinted memory samples. The percentages of the total number of errors which were singles, doubles, and triplets, were determined as a function of LET, dose, and soft or hard section of the devices. The experimental observations are compared to the predictions of simple binomial statistics.

Stassinopoulos, E. G.↗
DOE OSTI2025

Identifying Nuclear Data Correlated Through Predicting Bias in Integral Experiments via Applying Principal Component Analysis to Random Forest

ABSTRACT Nuclear data (ND) are the input data for neutron‐transport simulations to answer questions related to nuclear technologies. Subsets of ND, here > 20,000 data points, are validated with respect to thousands of criticality experiments that represent various applications on a small scale. The aim of validation with these experiments is to find errors in ND or methods. The key challenge here is that several hundreds of ND are used to simulate one integral value. Hence, one cannot clearly identify what ND are leading to bias in criticality measurements. In fact, a mistake in one nuclear‐data observable can be compensated with an error in another, and the predicted criticality value would still be predicted in agreement with experimental data. Random forest (RF) was previously employed to predict bias in criticality measurements using sensitivities of simulated criticality experiments to ND. The SHapley Additive exPlanations (SHAP) metric was then applied to attribute the importance of each ND experiment and observable to bias prediction. This, however, did not highlight what ND were jointly related to predicting bias. This is important as it could inform us about where compensating errors in ND could hide. We tackle this shortcoming here by first decomposing the ND sensitivities to integral‐experiment simulations into principal components. Then we use principal component projections to predict bias via the RF and SHAP. The SHAP values and principal components are employed to reconstruct detailed SHAP values for each ND observable. We demonstrate that these extended SHAP bias predictions are more robust, less noisy, and more efficient. In addition, we show that this approach accounts for covariance in ND sensitivities and automates the identification of where compensating errors could hide in ND.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1990

Intergration of system identification and robust controller designs for flexible structures in space

An approach is developed using experimental data to identify a reduced-order model and its model error for a robust controller design. There are three steps involved in the approach. First, an approximately balanced model is identified using the Eigensystem Realization Algorithm, which is an identification algorithm. Second, the model error is calculated and described in frequency domain in terms of the H(infinity) norm. Third, a pole placement technique in combination with a H(infinity) control method is applied to design a controller for the considered system. A set experimental data from an existing setup, namely the Mini-Mast system, is used to illustrate and verify the approach.

Juang, Jer-Nan↗
DOE OSTI2023

Investigation of 3D printed lightweight hybrid composites via theoretical modeling and machine learning

Hybrid composites combine two or more different fillers to achieve multifunctional or advanced material properties, such as lightweight and enhanced mechanical properties. The properties of the composites significantly depend on their microstructures, which can be tailored via advanced 3D printing processes. Understanding the process-structure-property relationships is critical to enable the design and engineering of novel hybrid composites for applications in aerospace, automotive, and protective coatings. Here, for this work, we develop 3D printable and lightweight hybrid composites and leverage the conventional design of experiments, a theoretical hybrid model, and an image-driven machine learning (ML) method to investigate their mechanical behaviors. The hybrid composites are formulated with elastomer matrix, microfillers, and thin-shell particles, enabling a significant degree of design freedom of microstructures with densities and mechanical properties varying up to 70% and 91%, respectively. Our statistical analysis indicates that the 3D printing path direction and the microfibers fraction are dominating process parameters with contribution percentages of 45.3% and 57.7% on the specific stiffness and strength, respectively. A hybrid mechanics model is developed based on a simple Weibull distribution function and classical single-filler models to effectively capture the variations in mechanical properties, however, it overestimates the values due to its statistical constraints and idealization of experimental uncertainty. The image-driven ML model leverages the microscale images directly without losing the structural details, shows more accurate predictions with experimental data, and has 48.6% lower root mean square error than the theoretical model.

3D printing↗
DOE OSTI2024

Energetic and Spectroscopic Properties of Astrophysically Relevant MgC 4 H Radicals Using High-Level Ab Initio Calculations

Considering the importance of magnesium-bearing hydrocarbon molecules (MgC n H; n = 2, 4, and 6) in the carbon-rich circumstellar envelopes (e.g., IRC+10216), a total of 28 constitutional isomers of MgC 4 H have been theoretically investigated using density functional theory (DFT) and coupled-cluster methods. The zero-point vibrational energy corrected relative energies at the ROCCSD(T)/cc-pCVTZ level of theory reveal that the linear isomer, 1-magnesapent-2,4-diyn-1-yl (1, 2 Σ + ), is the global minimum geometry on the MgC 4 H potential energy surface. The latter has been detected both in the laboratory and in the evolved carbon star, IRC+10216. The calculated spectroscopic data for 1 match well with the experimental observations (error ~ 0.78%) which validates our theoretical methodology. Plausible isomerization processes happening among different isomers are examined using DFT and coupled-cluster methods. CASPT2 calculations have been performed for a few isomers exhibiting multireference characteristics. The second most stable isomer, 1-ethynyl-1λ 3 -magnesacycloprop-2-ene-2,3-diyl (2, 2 A 1 , μ = 2.54 D), is 146 kJ mol –1 higher in energy than 1 and possibly the next promising candidate to be detected in the laboratory or in the interstellar medium in future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Impact of Limited Degree of Freedom Drag Coefficients on a Floating Offshore Wind Turbine Simulation

The worldwide effort to design and commission floating offshore wind turbines (FOWT) is motivating the need for reliable numerical models that adequately represent their physical behavior under realistic sea states. However, properly representing the hydrodynamic quadratic damping for FOWT remains uncertain, because of its dependency on the choice of drag coefficients (dimensionless or not). It is hypothesized that the limited degree of freedom (DoF) drag coefficient formulation that uses only translational drag coefficients causes mischaracterization of the rotational DoF drag, leading to underestimation of FOWT global loads, such as tower base fore-aft shear. To address these hydrodynamic modeling uncertainties, different quadratic drag models implemented in the open-source mid-fidelity simulation tool, OpenFAST, were investigated and compared with the experimental data from the Offshore Code Comparison Collaboration, Continued, with Correlation (OC5) project. The tower base fore-aft shear and up-wave mooring line tension were compared under an irregular wave loading condition to demonstrate the effects of the different damping models. Two types of hydrodynamic quadratic drag formulations were considered: (1) member-based dimensionless drag coefficients applied only at the translational DoF (namely limited-DoF drag model) and (2) quadratic drag matrix model (in dimensional form). Based on the results, the former consistently underestimated the 95th percentile peak loads and spectral responses when compared to the OC5 experimental data. In contrast, the drag matrix models reduced errors in estimates of the tower base shear peak load by 7–10% compared to the limited-DoF drag model. The underestimation in the tower base fore-aft shear was thus inferred be related to mischaracterization of the rotational pitch drag and the heave motion/drag by the limited-DoF model.

17 WIND ENERGY↗
NASA NTRS1972

RFI in hybrid loops - Simulation and experimental results.

A digital simulation of an imperfect second-order hybrid phase-locked loop (HPLL) operating in radio frequency interference (RFI) is described. Its performance is characterized in terms of phase error variance and phase error probability density function (PDF). Monte-Carlo simulation is used to show that the HPLL can be superior to the conventional phase-locked loops in RFI backgrounds when minimum phase error variance is the goodness criterion. Similar experimentally obtained data are given in support of the simulation data.

Ziemer, R. E.↗
NASA NTRS1990

Delivery performance of conventional aircraft by terminal-area, time-based air traffic control: A real-time simulation evaluation

A description and results are presented of a study to measure the performance and reaction of airline flight crews, in a full workload DC-9 cockpit, flying in a real-time simulation of an air traffic control (ATC) concept called Traffic Intelligence for the Management of Efficient Runway-scheduling (TIMER). Experimental objectives were to verify earlier fast-time TIMER time-delivery precision results and obtain data for the validation or refinement of existing computer models of pilot/airborne performance. Experimental data indicated a runway threshold, interarrival-time-error standard deviation in the range of 10.4 to 14.1 seconds. Other real-time system performance parameters measured include approach speeds, response time to controller turn instructions, bank angles employed, and ATC controller message delivery-time errors.

Credeur, Leonard↗
NASA NTRS1990

An improved method for simultaneous determination of frictional pressure drop and vapor volume fraction in vertical flow boiling

The two-phase frictional pressure drop and vapor volume fraction in the vertical boiling and adiabatic flow of the refrigerant, R11, have been simultaneously measured by a liquid balancing column and differential magnetic reluctance pressure transducers. An account is given of the experimental apparatus and procedure, data acquisition and analysis, and error estimation employed. All values of two-phase multipliers evaluated on the basis of the measured frictional pressure drop data in vertical upflow fall in the range bounded by the predictions of the Chisholm correlation and the homogeneous model.

Klausner, J. F.↗
NASA NTRS1993

Microspheres for laser velocimetry in high temperature wind tunnel

The introduction of non-intrusive measurement techniques in wind tunnel experimentation has been a turning point in error free data acquisition. Laser velocimetry has been progressively implemented and utilized in various wind tunnels; e.g. subsonic, transonic, and supersonic. The success of the laser velocimeter technique is based on an accurate measurement of scattered light by seeding particles introduced into the flow stream in the wind tunnel. Therefore, application of appropriate seeding particles will affect, to a large extent the acquired data. The seeding material used depends on the type of experiment being run. Among the seeding material for subsonic tunnel are kerosene, Kaolin, and polystyrene. Polystyrene is known to be the best because of being solid particles, having high index of refraction, capable of being made both spherical and monodisperse. However for high temperature wind tunnel testing seeding material must have an additional characteristic that is high melting point. Typically metal oxide powders such as Al2O3 with melting point 3660 F are used. The metal oxides are, however polydispersed, have a high density, and a tendency to form large agglomerate that does not closely follow the flow velocity. The addition of flame phase silica to metal oxide helps to break up the agglomerates, yet still results in a narrow band of polydispersed seeding. The less desirable utility of metal oxide in high temperature wind tunnels necessitates the search for a better alternative particle seeding which this paper addresses. The Laser Velocimetry (LV) characteristic of polystyrene makes it a prime candidate as a base material in achieving the high temperature particle seeding inexpensively. While polystyrene monodisperse seeding particle reported has been successful in a subsonic wind tunnel, it lacks the high melting point and thus is not practically usable in a high temperature wind tunnel. It is well known that rise in melting point of polystyrene can be achieved by a cross-linking technique. Since polystyrene already possesses all the desired characteristics for LV, to circumvent the low melting point, a cross-linking technique was investigated.

Ghorieshi, Anthony↗
NASA NTRS1993

On the Accuracy of CFD-Based Pressure Drop Predictions for Right-Angle Ducts

The predictive capability of computational fluid dynamics (CFD) codes for turbulent flow through curved ducts is of significant importance to the design and performance analysis of modern rocket engine flowpaths. Code calibration and validation studies for this class of flow are desireable to estimate the performance margin and operating range of components designed using Navier-Stokes methods. Parametric experimental studies such as that of Weske (NACA ARR W-39) provided a wealth of performance data for the design of single- and compound elbow configurations with various cross-sections, curvature and aspect ratios at varying Reynolds numbers. In that work, the majority of data is presented in the form of loss coefficients, characterizing pressure losses due to duct curvature, and including losses due to wall friction. Using measured friction coefficients, losses of equivalent straight lengths of duct are subtracted, resulting in performance curves useful for design computations. These data are currently used in a CFD-based parametric study covering a broad range of operating conditions. Of particular interest for the accuracy of CFD predictions are the effects on pressure loss due to inlet boundary layer thickness (dependent on upstream development length), and the wall treatment for the turbulence equations (conventional wall functions vs. wall integration using a two-layer model). The experimental data are reassessed in the form of an error analysis, and are compared with CFD predictions for 18 computational cases. Grid-independence, grid spacing, and convergence requirements of the cases are discussed. Conclusions regarding the relative importance of the parametric variables will be presented.

Brankovic, Andreja↗