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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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The proton charge radius extracted from the initial-state radiation experiment at MAMI

Abstract We report on a comprehensive reinterpretation of the existing cross-section data for elastic electron-proton scattering obtained by the initial-state radiation technique, resulting in a significantly improved accuracy of the extracted proton charge radius. By refining the external energy corrections we have achieved an outstanding description of the radiative tail, essential for a detailed investigation of the proton finite-size effects on the measured cross sections. This development, together with a novel framework for determining the radius, based on a regression analysis of the cross sections employing a polynomial model for the form factor, led us to a new value for the charge radius, which is $$(0.878 \pm 0.011_\mathrm {stat.}\pm 0.031_\mathrm {sys.}\pm 0.002_\mathrm {mod.})\,\mathrm {fm}$$ ( 0.878 ± 0 . 011 stat . ± 0 . 031 sys . ± 0 . 002 mod . ) fm

Physics↗
DOE OSTI2023

The Cross-sectional and Longitudinal Association Between Thyroid Function and Depression: A Population-Based Study

Abstract Context An association of thyroid function with mood disorders has been widely suggested, but very few studies have examined this association longitudinally. Objective We assessed the cross-sectional and longitudinal association between thyroid function and depression in a population-based cohort. Methods A total of 9471 individuals were included in cross-sectional analyses, of whom 8366 had longitudinal data. At baseline, we assessed thyroid function using serum samples (thyrotropin [TSH], free thyroxine (FT4), and thyroid peroxidase antibodies) and depressive symptoms using the Centre for Epidemiologic Studies Depression (CES-D) scale. Incident depressive events (n = 1366) were continuously followed up with the CES-D and clinical interviews. We analyzed the cross-sectional association of thyroid function and thyroid disease with depressive symptoms using linear and logistic regression, and the longitudinal association with Cox proportional hazard models for depressive events. Results Lower TSH levels and lower and higher FT4 levels were cross-sectionally associated with more depressive symptoms with a B value of −0.07 per 1 unit increase of natural log-transformed TSH (95% CI −0.11; −0.04). Furthermore, hypothyroidism was cross-sectionally associated with less depressive symptoms and hyperthyroidism with more depressive symptoms. Longitudinally, there was a U-shaped association between FT4 and incident depressive events but only in euthyroid participants. Conclusion We show a cross-sectional association between thyroid (dys)function with depressive symptoms, and a U-shaped association between FT4 and incident depressive events in euthyroid individuals. Our findings suggest an association of thyroid function with the risk of developing depression, albeit small. Reverse causation and additional underlying factors may also contribute to the association.

Roa Dueñas, Oscar Hernando (ORCID:0000000207522261↗
DOE OSTI2024

Inferring the Focal Depths of Small Earthquakes in Southern California Using Physics-Based Waveform Features

Determining the depths of small crustal earthquakes is challenging in many regions of the world, because most seismic networks are too sparse to resolve trade-offs between depth and origin time with conventional arrival-time methods. Precise and accurate depth estimation is important, because it can help seismologists discriminate between earthquakes and explosions, which is relevant to monitoring nuclear test ban treaties and producing earthquake catalogs that are uncontaminated by mining blasts. Here, we examine the depth sensitivity of several physics-based waveform features for ~8000 earthquakes in southern California that have well-resolved depths from arrival-time inversion. We focus on small earthquakes (2 < M L < 4) recorded at local distances (<150 km), for which depth estimation is especially challenging. We find that differential magnitudes (M w /M L –M c ) are positively correlated with focal depth, implying that coda wave excitation decreases with focal depth. We analyze a simple proxy for relative frequency content, Φ≡log 10 (M 0 )+3log 10 (f c ), and find that source spectra are preferentially enriched in high frequencies, or “blue-shifted,” as focal depth increases. Here, we also find that two spectral amplitude ratios Rg 0.5–2 Hz/Sg 0.5–8 Hz and Pg/Sg at 3–8 Hz decrease as focal depth increases. Using multilinear regression with these features as predictor variables, we develop models that can explain 11%–59% of the variance in depths within 10 subregions and 25% of the depth variance across southern California as a whole. We suggest that incorporating these features into a machine learning workflow could help resolve focal depths in regions that are poorly instrumented and lack large databases of well-located events. Some of the waveform features we evaluate in this study have previously been used as source discriminants, and our results imply that their effectiveness in discrimination is partially because explosions generally occur at shallower depths than earthquakes.

58 GEOSCIENCES↗
NASA NTRS1972

Development of a noise annoyance sensitivity scale

Examining the problem of noise pollution from the psychological rather than the engineering view, a test of human sensitivity to noise was developed against the criterion of noise annoyance. Test development evolved from a previous study in which biographical, attitudinal, and personality data was collected on a sample of 166 subjects drawn from the adult community of Raleigh. Analysis revealed that only a small subset of the data collected was predictive of noise annoyance. Item analysis yielded 74 predictive items that composed the preliminary noise sensitivity test. This was administered to a sample of 80 adults who later rate the annoyance value of six sounds (equated in terms of peak sound pressure level) presented in a simulated home, living-room environment. A predictive model involving 20 test items was developed using multiple regression techniques, and an item weighting scheme was evaluated.

Bregman, H. L.↗
NASA NTRS1975

Application of remote sensing for fishery resources assessment and monitoring

The author has identified the following significant results. The distribution and abundance of white marlin correlated with the chlorophyll, water temperature, and Secchi depth sea truth measurements. Results of correlation analyses for dolphin were inconclusive. Predicition models for white marlin were developed using stepwise multiple regression and discriminant function analysis techniques which demonstrated a potential for increasing the probability of game fishing success. The S190A and B imagery was density sliced/color enhanced with white marlin location superimposed on the image, but no density/white marlin relationship could be established.

Savastano, K. J.↗
NASA NTRS1981

Estimation of airplane stability and control derivatives at high angles of attack

A technique for extracting approximate aircraft stability and control derivatives over a broad range of angle of attack from a single large amplitude maneuver which encompasses that range is presented. The results from the application of this technique to flight data are shown to agree well with derivatives extracted from several maneuvers which are more restricted in range of angle of attack. The technique consists of dividing the data from the large maneuver into bins that are a function of angle of attack. The binned data is then analyzed with a modified stepwise regression. The identification of nonlinear terms in the aerodynamic model is shown to discern regions of rapid change in aircraft behavior.

Batterson, J. G.↗
NASA NTRS1984

Meteorological factors in Earth-Satellite propagation

A 5-meter paraboloidal antenna operated at 28 GHz showed gain changes of 2 dB due to rain. While precise estimation of the corresponding angle of arrival changes is difficult, they appear to have been on the order of 0.02 degrees. The attenuation at 28.6 GHz inferred from radiometry agreed well with that measured simultaneously over a satellite/Earth link at the same frequency. The radiometers so calibrated have been used to add to the available site diversity data base using a 9 km baseline. An improved empirical model of site diversity gain was obtained by applying regression techniques to available published data. A brief review of the literature has led to suggestions for two experimental programs, one dealing with multifrequency radiometry and the other with the effects of the stochastic properties of precipitation on wideband data transmission.

Levis, C. A.↗
NASA NTRS2000

Determining a Prony Series for a Viscoelastic Material From Time Varying Strain Data

In this study a method of determining the coefficients in a Prony series representation of a viscoelastic modulus from rate dependent data is presented. Load versus time test data for a sequence of different rate loading segments is least-squares fitted to a Prony series hereditary integral model of the material tested. A nonlinear least squares regression algorithm is employed. The measured data includes ramp loading, relaxation, and unloading stress-strain data. The resulting Prony series which captures strain rate loading and unloading effects, produces an excellent fit to the complex loading sequence.

Tzikang, Chen↗
NASA NTRS2001

Improving Space Project Cost Estimating with Engineering Management Variables

Current space project cost models attempt to predict space flight project cost via regression equations, which relate the cost of projects to technical performance metrics (e.g. weight, thrust, power, pointing accuracy, etc.). This paper examines the introduction of engineering management parameters to the set of explanatory variables. A number of specific engineering management variables are considered and exploratory regression analysis is performed to determine if there is statistical evidence for cost effects apart from technical aspects of the projects. It is concluded that there are other non-technical effects at work and that further research is warranted to determine if it can be shown that these cost effects are definitely related to engineering management.

Hamaker, Joseph W.↗
NASA NTRS2020

Recommendations for Using Noise Monitors to Estimate Noise Exposure During X-59 Community Tests

A low fidelity simulation approach is used to explore how to place and use noise monitors during X-59 QueSST community tests, where people’s annoyance to the noise produced by the X-59 aircraft will be gathered. Several recommendations are provided including: 1) the desired number of sparsely spaced noise monitor sites within the survey area, 2) whether to group and average measurements across multiple noise monitors located at a site, 3) what spacing should be used if grouped noise monitors are used, 4) an approach to mitigate ambient noise contamination at the measurement sites, 5) a method to combine empirical and predicted dose estimates to provide a single dose estimate for respondents, and 6) assessing how changes in turbulence intensity and array configuration affect dose uncertainty. To make these recommendations, the error that is expected when fitting contrived, smoothly varying sonic boom “reference exposure surfaces” is studied when a spatially sparse and scattered set of samples is used as responses for the fit. The reference exposure surfaces mimic the sonic boom exposure at ground level that might be expected in the X-59 survey area in the absence of atmospheric turbulence, ambient noise, and other localized effects. The spatial extent of these surfaces varies and is representative of the different survey area sizes that might be expected during future X-59 community overflight tests. These contrived reference surfaces are sampled, and those reference samples are then perturbed to mimic atmospheric turbulence, ambient noise and other localized effects that might affect noise monitor measurements within overflown communities. Two different surface fitting methods are investigated when fitting these perturbed samples to approximate the reference surface. The first method uses interpolation between the perturbed data at the scattered sites to compute the fit. The second method fits a polynomial surface model to the perturbed data using ordinary least squares regression analysis. For both fitting methods, the root mean square fit error is computed from the pointwise difference between the fit surface and the reference surface as the count and configuration of the sites is varied while also averaging the error across many different realizations of both the smooth variation of the reference exposure surface and the random, localized perturbations at the sample sites. Different site configurations are compared using this error statistic to make the recommendations noted above. Additionally, the two fitting approaches (interpolation vs linear regression) are compared based on the fit error observed in these simulations. These analyses, comparisons, and recommendations should inform future decisions on the noise monitor placement and the methods used to analyze the noise monitor data that is collected during X-59 community overflights.

sonic boom↗
NASA NTRSDate not supplied

High-Fidelity Flight Dynamic Analysis of Transonic Truss-Braced Wing

This paper presents a high-fidelity flight dynamic analysis of the Mach 0.8 Transonic Truss-Braced Wing (TTBW). Unsteady RANS CFD simulations of the Mach 0.8 TTBW in pitch, plunge, roll, and yaw oscillations are conducted in FUN3D. The time-domain data are transformed into the frequency-domain data by Fourier series. Transfer functions of the dynamic stability derivatives are then estimated by a frequency-domain re- gression. The dynamic stability derivatives with respect to the angle of attack are determined by the regression of the unsteady aerodynamic coefficients for the plunge motion. The dynamic stability derivatives with re- spect to the pitch rate are determined by the regression of the differential unsteady aerodynamic coefficients for the pitch motion upon the removal of the angle of attack contribution by the plunge motion. Similarly, dynamic stability derivatives with respect to the angle of sideslip, roll rate, and yaw rates are determined from the frequency domain regression. The longitudinal and lateral-direction flight dynamic models of the Mach 0.8 TTBW are constructed from these dynamic stability derivatives. The eigenvalues of the aircraft modes are analyzed to determine the vehicle stability.

Aircraft Stability and Control↗
DOE OSTI2026

Interpretable and flexible non-intrusive reduced-order models using reproducing kernel Hilbert spaces

This paper develops an interpretable, non-intrusive reduced-order modeling technique using regularized kernel interpolation. Existing non-intrusive approaches approximate the dynamics of a reduced-order model (ROM) by solving a data-driven least-squares regression problem for low-dimensional matrix operators. Our approach instead leverages regularized kernel interpolation, which yields an optimal approximation of the ROM dynamics from a user-defined reproducing kernel Hilbert space. We show that our kernel-based approach can produce interpretable ROMs whose structure mirrors full-order model structure by embedding judiciously chosen feature maps into the kernel. The approach is flexible and allows a combination of informed structure through feature maps and closure terms via more general nonlinear terms in the kernel. We also derive a computable a posteriori error bound that combines standard error estimates for intrusive projection-based ROMs and kernel interpolants. In conclusion, the approach is demonstrated in several numerical experiments that include comparisons to operator inference using both proper orthogonal decomposition and quadratic manifold dimension reduction.

Data-driven model reduction↗
DOE OSTI2026

Calibration and Rapid-Adoption Forecasting Techniques

CRAFT (Calibration and Rapid-Adoption Forecasting Techniques) CRAFT is a Python-based project for processing, analyzing, and modeling atmospheric or environmental data. It uses machine learning techniques, specifically Random Forest Regression, to create emulators for various environmental variables such as gross primary production and soil water content. It then uses these emulators to robustly test the parameter space of mechanistic models to provide posterior estimations of the free parameters.

Robins, Zachary↗
NASA NTRS2000

An Evaluation Technique for an F/A-18 Aircraft Loads Model Using F/A-18 Systems Research Aircraft Flight Data

A limited evaluation of the F/A-18 baseline loads model was performed on the Systems Research Aircraft at NASA Dryden Flight Research Center (Edwards, California). Boeing developed the F/A-18 loads model using a linear aeroelastic analysis in conjunction with a flight simulator to determine loads at discrete locations on the aircraft. This experiment was designed so that analysis of doublets could be used to establish aircraft aerodynamic and loads response at 20 flight conditions. Instrumentation on the right outboard leading edge flap, left aileron, and left stabilator measured the hinge moment so that comparisons could be made between in-flight-measured hinge moments and loads model-predicted values at these locations. Comparisons showed that the difference between the loads model-predicted and in-flight-measured hinge moments was up to 130 percent of the flight limit load. A stepwise regression technique was used to determine new loads derivatives. These derivatives were placed in the loads model, which reduced the error to within 10 percent of the flight limit load. This paper discusses the flight test methodology, a process for determining loads coefficients, and the direct comparisons of predicted and measured hinge moments and loads coefficients.

Olney, Candida D.↗
DOE OSTI2024

Enhancing Solar Power Forecasting with Regularized Constrained Quantile Regression Averaging and Bootstrapping Techniques

Probabilistic solar power forecasting (SPF) plays an essential role in optimizing power-grid operations by quantifying the forecast uncertainty. To improve the accuracy and robustness of probabilistic SPF, this paper introduces the regularized constrained quantile regression averaging (rCQRA) method to combine outputs from multiple PSPF models. In addition, a bootstrapping method was used to quantify model uncertainty, providing insights into the reliability and significance of each ensemble component. To evaluate its efficacy, the proposed rCQRA method is used to integrate four PSPF methods. The resulting SPF models are trained and validated using a real-world six-year dataset from a rooftop solar plant in the USA. The performance of the proposed rCQRA method is evaluated and compared with two benchmark methods under three categories of weather conditions. It is shown that the rCQRA method has superior performance in its forecast reliability, sharpness, and accuracy.

Ensemble learning, probabilistic solar power forec↗
DOE OSTI2024

Online Learning of Effective Turbine Wind Speed in Wind Farms

To develop better wind farm controllers that can meet more complex objectives, methods of modeling the wind turbine wakes at low computational expense are needed. Gaussian process (GP) regression offers a computationally inexpensive framework for learning complex functions from noisy measurements with very few datapoints. In this work, an online learning approach is presented to learn the rotor-averaged wind velocity at downstream wind turbines with GPs, using the available datastream of wind field measurements and wind turbine control set-points. This framework can readily be integrated into model-based controls methods because the model a) is updated online at low computational expense, b) assumes a mathematically favorable Gaussian form, and c) explicitly quantifies the stochastic nature of the wake field so that the trade-off between exploration and exploitation, and the uncertainty in the prediction, can be utilized. We show that a GP-learned model can match true values with errors within 0.5% on average, with as few as 5 training data points.

Gaussian process↗
NASA NTRS1995

Supporting technology for the development of Controlled Ecological Life Support Systems (CELSS)

To support the development of Controlled Ecological Life Support Systems (CELSS) in the space program, a metabolic simulator has been selected for use in a closed chamber to test functions of the CELSS. This metabolic simulator is a catalytic reactor which oxidizes the methyl acetate to produce carbon dioxide and water vapor. In this project, kinetic studies of catalytic oxidation of methyl acetate were conducted using monolithic and pellet catalysts with 0.5% (by weight) platinum (Pt) on aluminum oxide (Al2O3). The reaction was studied at a pressure of one atmosphere and at temperatures varying from 160 C to 420 C. By-products were identified at the exit of the preheater and reactor. For the kinetic study with the monolithic catalyst, a linear regression method was used to correlate the kinetic data with zero-order, first-order and Langmuir-Hinshelwood models. Results indicate that the first-order model represents the data adequately at low concentrations of methyl acetate. For higher concentrations of methyl acetate, the Langmuir-Hinshelwood model best represents the kinetic data. Both rate constant and adsorption equilibrium constants were estimated from the regression. A Taguchi orthogonal array (L(sub 9)) was used to investigate the effects of temperature, flow rate, and concentration on the catalytic oxidation of methyl acetate. For the monolithic catalyst, temperature exerts the most significant effect, followed by concentration of methyl acetate. For the pellet catalyst, reaction temperature is the most significant factor, followed by gas flow rate and methyl acetate concentration. Concentrations of either carbon dioxide or oxygen were seen to have insignificant effect on the methyl acetate conversion process. Experimental results indicate that the preheater with glass beads can accomplish thermal cracking and catalytic reaction of methyl acetate to produce acetic acid, methanol, methyl formate, and 1-propanol. The concentration of all by-products was measured in ppmv (parts per million by volume). At higher temperatures, greater amounts of these products are produced, as expected. In all cases, methanol was the predominant concentration detected, followed by methyl formate. At temperatures lower than 320 C for the P-type monolithic catalyst, methanol, acetic acid, and acetone were detected, whereas, for the E-type monolithic catalyst, only methanol was detected at 160 C. Both P and E types of the monolithic catalyst were specified with the same substrates (ceramic), washcoat (Al2O3), and promoter (Pt). However, the manufacturing and treatment procedures were quite different. It was therefore concluded that the performance of the E-type monolithic catalyst is superior to that of the P-type for oxidation of methyl acetate. At higher reaction temperatures, e.g., above 420 C, all reactants and byproducts were completely oxidized using these two types of monolithic catalyst to produce carbon dioxide and water vapor. A complex heterogenous catalytic reaction mechanism was proposed to explain the formation of the byproducts (methanol, acetic acid, and methyl formate) as the methyl acetate traveled through the preheater packed with glass beads. The by-product, 1-propanol, may be formed only through a homogeneous reaction, since it is difficult to develop a reasonable sequence of heterogeneous reaction steps to explain its formation. The homogeneous thermal decomposition of methyl acetate to form free radicals was proposed to explain the formation of 1-propanol, and also methanol, in the preheater. A dual-site catalytic reaction mechanism was proposed for the oxidation of methyl acetate over Pt/Al2O3 monolithic catalyst. The dual-site mechanism describes the chemisorption of oxygen molecules as well as a physical adsorption of methyl acetate on the active sites. On the active sites, methyl acetate is oxidized rapidly to form carbon dioxide and water vapor. A rate equation derived from this mechanism gives the Langmuir-Hinshelwood rate formula which has been observed from the experimental data obtained in this project for high methyl acetate concentration (greater than 1000 ppmv) over a monolithic catalyst. If the oxygen concentration is very high and methyl acetate concentration is very low, the reaction rate equation is then reduced to a first-order with respect to methyl acetate concentration. The first-order model has also been observed from the experimental data obtained in this project for low methyl acetate concentration (less than 1000 ppmv).

Li, Ku-Yen↗