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At least 325 records · Page 18

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DOE OSTI2023

Di-CNN: Domain-Knowledge-Informed Convolutional Neural Network for Manufacturing Quality Prediction

In manufacturing, convolutional neural networks (CNNs) are widely used on image sensor data for data-driven process monitoring and quality prediction. However, as purely data-driven models, CNNs do not integrate physical measures or practical considerations into the model structure or training procedure. Consequently, CNNs’ prediction accuracy can be limited, and model outputs may be hard to interpret practically. This study aims to leverage manufacturing domain knowledge to improve the accuracy and interpretability of CNNs in quality prediction. A novel CNN model, named Di-CNN, was developed that learns from both design-stage information (such as working condition and operational mode) and real-time sensor data, and adaptively weighs these data sources during model training. It exploits domain knowledge to guide model training, thus improving prediction accuracy and model interpretability. A case study on resistance spot welding, a popular lightweight metal-joining process for automotive manufacturing, compared the performance of (1) a Di-CNN with adaptive weights (the proposed model), (2) a Di-CNN without adaptive weights, and (3) a conventional CNN. The quality prediction results were measured with the mean squared error (MSE) over sixfold cross-validation. Model (1) achieved a mean MSE of 6.8866 and a median MSE of 6.1916, Model (2) achieved 13.6171 and 13.1343, and Model (3) achieved 27.2935 and 25.6117, demonstrating the superior performance of the proposed model.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Surrogate modeling for efficiently, accurately and conservatively estimating measures of risk

We present a surrogate modeling framework for conservatively estimating measures of risk from limited realizations of an expensive physical experiment or computational simulation. Risk measures combine objective probabilities with the subjective values of a decision maker to quantify anticipated outcomes. Given a set of samples, we construct a surrogate model that produces estimates of risk measures that are always greater than their empirical approximations obtained from the training data. These surrogate models limit over-confidence in reliability and safety assessments and produce estimates of risk measures that converge much faster to the true value than purely sample-based estimates. We first detail the construction of conservative surrogate models that can be tailored to a stakeholder’s risk preferences and then present an approach, based on stochastic orders, for constructing surrogate models that are conservative with respect to families of risk measures. Our surrogate models include biases that permit them to conservatively estimate the target risk measures. We provide theoretical results that show that these biases decay at the same rate as the L 2 error in the surrogate model. Numerical demonstrations confirm that risk-adapted surrogate models do indeed overestimate the target risk measures while converging at the expected rate.

42 ENGINEERING↗

NASA NTRS2000

Improvement of VLBI EOP Accuracy and Precision

In the CORE program, EOP measurements will be made with several different networks, each operating on a different day. It is essential that systematic differences between EOP derived by the different networks be minimized. Observed biases between the simultaneous CORE-A and NEOS-A sessions are about 60-130 micro(as) for PM, UT1 and nutation parameters. After removing biases, the observed rms differences are consistent with an increase in the formal precision of the measurements by factors ranging from 1.05 to 1.4. We discuss the possible sources of unmodeled error that account for these factors and the biases and the sensitivities of the network differences to modeling errors. We also discuss differences between VLBI and GPS PM measurements.

MacMillan, Daniel↗

DOE OSTI2023

High Impedance Fault Detection Through Quasi-Static State Estimation: A Parameter Error Modeling Approach

This paper presents a model for detecting high impedance faults using parameter error modeling and a two step per-phase weighted-least squares state estimation process. The proposed scheme leverages the use of Phasor Measurement Units and synthetic measurements to identify per-phase power flow and injection measurements which indicate a parameter error through ?2 Hypothesis Testing applied to the composed measurement error. Although current and voltage waveforms are commonly analyzed for high-impedance fault detection, wide area power flow and injection measurements, which are already inherent to the state estimation process, also show promise for real-world high-impedance fault detection applications. The error distributions after detection share the measurement function error spread observed in proven parameter error diagnostics and can be applied to high-impedance fault identification. Further, this error spread across measurement functions related to the fault will be clearly discerned from measurement error. Case studies are performed on the IEEE 33-Bus Distribution System along with the proposed model in Simulink.

Cooper, Austin↗

NASA NTRS1990

Dynamic sea surface topography, gravity, and improved orbit accuracies from the direct evaluation of Seasat altimeter data

A gravitational model incorporating Seasat altimetry, surface gravimetry, and satellite tracking data has been determined in terms of global spherical harmonics complete to degree and order 50. This model, PGS-3337, uses altimeter data as a dynamic observation of the satellite's height above the sea surface. A solution for the ocean's dynamic topography is recovered simultaneously with the orbit parameters, gravity, and ocean tidal terms. The recovered dynamic topography reveals the global long wavelength circulation of the oceans with a resolution of 2000 km and is very similar to the mean upper ocean dynamic height derived from historical ship observations. The PGS-3337 geoid has an uncertainty of 60 cm rms globally but 25 cm rms over the ocean because of the altimeter measurements. Seasat orbits determined in this solution have an estimated accuracy for the radial position of 20 cm rms. The difference between the altimeter observed sea height and the geoid plus dynamic topography model is 30 cm rms. Contained in these residuals are the sea height variability, as well as errors from the geoid, orbits, tidal models, and altimeter range measurement. This performance level is 2 to 3 times better than that achieved with previous Goddard gravitational models.

Marsh, J. G.↗

DOE OSTI2025

Interpretable ensemble learning unveils main aerosol optical properties in predicting cloud condensation nuclei number concentration

Variations in cloud condensation nuclei number concentration (N CCN ) significantly influence cloud microphysics, yet direct N CCN measurements remain challenging. Here, we present an N CCN ensemble learning (NEL) model utilizing ensemble learning and interpretability analysis on aerosol optical parameters. Validated at two land sites, two ocean sites and one polar site within the Atmospheric Radiation Measurement program, the mean absolute percentage error range of the NEL model across different environments is from 12% to 36%, demonstrating high accuracy. Key findings reveal that aerosol optical parameters can serve as predictors for N CCN . Aerosol scattering and backscattering coefficients, absorption coefficient, backscatter fraction (BSF), and Ångström exponent (AE) are positively correlated with N CCN , while single scattering albedo shows negative correlations. N CCN prediction at land sites is highly sensitive to BSF, largely driven by the backscattering coefficient, as fine particles dominate in these sites. At ocean sites, N CCN prediction is more sensitive to AE, primarily influenced by the scattering coefficient, due to the higher proportion of larger particles. At the polar site, N CCN prediction shows sensitivity to both BSF and AE, mainly driven by the scattering coefficient, as polar sites are cleaner and contain larger particles. These differences reflect the variation in particle size and number concentration across different environments.

Atmospheric science↗

DOE OSTI2024

Geophysical and Environmental Monitoring Data, and Subsurface Flow Modelling Results for Chicken Bone Meadow, Mt. Snodgrass, Crested Butte, CO

This dataset includes geoelectrical monitoring data acquired between October 2021 and November 2022, soil moisture and temperature data, groundwater data obtained from borehole SNIB covering the period from June 2021 to September 2022, and hydrological modelling results. The data were acquired to investigate how variations in bedrock type and topography, and vegetation cover control subsurface flow dynamics. To provide insights into the subsurface flow dynamics and their controls, a monitoring transect was installed at the Chicken Bone Meadow, Mt. Snodgrass, Crested Butte, CO, measuring the spatio-temporal variations of soil moisture, soil and snow temperature, subsurface electrical resistivity variations, and groundwater dynamics. Field data are organized in a folder structure, with Electrical Resistivity Tomography (ERT) data being provided as one file per measurement, and data of the soil moisture and temperature sensors being provided as text files covering the entire monitoring period. The ‘Locations.csv’ file contains the location of all sensors, given in NAD83 – UTM Zone 13N. ERT monitoring data has been processed to filter data based on reciprocal errors (data with errors > 30% were removed), a linear error model was fitted to each survey, and to ensure a constant set of measurements for time-lapse inversion, filtered data were interpolated and assigned a 100% measurement error. Soil moisture and temperature data were acquired at 15 min intervals, and averaged to provide 1h data. Weather data and borehole data (groundwater depth, conductivity and temperature) were acquired at 30 min intervals, and are provided as daily measurements; all measurements are averaged, except of precipitation values, which are given as daily accumulation. The hydrological model was set up along the ERT monitoring transect, and net infiltration was used as surface boundary condition and derived from the weather data. Four different results are provided, (1) results for a parameterization using hydraulic permeability and porosity as derived from the ERT data through petrophysical relationships, and (2) three simplified model results, using 1 to 3 geological layers above the bedrock. Modelling was performed using PFLOTRAN, and for each model the PFLOTRAN input files are provided. The result files include weekly hydrological modelling results (e.g., saturation, velocities, pressures), as well as the model parameterization. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1980

Simulation of a navigator algorithm for a low-cost GPS receiver

The analytical structure of an existing navigator algorithm for a low cost global positioning system receiver is described in detail to facilitate its implementation on in-house digital computers and real-time simulators. The material presented includes a simulation of GPS pseudorange measurements, based on a two-body representation of the NAVSTAR spacecraft orbits, and a four component model of the receiver bias errors. A simpler test for loss of pseudorange measurements due to spacecraft shielding is also noted.

Hodge, W. F.↗

DOE OSTI2026

Novel application of the modal strain energy technique for state-of-the-art damping predictions

Structural damping, which measures the energy dissipation of a vibrating structure, is a key modeling input for lightweight structures but is notoriously hard to predict. This work utilizes vibration-based measurements of centimeter-scale coupons and the modal strain energy approach to predict structural damping of a lightly damped structure. The approach was originally validated with panels shorter than a meter in length. This work extends the validation to a 2.75 m beam made of unidirectional and biaxial glass fiber laminates bonded by adhesive. The comparison between three-dimensional finite element model predictions and full-scale experimental measurements of damping show an average error of 5.2% for the first five modes. Additionally, the modal strain energy approach is newly applied with a one-dimensional geometrically exact beam theory model and a two-dimensional sectional analysis solver. This beam approach accurately predicts the damping behavior of the first bending modes but loses accuracy for higher order modes that are dominated by three-dimensional effects. This novel approach provides faster simulations while allowing arbitrary beam cross sections. The paper also investigates traditional and high-force dynamic mechanical analysis to measure structural damping of coupons. Both alternatives show significant errors in attempted validation against the theoretical thermoelastic damping of aluminum coupons.

17 WIND ENERGY↗

NASA NTRS1986

Precision of vertical position estimates from very long baseline interferometry

It is found that the precision of VLBI measurements of vertical crustal motions is limited by errors in the modeling of the propagation delay through the earth's neutral atmosphere, and by errors in finding the orientation of a fixed crust coordinate system in the VLBI reference frame. The repeatability of baseline length measurements study indicates a vertical position precision of about 8 cm, averaged over 13 sites and 4.5 years of data, while the repeatability of vertical position estimates for a Richmond, FL site is found to yield a precision of about 7 cm for 42 observations made over an 11-month period. An overall precision of 8 cm for a 24-hour VLBI observing session is obtained, and the effects of earth orientation parameter errors is contingent on the distances between the VLBI sites.

Herring, T. A.↗

DOE OSTI2026

Transfer learning for analysis of collective and non-collective Thomson scattering spectra

Thomson scattering (TS) diagnostics provide reliable, minimally perturbative measurements of fundamental plasma parameters, such as electron density (⁠n e ) and electron temperature (⁠T e ⁠). Deep neural networks can provide accurate estimates of ⁠n e and T e when conventional fitting algorithms may fail, such as when TS spectra are dominated by noise, or when fast analysis is required for real-time operation. Although deep neural networks typically require large training sets, transfer learning can improve model performance on a target task with limited data by leveraging pre-trained models from related source tasks, where select hidden layers are further trained using target data. We present five architecturally diverse deep neural networks, pre-trained on synthetic TS data and adapted for experimentally measured TS data, to evaluate the efficacy of transfer learning in estimating n e and T e in both the collective and non-collective scattering regimes. We evaluate errors in n e and T e estimates as a function of training set size for models trained with and without transfer learning, and we observe decreases in model error from transfer learning when the training set contains ≲ 200 experimentally measured spectra.

Artificial neural networks↗

NASA NTRS2012

Frequency Shift During Mass Properties Testing Using Compound Pendulum Method

During mass properties testing on the X-48B Blended Wing Body aircraft (The Boeing Company, Chicago, Illinois) at the National Aeronautics and Space Administration Dryden Flight Research Center, Edwards, California, large inertia measurement errors were observed in results from compound pendulum swings when compared to analytical models. By comparing periods of oscillations as measured from an average over the test period versus the period of each oscillation, it was noticed that the frequency of oscillation was shifting significantly throughout the test. This phenomenon was only noticed during compound pendulum swings, and not during bifilar pendulum swings. The frequency shift was only visible upon extensive data analysis of the frequency for each oscillation, and did not appear in averaged frequency data over the test period. Multiple test articles, test techniques, and hardware setups were used in attempts to eliminate or identify the cause of the frequency shift. Plotting the frequency of oscillation revealed a region of minimal shift that corresponded to a larger amplitude range. This region of minimal shift provided the most accurate results compared to a known test article; however, the amplitudes that produce accurate inertia measurements are amplitudes larger than those generally accepted in mass properties testing. This paper examines two case studies of the frequency shift, using mass properties testing performed on a dummy test article, and on the X-48B Blended Wing Body aircraft.

Wolfe, David↗

NASA NTRS1999

Real-Time Parameter Estimation in the Frequency Domain

A method for real-time estimation of parameters in a linear dynamic state space model was developed and studied. The application is aircraft dynamic model parameter estimation from measured data in flight for indirect adaptive or reconfigurable control. Equation error in the frequency domain was used with a recursive Fourier transform for the real-time data analysis. Linear and nonlinear simulation examples and flight test data from the F-18 High Alpha Research Vehicle HARV) were used to demonstrate that the technique produces accurate model parameter estimates with appropriate error bounds. Parameter estimates converged in less than 1 cycle of the dominant dynamic mode natural frequencies, using control surface inputs measured in flight during ordinary piloted maneuvers. The real-time parameter estimation method has low computational requirements, and could be implemented aboard an aircraft in real time.

Morelli, Eugene A.↗

NASA NTRS2021

Improved Representation of the Global Dust Cycle Using Observational Constraints on Dust Properties and Abundance

Even though desert dust is the most abundant aerosol by mass in Earth’s atmosphere, atmospheric models 25 struggle to accurately represent its spatial and temporal distribution. These model errors are partially caused byfundamental difficulties in simulating dust emission in coarse-resolution models and in accurately representing dust microphysical properties. Here we mitigate these problems by developing a new methodology that yields an improved representation of the global dust cycle. We present an analytical framework that uses inverse modeling to integrate an ensemble of global model simulations with observational constraints on the dust size distribution, 30 extinction efficiency, and regional dust aerosol optical depth. We then compare the inverse model results against independent measurements of dust surface concentration and deposition flux and find that errors are reduced by approximately a factor of two relative to current model simulations of the Northern Hemisphere dust cycle. The inverse model results show smaller improvements in the less dusty Southern Hemisphere, most likely because both the model simulations and the observational constraints used in the inverse model are less accurate. On a global 35 basis, we find that the emission flux of dust with geometric diameter up to 20 μm (PM20) is approximately 5,000 Tg/year, which is greater than most models account for. This larger PM20 dust flux is needed to match observational constraints showing a large atmospheric loading of coarse dust. We obtain gridded data sets of dust emission, vertically integrated loading, dust aerosol optical depth, (surface) concentration, and wet and dry deposition fluxes that are resolved by season and particle size. As our results indicate that this data set is more accurate than current 40 model simulations and the MERRA-2 dust reanalysis product, it can be used to improve quantifications of dust impacts on the Earth system.

aerosols, dust↗

NASA NTRS2021

Improved representation of the global dust cycle using observational constraints on dust properties and abundance

Even though desert dust is the most abundant aerosol by mass in Earth's atmosphere, atmospheric models struggle to accurately represent its spatial and temporal distribution. These model errors are partially caused by fundamental difficulties in simulating dust emission in coarse-resolution models and in accurately representing dust microphysical properties. Here we mitigate these problems by developing a new methodology that yields an improved representation of the global dust cycle. We present an analytical framework that uses inverse modeling to integrate an ensemble of global model simulations with observational constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth. We then compare the inverse model results against independent measurements of dust surface concentration and deposition flux and find that errors are reduced by approximately a factor of 2 relative to current model simulations of the Northern Hemisphere dust cycle. The inverse model results show smaller improvements in the less dusty Southern Hemisphere, most likely because both the model simulations and the observational constraints used in the inverse model are less accurate. On a global basis, we find that the emission flux of dust with a geometric diameter up to 20 µm (PM20) is approximately 5000 Tg yr−1, which is greater than most models account for. This larger PM20 dust flux is needed to match observational constraints showing a large atmospheric loading of coarse dust. We obtain gridded datasets of dust emission, vertically integrated loading, dust aerosol optical depth, (surface) concentration, and wet and dry deposition fluxes that are resolved by season and particle size. As our results indicate that this dataset is more accurate than current model simulations and the MERRA-2 dust reanalysis product, it can be used to improve quantifications of dust impacts on the Earth system.

Dust cycle↗

DOE OSTI2025

Assessment of errors in analytic modeling of permanent magnet electron spectrometers for laser-plasma accelerators

Here, we assess the error in three treatments of a critical electron beam diagnostic for laser-plasma acceleration (LPA) experiments: a permanent magnet electron spectrometer. Since LPA electron spectrometers are often difficult to calibrate due to the scarcity of well-characterized, tunable electron beam sources in the appropriate energy range and the mechanical complexity of electron spectrometers, the standard of calibration is Hall probe measurements. We first compare the electron spectrometer performance between SIMION calculations and the Hall probe measurements. We find up to a 60% (<8%) error in determining the absolute energy for electrons below (above) 80 MeV when using the SIMION-modeled magnetic field vs the measured magnetic field due to SIMION overpredicting fringe field strength. The difference in spectrometer energy resolution is within ∼10% between the modeled and measured fields. We then assess a simple block model, which is commonly used. We find that the simple block model presented here sufficiently captures the predicted as-built spectrometer performance for any application provided that the user can tolerate uncertainties on absolute energy determination up to 5% and errors on energy resolution up to 1%.

Electromagnetic optics↗

NASA NTRS2012

Airborne Measurements of Atmospheric Pressure made Using an IPDA Lidar Operating in the Oxygen A-Band

We report airborne measurements of atmospheric pressure made using an integrated path differential absorption (IPDA) lidar that operates in the oxygen A-band near 765 nm. Remote measurements of atmospheric temperature and pressure are needed for NASA s Active Sensing of CO2 Emissions Over Nights, Days, and Seasons (ASCENDS) mission to measure atmospheric CO2. Accurate measurements of tropospheric CO2 on a global scale are very important in order to better understand its sources and sinks and to improve our predictions of climate change. The goal of ASCENDS is to determine the CO2 dry mixing ratio with lidar measurements from space at a level of ~1 ppm. Analysis to date shows that with current weather models, measurements of both the CO2 column density and the column density of dry air are needed. Since O2 is a stable molecule that uniformly mixed in the atmosphere, measuring O2 absorption in the atmosphere can be used to infer the dry air density. We have developed an airborne (IPDA) lidar for Oxygen, with support from the NASA ESTO IIP program. Our lidar uses DFB-based seed laser diodes, a pulsed modulator, a fiber laser amplifier, and a non-linear crystal to generate wavelength tunable 765 nm laser pulses with a few uJ/pulse energy. The laser pulse rate is 10 KHz, and average transmitted laser power is ~20 mW. Our lidar steps laser pulses across a selected line O2 doublet near 764.7 nm in the Oxygen A-band. The direct detection lidar receiver uses a 20 cm diameter telescope, a Si APD detector in Geiger mode, and a multi-channel scalar to detect and record the time resolved laser backscatter in 40 separate wavelength channels. Subsequent analysis is used to estimate the transmission line shape of the doublet for the laser pulses reflected from the ground. Ground based data analysis allows averaging from 1 to 60 seconds to increase SNR in the transmission line shape of the doublet. Our retrieval algorithm fits the expected O2 lineshapes against the measurements and determines the atmospheric pressure by minimizing the error between the observations and model. We first demonstrated our airborne lidar during flights during summer 2010. We made several improvements and made measurements during the Ascends flights during July 2011. More information about the technique, lidar instrument, airborne measurements, and pressure estimates will be described in the presentation.

Riris, Haris↗

NASA NTRS1983

Accuracy of earth albedo estimates from wide-angle radiation measurements

The instantaneous resolution and accuracy that can be expected from two inversion techniques to be used in the Earth Radiation Budget Experiment (ERBE) are examined. It is shown that measurement errors are magnified by the numerical filter and that this magnification is a function of the magnitude of the inversion factor. By using singular value decomposition, these magnitudes can be reduced to improve the estimates. A simulation of the estimation process using an albedo field derived from scanning radiometer data shows that retaining 6 of the 13 singular values gives the best results. If there are no bidirectional model errors the medium-field-of-view data give the best estimates. However, when bidirectional model errors are considered, the wide-field-of-view measurements give better estimates since they are less sensitive to these errors.

Green, R. N.↗