Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “timing error evaluation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Identifying Optimal Temporal Scale for the Correlation of AOD and Ground Measurements of PM2.5 to Improve the Model Performance in a Real-time Air Quality Estimation System

Aerosol optical depth (AOD), an indirect estimate of particle matter using satellite observations, has shown great promise in improving estimates of PM 2.5 air quality surface. Currently, few studies have been conducted to explore the optimal way to apply AOD data to improve the model accuracy of PM 2.5 surface estimation in a real-time air quality system. We believe that two major aspects may be worthy of consideration in that area: 1) the approach to integrate satellite measurements with ground measurements in the pollution estimation, and 2) identification of an optimal temporal scale to calculate the correlation of AOD and ground measurements. This paper is focused on the second aspect on the identifying the optimal temporal scale to correlate AOD with PM2.5. Five following different temporal scales were chosen to evaluate their impact on the model performance: 1) within the last 3 days, 2) within the last 10 days, 3) within the last 30 days, 4) within the last 90 days, and 5) the time period with the highest correlation in a year. The model performance is evaluated for its accuracy, bias, and errors based on the following selected statistics: the Mean Bias, the Normalized Mean Bias, the Root Mean Square Error, Normalized Mean Error, and the Index of Agreement. This research shows that the model with the temporal scale of within the last 30 days displays the best model performance in this study area using 2004 and 2005 data sets.

Li, Hui↗
NASA NTRSDate not supplied

Global Surface Skin Temperature Monitoring from Satellites

Surface skin temperature has been retrieved from IASI measurements. Monthly and spatially gridded surface skin temperature is produced to show some phenomena of its natural variability, which is also reflected in the surface emissivity and/or soil moisture derived from the same time series of measurements. The anomalies of surface skin temperature are used to estimate its trend. Error estimation and/or evaluation has been performed and discussed to understand the uncertainty in the trends. The trend of IASI global surface skin temperature is compared with that of NASA GISS global surface air temperature. Despite the physical differences between surface skin and air temperatures, agreement is shown between these two datasets indicating consistency and global surface warming during the past 15 years. The trend of IASI global surface skin temperature reports a positive increase has evolved during 2007–2022. This warming trend is more pronounced in the northern hemisphere. Retrieving, analyzing, and monitoring surface parameters from such advanced hyperspectral infrared sounders will continue.

global warming↗
NASA NTRS1975

Terminal guidance and navigation for comet and asteroid rendezvous

A terminal guidance and navigation scheme developed in earlier work was modified and evaluated for a solar electric propulsion rendezvous mission to comet Encke. The scheme is intended for autonomous, on-board use. The guidance algorithm is based on optimal control theory and minimizes the time integrated square of thrust acceleration. The navigation algorithm employs a modified Kalman filter set in measurement variables. Random sequences were generated to simulate measurement errors, and the evaluation was conducted with detailed numerical computations which include actual motions of spacecraft and comet. The evaluations showed that the scheme attains rendezvous and maintains station after rendezvous within less than 10 km for estimated best measurements and within less than 100 km for estimated worst measurements. The measurements required are angles, range, and range rate. Angles and range appear to be absolutely necessary; range rate is not as strong a measurement type, and further modifications of the filter will allow a scheme that does not require the rate measurements.

Bennett, A. G.↗
NASA NTRS1991

A method for computing instantaneous downtrack error from TDRS Doppler residuals

A simple method has been developed which calculates the instantaneous downtrack error in an orbiting vehicle's state vector from TDRS (Tracking and Data Relay Satellite) Doppler observation residuals. The downtrack error is expressed as a delta-time correction to the timetag of the vehicle's state vector. The method requires only a few easily obtained parameters and is easy to implement. This paper details the derivation of computing a delta-time correction from TDRS Doppler observation residuals and evaluates the method's assumptions and limitations. The results presented show that typically the method computes delta-time corrections that are less than five percent off from the true timetag error for Doppler observation residuals within twenty minutes of closest approach to the TDRS.

Haas, Franklin M., Jr.↗
DOE OSTI2026

Fast and Accurate Intersections on a Sphere

We introduce a fast, high-precision algorithm for calculating intersections between great circle arcs and lines of constant latitude on the unit sphere. We first propose a simplified intersection point formula with improved speed and numerical robustness over the ones traditionally implemented in geoscience software. We then show how algorithms based on the concept of error-free transformations (EFT) can be applied to evaluate this formula within a relative error bound that is on the order of machine precision. Here, we demonstrate that, with a vectorized and parallelized implementation, this enhanced accuracy is achieved with no compute time overhead compared to a direct calculation in hardware floating point, making our algorithm suitable for performance-sensitive applications like regridding of high-resolution climate data. In contrast, evaluating our formula using high-precision data types like quadruple precision and arbitrary precision, or using the robust intersection computation routines from the Computational Geometry Algorithms Library, leads to significant computational overhead, especially since these alternatives inhibit vectorization. More generally, our work demonstrates how EFT techniques can be combined and extended to implement nontrivial geometric calculations with high accuracy and speed.

Environmental sciences↗
DOE OSTI2023

Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation

Abstract This study proposes and assesses a methodology to obtain high-quality probabilistic predictions and uncertainty information of near-landfall tropical cyclone–driven (TC-driven) storm tide and inundation with limited time and resources. Forecasts of TC track, intensity, and size are perturbed according to quasi-random Korobov sequences of historical forecast errors with assumed Gaussian and uniform statistical distributions. These perturbations are run in an ensemble of hydrodynamic storm tide model simulations. The resulting set of maximum water surface elevations are dimensionality reduced using Karhunen–Loève expansions and then used as a training set to develop a polynomial chaos (PC) surrogate model from which global sensitivities and probabilistic predictions can be extracted. The maximum water surface elevation is extrapolated over dry points incorporating energy head loss with distance to properly train the surrogate for predicting inundation. We find that the surrogate constructed with third-order PCs using elastic net penalized regression with leave-one-out cross validation provides the most robust fit across training and test sets. Probabilistic predictions of maximum water surface elevation and inundation area by the surrogate model at 48-h lead time for three past U.S. landfalling hurricanes (Irma in 2017, Florence in 2018, and Laura in 2020) are found to be reliable when compared to best track hindcast simulation results, even when trained with as few as 19 samples. The maximum water surface elevation is most sensitive to perpendicular track-offset errors for all three storms. Laura is also highly sensitive to storm size and has the least reliable prediction. Significance Statement The purpose of this study is to develop and evaluate a methodology that can be used to provide high-quality probabilistic predictions of hurricane-induced storm tide and inundation with limited time and resources. This is important for emergency management purposes during or after the landfall of hurricanes. Our results show that sampling forecast errors using quasi-random sequences combined with machine learning techniques that fit polynomial functions to the data are well suited to this task. The polynomial functions also have the benefit of producing exact sensitivity indices of storm tide and inundation to the forecasted hurricane properties such as path, intensity, and size, which can be used for uncertainty estimation. The code implementing the presented methodology is publicly available on GitHub.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1978

An evaluation of electrochemical concentration Cell (ECC) sonde measurements of atmospheric ozone

Using Dobson spectrophotometer measurements of total ozone as a comparison, an analysis of the electrochemical concentration cell (ECC) ozonesonde's measurement accuracy is presented. Days of conjunctive ECC-Dobson observations (from 1970 to 1976 at Wallops Flight Center) provide a set of 123 pairs of total ozone values. Sample set statistics are generated with means and standard deviations of total ozone values and differences being noted. An in-depth study of factors such as time assumptions used in calculating residual ozone, and other possible sources of errors are examined. A study of ECC ozone profiles is also presented with an evaluation of sonde measurement of seasonal trends, altitude or peak ozone concentration, and other important ozone parameters. Short-period changes in total ozone using Dobson data during the observational period are also described.

Geraci, M. J.↗
NASA NTRS1977

Autonomous satellite navigation with the Global Positioning System

This paper discusses the potential of using the Global Positioning System (GPS) to provide autonomous navigation capability to NASA satellites in the 1980 era. Some of the driving forces motivating autonomous navigation are presented. These include such factors as advances in attitude control systems, onboard science annotation, and onboard gridding of imaging data. Simulation results which demonstrate baseline orbit determination accuracies using GPS data on Seasat, Landsat-D, and the Solar Maximum Mission are presented. Emphasis is placed on identifying error sources such as GPS time, GPS ephemeris, user timing biases, and user orbit dynamics, and in a parametric sense on evaluating their contribution to the orbit determination accuracies.

A J Fuchs↗
DOE OSTI2022

Limitations of reanalysis data for wind power applications

Wind energy resource estimates commonly depend on simulated wind speed profiles generated by reanalysis or weather models due to the lack of long time series measurements with sufficient coverage at relevant heights (roughly 90 m above ground). However, modeled data, including reanalyses, can be noisy and display a wide range of biases and errors, variously attributed to terrain effects, poor coverage of assimilated inputs, and model resolution. Wind generation records, if available at high temporal and geographical resolution, can provide a proxy for wind measurements and allow for evaluation of reanalyses and weather model wind time series. We use a 7-year-long data set of hourly, plant-level generation records from over 100 wind plants across Texas to evaluate two commonly used reanalysis data sets (MERRA2 and ERA5). Additionally, we use 1-year of records (2019) to evaluate an operational, high-resolution regional weather modeling product (HRRR v3). We find that across the region, and across all modeling products, the modeled representation of wind generation (i.e., wind speeds at hub heights passed through a power curve) has relatively small mean errors when aggregated daily, but that accuracy and hourly correlation have a strong diurnal sensitivity. Accuracy and correlation systematically decline through the evening and markedly improve after sunrise. These diurnal patterns persist even in the highest resolution model tested (HRRR v3). We hypothesize the nighttime decline in accuracy is mostly due to poorly represented boundary layer conditions, perhaps related to model representation of stability, while other uncertainties (such as wake effects) play a secondary role.

17 WIND ENERGY↗
DOE OSTI2024

A chemical kinetic analysis of knock propensity of methanol-to-gasoline fuel

Production of low carbon gasoline-like fuels such as methanol-to-gasoline (MTG) is a promising approach to achieve rapid greenhouse gas emission reduction of the transportation sector. Despite the fact that gasoline that meets the ASTM D4814 standard for automotive spark-ignition engine fuel can be readily produced from these processes, it is unclear how the composition of MTG may affect engine performance and emissions. Here, in this paper, a surrogate for an MTG is used to numerically study the effects of gasoline composition on knock propensity and on the sensitivity of knock to thermal and fuel stratification, to oxygen dilution and to nitric oxide from exhaust gas recirculation of residual gases. Simulations were performed in ANSYS CHEMKIN-PRO using a comprehensive chemical kinetic mechanism for gasoline surrogates, and results of the MTG surrogate were compared against those of a petroleum-based regular E10 gasoline, termed PACE-20. A premium-grade MTG fuel was also formulated by adding ethanol to the MTG surrogate, and results were compared against those of four premium-grade, gasoline-like fuels representative of future alternative gasoline formulations. Surrogates and mechanism were evaluated by comparison against experimental engine data, and the model showed high accuracy at stoichiometric conditions (mean absolute error of ignition timing equal to 1.46 crank angle degrees) but larger deviations at lean conditions (mean absolute error of ignition timing equal to 5.52 crank angle degrees). Despite the fact that the MTG surrogate has a RON 1.1 units higher than that of PACE-20, it may show higher knock propensity at medium temperature conditions due to a less intense NTC behavior. MTG autoignition was more temperature- and equivalence ratio-sensitive than that of PACE20, suggesting that MTG can benefit more from naturally-occurring thermal stratification or from induced fuel stratification of the end gas to mitigate knock intensity. The sensitivity of autoignition reactivity to oxygen dilution and to NO concentration was higher for MTG than for regular gasoline at medium loads, but the opposite trend was observed at high loads due to the effect of pressure on the low-temperature chemistry of regular gasoline. Approximately 14 % vol ethanol content was required to upgrade the octane rating of MTG from regular grade to premium grade. Adding 13.6 % vol ethanol made the fuel autoignition less sensitive to both oxygen dilution and NO content (ignition time varies approx. 17 % and 50 % less with oxygen dilution and NO addition, respectively, when adding ethanol at high engine loads).

02 PETROLEUM↗
DOE OSTI2022

Classifying handedness in chiral nanomaterials using label error robust deep learning

Abstract High-throughput scanning electron microscopy (SEM) coupled with classification using neural networks is an ideal method to determine the morphological handedness of large populations of chiral nanoparticles. Automated labeling removes the time-consuming manual labeling of training data, but introduces label error, and subsequently classification error in the trained neural network. Here, we evaluate methods to minimize classification error when training from automated labels of SEM datasets of chiral Tellurium nanoparticles. Using the mirror relationship between images of opposite handed particles, we artificially create populations of varying label error. We analyze the impact of label error rate and training method on the classification error of neural networks on an ideal dataset and on a practical dataset. Of the three training methods considered, we find that a pretraining approach yields the most accurate results across label error rates on ideal datasets, where size and other morphological variables are held constant, but that a co-teaching approach performs the best in practical application.

36 MATERIALS SCIENCE↗
NASA NTRS1982

Experimental evaluation of the concept of supevisory manipulation

A computer-controlled teleoperator system which is based on task-referenced sensor-aided control has been developed to study supervisory manipulation. This system, called SUPERMAN, is capable of performing complicated tasks in real-time by utilizing the operator for high-level functions related to the unpredictable portions of a task, while the subordinate machine performs the more well-defined subtasks under human supervison. To determine whether supervisory control schemes such as these offer any advantage over manual control under real-time conditions, a number of experiments involving both simple and complicated tasks were performed. Six representative tasks were chosen for the study: (1) obtaining a tool from a rack, (2) returning the tool to the rack, (3) removing a nut, (4) placing samples in a storage bin, (5) opening and closing a valve, and (6) digging with a shovel. The experiments were performed under simulated conditions using four forms of manual control (i.e., switch rate, joystick rate, master-slave position control, and master-slave with force feedback), as well as supervisory control. Through these experiments the effectiveness and quality of control were evaluated on the basis of the time required to complete each portion of the task and the type and number of errors which occurred.

Brooks, T. L.↗
NASA NTRS1992

Signal processing for airborne doppler radar detection of hazardous wind shear as applied to NASA 1991 radar flight experiment data

Radar data collected during the 1991 NASA flight tests have been selectively analyzed to support research directed at developing both improved as well as new algorithms for detecting hazardous low-altitude windshear. Analysis of aircraft attitude data from several flights indicated that platform stability bandwidths were small compared to the data rate bandwidths which should support an assumption that radar returns can be treated as short time stationary. Various approaches at detection of weather returns in the presence of ground clutter are being investigated. Non-coventional clutter rejection through spectrum mode tracking and classification algorithms is a subject of continuing research. Based upon autoregressive modeling of the radar return time sequence, this approach may offer an alternative to overcome errors in conventional pulse-pair estimates. Adaptive filtering is being evaluated as a means of rejecting clutter with emphasis on low signal-to-clutter ratio situations, particularly in the presence of discrete clutter interference. An analysis of out-of-range clutter returns is included to illustrate effects of ground clutter interference due to range aliasing for aircraft on final approach. Data are presented to indicate how aircraft groundspeed might be corrected from the radar data as well as point to an observed problem of groundspeed estimate bias variation with radar antenna scan angle. A description of how recorded clutter return data are mixed with simulated weather returns is included. This enables the researcher to run controlled experiments to test signal processing algorithms. In the summary research efforts involving improved modelling of radar ground clutter returns and a Bayesian approach at hazard factor estimation are mentioned.

Baxa, Ernest G., Jr.↗
NASA NTRS1998

Global Warming Estimation from MSU

Microwave Sounding Unit (MSU) radiometer observations in Ch 2 (53.74 GHz) from sequential, sun-synchronous, polar-orbiting NOAA satellites contain small systematic errors. Some of these errors are time-dependent and some are time-independent. Small errors in Ch 2 data of successive satellites arise from calibration differences. Also, successive NOAA satellites tend to have different Local Equatorial Crossing Times (LECT), which introduce differences in Ch 2 data due to the diurnal cycle. These two sources of systematic error are largely time independent. However, because of atmospheric drag, there can be a drift in the LECT of a given satellite, which introduces time-dependent systematic errors. One of these errors is due to the progressive chance in the diurnal cycle and the other is due to associated chances in instrument heating by the sun. In order to infer global temperature trend from the these MSU data, we have eliminated explicitly the time-independent systematic errors. Both of the time-dependent errors cannot be assessed from each satellite. For this reason, their cumulative effect on the global temperature trend is evaluated implicitly. Christy et al. (1998) (CSL). based on their method of analysis of the MSU Ch 2 data, infer a global temperature cooling trend (-0.046 K per decade) from 1979 to 1997, although their near nadir measurements yield near zero trend (0.003 K/decade). Utilising an independent method of analysis, we infer global temperature warmed by 0.12 +/- 0.06 C per decade from the observations of the MSU Ch 2 during the period 1980 to 1997.

Prabhakara, C.↗
DOE OSTI2020

Cross-Market Price Difference Forecast Using Deep Learning for Electricity Markets

Price forecasting is in the center of decision making in electricity markets. Many researches have been done in forecasting energy prices while little research has been reported on forecasting price difference between day-ahead and realtime markets due to its high volatility, which however plays a critical role in virtual trading. To this end, this paper takes the first attempt to employ novel deep learning architecture with Bidirectional Long-Short Term Memory (LSTM) units to forecast the price difference between day-ahead and real-time markets for the same node. The raw data is collected from PJM market, processed and fed into the proposed network. The Root Mean Squared Error (RMSE) and customized performance metric are used to evaluate the performance of the proposed method. Case studies show that it outperforms the traditional statistical models like ARIMA, and machine learning models like XGBoost and SVR methods in both RMSE and the capability of forecasting the sign of price difference. Additionally, to cross-market price difference forecast, the proposed approach has the potential to be applied to solve other forecasting problems such as price spread forecast in DA market for Financial Transmission Right (FTR) trading purpose.

DA/RT price difference↗
NASA NTRS2004

Advancing Solid Earth Science through Improved Atmosphere Modeling

We proposed to investigate and develop better models for the effect of the hydrostatic and water vapor components of the neutral atmosphere on delay for VLBI and GPS by using a Numerical Weather Model to better simulate realistic atmosphere conditions. By using a raytrace calculation through the model atmosphere at the times of actual VLBI observations, the potential improvement in geodetic results can be evaluated. Also, by calculating the actual variation of delays with elevation and azimuth, the errors in current mapping function models can be assessed. The VLBI data to be initially analyzed are the fifteen days of the CONT02 sessions of 2002 October which included eight stations. There are three segments to the research. 1) The PSU/NCAR fifth generation mesoscale numerical weather model (MM5) will be used to provide the state of the atmosphere with highest horizontal resolution of 3 km. 2) A three-dimensional raytrace program will be developed to determine the delays through the model atmosphere at the times and in the directions of the VLBI observations for each of the sites. 3) The VLBI data will be analyzed using both standard models for the atmosphere mapping functions and the mapping functions derived from the NWM raytracing.

Niell, A. E.↗
DOE OSTI2026

APSO-enhanced algebraic derivative estimation approach for real-time traffic flow prediction on critical road sections during wildfire evacuation

In rapid-onset disaster scenarios such as wildfires, evacuation traffic often significantly deviates from historical patterns, rendering conventional data-driven forecasting methods less effective. To address this challenge, we propose an improved algebraic derivative estimation (ADE) incorporating particle swarm optimization (PSO) for real-time traffic flow prediction. Our approach dynamically adjusts the ADE prediction time window at each step by minimizing a cost function based on the mean and variance of accumulated forecasting errors within the window, thereby balancing bias and variability. We evaluate the method using traffic data from the January 2025 California wildfires, focusing on key road segments critical for large-scale evacuations. The results demonstrate that our approach surpasses established machine learning and deep learning models—XGBoost, LSTM, and GRU—in predictive accuracy and maintains high computational efficiency. Notably, the proposed method eliminates the need for offline model training. Moreover, rapid PSO-based tuning enables real-time deployment, which provides a crucial advantage in scenarios where evacuation timings and road closures change dynamically. In conclusion, these findings highlight the benefits of the PSO-enhanced ADE framework for emergency traffic management, where rapid, data-sparse forecasts are essential for effective evacuation planning.

Algebraic derivative estimation↗
NASA NTRS1992

Summary: Experimental validation of real-time fault-tolerant systems

Testing and validation of real-time systems is always difficult to perform since neither the error generation process nor the fault propagation problem is easy to comprehend. There is no better substitute to results based on actual measurements and experimentation. Such results are essential for developing a rational basis for evaluation and validation of real-time systems. However, with physical experimentation, controllability and observability are limited to external instrumentation that can be hooked-up to the system under test. And this process is quite a difficult, if not impossible, task for a complex system. Also, to set up such experiments for measurements, physical hardware must exist. On the other hand, a simulation approach allows flexibility that is unequaled by any other existing method for system evaluation. A simulation methodology for system evaluation was successfully developed and implemented and the environment was demonstrated using existing real-time avionic systems. The research was oriented toward evaluating the impact of permanent and transient faults in aircraft control computers. Results were obtained for the Bendix BDX 930 system and Hamilton Standard EEC131 jet engine controller. The studies showed that simulated fault injection is valuable, in the design stage, to evaluate the susceptibility of computing sytems to different types of failures.

Iyer, R. K.↗