Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Model error”

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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Development of Neural Network for X-59 Air Data Probe Calibration

Two wind tunnel tests were conducted in the NASA Glenn Research Center (GRC) 8- by 6-Foot Supersonic Wind Tunnel to calibrate the nose-mounted air data probe for the X-59. The probe will be the primary instrument for the flight test of the X-59 for determining angle of attack, angle of sideslip, airspeed, and pressure altitude of the aircraft in flight. This paper documents the development of several neural net algorithms that can be used to relate air data probe port pressures from those tests to relevant X-59 flight parameters, such as angle of attack, angle of sideslip, mach number, and static and dynamic pressure. These calibration algorithms address accuracy effects for significant real-world conditions, such as initial sensor measurement uncertainties, assumed parameter constraints on NN modelling errors, and malfunctioning or plugged ports. Resultant integrated uncertainties are provided for each algorithm; these uncertainties are then used to estimate the effects on relevant X-59 mission parameters.

Kurtis Roy Long↗
NASA NTRS2021

Maneuver Design Implementation and Verification for the Mars 2020 Mission

On February 18, 2021, the Perseverance rover landed in Jezero Crater on Mars, transported by the Mars 2020 spacecraft on a nearly seven-month journey to the Red Planet. The execution of three propulsive maneuvers during the interplanetary cruise phase was required to remove the launch-injection bias and deliver the spacecraft to the Mars atmospheric entry point. This paper focuses on the maneuver implementation and verification process between the navigation and spacecraft teams. Additionally, this paper discusses the execution error models that were used to determine maneuver performance and delivery accuracy at the atmospheric entry interface point.

Kruizinga, Gerhard↗
NASA NTRSDate not supplied

Probabalistic Risk Analysis and Thermal Margin Process for an Inflatable Aeroshell

Uncertainties always exist in atmospheric entry aeroheating environments and the thermal response of thermal protection system (TPS) material. These uncertainties are mitigated in the design by ap-plying margin and factors of safety to the TPS. Entry vehicle TPS is often conservatively over-sized for the heat loads that are experienced along the entry trajectory by stacking worst-case scenarios together. Additionally, the current TPS design and margin process used by NASA offers very little insight into the risk of over-temperature during flight and the reliability of the heat shield performance [1,3]. A probabilistic margin process can be used to calculate the amount of TPS margin necessary to survive a given entry heat load at a specified level of risk [2,3,4]. The vehicle’s initial entry state (entry velocity, flight path angle, and entry mass) determines the expected atmospheric entry environmental conditions and resulting heat load that the entry vehicle will experience. If there is flexibility in the entry state, then this process can be used to select an appropriate combination of entry state parameters and TPS size to target a desired reentry reliability. This probabilistic margin process allows engineers to make informed aeroshell design, entry-trajectory design, and TPS performance risk trades while preventing excessive TPS margin from being applied. The probabilistic TPS margin process has been performed to determine TPS thickness and entry heating constraints given an acceptable risk level for the Low Earth Orbit Flight Experiment of an Inflatable Decelerator (LOFTID) flight project. The process is used in a manner to size the entry heat load for a given flexible TPS (FTPS) thickness so that it meets project reliability standards while allowing the FTPS and the underlying inflatable structure (IS) to be pushed to adequately high temperatures. Since the LOFTID project is an experimental flight demonstration, it is de-sired to drive the FTPS and IS to temperatures that cover a large range of their thermal response models’ applicability. This will allow the thermal response models to be better improved and validated post-flight using LOFTID’s extensive instrumentation embedded within the aeroshell. The presentation demonstrates how uncertainty analysis is carried out using an end-to-end Monte Carlo process where three separate Monte Carlo simulations are run in sequence. The first Monte Carlo simulation operates on the entry trajectory model to generate trajectory parameter dispersions that are fed into the second Monte Carlo simulation. The second Monte Carlo simulation operates on the aerothermodynamics model to generate aeroheating parameter dispersions that are fed into the third Monte Carlo simulation. The third Monte Carlo simulation operates on the FTPS material thermal response model to generate the final FTPS/IS thermal response dispersions. The end-to-end Monte Carlo simulation propagates the uncertainties of each model into the next to quantify the resulting uncertainty of the FTPS/IS thermal response. The fractional contributions of the uncertain parameters in the trajectory, aerothermal, and thermal response models to the variance in the FTPS/IS thermal response is determined as a byproduct of the Monte Carlo analysis. The structural uncertainty of the FTPS thermal response model is evaluated by flight relevant ground testing and model error analysis using test measurements. This probabilistic TPS margin process had never been applied to an entry vehicle and it is one of the LOFTID project’s goals to demonstrate its merits.

Steven A. Tobin↗
NASA NTRS2023

Can Remotely Sensed Snow Disappearance Explain Seasonal Water Supply?

Understanding the relationship between remotely sensed snow disappearance and seasonal water supply may become vital in coming years to supplement limited ground based, in situ measurements of snow in a changing climate. For the period 2001–2019, we investigated the relationship between satellite derived Day of Snow Disappearance (DSD)—the date at which snow has completely disappeared—and the seasonal water supply, i.e., the April—July total streamflow volume, for 15 snow dominated basins across the western U.S. A Monte Carlo framework was applied, using linear regression models to evaluate the predictive skill—defined here as a model’s ability to accurately predict seasonal flow volumes—of varied predictors, including DSD and in situ snow water equivalent (SWE), across a range of spring forecast dates. In all basins there is a statistically significant relationship between mean DSD and seasonal water supply (p ≤ 0.05), with mean DSD explaining roughly half of the variance. Satellite-based model skill improves later in the forecast season, surpassing the skill of in-situ-based (SWE) models in skill in 10 of the 15 basins by the latest forecast date. We found little to no correlation between model error and basin characteristics such as elevation and the ratio of snow water equivalent to total precipitation. Despite a relatively short data record, this exploratory analysis shows promise for improving seasonal water supply prediction, in particular for snow dominated basins lacking in situ observations.

snow remote sensing↗
NASA NTRS2024

Adiabatic Gravitational Waveform Model for Compact Objects Undergoing Quasi-Circular Inspirals Into Rotating Massive Black Holes

We present bhpwave: a new python-based, open-source tool for generating the gravitational waveforms of stellar-mass compact objects undergoing quasicircular inspirals into rotating massive black holes. These binaries, known as extreme-mass-ratio inspirals (EMRIs), are exciting mHz gravitational wave sources for future space-based detectors such as the Laser Interferometer Space Antenna (LISA). Relativistic models of EMRI gravitational wave signals are necessary to unlock the full scientific potential of mHz detectors, yet few open-source EMRI waveform models exist. Thus we built bhpwave, which uses the adiabatic approximation from black hole perturbation theory to rapidly construct gravitational waveforms based on the leading-order inspiral dynamics of the binary. In this work, we present the theoretical and numerical foundations underpinning bhpwave. We also demonstrate how bhpwave can be used to assess the impact of EMRI modeling errors on LISA gravitational wave data analysis. In particular, we find that for retrograde orbits and slowly spinning black holes we can mismodel the gravitational wave phasing by as much as ∼10 radians without significantly biasing EMRI parameter estimation.

Zachary Nasipak↗
NASA NTRS2024

Pre-Launch Calibration and Validation of the Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) Instrument

The Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) is a new imaging polarimeter instrument capable of sampling a single Earth target from up to 120 viewing angles, in four spectral channels, and in three linear polarization states across a 114° field of view (FOV). AirHARP is telecentric in the image space and simultaneously images three linear polarization states with no moving parts. These two aspects of the design allow for a simple and efficient quantitative calibration. Using coefficients derived at the center of the lens and the detector flatfields, we can calibrate the entire AirHARP sensor in a variety of laboratory, field, and space environments. We show that this telecentric calibration technique yields a 1σ absolute uncertainty of 0.25 % in degree of linear polarization (DOLP) in the laboratory for all channels and for pixels around the optical axis. To validate across the FOV, we compare our multi-angle reflectance and polarization data with the Research Scanning Polarimeter (RSP) over targets sampled during the NASA Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign. We use the error-normalized difference technique to estimate how well the instruments compare relative to their error models. We find that AirHARP and the RSP reasonably agree for reflectance and DOLP within 2 standard deviations of their mutual uncertainty at 550, 670, and 870 nm and over a limited set of ocean and desert scenes. This calibration technique makes the Hyper-Angular Rainbow Polarimeter (HARP) design attractive for new spaceborne climate missions: HARP CubeSat (2020–2022), HARP2 (2024–) on the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE), and beyond.

Airborne Hyper-Angular Rainbow Polarimeter↗
NASA NTRSDate not supplied

Sensitivity of Simulated Radiative Emission from Nitrogen Flows to Chemical Parameters

Numerical models of hypersonic flows rely on chemistry data inferred from experiments and quantum calculations. Experiments from NASA's Electric Arc Shock Tube (EAST) measure the spectral emission of strong shocks in pure nitrogen flows. These spectral profiles are only approximately captured by numerical simulations. To address these discrepancies, one wishes to calibrate some of the chemical parameters to better match experiment. Because there are hundreds of parameters in the numerical model, a sensitivity analysis was carried out on the entire chemistry database to determine which parameters are most important to analyze in the future. In order to perform this work it was necessary to generate a consistent chemical database that interfaced with both the CFD and line-by-line radiation solver. Using Monte Carlo methods, the total Sobol index was calculated for important parameters. Twenty parameters were identified as important across the nitrogen system. However, it was noted that in some spectral regions, the parametric uncertainty was unable to bound EAST measurements. Further improvements could be made by increasing the parameter uncertainties or addressing other sources of modeling error.

Kaelan B Hansson↗
NASA NTRSDate not supplied

Sensitivity of Simulated Radiative Emission from Nitrogen Flows to Chemical Parameters

Numerical models of hypersonic flows rely on chemistry data inferred from experiments and quantum calculations. Experiments from NASA's Electric Arc Shock Tube (EAST) measure the spectral emission of strong shocks in pure nitrogen flows. These spectral profiles are only approximately captured by numerical simulations. To address these discrepancies, one wishes to calibrate some of the chemical parameters to better match experiment. Because there are hundreds of parameters in the numerical model, a sensitivity analysis was carried out on the entire chemistry database to determine which parameters are most important to analyze in the future. In order to perform this work it was necessary to generate a consistent chemical database that interfaced with both the CFD and line-by-line radiation solver. Using Monte Carlo methods, the total Sobol index was calculated for important parameters. Twenty parameters were identified as important across the nitrogen system. However, it was noted that in some spectral regions, the parametric uncertainty was unable to bound EAST measurements. Further improvements could be made by increasing the parameter uncertainties or addressing other sources of modeling error.

Kaelan Hansson↗
DOE OSTI2024

\texttt{qec\_code\_sim}: An open-source Python framework for estimating the effectiveness of quantum-error correcting codes on superconducting qubits

Quantum computers are highly susceptible to errors due to unintended interactions with their environment. It is crucial to correct these errors without gaining information about the quantum state, which would result in its destruction through back-action. Quantum Error Correction (QEC) provides information about occurred errors without compromising the quantum state of the system. However, the implementation of QEC has proven to be challenging due to the current performance levels of qubits -- break-even requires fabrication and operation quality that is beyond the state-of-the-art. Understanding how qubit performance factors into the success of a QEC code is a valuable exercise for tracking progress towards fault-tolerant quantum computing. Here we present \texttt{qec\_code\_sim}, an open-source, lightweight Python framework for studying the performance of small quantum error correcting codes under the influence of a realistic error model appropriate for superconducting transmon qubits, with the goal of enabling useful hardware studies and experiments. \texttt{qec\_code\_sim} requires minimal software dependencies and prioritizes ease of use, ease of change, and pedagogy over execution speed. As such, it is a tool well-suited to small teams studying systems on the order of one dozen qubits.

Lopez, Santiago↗
DOE OSTI2023

DUNE-PRISM – A New Method to Measure Neutrino Oscillations

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long baseline neutrino oscillation experiment designed to make precision measurements in a 1.2 2.4 MW neutrino beam, which is directed 1285 km from the Fermi National Accelerator Laboratory (Fermilab) to the Sandford Underground Research Facility (SURF) in South Dakota. Neutrinos are measured at two detector facilities: a near detector located at Fermilab close where the beam is produced and a far detector at SURF. The neutrino beam can be configured to be composed primarily of either muon or anti-muon neutrinos. DUNE measures the disappearance of muon and anti-muon neutrinos and appearance electron and anti-electron neutrinos in the neutrino beam. Measuring these neutrino flavour transitions provides DUNE with sensitivity to the neutrino mass ordering, $\delta_{CP}$, $\theta_{13}$, $\theta_{23}$ and the magnitude of $\Delta m^2_{23}$. The DUNE Precision Reaction Independent Spectrum Measurement (DUNE-PRISM) concept presents a novel way to perform a neutrino oscillation analysis, which has the potential to significantly reduce the impact of large systematic uncertainties in the neutrino interaction model. The PRISM method linearly combines measurements of off-axis neutrino interactions at the DUNE near detector to produce data-driven predictions of the oscillated neutrino event rate spectrum at the far detector. By building an oscillated far detector prediction directly from data, any unknown or poorly modelled neutrino interaction effects will be naturally incorporated into the measurement of the parameters of the neutrino oscillation model. This thesis presents the first complete neutrino oscillation analysis for DUNE using the PRISM method. Details of the methodology are fully explained and the prospects for further improvements to the techniques described are highlighted. The expected impact and relative importance of the neutrino flux, cross section and detector systematic uncertainties are described in detail. Finally, this thesis demonstrates that the PRISM method is capable of performing a measurement of the oscillation parameters that is robust against neutrino interaction modelling errors.

Hasnip, Ciaran↗
NASA NTRS2013

The Role of Model and Initial Condition Error in Numerical Weather Forecasting Investigated with an Observing System Simulation Experiment

A series of experiments that explore the roles of model and initial condition error in numerical weather prediction are performed using an observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO). The use of an OSSE allows the analysis and forecast errors to be explicitly calculated, and different hypothetical observing networks can be tested with ease. In these experiments, both a full global OSSE framework and an 'identical twin' OSSE setup are utilized to compare the behavior of the data assimilation system and evolution of forecast skill with and without model error. The initial condition error is manipulated by varying the distribution and quality of the observing network and the magnitude of observation errors. The results show that model error has a strong impact on both the quality of the analysis field and the evolution of forecast skill, including both systematic and unsystematic model error components. With a realistic observing network, the analysis state retains a significant quantity of error due to systematic model error. If errors of the analysis state are minimized, model error acts to rapidly degrade forecast skill during the first 24-48 hours of forward integration. In the presence of model error, the impact of observation errors on forecast skill is small, but in the absence of model error, observation errors cause a substantial degradation of the skill of medium range forecasts.

Model Error↗
NASA NTRS1981

Modeling human tracking error in several different anti-tank systems

An optimal control model for generating time histories of human tracking errors in antitank systems is outlined. Monte Carlo simulations of human operator responses for three Army antitank systems are compared. System/manipulator dependent data comparisons reflecting human operator limitations in perceiving displayed quantities and executing intended control motions are presented. Motor noise parameters are also discussed.

Kleinman, D. L.↗
NASA NTRS2016

Benefit of Modeling the Observation Error in a Data Assimilation Framework Using Vegetation Information Obtained From Passive Based Microwave Data

A primary operational goal of the United States Department of Agriculture (USDA) is to improve foreign market access for U.S. agricultural products. A large fraction of this crop condition assessment is based on satellite imagery and ground data analysis. The baseline soil moisture estimates that are currently used for this analysis are based on output from the modified Palmer two-layer soil moisture model, updated to assimilate near-real time observations derived from the Soil Moisture Ocean Salinity (SMOS) satellite. The current data assimilation system is based on a 1-D Ensemble Kalman Filter approach, where the observation error is modeled as a function of vegetation density. This allows for offsetting errors in the soil moisture retrievals. The observation error is currently adjusted using Normalized Difference Vegetation Index (NDVI) climatology. In this paper we explore the possibility of utilizing microwave-based vegetation optical depth instead.

Vegetation↗
DOE OSTI2023

diffReplication - An Energy-Aware Fault Tolerance Model for Silent Error Detection and Mitigation in Heterogeneous Extreme-scale Computing Environment

At extreme scale, the frequency of silent errors – a class of errors that remain undetected by low-level error detection mechanisms – increases significantly with the computational complexity of the application and the scale of the computing infrastructure. As hardware and software advances are made to usher in the next scientific era of computing, developing new approaches to mitigate the impact of silent errors remains a challenging problem. In this work, we propose an energy-aware fault-tolerance model, referred to diffReplication to overcome silent errors. In the proposed model, the main process is associated with one replica that executes at the same rate as the main process, and one diffReplica that is executed at a fraction of the main process' execution rate. If the main and its replica reach consensus at the end of a computation phase, the state of the diffReplica is updated and computation is resumed. If the synchronization attempt results in a disagreement, however, the diffReplica increases its execution speed to complete the computation and quickly reach the synchronization barrier. Assuming a single error over any given synchronization interval, a majority voting is used to reach consensus and tolerate silent errors. To further enhance its performance, diffReplication is augmented with speculative execution, whereby the main or its fast replica is selected to continue execution without waiting for the diffReplica. The selection process is based on the previous behaviour of the main and its replica. A performance analysis study is carried out to assess the performance of diffReplication, in terms of the energy saving and time-to-completion reduction achieved by the diffReplication scheme. The experiment shows that speculative execution reduces the time to completion with additional energy, and dynamic decision-making balances the energy consumption and time to completion.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1983

Uncertainty methodology for in-flight thrust determination

A methodology is proposed for the evaluation of uncertainty in the in-flight determination of aircraft thrust, which provides error traceability to a national standards laboratory, and is independent of the procedure used to calculate or measure thrust in flight, thereby yielding a consistent means for the evaluation of measurement capabilities. Attention is given to the factors of measurement error, precision, bias, uncertainty, error estimation and classification, error propagation, ground testing, and the related problems of model bias error, model precision error, and the uncertainty limit.

Adams, G. R.↗
DOE OSTI2020

A Study on Modeled Wind Speed Errors Using the U.S. Department of Energy Buoys

Pacific Northwest National Laboratory (PNNL) operates two AXYS WindSentinel lidar buoys for the U.S. Department of Energy’s Wind Energy Technologies Office. The purpose of these buoys is to collect hub-height winds and supporting meteorological and oceanographic information to facilitate the development of wind energy in the U.S. waters. The first deployment for one buoy was off the coast of Virginia from December 2014 to May 2016, and the first deployment for the other buoy was off the coast of New Jersey from November 2015 until February 2017. This report describes recent analysis of data collected during these first two deployments. Specifically, we compare hub-height wind speed estimates using Monin-Obukhov Similarity Theory (MOST) to the lidar measurements, and examine how those errors are affected by wind direction, atmospheric stability, wind-wave direction differences, and various measures of the wave-state. The comparisons are done using standard similarity functions based on MOST; including the Businger - Dyer, the Beljaars & Holtslag and the Vickers & Mahrt similarity functions. All models produce large errors over the range of atmospheric stabilities that were observed, with the largest errors occurring for stable flows. The Vickers & Mahrt function resulted in the largest overall bias and standard deviation, while Beljaars & Holtslag function gave the smallest bias and standard deviation due to its better performance under stable conditions. The models perform best under unstable conditions, but even in this regime there is a consistent overestimation of the wind speed of between roughly 0 to 1 ms -1 compared to the lidar measurements. We identify specific metocean conditions (i.e. stability and wind and wave directions) at each of the deployment locations that lead to large errors in MOST predictions. Finally, a coupled ocean-atmosphere model framework was investigated to simulate large errors in weather research forecasting (WRF).

17 WIND ENERGY↗
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 NTRS1981

Statistical model to perform error analysis of curve fits of wind tunnel test data using the techniques of analysis of variance and regression analysis

The considered research had the objective to design a statistical model that could perform an error analysis of curve fits of wind tunnel test data using analysis of variance and regression analysis techniques. Four related subproblems were defined, and by solving each of these a solution to the general research problem was obtained. The capabilities of the evolved true statistical model are considered. The least squares fit is used to determine the nature of the force, moment, and pressure data. The order of the curve fit is increased in order to delete the quadratic effect in the residuals. The analysis of variance is used to determine the magnitude and effect of the error factor associated with the experimental data.

Alston, D. W.↗