Search NASA⌕ Search

SEARCH · Search NASA

Results for “error modeling”

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 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Exact and Model Exchange-Correlation Potentials for Open-Shell Systems

The conventional approaches to the inverse density functional theory problem typically assume nondegeneracy of the Kohn–Sham (KS) eigenvalues, greatly hindering their use in open-shell systems. Here, we present a generalization of the inverse density functional theory problem that can seamlessly admit degenerate KS eigenvalues. Additionally, we allow for fractional occupancy of the Kohn–Sham orbitals to also handle noninteracting ensemble-v-representable densities, as opposed to just noninteracting pure-v-representable densities. We present the exact exchange-correlation (XC) potentials for six open-shell systems–four atoms (Li, C, N, and O) and two molecules (CN and CH 2 )–using accurate ground-state densities from configuration interaction calculations. We compare these exact XC potentials with model XC potentials obtained using nonlocal (B3LYP, SCAN0) and local/semilocal (SCAN, PBE, PW92) XC functionals. Although the relative errors in the densities obtained from these DFT functionals are of $O$(10 –3 to 10 –2 ), the relative errors in the model XC potentials remain substantially large–$O$(10 –1 to 10 0 ).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

SU-F-T-105: The Predicted Radiation Response of Non-Small Cell Lung Cancer for SRS, SBRT and HDR Brachytherapy

Purpose: The data on the α/β ratio of non-small cell lung cancer (NSCLC) is scarce in the literature. We have previously proposed a generalized LQ (gLQ) model to address the high dose dilemma of the LQ model. In this study, we applied the gLQ model to both the patients and in vitro cell irradiation data treated with a large range of doses, and investigated the α/β ratio in NSCLC. Methods: 150 patients with T1T2 and non-T1T2 stages were treated with stereotactic body radiotherapy (SBRT). In vitro datasets of 14 NSCLC cell lines from the National Cancer Institute published in Eur J Cancer Clin Oncol. 25(3):527–534 (1989) and 7 NSCLC cell lines published in Cancer Res 57:4285–300 (1997) were included. The gLQ model was used to fit datasets. The least χ2 method was adopted to determine the goodness of fit. Errors of the model parameters were determined by propagating minimal χ2. The α/β ratios from both the patients and these in vitro NSCLC cell lines were obtained. Results: The average of α/β ratios for T1T2 and non-T1T2 NSCLC was 1.45 Gy. The same type of cell lines irradiated with different modalities but almost the same dose rate yielded approximately the same α/β ratio. The average of α/β ratios for NSCLC cell lines in this study was 5.45 Gy. Conclusion: The difference in the α/β ratios between the patients and in vitro cell data is expected and the lower α/β ratio for patients suggests the higher radiosensitivity, which could be associated with higher tumor perfusion or other tumor microenvironmental effects. The α/β ratios derived from the gLQ model can be used in high dose regions or high fraction sizes and are useful to extend our clinical experience accumulated from conversional low-dose fractionation to high dose irradiation schedules.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1978

A numerical model for low-latitude ionospheric TEC

A numerical model of total electron content (TEC) at low latitudes has been constructed from the Faraday rotation data recorded at a number of locations in India during the period Oct. 1975-July 1976 when the geostationary satellite ATS-6 was located at 35 deg E. In all, 36 coefficients for each season are required to represent the model. The model can be used for satellite tracking systems in estimating quickly the range, range rate and angular refraction errors. The present model is an improvement over the previously constructed model based on orbiting satellite data.

Sethia, G.↗
NASA NTRS2006

Variational Assimilation of Global Microwave Rainfall Retrievals: Physical and Dynamical Impact on GEOS Analyses and Forecasts

Global microwave rainfall retrievals from a 5-satellite constellation, including TMI from TRMM, SSWI from DMSP F13, F14 and F15, and AMSR-E from EOS-AQUA, are assimilated into the NASA Goddard Earth Observing System (GEOS) Data Assimilation System (DAS) using a 1-D variational continuous assimilation (VCA) algorithm. The physical and dynamical impact of rainfall assimilation on GEOS analyses and forecasts is examined at various temporal and spatial scales. This study demonstrates that the 1-D VCA algorithm, which was originally developed and evaluated for rainfall assimilations over tropical oceans, can effectively assimilate satellite microwave rainfall retrievals and improve GEOS analyses over both the Tropics and the extratropics where the atmospheric processes are dominated by different large-scale dynamics and moist physics, and also over the land, where rainfall estimates from passive microwave radiometers are believed to be less accurate. Results show that rainfall assimilation renders the GEOS analysis physically and dynamically more consistent with the observed precipitation at the monthly-mean and 6-hour time scales. Over regions where the model precipitation tends to misbehave in distinctly different rainy regimes, the 1-D VCA algorithm, by compensating for errors in the model s moist time-tendency in a 6-h analysis window, is able to bring the rainfall analysis closer to the observed. The radiation and cloud fields also tend to be in better agreement with independent satellite observations in the rainfall-assimilation m especially over regions where rainfall analyses indicate large improvements. Assimilation experiments with and without rainfall data for a midlatitude frontal system clearly indicates that the GEOS analysis is improved through changes in the thermodynamic and dynamic fields that respond to the rainfall assimilation. The synoptic structures of temperature, moisture, winds, divergence, and vertical motion, as well as vorticity are more realistically captured across the front. Short-term forecasts using initial conditions assimilated with rainfall data also show slight improvements. 1

Lin, Xin↗
NASA NTRS2015

Pressure Loss Predictions of the Reactor Simulator Subsystem at NASA GRC

Testing of the Fission Power System (FPS) Technology Demonstration Unit (TDU) is being conducted at NASA GRC. The TDU consists of three subsystems: the Reactor Simulator (RxSim), the Stirling Power Conversion Unit (PCU), and the Heat Exchanger Manifold (HXM). An Annular Linear Induction Pump (ALIP) is used to drive the working fluid. A preliminary version of the TDU system (which excludes the PCU for now), is referred to as the RxSim subsystem and was used to conduct flow tests in Vacuum Facility 6 (VF 6). In parallel, a computational model of the RxSim subsystem was created based on the CAD model and was used to predict loop pressure losses over a range of mass flows. This was done to assess the ability of the pump to meet the design intent mass flow demand. Measured data indicates that the pump can produce 2.333 kg/sec of flow, which is enough to supply the RxSim subsystem with a nominal flow of 1.75 kg/sec. Computational predictions indicated that the pump could provide 2.157 kg/sec (using the Spalart-Allmaras turbulence model), and 2.223 kg/sec (using the k- turbulence model). The computational error of the predictions for the available mass flow is -0.176 kg/sec (with the S-A turbulence model) and -0.110 kg/sec (with the k-epsilon turbulence model) when compared to measured data.

Turbulence Models↗
NASA NTRS2016

Pressure Loss Predictions of the Reactor Simulator Subsystem at NASA Glenn Research Center

Testing of the Fission Power System (FPS) Technology Demonstration Unit (TDU) is being conducted at NASA Glenn Research Center. The TDU consists of three subsystems: the reactor simulator (RxSim), the Stirling Power Conversion Unit (PCU), and the heat exchanger manifold (HXM). An annular linear induction pump (ALIP) is used to drive the working fluid. A preliminary version of the TDU system (which excludes the PCU for now) is referred to as the "RxSim subsystem" and was used to conduct flow tests in Vacuum Facility 6 (VF 6). In parallel, a computational model of the RxSim subsystem was created based on the computer-aided-design (CAD) model and was used to predict loop pressure losses over a range of mass flows. This was done to assess the ability of the pump to meet the design intent mass flow demand. Measured data indicates that the pump can produce 2.333 kg/sec of flow, which is enough to supply the RxSim subsystem with a nominal flow of 1.75 kg/sec. Computational predictions indicated that the pump could provide 2.157 kg/sec (using the Spalart-Allmaras (S‒A) turbulence model) and 2.223 kg/sec (using the k- turbulence model). The computational error of the predictions for the available mass flow is ‒0.176 kg/sec (with the S-A turbulence model) and -0.110 kg/sec (with the k- turbulence model) when compared to measured data.

Mass Flow↗
NASA NTRSDate not supplied

Enhancing Air Traffic Control Planning with Automatic Speech Recognition

The decisions made during the Federal Aviation Administration Air Traffic Control System Command Center's planning teleconferences hold significant sway over the National Airspace System. Held every two hours, these teleconferences convene air traffic managers and stakeholders from across the nation to discuss airspace conditions, weather, and constraints, leading to the formulation and adjustment of traffic management initiatives. Given the critical nature of these decisions, the need for accurate and efficient record-keeping is paramount. In recent years, the application of automatic speech recognition has gained popularity across diverse industries, including aviation. While traditional applications focus on transcribing air traffic control communication, this paper explores a unique application of automatic speech recognition by converting the audio from planning teleconferences into text transcriptions. This innovative approach addresses key challenges in the field, presenting potential benefits for quality assurance, real-time participation, and downstream natural language processing tasks. A notable breakthrough in the machine learning community, namely the transformer neural network architecture, forms the backbone of the proposed solution in this paper. The transformer architecture's role in this research represents a paradigm shift in the efficiency of automatic speech recognition models. By reducing the amount of in-domain training data required, this architecture allows for the fine-tuning of such models like Whisper, originally pretrained on vast English speech datasets. The adaptability of the transformer architecture proves invaluable in capturing the nuances of aviation terminology and specific language used in planning teleconferences. Leveraging the Whisper model as a baseline, our research details the fine-tuning and validation using a dataset comprising 20 hours of meticulously transcribed planning teleconferences. Notably, the baseline pretrained Whisper model exhibited a word error rate of 18.77%. Through the fine-tuning process, the model achieved a substantial improvement, demonstrating an impressive performance with a reduced word error rate of 6.82%. This substantial decrease in WER not only highlights the effectiveness of the transformer architecture but also emphasizes the practical advancements achieved through the application of automatic speech recognition in this specific domain. The utilization of automatic speech recognition in planning teleconferences in this work introduces several novelties. Firstly, the creation of text transcriptions offers a valuable tool for quality assurance and facilitates the efficient review of teleconferences. This is an important aspect of the proposed solution, given the time-sensitive and high-stakes nature of decisions made during these meetings. Furthermore, text-searchable transcriptions provide a streamlined approach for locating and validating critical information, potentially saving hours of manual effort in searching through audio recordings. Moreover, our research identifies a key use case for external facilities and stakeholders. In situations where attendance at the planning teleconference is not feasible, having access to text transcriptions in real-time or shortly after the teleconference ends, proves to be a time-saving and informative resource. This feature enhances collaboration and ensures that stakeholders can stay abreast of important discussions and decisions even in their absence. Despite the efficiency gains facilitated by the transformer architecture in automatic speech recognition technology, it is essential to acknowledge the human factors in data creation. Subject matter experts play a crucial role in accurately transcribing planning teleconferences due to the specificity and complexity of the information discussed. The research dataset, consisting of 20 hours of transcribed planning teleconferences, forms the foundation for fine-tuning and validating the Whisper model. The achieved word error rate of 6.82% demonstrates promising advancements, particularly in recognizing essential aviation terminology within the teleconferences. In conclusion, this paper presents a comprehensive exploration of the application of automatic speech recognition in Air Traffic Control System Command Center planning teleconferences, leveraging the transformer architecture for enhanced efficiency. The novel contributions lie in the improved accessibility of decision-making records, real-time participation opportunities for external stakeholders, and the potential for downstream natural language processing advancements. As the aviation industry continues to evolve, the integration of automatic speech recognition technologies holds the promise of revolutionizing decision-making processes and contributing to the overall safety and efficiency of air traffic management.

ATM↗
NASA NTRS1996

Uncertainty Modeling for Structural Control Analysis and Synthesis

The development of an accurate model of uncertainties for the control of structures that undergo a change in operational environment, based solely on modeling and experimentation in the original environment is studied. The application used throughout this work is the development of an on-orbit uncertainty model based on ground modeling and experimentation. A ground based uncertainty model consisting of mean errors and bounds on critical structural parameters is developed. The uncertainty model is created using multiple data sets to observe all relevant uncertainties in the system. The Discrete Extended Kalman Filter is used as an identification/parameter estimation method for each data set, in addition to providing a covariance matrix which aids in the development of the uncertainty model. Once ground based modal uncertainties have been developed, they are localized to specific degrees of freedom in the form of mass and stiffness uncertainties. Two techniques are presented: a matrix method which develops the mass and stiffness uncertainties in a mathematical manner; and a sensitivity method which assumes a form for the mass and stiffness uncertainties in macroelements and scaling factors. This form allows the derivation of mass and stiffness uncertainties in a more physical manner. The mass and stiffness uncertainties of the ground based system are then mapped onto the on-orbit system, and projected to create an analogous on-orbit uncertainty model in the form of mean errors and bounds on critical parameters. The Middeck Active Control Experiment is introduced as experimental verification for the localization and projection methods developed. In addition, closed loop results from on-orbit operations of the experiment verify the use of the uncertainty model for control analysis and synthesis in space.

Campbell, Mark E.↗
NASA NTRS1983

Distributed control of large space antennas

A systematic way to choose control design parameters and to evaluate performance for large space antennas is presented. The structural dynamics and control properties for a Hoop and Column Antenna and a Wrap-Rib Antenna are characterized. Some results of the effects of model parameter uncertainties to the stability, surface accuracy, and pointing errors are presented. Critical dynamics and control problems for these antenna configurations are identified and potential solutions are discussed. It was concluded that structural uncertainties and model error can cause serious performance deterioration and can even destabilize the controllers. For the hoop and column antenna, large hoop and long meat and the lack of stiffness between the two substructures result in low structural frequencies. Performance can be improved if this design can be strengthened. The two-site control system is more robust than either single-site control systems for the hoop and column antenna.

Cameron, J. M.↗
DOE OSTI2016

TU-FG-BRB-10: A New Approach to Proton Radiography Using the Beamline X-Ray Flat Panel

Purpose: Proton radiography, which images the patients with the same type of particles that they are to be treated with, is a promising approach for image guidance and range uncertainties reduction. This study aimed to realize quality proton radiography by measuring dose rate functions (DRF) in time domain using a single flat panel and retrieve water equivalent path length (WEPL) from them. Methods: An amorphous silicon flat panel (PaxScan™ 4030CB, Varian Medical Systems, Inc., Palo Alto, CA) was placed behind phantoms to measure DRFs from a proton beam modulated by the modulator wheel. To retrieve WEPL and RSP, calibration models based on the intensity of DRFs only, root mean square (RMS) of DRFs only and the intensity weighted RMS were tested. The quality of obtained WEPL images (in terms of spatial resolution and level of details) and the accuracy of WEPL were compared. Results: RSPs for most of the Gammex phantom inserts were retrieved within ± 1% errors by calibration models based on the RMS and intensity weighted RMS. The mean percentage error for all inserts was reduced from 1.08% to 0.75% by matching intensity in the calibration model. In specific cases such as the insert with a titanium rod, the calibration model based on RMS only fails while the that based on intensity weighted RMS is still valid. The quality of retrieved WEPL images were significantly improved for calibration models including intensity matching. Conclusion: For the first time, a flat panel, which is readily available in the beamline for image guidance, was tested to acquire quality proton radiography with WEPL accurately retrieved from it. This technique is promising to be applied for image-guided proton therapy as well as patient specific RSP determination to reduce uncertainties of beam ranges.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Tula: Optimizing Time, Cost, and Generalization in Distributed Large-Batch Training

Distributed training increases the number of batches processed per iteration either by scaling-out (adding more nodes) or scaling-up (increasing the batch-size). However, the largest configuration does not necessarily yield the best performance. Horizontal scaling introduces additional communication overhead, while vertical scaling is constrained by computation cost and device memory limits. Thus, simply increasing the batch-size leads to diminishing returns: training time and cost decrease initially but eventually plateaus, creating a knee-point in the time/cost vs. batch-size pareto curve. The optimal batch-size therefore depends on the underlying model, data and available compute resources. Large batches also suffer from worse model quality due to the well-known “generalization gap”. In this paper, we present Tula, an online service that automatically optimizes time, cost, and convergence quality for large-batch training of convolutional models. It combines parallel-systems modeling with statistical performance prediction to identify the optimal batchsize. Tula predicts training time and cost within 7.5−14% error across multiple models, and achieves up to 20× overall speedup and improves test accuracy by ≈9% on average over standard large-batch training on various vision tasks, thus successfully mitigating the generalization gap and accelerating training at the same time.

Tyagi, Sahil [ORNL] (ORCID:0009000783144745)↗
DOE OSTI2018

Woody plants optimise stomatal behaviour relative to hydraulic risk

Abstract Stomatal response to environmental conditions forms the backbone of all ecosystem and carbon cycle models, but is largely based on empirical relationships. Evolutionary theories of stomatal behaviour are critical for guarding against prediction errors of empirical models under future climates. Longstanding theory holds that stomata maximise fitness by acting to maintain constant marginal water use efficiency over a given time horizon, but a recent evolutionary theory proposes that stomata instead maximise carbon gain minus carbon costs/risk of hydraulic damage. Using data from 34 species that span global forest biomes, we find that the recent carbon‐maximisation optimisation theory is widely supported, revealing that the evolution of stomatal regulation has not been primarily driven by attainment of constant marginal water use efficiency. Optimal control of stomata to manage hydraulic risk is likely to have significant consequences for ecosystem fluxes during drought, which is critical given projected intensification of the global hydrological cycle.

Anderegg, William R. L.↗
NASA NTRS1988

Impact of device level faults in a digital avionic processor

This paper describes an experimental analysis of the impact of gate and device-level faults in the processor of a flight control system. Via mixed mode simulation faults were injected both at the gate (stuck-at) and at the transistor levels, and their propagation through the chip to the output pins was measured. The results show that there is little correspondence between a stuck-at and a device-level fault model insofar as error activity or detection within a functional unit is concerned. Insofar as error activity outside the injected unit and at the output pins are concerned, the stuck-at and device models track each other, although the stuck-at model overestimates, by over one hundred percent, the probability of fault propagation to the output pins. The stuck-at model significantly underestimates the impact of an internal chip fault on the output pins.

Kim, S.↗
NASA NTRS1994

Anticipatory Neurofuzzy Control

Technique of feedback control, called "anticipatory neurofuzzy control," developed for use in controlling flexible structures and other dynamic systems for which mathematical models of dynamics poorly known or unknown. Superior ability to act during operation to compensate for, and adapt to, errors in mathematical model of dynamics, changes in dynamics, and noise. Also offers advantage of reduced computing time. Hybrid of two older fuzzy-logic control techniques: standard fuzzy control and predictive fuzzy control.

Mccullough, Claire L.↗
NASA NTRS2000

Tidal Implications for the GRACE Satellite Mission

A fundamental part of the processing of the GRACE satellite data will be correction for the gravitational effect of tides. This includes solid earth, ocean, and atmospheric tides. The most problematic are ocean tides in shallow seas and in high latitudes (above the 66$^{\circ}$ inclination of Topex). Somewhat smaller, but still important, errors are associated with the S$_1$ air tide and the ocean's dynamic response to it. In this paper we explore several facets of the tidal error budget. The ocean tide error spectrum is estimated (a) by inverse methods and (b) by simple differencing of several of the best tidal models. The tidal error spectrum is flatter than the tidal signal spectrum, and it exceeds 10\% of the signal at degrees 15 and above. The tide errors also exceed the anticipated sensitivity of GRACE for all degrees below 40, and possibly below 50. The assumption of constant seawater density (of about 1035 kg\,M$^{-3}$) is a minor error source. Owing to GRACE's high inclination, solar tides are aliased to long periods, which is potentially problematic for geophysical interpretation of the gravity signal. Simulations of the effect of high-latitude solar-tide errors on gravity estimates around Antarctica will be discussed.

Ray, Richard D.↗
NASA NTRS2000

Sampling Errors of SSM/I and TRMM Rainfall Averages: Comparison with Error Estimates from Surface Data and a Sample Model

Quantitative use of satellite-derived maps of monthly rainfall requires some measure of the accuracy of the satellite estimates. The rainfall estimate for a given map grid box is subject to both remote-sensing error and, in the case of low-orbiting satellites, sampling error due to the limited number of observations of the grid box provided by the satellite. A simple model of rain behavior predicts that Root-mean-square (RMS) random error in grid-box averages should depend in a simple way on the local average rain rate, and the predicted behavior has been seen in simulations using surface rain-gauge and radar data. This relationship was examined using satellite SSM/I data obtained over the western equatorial Pacific during TOGA COARE. RMS error inferred directly from SSM/I rainfall estimates was found to be larger than predicted from surface data, and to depend less on local rain rate than was predicted. Preliminary examination of TRMM microwave estimates shows better agreement with surface data. A simple method of estimating rms error in satellite rainfall estimates is suggested, based on quantities that can be directly computed from the satellite data.

Bell, Thomas L.↗
DOE OSTI2026

Benchmarking the performance of uncertainty quantification methods for neural network-based interatomic potentials

Machine-learned interatomic potentials (ML-IAPs) continue to gain popularity as accurate, computationally efficient replacements for traditional, physics-based interatomic potentials and expensive ab initio methods. Uncertainty quantification (UQ) of ML-IAPs is a growing area of research as UQ is critical in many applications of IAPs, such as developing curated datasets, active learning-based data augmentation, self-improving models, and estimating the uncertainty of molecular dynamics simulations. In this paper, we construct and benchmark a series of different neural network potentials (NNPs) with varying network architectures to determine the performance of these models with respect to both the mean and uncertainty calibration error. Each NNP method is specifically designed to predict either epistemic or aleatoric uncertainty with particular focus on the differences in behavior between the epistemic and aleatoric uncertainty estimates. We benchmark these methods using multiple datasets common in the ML-IAP literature. The results show that the aleatoric uncertainty from single-shot model architectures is a competitive alternative to ensemble-based epistemic uncertainty predictions in regions of sufficient data-density. However, in regions where the representative data is sparse, aleatoric uncertainty models tend to overpredict and epistemic methods tend to underpredict the actual model error. We conclude that the type of UQ is crucial when discussing performance of probabilistic model results as different methods have different performance characteristics depending on the regime in which they are evaluated. Therefore, the type of UQ method should be carefully evaluated against both the data characteristics and requirements for the intended application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Exploring Autoencoder-based Error-bounded Compression for Scientific Data

Error-bounded lossy compression is becoming an indispensable technique for the success of today's scientific projects with vast volumes of data produced during the simulations or instrument data acquisitions. Not only can it significantly reduce data size, but it also can control the compression errors based on user-specified error bounds. Autoencoder (AE) models have been widely used in image compression, but few AE-based compression approaches support error-bounding features, which are highly required by scientific applications. To address this issue, we explore using convolutional autoencoders to improve error-bounded lossy compression for scientific data, with the following three key contributions. (1) We provide an in-depth investigation of the characteristics of various autoencoder models and develop an error-bounded autoencoder-based framework in terms of the SZ model. (2) We optimize the compression quality for main stages in our designed AE-based error-bounded compression framework, fine-tuning the block sizes and latent sizes and also optimizing the compression efficiency of latent vectors. (3) We evaluate our proposed solution using five real-world scientific datasets and comparing them with six other related works. Experiments show that our solution exhibits a very competitive compression quality from among all the compressors in our tests. In absolute terms, it can obtain a much better compression quality (100%similar to 800% improvement in compression ratio with the same data distortion) compared with SZ2.1 and ZFP in cases with a high compression ratio.

Liu, Jinyang↗