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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

An experimental extrapolation technique using the Gafchromic EBT3 film for relative output factor measurements in small x-ray fields

Purpose: An experimental extrapolation technique is presented, which can be used to determine the relative output factors for very small x-ray fields using the Gafchromic EBT3 film. Methods: Relative output factors were measured for the Brainlab SRS cones ranging in diameters from 4 to 30 mm{sup 2} on a Novalis Trilogy linear accelerator with 6 MV SRS x-rays. The relative output factor was determined from an experimental reducing circular region of interest (ROI) extrapolation technique developed to remove the effects of volume averaging. This was achieved by scanning the EBT3 film measurements with a high scanning resolution of 1200 dpi. From the high resolution scans, the size of the circular regions of interest was varied to produce a plot of relative output factors versus area of analysis. The plot was then extrapolated to zero to determine the relative output factor corresponding to zero volume. Results: Results have shown that for a 4 mm field size, the extrapolated relative output factor was measured as a value of 0.651 ± 0.018 as compared to 0.639 ± 0.019 and 0.633 ± 0.021 for 0.5 and 1.0 mm diameter of analysis values, respectively. This showed a change in the relative output factors of 1.8% and 2.8% at these comparative regions of interest sizes. In comparison, the 25 mm cone had negligible differences in the measured output factor between zero extrapolation, 0.5 and 1.0 mm diameter ROIs, respectively. Conclusions: This work shows that for very small fields such as 4.0 mm cone sizes, a measureable difference can be seen in the relative output factor based on the circular ROI and the size of the area of analysis using radiochromic film dosimetry. The authors recommend to scan the Gafchromic EBT3 film at a resolution of 1200 dpi for cone sizes less than 7.5 mm and to utilize an extrapolation technique for the output factor measurements of very small field dosimetry.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Constrained Extrapolation Problem and Order-Dependent Mappings

In this work, we consider the problem of extrapolating the perturbation series for the dilute Fermi gas in three dimensions to the unitary limit of infinite scattering length and into the BEC region, using the available strong-coupling information to constrain the extrapolation problem. In this constrained extrapolation problem (CEP) the goal is to find classes of approximants that give well converged results already for low perturbative truncation orders. First, we show that standard Padé and Borel methods are too restrictive to give satisfactory results for this CEP. A generalization of Borel extrapolation is given by the so-called Maximum Entropy extrapolation method (MaxEnt). However, we show that MaxEnt requires extensive elaborations to be applicable to the dilute Fermi gas and is thus not practical for the CEP in this case. Instead, we propose order-dependent-mapping extrapolation (ODME) as a simple, practical, and general method for the CEP. Here, we find that the ODME approximants for the ground-state energy of the dilute Fermi gas are robust with respect to changes of the mapping choice and agree with results from quantum Monte Carlo simulations within uncertainties.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Adaptive stratified importance sampling: hybridization of extrapolation and importance sampling Monte Carlo methods for estimation of wind turbine extreme loads

Wind turbine extreme load estimation is especially difficult because turbulent inflow drives nonlinear turbine physics and control strategies; thus there can be huge differences in turbine response to essentially equivalent environmental conditions. The two main current approaches, extrapolation and Monte Carlo sampling, are both unsatisfying: extrapolation-based methods are dangerous because by definition they make predictions outside the range of available data, but Monte Carlo methods converge too slowly to routinely reach the desired 50-year return period estimates. Thus a search for a better method is warranted. We introduce an adaptive stratified importance sampling approach that allows for treating the choice of environmental conditions at which to run simulations as a stochastic optimization problem that minimizes the variance of unbiased estimates of extreme loads. Furthermore, the framework, built on the traditional bin-based approach used in extrapolation methods, provides a close connection between sampling and extrapolation, and thus allows the solution of the stochastic optimization (i.e., the optimal distribution of simulations in different wind speed bins) to guide and recalibrate the extrapolation. Results show that indeed this is a promising approach, as the variance of both the Monte Carlo and extrapolation estimates are reduced quickly by the adaptive procedure. We conclude, however, that due to the extreme response variability in turbine loads to the same environmental conditions, our method and any similar method quickly reaches its fundamental limits, and that therefore our efforts going forward are best spent elucidating the underlying causes of the response variability.

17 WIND ENERGY↗
DOE OSTI2020

How accurate is a machine learning-based wind speed extrapolation under a round-robin approach?

As the size of commercial wind turbines keeps increasing, having accurate ways to vertically extrapolate wind speed is essential to obtain a precise characterization of the wind resource for wind energy production. Recently, machine learning has been proposed and applied to extrapolate wind speed to hub heights. However, previous studies trained and tested the machine learning methods at the same site, giving them an unfair advantage over the conventional extrapolation techniques, which are instead more universal. Here, we use data from four sites in Oklahoma to test a round-robin validation approach for machine learning, under which we train a random forest at a site, and test it at a different site, where the model has no prior knowledge of the wind resource. We quantify how the accuracy of this technique varies with distance from the training site, and we find that it outperforms conventional techniques for wind extrapolation at all the considered spatial separations. We then assess how the accuracy of the machine-learning based approach varies when it is used to predict wind speed in a wind farm far wake. Finally, we explore as case study the performance of the random forest in extrapolating winds during a low-level jet event.

17 WIND ENERGY↗
DOE OSTI2019

Resurgent extrapolation: rebuilding a function from asymptotic data. Painlevé I

Extrapolation is a generic problem in physics and mathematics: how to use asymptotic data in one parametric regime to learn about the behavior of a function in another parametric regime. For example: extending weak coupling expansions to strong coupling, or high temperature expansions to low temperature, or vice versa. Such extrapolations are particularly interesting in systems possessing dualities. Here we study numerical procedures for performing such an extrapolation, combining ideas from resurgent asymptotics with well-known techniques of Borel summation, Padé approximants and conformal mapping. We illustrate the method with the concrete example of the Painlevé I equation, which has applications in many branches of physics and mathematics. Starting solely with a finite number of coefficients from asymptotic data at infinity on the positive real line, we obtain a high precision extrapolation of the function throughout the complex plane, even across the phase transition into the pole region. The precision far exceeds that of state-of-the-art numerical integration methods along the real axis. Furthermore, the methods used are both elementary and general, not relying on Painlevé integrability properties, and so are applicable to a wide class of extrapolation problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Patient-bounded extrapolation using low-dose priors for volume-of-interest imaging in C-arm CT

Purpose: Three-dimensional (3D) volume-of-interest (VOI) imaging with C-arm systems provides anatomical information in a predefined 3D target region at a considerably low x-ray dose. However, VOI imaging involves laterally truncated projections from which conventional reconstruction algorithms generally yield images with severe truncation artifacts. Heuristic based extrapolation methods, e.g., water cylinder extrapolation, typically rely on techniques that complete the truncated data by means of a continuity assumption and thus appear to be ad-hoc. It is our goal to improve the image quality of VOI imaging by exploiting existing patient-specific prior information in the workflow. Methods: A necessary initial step prior to a 3D acquisition is to isocenter the patient with respect to the target to be scanned. To this end, low-dose fluoroscopic x-ray acquisitions are usually applied from anterior–posterior (AP) and medio-lateral (ML) views. Based on this, the patient is isocentered by repositioning the table. In this work, we present a patient-bounded extrapolation method that makes use of these noncollimated fluoroscopic images to improve image quality in 3D VOI reconstruction. The algorithm first extracts the 2D patient contours from the noncollimated AP and ML fluoroscopic images. These 2D contours are then combined to estimate a volumetric model of the patient. Forward-projecting the shape of the model at the eventually acquired C-arm rotation views gives the patient boundary information in the projection domain. In this manner, we are in the position to substantially improve image quality by enforcing the extrapolated line profiles to end at the known patient boundaries, derived from the 3D shape model estimate. Results: The proposed method was evaluated on eight clinical datasets with different degrees of truncation. The proposed algorithm achieved a relative root mean square error (rRMSE) of about 1.0% with respect to the reference reconstruction on nontruncated data, even in the presence of severe truncation, compared to a rRMSE of 8.0% when applying a state-of-the-art heuristic extrapolation technique. Conclusions: The method we proposed in this paper leads to a major improvement in image quality for 3D C-arm based VOI imaging. It involves no additional radiation when using fluoroscopic images that are acquired during the patient isocentering process. The model estimation can be readily integrated into the existing interventional workflow without additional hardware.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-D-204-02: BED Consistent Extrapolation of Mean Dose Tolerances

Purpose: The safe use of radiotherapy requires the knowledge of tolerable organ doses. For experimental fractionation schemes (e.g. hypofractionation) these are typically extrapolated from traditional fractionation schedules using the Biologically Effective Dose (BED) model. This work demonstrates that using the mean dose in the standard BED equation may overestimate tolerances, potentially leading to unsafe treatments. Instead, extrapolation of mean dose tolerances should take the spatial dose distribution into account. Methods: A formula has been derived to extrapolate mean physical dose constraints such that they are mean BED equivalent. This formula constitutes a modified BED equation where the influence of the spatial dose distribution is summarized in a single parameter, the dose shape factor. To quantify effects we analyzed 14 liver cancer patients previously treated with proton therapy in 5 or 15 fractions, for whom also photon IMRT plans were available. Results: Our work has two main implications. First, in typical clinical plans the dose distribution can have significant effects. When mean dose tolerances are extrapolated from standard fractionation towards hypofractionation they can be overestimated by 10–15%. Second, the shape difference between photon and proton dose distributions can cause 30–40% differences in mean physical dose for plans having the same mean BED. The combined effect when extrapolating proton doses to mean BED equivalent photon doses in traditional 35 fraction regimens resulted in up to 7–8 Gy higher doses than when applying the standard BED formula. This can potentially lead to unsafe treatments (in 1 of the 14 analyzed plans the liver mean dose was above its 32 Gy tolerance). Conclusion: The shape effect should be accounted for to avoid unsafe overestimation of mean dose tolerances, particularly when estimating constraints for hypofractionated regimens. In addition, tolerances established for a given treatment modality cannot necessarily be applied to other modalities with drastically different dose distributions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-579: Extrapolation Techniques for Small Field Dosimetry Using Gafchromic EBT3 Film

Purpose: The purpose of this project is to test an experimental approach using an extrapolation technique for Gafchromic EBT3 film for small field x-ray dosimetry. Methods: Small fields from a Novalis Tx linear accelerator with HD Multileaf Collimators with 6 MV was used. The field sizes ranged from 5 × 5 to 50 × 50 mm2 MLC fields and a range of circular cones of 4 to 30 mm2 diameters. All measurements were performed in water at an SSD of 100 cm and at a depth of 10 cm. The relative output factors (ROFs) were determined from an extrapolation technique developed to eliminate the effects of partial volume averaging in film scan by scanning films with high resolution (1200 DPI). The size of the regions of interest (ROI) was varied to produce a plot of ROFs versus ROI which was then extrapolated to zero ROI to determine the relative output factor. The results were compared with other solid state detectors with proper correction, namely, IBA SFD diode, PTW 60008 and PTW 60012 diode. Results: For the 4 mm cone, the extrapolated ROF had a value of 0.658 ± 0.014 as compared to 0.642 and 0.636 for 0.5 mm and 1 mm2 ROI analysis, respectively. This showed a change in output factor of 2.4% and 3.3% at this comparative ROI sizes. In comparison, the 25 mm cone had a difference in measured output factor of 0.3% and 0.5% between 0.5 and 1.0 mm, respectively compared to zero volume. For the fields defined by MLCs a difference of up to 2% for 5×5 mm2 was observed. Conclusion: A measureable difference can be seen in ROF based on the ROI when radiochromic film is used. Using extrapolation technique from high resolution scanning a good agreement can be achieved.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Decreasing wind speed extrapolation error via domain-specific feature extraction and selection

Abstract. Model uncertainty is a significant challenge in the wind energy industry and can lead to mischaracterization of millions of dollars' worth of wind resources. Machine learning methods, notably deep artificial neural networks (ANNs), are capable of modeling turbulent and chaotic systems and offer a promising tool to produce high-accuracy wind speed forecasts and extrapolations. This paper uses data collected by profiling Doppler lidars over three field campaigns to investigate the efficacy of using ANNs for wind speed vertical extrapolation in a variety of terrains, and it quantifies the role of domain knowledge in ANN extrapolation accuracy. A series of 11 meteorological parameters (features) are used as ANN inputs, and the resulting output accuracy is compared with that of both standard log-law and power-law extrapolations. It is found that extracted nondimensional inputs, namely turbulence intensity, current wind speed, and previous wind speed, are the features that most reliably improve the ANN's accuracy, providing up to a 65 % and 52 % increase in extrapolation accuracy over log-law and power-law predictions, respectively. The volume of input data is also deemed important for achieving robust results. One test case is analyzed in depth using dimensional and nondimensional features, showing that the feature nondimensionalization drastically improves network accuracy and robustness for sparsely sampled atmospheric cases.

17 WIND ENERGY↗
DOE OSTI2017

Projecting species’ vulnerability to climate change: Which uncertainty sources matter most and extrapolate best?

Species distribution models (SDMs) are commonly used to assess potential climate change impacts on biodiversity, but several critical methodological decisions are often made arbitrarily. We compare variability arising from these decisions to the uncertainty in future climate change itself. We also test whether certain choices offer improved skill for extrapolating to a changed climate and whether internal cross-validation skill indicates extrapolative skill.We compared projected vulnerability for 29 wetland-dependent bird species breeding in the climatically dynamic Prairie Pothole Region, USA. For each species we built 1,080 SDMs to represent a unique combination of: future climate, class of climate covariates, collinearity level, and thresholding procedure. We examined the variation in projected vulnerability attributed to each uncertainty source. To assess extrapolation skill under a changed climate, we compared model predictions with observations from historic drought years. Uncertainty in projected vulnerability was substantial, and the largest source was that of future climate change. Large uncertainty was also attributed to climate covariate class with hydrological covariates projecting half the range loss of bioclimatic covariates or other summaries of temperature and precipitation. We found that choices based on performance in cross-validation improved skill in extrapolation. Qualitative rankings were also highly uncertain. Given uncertainty in projected vulnerability and resulting uncertainty in rankings used for conservation prioritization, a number of considerations appear critical for using bioclimatic SDMs to inform climate change mitigation strategies. Our results emphasize explicitly selecting climate summaries that most closely represent processes likely to underlie ecological response to climate change. For example, hydrological covariates projected substantially reduced vulnerability, highlighting the importance of considering whether water availability may be a more proximal driver than precipitation. However, because cross-validation results were correlated with extrapolation results, the use of cross-validation performance metrics to guide modeling choices where knowledge is limited was supported.

thresholds↗
DOE OSTI2015

Improving in vitro to in vivo extrapolation by incorporating toxicokinetic measurements: A case study of lindane-induced neurotoxicity

Approaches for extrapolating in vitro toxicity testing results for prediction of human in vivo outcomes are needed. The purpose of this case study was to employ in vitro toxicokinetics and PBPK modeling to perform in vitro to in vivo extrapolation (IVIVE) of lindane neurotoxicity. Lindane cell and media concentrations in vitro, together with in vitro concentration-response data for lindane effects on neuronal network firing rates, were compared to in vivo data and model simulations as an exercise in extrapolation for chemical-induced neurotoxicity in rodents and humans. Time- and concentration-dependent lindane dosimetry was determined in primary cultures of rat cortical neurons in vitro using “faux” (without electrodes) microelectrode arrays (MEAs). In vivo data were derived from literature values, and physiologically based pharmacokinetic (PBPK) modeling was used to extrapolate from rat to human. The previously determined EC{sub 50} for increased firing rates in primary cultures of cortical neurons was 0.6 μg/ml. Media and cell lindane concentrations at the EC{sub 50} were 0.4 μg/ml and 7.1 μg/ml, respectively, and cellular lindane accumulation was time- and concentration-dependent. Rat blood and brain lindane levels during seizures were 1.7–1.9 μg/ml and 5–11 μg/ml, respectively. Brain lindane levels associated with seizures in rats and those predicted for humans (average = 7 μg/ml) by PBPK modeling were very similar to in vitro concentrations detected in cortical cells at the EC{sub 50} dose. PBPK model predictions matched literature data and timing. These findings indicate that in vitro MEA results are predictive of in vivo responses to lindane and demonstrate a successful modeling approach for IVIVE of rat and human neurotoxicity. - Highlights: • In vitro to in vivo extrapolation for lindane neurotoxicity was performed. • Dosimetry of lindane in a micro-electrode array (MEA) test system was assessed. • Cell concentrations at the MEA EC{sub 50} equaled rat brain levels associated with seizure. • PBPK-predicted human brain levels at seizure also equaled EC{sub 50} cell concentrations. • In vitro MEA results are predictive of lindane in vivo dose–response in rats/humans.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Use of the Wilshire Equations to Correlate and Extrapolate Creep Data of Inconel 617 and Nimonic 105

Advanced power plant alloys must endure high temperatures and pressures for durations at which creep data are often not available, necessitating the extrapolation of creep life. A recently developed creep life extrapolation method is the Wilshire equations, with which multiple approaches can be used to increase the goodness of fit of available experimental data and improve the confidence level of calculating long-term creep strength at times well beyond the available experimental data. In this article, the Wilshire equation is used to extrapolate the creep life of Inconel 617 and Nimonic 105 to 100,000 h. The use of (a) different methods to determine creep activation energy, (b) region splitting, (c) heat- and processing-specific tensile strength data, and (d) short-duration test data were investigated to determine their effects on correlation and extrapolation. For Inconel 617, using the activation energy of lattice self-diffusion as Q C * resulted in a poor fit with the experimental data. Additionally, the error of calculated rupture times worsened when splitting regions. For Nimonic 105, the error was reduced when heat- and processing-specific tensile strengths were used. Extrapolating Inconel 617 creep strength to 100,000 h life gave conservative results when compared to values calculated by the European Creep Collaborative Committee.

36 MATERIALS SCIENCE↗
NASA NTRS1992

Extrapolation limitations of multilayer feedforward neural networks

The limitations of backpropagation used as a function extrapolator were investigated. Four common functions were used to investigate the network's extrapolation capability. The purpose of the experiment was to determine whether neural networks are capable of extrapolation and, if so, to determine the range for which networks can extrapolate. The authors show that neural networks cannot extrapolate and offer an explanation to support this result.

Haley, Pamela J.↗
NASA NTRS1992

The Extrapolation of Elementary Sequences

We study sequence extrapolation as a stream-learning problem. Input examples are a stream of data elements of the same type (integers, strings, etc.), and the problem is to construct a hypothesis that both explains the observed sequence of examples and extrapolates the rest of the stream. A primary objective -- and one that distinguishes this work from previous extrapolation algorithms -- is that the same algorithm be able to extrapolate sequences over a variety of different types, including integers, strings, and trees. We define a generous family of constructive data types, and define as our learning bias a stream language called elementary stream descriptions. We then give an algorithm that extrapolates elementary descriptions over constructive datatypes and prove that it learns correctly. For freely-generated types, we prove a polynomial time bound on descriptions of bounded complexity. An especially interesting feature of this work is the ability to provide quantitative measures of confidence in competing hypotheses, using a Bayesian model of prediction.

Laird, Philip↗
NASA NTRS1996

Implicit Extrapolation Methods for Variable Coefficient Problems

Implicit extrapolation methods for the solution of partial differential equations are based on applying the extrapolation principle indirectly. Multigrid tau-extrapolation is a special case of this idea. In the context of multilevel finite element methods, an algorithm of this type can be used to raise the approximation order, even when the meshes are nonuniform or locally refined. Here previous results are generalized to the variable coefficient case and thus become applicable for nonlinear problems. The implicit extrapolation multigrid algorithm converges to the solution of a higher order finite element system. This is obtained without explicitly constructing higher order stiffness matrices but by applying extrapolation in a natural form within the algorithm. The algorithm requires only a small change of a basic low order multigrid method.

Jung, M.↗
NASA NTRS2007

The Extrapolation of High Altitude Solar Cell I(V) Characteristics to AM0

The high altitude aircraft method has been used at NASA GRC since the early 1960's to calibrate solar cell short circuit current, ISC, to Air Mass Zero (AMO). This method extrapolates ISC to AM0 via the Langley plot method, a logarithmic extrapolation to 0 air mass, and includes corrections for the varying Earth-Sun distance to 1.0 AU and compensating for the non-uniform ozone distribution in the atmosphere. However, other characteristics of the solar cell I(V) curve do not extrapolate in the same way. Another approach is needed to extrapolate VOC and the maximum power point (PMAX) to AM0 illumination. As part of the high altitude aircraft method, VOC and PMAX can be obtained as ISC changes during the flight. These values can then the extrapolated, sometimes interpolated, to the ISC(AM0) value. This approach should be valid as long as the shape of the solar spectra in the stratosphere does not change too much from AMO. As a feasibility check, the results are compared to AMO I(V) curves obtained using the NASA GRC X25 based multi-source simulator. This paper investigates the approach on both multi-junction solar cells and sub-cells.

Snyder, David B.↗
DOE OSTI2015

Short-range stabilizing potential for computing energies and lifetimes of temporary anions with extrapolation methods

The energy of a temporary anion can be computed by adding a stabilizing potential to the molecular Hamiltonian, increasing the stabilization until the temporary state is turned into a bound state, and then further increasing the stabilization until enough bound state energies have been collected so that these can be extrapolated back to vanishing stabilization. The lifetime can be obtained from the same data, but only if the extrapolation is done through analytic continuation of the momentum as a function of the square root of a shifted stabilizing parameter. This method is known as analytic continuation of the coupling constant, and it requires—at least in principle—that the bound-state input data are computed with a short-range stabilizing potential. In the context of molecules and ab initio packages, long-range Coulomb stabilizing potentials are, however, far more convenient and have been used in the past with some success, although the error introduced by the long-rang nature of the stabilizing potential remains unknown. Here, we introduce a soft-Voronoi box potential that can serve as a short-range stabilizing potential. The difference between a Coulomb and the new stabilization is analyzed in detail for a one-dimensional model system as well as for the {sup 2}Π{sub u} resonance of CO{sub 2}{sup −}, and in both cases, the extrapolation results are compared to independently computed resonance parameters, from complex scaling for the model, and from complex absorbing potential calculations for CO{sub 2}{sup −}. It is important to emphasize that for both the model and for CO{sub 2}{sup −}, all three sets of results have, respectively, been obtained with the same electronic structure method and basis set so that the theoretical description of the continuum can be directly compared. The new soft-Voronoi-box-based extrapolation is then used to study the influence of the size of diffuse and the valence basis sets on the computed resonance parameters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Can Tauc plot extrapolation be used for direct-band-gap semiconductor nanocrystals?

Despite that Tauc plot extrapolation has been widely adopted for extracting bandgap energies of semiconductors, there is a lack of theoretical support for applying it to nanocrystals. In this paper, direct-allowed optical transitions in semiconductor nanocrystals have been formulated based on a purely theoretical approach. This result reveals a size-dependant transition of the power factor used in Tauc plot, increasing from one half used in the 3D bulk case to one in the 0D case. This size-dependant intermediate value of power factor allows a better extrapolation of measured absorption data. Being a material characterization technique, the generalized Tauc extrapolation gives a more reasonable and accurate acquisition of the intrinsic bandgap, while the unjustified purpose of extrapolating any elevated bandgap caused by quantum confinement is shown to be incorrect.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗