scholarly journals Interpolation of equation-of-state data

2019 ◽  
Vol 626 ◽  
pp. A108
Author(s):  
V. A. Baturin ◽  
W. Däppen ◽  
A. V. Oreshina ◽  
S. V. Ayukov ◽  
A. B. Gorshkov

Aims. We use Hermite splines to interpolate pressure and its derivatives simultaneously, thereby preserving mathematical relations between the derivatives. The method therefore guarantees that thermodynamic identities are obeyed even between mesh points. In addition, our method enables an estimation of the precision of the interpolation by comparing the Hermite-spline results with those of frequent cubic (B-) spline interpolation. Methods. We have interpolated pressure as a function of temperature and density with quintic Hermite 2D-splines. The Hermite interpolation requires knowledge of pressure and its first and second derivatives at every mesh point. To obtain the partial derivatives at the mesh points, we used tabulated values if given or else thermodynamic equalities, or, if not available, values obtained by differentiating B-splines. Results. The results were obtained with the grid of the SAHA-S equation-of-state (EOS) tables. The maximum lgP difference lies in the range from 10−9 to 10−4, and Γ1 difference varies from 10−9 to 10−3. Specifically, for the points of a solar model, the maximum differences are one order of magnitude smaller than the aforementioned values. The poorest precision is found in the dissociation and ionization regions, occurring at T ∼ 1.5 × 103−105 K. The best precision is achieved at higher temperatures, T >  105 K. To discuss the significance of the interpolation errors we compare them with the corresponding difference between two different equation-of-state formalisms, SAHA-S and OPAL 2005. We find that the interpolation errors of the pressure are a few orders of magnitude less than the differences from between the physical formalisms, which is particularly true for the solar-model points.

2016 ◽  
Author(s):  
Nicole Kovacs ◽  
Terry Peters ◽  
Elvis Chen

A cubic spline is a spline where each curve is defined by a third-order polynomial, while a Hermite spline has each polynomial specified in Hermite form, being computed using tangent information as well as the position of the points. We propose two new classes for VTK, vtkCubicSpline and vtkHermiteSpline, which compute interpolating splines using a Cubic and a Hermite Spline Interpolation function, respectively. We also propose two new auxiliary classes, vtkParametricCubicSpline and vtkParametricHermiteSpline, that create parametric functions for the 1D interpolating aforementioned splines.


Author(s):  
Carlo Ciulla

This chapter reviews the extensive and comprehensive literature on B-Splines. In the forthcoming text emphasis is given to hierarchy and formal definition of polynomial interpolation with specific focus to the subclass of functions that are called B-Splines. Also, the literature is reviewed with emphasis on methodologies and applications of B-Splines within a wide array of scientific disciplines. The review is conducted with the intent to inform the reader and also to acknowledge the merit of the scientific community for the great effort devoted to B-Splines. The chapter concludes emphasizing on the proposition that the unifying theory presented throughout this book has for what concerns two specific cases of B-Spline functions: univariate quadratic and cubic models.


2017 ◽  
Vol 75 (3) ◽  
pp. 988-998 ◽  
Author(s):  
Jennifer L Shepperson ◽  
Niels T Hintzen ◽  
Claire L Szostek ◽  
Ewen Bell ◽  
Lee G Murray ◽  
...  

Abstract Understanding the distribution of fishing activity is fundamental to quantifying its impact on the seabed. Vessel monitoring system (VMS) data provides a means to understand the footprint (extent and intensity) of fishing activity. Automatic Identification System (AIS) data could offer a higher resolution alternative to VMS data, but differences in coverage and interpretation need to be better understood. VMS and AIS data were compared for individual scallop fishing vessels. There were substantial gaps in the AIS data coverage; AIS data only captured 26% of the time spent fishing compared to VMS data. The amount of missing data varied substantially between vessels (45–99% of each individuals' AIS data were missing). A cubic Hermite spline interpolation of VMS data provided the greatest similarity between VMS and AIS data. But the scale at which the data were analysed (size of the grid cells) had the greatest influence on estimates of fishing footprints. The present gaps in coverage of AIS may make it inappropriate for absolute estimates of fishing activity. VMS already provides a means of collecting more complete fishing position data, shielded from public view. Hence, there is an incentive to increase the VMS poll frequency to calculate more accurate fishing footprints.


1989 ◽  
Vol 6 (2) ◽  
pp. 177-179 ◽  
Author(s):  
Mark S. Mummy

Author(s):  
Carlo Ciulla

The results obtained processing the MRI database with classic and SRE-based one dimensional quadratic and cubic B-Splines are presented in this chapter. The chapter opens up with information relevant to the image resolution of the MRI database employed for validation. The assessment of the performance of the two classes of interpolators (classic and SRE-based) is conducted both quantitatively and qualitatively. The RSME Ratio is plotted to ascertain which ones of the classic or the SRE-based models deliver the smaller interpolation error. Also, the analysis of error images obtained after processing with either of the two model interpolators and the display of the maps of novel re-sampling locations along with spectral power evolutions corroborates the presentation of the characteristic features of the performances of the interpolation functions treated in this chapter.


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