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Results for “radial basis function interpolation”

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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21 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Natural element method for radiative heat transfer in a semitransparent medium with irregular geometries

This paper develops a numerical solution to the radiative heat transfer problem coupled with conduction in an absorbing, emitting and isotropically scattering medium with the irregular geometries using the natural element method (NEM). The walls of the enclosures, having temperature and mixed boundary conditions, are considered to be opaque, diffuse as well as gray. The NEM as a meshless method is a new numerical scheme in the field of computational mechanics. Different from most of other meshless methods such as element-free Galerkin method or those based on radial basis functions, the shape functions used in NEM are constructed by the natural neighbor interpolations, which are strictly interpolant and the essential boundary conditions can be imposed directly. The natural element solutions in dealing with the coupled heat transfer problem for the mixed boundary conditions have been validated by comparison with those from Monte Carlo method (MCM) generated by the authors. For the validation of the NEM solution to radiative heat transfer in the semicircular medium with an inner circle, the results by NEM have been compared with those reported in the literatures. For pure radiative transfer, the upwind scheme is employed to overcome the oscillatory behavior of the solutions in some conditions. The steady state and transient heat transfer problem combined with radiation and conduction in the semicircular enclosure with an inner circle are studied. Effects of various parameters such as the extinction coefficient, the scattering albedo, the conduction–radiation parameter and the boundary emissivity are analyzed on the radiative and conductive heat fluxes and transient temperature distributions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Educating local radial basis functions using the highest gradient of interest in three dimensional geometries: EDUCATING LOCALIZED RBF-SCHEMES

We present a novel methodology to effectively localize radial basis function approximation methods in three dimensions. The local scheme requires shape parameter-dependent functions that can be used to approximate gradients of scattered data and to solve partial differential operators. The optimum shape parameter is obtained from the highest gradient of interest, where a known analytical function, when boundary conditions are not present, or a shape parameter-free global approximation are used to educate the localized scheme. The later option is applicable to problems where the operator needs to be solved multiple times, like in time evolution or stochastic integration. Past shape parameter's optimizations, for two-dimensional domains, based on the condition number of the interpolant matrix, were unable to provide satisfactory approximations. The applicability of our method is illustrated in the context of an analytical expression interpolation and during a Ginzburg-Landau relaxation of a free energy functional. In general, the optimum shape parameter depends on geometry, node distribution, and density, whereas the approximation errors decrease as the node density and the local stencil size increase. The effective localization of radial basis functions motivates its use in moving boundary problems and accelerates solutions through sparse matrix solvers.

free energy functional↗
DOE OSTI2015

MO-F-CAMPUS-J-02: Automatic Recognition of Patient Treatment Site in Portal Images Using Machine Learning

Purpose: To investigate the method to automatically recognize the treatment site in the X-Ray portal images. It could be useful to detect potential treatment errors, and to provide guidance to sequential tasks, e.g. automatically verify the patient daily setup. Methods: The portal images were exported from MOSAIQ as DICOM files, and were 1) processed with a threshold based intensity transformation algorithm to enhance contrast, and 2) where then down-sampled (from 1024×768 to 128×96) by using bi-cubic interpolation algorithm. An appearance-based vector space model (VSM) was used to rearrange the images into vectors. A principal component analysis (PCA) method was used to reduce the vector dimensions. A multi-class support vector machine (SVM), with radial basis function kernel, was used to build the treatment site recognition models. These models were then used to recognize the treatment sites in the portal image. Portal images of 120 patients were included in the study. The images were selected to cover six treatment sites: brain, head and neck, breast, lung, abdomen and pelvis. Each site had images of the twenty patients. Cross-validation experiments were performed to evaluate the performance. Results: MATLAB image processing Toolbox and scikit-learn (a machine learning library in python) were used to implement the proposed method. The average accuracies using the AP and RT images separately were 95% and 94% respectively. The average accuracy using AP and RT images together was 98%. Computation time was ∼0.16 seconds per patient with AP or RT image, ∼0.33 seconds per patient with both of AP and RT images. Conclusion: The proposed method of treatment site recognition is efficient and accurate. It is not sensitive to the differences of image intensity, size and positions of patients in the portal images. It could be useful for the patient safety assurance. The work was partially supported by a research grant from Varian Medical System.

60 APPLIED LIFE SCIENCES↗