SUMMARYA T-spline surface is a nonuniform rational B-spline (NURBS) surface with T-junctions, and is defined by a control grid called T-mesh. The T-mesh is similar to a NURBS control mesh except that in a T-mesh, a row or column of control points is allowed to terminate in the inner parametric space. This property of T-splines makes local refinement possible. In the present study, shell formulation based on the T-spline finite element method (FEM) is presented. Shell formulation based on NURBS or T-splines has fundamental limitations because rotational DOFs, which are necessary in the shell formulation, cannot be defined on control points. In this study, the simple mapping scheme, in which every control point is mapped into one geometric point on the surface, is employed to eliminate the limitations. Using this mapping scheme, T-spline FEM can be easily extended to the analysis of shells. The proposed shell formulation is verified through various benchmarking problems. This study is a part of the efforts by the authors for the integration of CAD-CAE processes.
T-splines are recently proposed geometric modeling tools. A T-spline surface is a NURBS surface with Tjunctions and is defined by a control grid called T-mesh. Local refinement can be performed very easily for Tsplines while it is limited for B-splines or NURBS. Using T-splines, patches with unmatched boundaries can be combined easily without special technique. In this study, the analysis methodology using T-splines is proposed. In this methodology, T-splines are used both for description of geometries and for approximation of solution spaces. Two-dimensional linear elastic and dynamic problems will be solved by employing the proposed T-spline finite element method, and the effectiveness of the current analysis methodology will be verified.
Performance characteristics of the locally optimum detector for composite signals in additive and multiplicative noise are investigated. A generalized model with which we can represent the effect of multiplicative noise as well as that of additive noise is considered for a signal detection problem. The signal considered here is a composite signal which contains both deterministic and stochastic signal components. To illustrate the performance of the locally optimum detector, finite sample-size performance characteristics of the locally optimum detector are obtained and compared with those of other detectors.
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