Universal Parametric Geometry Representation Method

By B. Kulfan. 2008

805 citations recorded by the source corpus.

Subfields: Computational Mechanics, Mechanical Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/16604c9fd31cf8d70dccba2f17f0871792ac7a9f

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In Computational Mechanics

In Mechanical Engineering

In Statistics and Probability

Citations in the corpus

Metric-Based Mathematical Derivation of Efficient Airfoil Design Variables (2015, methodology): “The wind-tunnel tolerance value of 5 × 10−4 was taken from Kulfan [12] to be the tolerance that the results should be within to be able to state that the airfoil shape had been recovered sufficiently.”
A Geometric Comparison of Aerofoil Shape Parameterisation Methods (2016, neutral): “P0 = (0, 0), Pi = ( i − 1 n , ai ) , Pn−1 = (1, zte), (11)”
An adaptive geometry parametrization for aerodynamic shape optimization (2014, background): “As denoted in the work of Kulfan (2008), for an airfoil with a round nose, the following centripetal parametrization (Farin 1990) gives a desirable leading edge geometry: ξ1 = 0 (2) ξ j = n− k−1 LT j−1 ∑ m=1 √ Lm j = 2, . . . ,N (3) where N is the total number of surface points on the airfoil, Lm…”
Active Subspaces of Airfoil Shape Parameterizations (2017, methodology): “, round nose) airfoils: parametric sections (PARSEC) [12] and classshape transformations (CST) [13], which we discuss in the following sections.”
Two-Level Free-Form and Axial Deformation for Exploratory Aerodynamic Shape Optimization (2015, methodology): “Kulfan’s CST method [15] extends this idea to a set of fundamental “classes” of aerodynamic bodies.”
High-fidelity aerodynamic shape optimization using efficient orthogonal modal design variables with a constrained global optimizer (2017, methodology): “Examples of these methods are CST [26], PARSEC [27], PDEs [28] and splines [29].”
Intuitive Class/Shape Function Parameterization for Airfoils (2014, methodology): “A well-behaved parameterization method should have the following properties [1,2]: 1) high flexibility to cover the potential optimal solution in the design space, 2) small number of key design variables, 3) smoothness and realizability of the shapes, and 4) intuitiveness of the design parameters fo”
Full Configuration Drag Estimation (2009, methodology): “Shape-function methods are also used for more complicated geometry, including full configurations: for example, Vachris and Yaeger’s [21] quickgeometry method and Kulfan’s [22] CST (class-function/shapefunction transformation).”