A leading-edge radius fitting window is the chosen neighborhood of nose coordinates used in leading-edge radius estimation from sampled coordinates. It can be specified by coordinate bounds, arc length or a point-selection rule. The choice controls both how closely a local shape model applies and how strongly coordinate errors affect the inferred airfoil leading-edge radius.
Consider the original local model , with known nose origin, exact coordinates and . A quadratic least squares fit with positive fixed weights givesThe quartic term therefore causes a deterministic coefficient error even with exact observations. For a dense, uniformly weighted window , the ratio of sums tends to . Expanding the window admits more of the omitted shape term.
Shrinking the window has a different cost. For unweighted observations with independent horizontal errors of common variance ,At a fixed number of similarly distributed points, scaling every by one half multiplies this variance by sixteen. Very close points can have tiny horizontal displacements relative to the instrument's coordinate error. The window thus presents a concrete bias-variance tradeoff, not a rule that the smallest neighborhood is always best.
For stipulated mm and , the dense-window model gives fitted radii about 2.9682 mm at mm and 2.7362 mm at mm, before any noise is added. These are calculations on a specified quartic curve, not observed airfoil data. The broader window has less sensitivity to fixed coordinate noise but more quadratic-model bias.
A useful measurement report states the origin estimate, coordinate uncertainty, fitting model and exact window, then examines sensitivity to a few nearby windows. If both coordinates are uncertain or the nose location is fitted, the displayed variance formula no longer covers the complete estimation problem. A known analytic profile supplies an implementation check: the four-digit thickness envelope has the radius relation .[ref] That check cannot remove model error for an unrelated measured nose.
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