Digital-Twin-Driven Inversion of Gap Deformation in a Passenger Aircraft Door–Frame Assembly

Accurate prediction and inversion of time-dependent gap deformation between a passenger aircraft door and its frame remain difficult under slowly varying pressurization because the response is governed by coupled geometry, lock restraint, and nonlinear contact. This study develops a digital-twin-driven inversion framework for door–frame gap deformation. A physics-based parametric twin is established using feature associations and a unified assembly datum, and the outer skin, stiffeners, load-bearing frame, inner panel, locks, and door frame are represented in a nonlinear quasi-static finite-element model. Geometric nonlinearity and a separable door–frame contact are retained so that load-dependent opening, local slip, and constraint effects can be captured. A full-scale test platform with binocular vision provides three-directional relative-displacement measurements. The numerical model is updated through equivalent-stiffness correction, local nodal adjustment, and global scaling, with physical restrictions imposed on the adjustable region and correction amplitudes to avoid unconstrained point-wise fitting; corrected simulation and experimental data are then fused for surrogate training. An improved Gaussian process regression model uses the normalized pressure level, spatial coordinates of the registered key-point pairs, and displacement-direction encoding as inputs and the corresponding fused directional gap displacement as the output; a squared-exponential kernel and input-dependent noise model represent nonlinear response and uncertainty. Under an identical within-profile training–validation partition, the reported GPR implementation yields lower aggregate error than the corresponding RBF-NN and random-forest benchmark runs, with a root-mean-square error of 0.21, a mean relative error of 2.8%, and a coefficient of determination of 0.985. These metrics quantify interpolation within the calibrated slowly varying quasi-static loading domain rather than validated extrapolation to an independent unseen load history. The framework provides a mechanism-consistent route for virtual–physical updating and surrogate-based inversion of aircraft door–frame gap states.