One-Way Thermo-Mechanical Coupled System Identification Using Displacement and Temperature Measurements

Abstract

Structural system identification in the presence of thermal loads is challenging, as unmeasured or poorly modeled thermal effects can mask or mimic damage, leading to unreliable conclusions. This work presents an optimization-driven, adjoint-based high-fidelity system identification framework for localizing structural weakness and recovering the temperature field in one-way thermo-mechanical coupled structures. The methodology builds upon a standard optimization formulation that minimizes weighted discrepancies between simulated responses and measured data from a sparse displacement and temperature sensor network. To account for thermal effects, two strategies are proposed: a monolithic approach, which simultaneously identifies Young's modulus and temperature distributions, and a partitioned approach, which iteratively couples two inexact sub-problems through a Gauss-Seidel type fixed-point scheme. The proposed approaches are evaluated using two numerical examples -- a Plate With a Hole and a Footbridge model -- under linearly varying and localized thermal fields, and for different sensor layouts. Both approaches successfully recover the Young's modulus and temperature distributions, even when sensor placement does not fully capture the underlying thermal trends. Compared with a constant-temperature assumption and interpolation of the temperature field from sensor data, the proposed approach achieves the most accurate damage localization and temperature reconstruction. The largest gains occur when localized thermal features are poorly sampled by sensors, where interpolation and constant-temperature assumptions underperform. Furthermore, results show that the location of the temperature sensors is as influential as the number of sensors: well-placed sensors substantially improve identification, while additional sensors that miss critical thermal features provide limited benefit.

Figures (41)

• Figure 1: Gauss--Seidel--type partitioned fixed point optimization with inexact subproblem solves, where $r$ refers to the coupling iteration and $\beta$ refers to the relaxation parameter.
• Figure 2: Plate with Hole.(a) Mesh and (b) location of the 14 displacement sensors.
• Figure 3: Plate with Hole. Target Young's modulus distribution, i.e., localized damages.
• Figure 4: Plate with Hole. Target temperature distributions: (a) Linearly varying thermal field, and (b) Localized thermal field.
• Figure 5: Plate with Hole. Identified Young's modulus distributions when thermal load is not considered during SI, but the actual structure is subjected to a: (a) Linearly varying thermal field, (b) Localized thermal field.