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S. Corcione

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Open access Aug 2026

Cost Benefits of Introducing Structural Health Monitoring in New Generation Aircraft

Structural Health Monitoring (SHM) is increasingly regarded as a key enabling technology for future composite aircraft, not only for maintenance optimization but also for a possible re-interpretation of structural sizing constraints. In civil aviation, SHM is generally associated with condition-based maintenance and reduced downtime. However, its real strategic value may be larger when composite airframes are considered, since certification-driven safety margins are strongly influenced by the limited detectability of barely visible damage and hidden internal flaws. In this work, the aircraft-level value of SHM is revisited with stronger attention to the monitoring rationale, the integration burden, and the system implications of certification-aware structural design. Starting from an A220-like reference aircraft, a multidisciplinary analysis is used to quantify how permanently installed SHM sensors affect operating empty weight, mission fuel, field performance, and direct operating costs. Three configurations are compared by varying sensor density and safety-margin assumptions. The results show that SHM becomes especially attractive when it is not treated only as a maintenance add-on, but as a design enabler capable of partially relaxing conservative composite sizing assumptions. Under the investigated assumptions, a reduced structural safety margin combined with a limited sensor density leads to nearly unchanged aircraft performance together with lower cash operating costs and lower block fuel. The study highlights that the system-level effectiveness of SHM depends on the balance between sensing coverage, installation mass, and the certification credit granted to the monitoring capability.

V. Memmolo, Vincenzo Cusati, S. Corcione · 0 citations
Open access Aug 2026

Machine Learning-Based Methodology for Predicting 2D Propeller–Airfoil–Flap Interactions

Aero-propulsive interactions in flapped configurations are a critical consideration for Short Take-Off and Landing (stol) aircraft, where extreme operational requirements demand robust, optimization-ready methodologies during preliminary design. This study develops a surrogate modeling framework that predicts the section-level aerodynamic response of a propeller–airfoil–flap configuration across a multi-dimensional space of propeller positioning, flap geometry, and operational conditions. A paired powered and unpowered design of experiments isolates the propulsive contribution to lift, drag, and pitching moment, while a virtual-disk propeller model parameterized by volumetric thrust decouples the prediction from any specific blade design. The framework couples two-dimensional steady Reynolds-averaged Navier–Stokes (rans) dataset generation with a Deep Neural Network (dnn) surrogate, which achieves coefficient of determination values above 0.96 for all three coefficients and reduces evaluation cost by several orders of magnitude relative to direct cfd, a benefit that is decisive in optimization. Single- and multi-objective optimization identify Pareto-optimal configurations, and independent cfd verification confirms prediction accuracies within 5 to 10% across the operational envelope. The resulting surrogate enables rapid, optimization-ready exploration of propeller–airfoil–flap configurations, providing actionable trade-off information for the preliminary design of stol aircraft.

Gabriele Morra, S. Corcione, F. Nicolosi · 0 citations

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