These slides were presented at the NAFEMS World Congress 2025, held in Salzburg, Austria from May 19–22, 2025.

Abstract

In the aerospace industry, ensuring the reliability of simulation models is a critical step in the design and validation of complex structures. These models play a pivotal role in reducing development costs and time by minimizing the need for extensive physical testing, while also ensuring structural integrity and safety under extreme operational conditions. However, achieving high levels of confidence in simulation models can be challenging, especially when working with limited experimental data. This challenge is particularly relevant in projects such as the Dual Launch Structure (DLS) of the Ariane 6 launcher, where the ability to predict performance accurately is essential for operational success. This study explores the use of advanced validation techniques, focusing on integrating data fusion methodologies and Digital Image Correlation (DIC) technology. These approaches enable engineers to extract more information from experimental tests, align diverse datasets, and improve the accuracy of numerical simulations. By combining data from multiple sources'”such as DIC measurements, strain gauges, and fiber optics'”into a comprehensive validation framework, this methodology addresses the inherent limitations of traditional validation processes and enhances the credibility of simulation models. The Ariane 6 launcher project, led by ArianeGroup, presents an exemplary case study for these techniques. The DLS, a critical component of the launcher, is designed to accommodate dual payloads during launch, ensuring structural reliability under severe loading conditions. Its validation process involves a full-scale test that incorporates a variety of data sources to capture deformation, strain, and overall structural behavior. EikoSim contributed to this process by introducing a tailored Smart Testing framework, which integrates disparate data streams into a cohesive model. This framework supports efficient preprocessing and alignment of experimental and numerical data, ensuring compatibility and reducing potential sources of error. A key innovation in this validation process is the use of Digital Image Correlation (DIC). This optical measurement technique captures full-field strain and deformation by analyzing surface displacements in real time. Unlike traditional point-based sensors, such as strain gauges, DIC provides a more holistic view of structural responses, making it a valuable tool for understanding complex behaviors. During the DLS validation, three DIC systems were deployed in critical regions identified through Finite Element Analysis (FEA). These systems complemented traditional sensors, such as strain gauges and displacement transducers, to create a robust dataset for comparison with simulation predictions. Data fusion played a pivotal role in enhancing the validation process. By integrating data from multiple sources, the methodology leverages the complementary strengths of each measurement technique. For example, strain gauges provide high-precision local measurements, while DIC captures global structural responses, and fiber optic sensors offer continuous data along predefined paths. This multi-faceted approach enabled the identification of discrepancies between FEA predictions and experimental results, with most deviations remaining within acceptable limits of less than 20%. These differences were attributed to factors such as nominal boundary conditions, material property assumptions, and simplifications in the numerical model. The results of this study underscore the importance of robust validation frameworks in aerospace engineering. The combination of advanced data fusion and DIC techniques allowed for the creation of a highly accurate simulation model of the DLS, reducing the need for repeated physical tests and enabling the identification of areas for model refinement. This approach not only validated the DLS for operational use in Ariane 6 but also set a benchmark for future projects involving complex structural components.