Uncertainty exists throughout the life cycle of an aerostructure, from the design of individual components and material selection to manufacturing processes, operational conditions, environmental factors, and end-of-life recycling (cradle-to-cradle). To meet aviation safety and sustainability goals, Virtual Prototyping (covering design, qualification, and manufacturing) combined with Virtual Certification (VC) is emerging as a critical enabling technology.
However, variations in materials and manufacturing processes limit the reliability of purely deterministic simulations. While standards such as SAE TAHB0009A and ARP 4761 provide frameworks for aviation safety, inherent structural randomness often necessitates larger safety margins and complex certification procedures.
Understanding and quantifying these uncertainties in components and full aviation structures is essential for accurate risk and safety assessment. UPBEAT (Uncertainty Prediction & Bias Elimination in Aviation Technology for Safety) addresses this challenge by developing advanced methods for Uncertainty Quantification (UQ). These methods integrate mathematical modeling, statistics, machine learning, and simulation techniques, correlating uncertainty with severity and probability of occurrence. The ultimate goal is to enable efficient risk assessment for each failure mode of a selected engine aerostructure, the Outlet Guide Vane (OGV).
The project aims to achieve: 20-40% reduced weight and 50-70% fewer defects. Advanced predictive capabilities streamline product development, reducing qualification time by 30-40% and costs by 25-35%. In-line quality assurance (QA) support lowers manufacturing costs by 30-50% & time by 20-30%.