Quantification of Uncertainties in Life Cycle Assessment for Aviation

Kurzfassung

With aviation increasingly contributing to the issue of climate change, enabling sustainability via technological solutions is becoming a priority. To evaluate the environmental impact of innovative concepts, Life Cycle Assessment (LCA) is a broadly used tool. However, uncertainty in LCA can significantly affect the reliability of results, especially for emerging aircraft concepts that lack established data. This thesis addresses these uncertainties by creating a framework for uncertainty and sensitivity analysis. It is tailored to DLR’s Life Cycle Cash Flow Environment (LYFE) tool, which uses discrete-event simulation to model the environmental impacts over an aircraft’s full life cycle including production, flight operations, maintenance and end-oflife. Uncertainties affecting the input parameters to the simulation are characterised by their nature, location and label. They can be quantified through probability distributions, which the Pedigree Approach helps define. These uncertainties are then propagated with Monte Carlo Simulation, for which a convergence analysis is conducted. Min-mean-max propagation offers a less runtime-intensive alternative, though it provides a more limited level of insight. Sensitivity analysis and diagnostic diagrams highlight the input parameters that influence the overall result quality the most. The application to a case study on a hydrogen-powered aircraft shows potential insights that the crated framwork provides. Its LCA results are highly dependent on the maintenance events and impacted by LYFE’s non-linear behaviour. The established framework provides a structured approach to uncertainty analysis that aligns with ISO 14040 and 14044 directives. In a broader context, this work contributes to improving the reliability of LCA in aviation, supporting its transition toward sustainability.