Environmental grading of materials as an eco-design tool

Abstract

The main goal of this master thesis is to develop an Eco-design tool for the aeronautical sectors comparing the traditional aeronautical materials (aluminium vs carbon composite). This Eco-design tool assesses the environmental performance of materials into grades and those grades are implement in traditional design tool. The environmental grades are determined using a life cycle assessment (LCA) approach. The method used to answer those question is: i. performing an environmental assessment of the selected materials, ii. translating the environmental assessment into grades and iii. evaluating the LCA uncertainties of the obtained grade thanks to characterization and numerical simulations. First, the comparison of materials environmental impacts during the design process relies on environmental assessment of materials. This environmental assessment is performed using the LCA method and the LCA software GaBi. The scope on the LCA is limited to the manufacturing of the final materials from virgin and (if possible) secondary materials. The impact assessment methods used are ReCiPe 2016 and Environmental Footprint 2.0. The environmental assessment gives the environmental impacts on 16 impacts categories including acidification, human toxicity, climate change or energy resource. The raw results of the conducted LCA are not on an easy-accessible format and that is the reason why the LCA results are transformed into environmental grades. Thus, the impacts of a material are translated into grade by normalizing the environmental assessment results and a weighting method. The normalization allows to make the different impacts dimensionless and thus be able to combine the different impact into a grade. The weighting method provides weighting factors for each impact category and allows to obtain the final environmental grade. The weighting factors represent the importance of each impact category, these factors were determined by Castenalli (Castellani et al., 2016). Finally, the uncertainties are determined by applying standard deviation analysis to the critical parameters and Monte Carlo simulations.