A set-based design space exploration framework for hybrid-electric aicraft design - Spinelli, Andrea (Cranfield University, 2023)
Engineering design is characterised by uncertainty caused by a lack of experience and information. The traditional approach focuses on iterating and refining an initial conceptual design, which often is similar to the final one. Although this method serves well in the case of evolutionary design, it is unsuitable for innovation. In fact, without a suitable initial starting point, many rework iterations may be required to correct early inadequate design decisions. In addition, it may be challenging to map the requirements directly onto the design space. This dissertation aims at developing a methodology to address this problem. The developed framework starts from the hypothesis, and the knowledge to carry out the mapping of requirements onto the input parameters is embedded in the simulation model, and hence no additional rules are required. Instead, a probabilistic surrogate model based on Gaussian processes is used in conjunction with Bayesian statistics to find and eliminate unfeasible areas of the design space. This selection criterion is used in a set-based design approach to explore pockets of the entire continuous design space. Finally, sets with a sufficient likelihood of satisfying the requirements are searched with a local multidisciplinary optimisation algorithm to recover the individual design points. This process reduced the computational cost of the design space exploration by 80% without sacrificing the number of alternative solutions. Thanks to the large amount of data obtained, it was possible to produce new knowledge on hybrid-electric aircraft design. Specifically, it was found that linear segments are sufficient for defining energy management strategies, and the reduction of NOx emissions and fuel consumption are associated with climb and cruise, respectively. Furthermore, when studying regional aircraft operating missions, it was found that partial recharge is necessary to maintain the design performance. However, this could reduce the duration of the battery. The battery ageing rate correlates with the EMS’s demand for electrical energy. Finally, it was found that the battery’s energy density is a determinant of the pack’s durability and the feasibility of HE aircraft. The rate of improvement in emissions and fuel consumption is non-linear, suggesting that investing in considerable technological improvements has better returns. Indeed, the required technological level will not be available until the 2040s without an exponential increment of the cell energy density.
Keywords: Set-based design; Design space exploration; Bayesian probability; Probabilistic surrogate modelling; Hybrid-electric aircraft; Energy Management Strategies; Technological uncertainty