The energy management strategy of a hybrid-electric aircraft is coupled with the design of the propulsion system itself. A new design space exploration methodology based on Set-Based Design is introduced to analyse the effects of different strategies on the fuel consumption, ${\mathrm{NO}}_{\mathrm{x}}$ and take-off mass. Probabilities are used to evaluate and discard areas of the design space not capable of satisfying the constraints and requirements, saving computational time corresponding to an average of 75%. The study is carried on a 50-seater regional turboprop with a parallel hybrid-electric architecture. The strategies are modelled as piecewise linear functions of the degree of hybridisation and are applied to different mission phases to explore how the strategy complexity and the number of hybridised segments can influence the behaviour of the system. The results indicate that the complexity of the parametrisation does not affect the trade-off between fuel consumption and ${\mathrm{NO}}_{\mathrm{x}}$ emissions. On the contrary, a significant trade-off is identified on which phases are hybridised. That is, the least fuel consumption is obtained only by hybridising the longest mission phase, while less ${\mathrm{NO}}_{\mathrm{x}}$ emissions are generated if more phases are hybridised. Finally, the maximum take-off mass was investigated as a parameter, and the impact to the trade-off between the objectives was analysed. Three energy management strategies were suggested from these findings, which achieved a reduction to the fuel consumption of up to 10% and a reduction to ${\mathrm{NO}}_{\mathrm{x}}$ emissions of up to 15%.

This paper presents a study of the effects of the durability and level of energy storage technology on energy management strategies and the performance of hybrid electric turboprops. The results highlight the key role of battery energy density on the durability of the battery pack and the viability of the concept of hybrid electric aircraft. Additionally, the trade-off between zero-day environmental compatibility and battery lifetime is identified, caused by the size of the pack. The effective energy density would decrease with the aging of the cells, leaving a significant inert mass and increasing fuel consumption. Optimal energy management strategies are suggested in light of this new information. Higher specific energy of the pack would mitigate this aspect, along with a reduction in fuel consumption and ${\rm{N}}{{\rm{O}}_{\rm{x}}}$ emissions. Indeed, the improvement of environmental compatibility was found to be nonlinear with a positive rate, suggesting high returns in investing in great improvements in energy density over a gradual increase. This result relates to the results of the statistical technological forecast presented in this study, which, without an increase in funding, predicts the availability of the specific energy required to match the fuel-only baseline in the 2040–2050 decade.

Contemporary engineering design is characterised by products and systems with increasing complexity coupled with tighter requirements and tolerances. This leads to high epistemic uncertainty due to numerous possible configurations and a high number of design parameters. Set-Based Design is a methodology capable of handling these design problems, by exploring and evaluating as many alternatives as possible, before committing to a specific solution.

Sustainability is a key requirement in contemporary engineering design, but it is difficult to quantify due to its multidimensionality. We propose the application of Bayesian Networks for modeling the cause and effect of engineering systems and their environment. Emphasis is placed on capturing the impact on sustainability indicators of design decisions. These include the performance of the system, its economic viability in terms of cost, and its environmental and societal impacts. The method leverages data from simulation models, enabling the designer to perform assumption-free inferences on the variables at play.

This paper presents a web-based visual analytics framework for robust multi-objective aircraft wing design. Aerodynamic and structural simulation data are generated for a redesigned CRM wing, with aspect ratio and skin root thickness as key variables. Ordinary Kriging surrogates are coupled with NSGA-III to explore trade-offs among lift-to-drag ratio, wing mass, and range. Input design uncertainties are propagated using Monte Carlo Simulation with Halton sampling, enabling low-cost robustness assessment. An interactive HTML–Python dashboard provides contour plots, sampled design points, and Pareto fronts, allowing engineers to perform what-if analyses and rapidly identify robust Pareto-optimal designs. Results show that a higher aspect ratio with lower skin thickness improves aerodynamic efficiency and range, while structural constraints and uncertainty bounds define feasible regions. The Kriging surrogate achieves a Surrogate Speed-Up Index (SSI) of $\mathcal{O}\left({10}^3\right)$, offering comparable insight into wing mass, range, and $L/D$ at roughly three-orders-of-magnitude-lower computational cost than direct mid-fidelity simulations.