When assessing new technologies at the overall aircraft level, it is crucial to establish an appropriate benchmark to evaluate the resulting performance of the final concept aircraft. This publication defines a short-to medium-range baseline aircraft for entry into service in 2035, which serves as a benchmark for the HOPE project. This aircraft is sized using the Bauhaus Luftfahrt Aircraft Design Environment (BLADE), and its engine model is derived using the in-house tool Aircraft Propulsion System Simulation (APSS). The top-level aircraft requirements and technology assumptions for the entire project timeline are also defined and used for the baseline aircraft. Compared to a state-of-the-art aircraft (entry into service in 2015), the baseline aircraft consumes 21.4% less block fuel during the design mission and 20.9% less block fuel during the typical mission, since its operating empty mass is reduced by 13.6%, its aerodynamic performance is improved by 8.5%, and it has 6.4% more efficient engines.

Liquid hydrogen (LH₂) has been identified as a potential solution to the ever-growing climate impact of the aviation sector. One of the key problems for the industry remains the provision of the necessary storage volume, which results from the low density of hydrogen. The objective of this paper is to quantify the potential for structurally integrated conformal wing tanks for liquid hydrogen. The three wing tanks derived for the CHoSe project contain internal rib structures and are placed inside the center wingbox as well as from wing root to kink. The multidisciplinary aircraft design environment BLADE has been extended by the capabilities to complement liquid hydrogen fuselage tanks with wing tanks of varying area mass. Comparing short-to-medium range (SMR) aircraft with only fuselage tanks and with additional wing tanks resulted in key findings: for similar area mass assumptions for fuselage and wing tanks of 20 kg/m², no fuel burn benefit could be achieved. The decrease in fuselage length could not compensate for the increased structural tank masses. No significant load alleviation effect on the wing structure can be expected due to the limited mass and lever arm of the tanks inside the wing. Small efficiency gains can only be expected when synergistic stiffening effects with the load-carrying structure of the wings reduce the effective added area mass to lower values than for the fuselage tanks. Adding tanks further outbound than the wing kink deteriorates the performance, even for the most optimistic tank assumptions.

Spatial interpolation methods (SIMs) are widely used in local environmental assessments, yet their computational cost and performance vary substantially across datasets. High resolution modelling of elements of local environmental studies, including pollutant concentrations and noise, is often computationally demanding. As a result, SIMs are widely used to estimate values between modelled points and construct exposure contour maps. The choice of interpolation method therefore has a direct influence on both computational efficiency and the accuracy of subsequent impact assessments.This work develops a structured framework for comparing and selecting SIMs by examining how they differ in three practical respects: how strongly they smooth or preserve sharp spatial features, how sensitive they are to the spacing of available data points, and how computationally demanding they are to apply. To demonstrate these distinctions, we analyse pollutant concentration and noise datasets using airport sites as the case study. High resolution model outputs are used as reference values, against which we evaluate interpolated estimates derived from coarser grids across a range of SIMs. This enables a systematic assessment of method behaviour under realistic sampling conditions typical of local environmental modelling.We compare commonly used SIMs including nearest neighbour, inverse distance weighting, linear and Clough–Tocher triangulation, radial basis functions and several kriging variants across multiple sampling densities. Errors relative to fine grid values are analysed together with measures of local spatial gradients, allowing us to identify when methods smooth peaks, distort steep transitions or perform reliably in more uniform regions. The study also reviews recent machine learning and hybrid interpolation approaches and summarises current software support for SIMs.The outcome has two components. First, we present a decision tree that groups SIMs according to their ability to represent sharp spatial changes, their sensitivity to spatial sampling and their computational requirements. This framework provides a general guide for method selection in local environmental assessments. Second, the case studies show that interpolation performance depends strongly on the structure of the dataset being modelled, meaning that method choice should always be verified for the specific application. Together, the framework and case study findings offer both a basis for SIM selection and insight into how different methods perform in practice when balancing accuracy and computational cost.