The electrification of the commuter aircraft is instrumental in the development of novel propulsion systems. The scope of this work aims to explore the design space of a parallel hybrid-electric configuration with an entry into service date of 2030 and beyond and determine the impact of other disciplines on conceptual design, such as components positioning, aircraft stability and structural integrity. Three levels of conceptual sizing are applied and linked with a parametric aircraft geometry tool, to generate the aircraft’s geometry and position the components. Subsequently, the structural optimisation of the wing box is performed, providing the centre of gravity of the components placed inside the wing, that minimise the induced stresses. Furthermore, the stability and trim analysis follow, with the former being highly affected by the positioning of components. Results are compared to a similar aircraft with entry into service technology of 2014 and it is indicated that in terms of block fuel reduction the total electrification benefit increases with the increase of degree of hybridisation, if aircraft mass is kept constant. On the other hand, if battery specific energy is kept constant, similar block fuel reduction is possible with lower hybridisation degrees. The potential for improvement in terms of carbon dioxide emissions and block fuel reduction ranges from 15.73% to 21.44% compared to the conventional aircraft, for levels of battery specific energy of 0.92 and 1.14 kWh/kg respectively. Finally, the component positioning evaluation indicates a maximum weight limitation of 240 kg for the addition of an aft boundary layer ingestion fan to a tube and wing aircraft configuration, without compromising the aircraft static stability.

Hybrid-electric propulsion is a promising alternative to sustainable aviation and is mainly considered for the commuter and regional aircraft class. However, the development of hybrid-electric propulsion variants is affected by the technology readiness level of electric components. The components’ technology will determine the electrification benefit, compared to a conventional aircraft, and will suggest which is the most beneficial variant and which has a closer entry into service date. Within this work, three different dates are explored, namely 2027, 2030 and 2040, to size three Parallel and three Series hybrid-electric architecture variants using an in-house aircraft sizing tool. All variants are compared to a conventional configuration sized using technological assumptions of 2014, with the main comparison metrics being the aircraft block fuel, energy consumption, direct operating cost and holistic environmental impact. On one hand, the Parallel configurations have reduced maximum take-off mass and mission energy consumption compared to the Series, however, the latter show a greater potential for block fuel reduction and require less onboard energy for the same mission. The annual operating cost evaluation indicates that the Parallel hybrid variant of 2030 has greater operational costs than the respective Series variant; however, it has reduced capital costs compared to the latter, making it more economical to operate considering both costs. Additionally, in the case of an energy recession, both hybrid variants of 2030 show a further cost reduction, with the Series having a total reduction of 10.4% excluding capital costs, compared to the reference aircraft. Moreover, the life cycle assessment shows that the Series variants have a lower environmental impact, both compared to the reference aircraft and the Parallel variants. The former could be up to 59.7% less detrimental to the environment than the reference aircraft, whereas the latter up to 23.9%, with the integration of renewable sources for electricity production. Finally, by the year 2040, the Series variant shows outstanding performance in all comparison metrics, compared to the Parallel and the reference aircraft.

This study deals with the optimization of performance for a hybrid-electric propulsion system. It focuses on the modeling and power management frameworks, while evaluation is done on a single flight basis. The main objective is to extract the maximum out of the novel powertrain archetype. Two hybridization factors are considered. The pair helps to describe the degree of hybridization at the power supply and power consumption levels. Their revised mathematical definition facilitates a unique method of hybrid-electric propulsion system modeling that maximizes the conveyed amount of information. An in-house computational tool is developed. It employs a genetic algorithm optimizer in the interest of managing power usage during flight. Energy consumption is set as the objective function. The operation of a 19-seater, commuter aircraft is investigated. Turbo-electric, series-hybrid, parallel-hybrid, and series-parallel variants are derived from a generic composition. An analysis on their optimized performance, with different technological readiness levels for 2020 and 2035, is aimed at identifying where each system performs best. Considering 2020 technology, it does not yield a viable hybrid-electric configuration, without suffering significant payload penalties. Architectures relying on mechanical propulsors show promise of 15% reduction to energy consumption, accounting for 2035 readiness levels. The concepts of Boundary Layer Ingestion and Distributed Propulsion display the potential to boost electrified propulsion. The series-hybrid and series-parallel configurations are the primary beneficiaries of these concepts, displaying up to 30% reduction in fuel and 20% reduction in energy consumption.

Hybrid-electric commuter aircraft segment is playing a significant role in the electrification of air transportation. Towards the achievement of efficient and robust transportation, design and optimization processes are necessary to evaluate the different aircraft components. Within this context, the current work investigates the impact of the positioning of the propulsion system and spars on the structural integrity of a hybrid-electric commuter aircraft. The proposed approach is based on an in-house aircraft sizing tool, along with geometry generation and high-fidelity structural evaluation models. These tools are tailored in an automated computational pipeline, that includes pre-processing, model evaluation and post-processing tasks, able to perform design space exploration and optimization over different loading conditions of a selected mission envelope. This work focuses on the assessment of the impact of the additional non-structural weight e.g., batteries, fuel, and propulsion components, inside the wing box, on the stress, deformation and spanwise thickness distribution of the structure. The effect of spars and propulsion system positioning on the available storage space, maximum stress and displacement is discussed, with the aft spar having the greatest impact. Finally, the structural model is optimized to minimize the mass, resulting in a 29% weight reduction, compared to the initial design.

This study aims to illustrate a sequence that optimizes the flight-path trajectory for a hybrid-electric aircraft at mission level, in addition to identifying the respective optimum power management strategy. An in-house framework for hybrid-electric propulsion system modeling is utilized. A hybrid-electric commuter aircraft serves as a virtual test-bench. Vectorized calculations, decision variable count, and optimization algorithms are considered for reducing the computational time of the framework. Performance improvements are evaluated for the aircraft's design mission profile. Total energy consumption is set as the objective function. Emphasis lies on minimizing the average value and standard deviation of the energy consumption and timeframe metrics. The best performing application decreases computational time by two orders of magnitude, while retaining equal accuracy and consistency as the original model. It is employed for creating a dataset for training an artificial neural network (ANN) against random mission patterns. The trained network is integrated into a surrogate model. The latter part of the analysis evaluates optimized mission profile characteristics with respect to energy consumption, against a benchmark flight-path. The combined optimization process decreases the multihour-scale timeframe by two orders of magnitude to a 3-min sequence. Using the novel framework, a 12% average energy consumption benefit is calculated for short, medium, and long regional missions, against equivalent benchmark profiles.

This work presents a numerical investigation targeting to simulate the slice of a small aircraft cabin as an experimental facility with a controlled environment, to assess passenger comfort when exposed to high volatile organic compound (VOC) concentrations. The mixing and transport of chemical species are evaluated using computational fluid dynamics for 800 s of in-cabin actual flow time and measurements are taken every 10 s from selected computational nodes close to the passengers’ noses. The results are used to create a dataset that trains four different machine learning classifiers, namely, the Random Forest, Support Vector Machine, Logistic Regression and Naive Bayes, and their performance is compared. Moreover, an additional simulation of the cabin with a filtering system utilising high-efficiency particulate air and activated carbon filters is conducted, to evaluate the impact of the molecular weight of the compounds on their residence time, and compare it to the simulation without the filters. Results indicate that the model is insensitive to the inlet air mass flow variation and that the mass of the VOCs measured in the monitored computational nodes remains relatively unaffected, meaning that the impact of the air-conditioning system setting is minor. Additionally, a Boruta feature selection algorithm is used to determine the importance of each measurement of the simulation and to form a dataset that will train the four machine learning classifiers. Furthermore, the comparison of the two simulations, the one with and the one without the filters, indicates that the residence time (RT) of the compounds is independent of their molecular weight, as they all show equivalent percentile reductions, with the naphthalene and styrene showing a 28.5% and 28.3% reduction respectively, compared to the simulation without the filters. Finally, in-cabin flow irregularities are present, disrupting the flow symmetry and suggesting that not all passengers share the same traveling experience.