Browsing by Author "Spinelli, Andrea"

Now showing 1 - 11 of 11
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    Aircraft design system requirements analysis with Bayesian Networks
    (AIAA, 2025-01-06) Spinelli, Andrea; Kipouros, Timoleon
    This paper introduces readers with the application of Bayesian Networks in conceptual design tasks. These models can represent in a graph the cause-and-effect logic of requirements and enable the analysis of uncertainty in decision making. A syntax for converting a requirement into a Bayesian network query is also presented. These concepts are applied on a wing design example to demonstrate the flexibility of a Bayesian Network in answering design-related questions. In particular, a feasible design space is identified through inference of the requirements onto the input parameters. This is useful to narrow down configurations for further detailed analysis with an optimization algorithm. The model is capable of estimating the quantities of interest and the effect of the requirement uncertainty on the system variables. Through proper data collection and integration with digital engineering methods, a system-wide Bayesian network would enable reasoning between systems and components, accelerating the industrial development of complex products.
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    Application of probabilistic principles to set-based design for the optimisation of a hybrid-electric propulsion system
    (IOP, 2022-02-15) Spinelli, Andrea; Anderson, Luchien; Balaghi Enalou, Hossein; Zaghari, Bahareh; Kipouros, Timoleon; Laskaridis, Panagiotis
    Current research in hybrid-electric aircraft propulsion has outlined the increased complexity in design when compared with traditional propulsion. However, current design methodologies rely on aircraft-level analysis and do not include the consideration of the impact of new technologies and their uncertainty. This can be a key factor for the development of future hybrid-electric propulsion systems. In this paper, we present a methodology for exploring the design space using the principles of Set-Based Design, which incorporates probabilistic assessment of requirements and multidisciplinary optimisation with uncertainty. The framework can explore every design parameter combination using a provided performance model of the system under design and evaluate the probability of satisfying a minimum required figure of merit. This process allows to quickly discard configurations incapable of meeting the goals of the optimiser. A multidisciplinary optimiser then is used to obtain the best points in each surviving configuration, together with their uncertainty. This information is used to discard undesirable configurations and build a set of Pareto optimal solutions. We demonstrate an early implementation of the framework for the design of a parallel hybrid-electric propulsion system for a regional aircraft of 50 seats. We achieve a considerable reduction to the required function evaluations and optimisation run time by avoiding the ineffective areas of the design space but at the same time maintaining the optimality potential of the selected sets of design solutions.
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    Application of probabilistic set-based design exploration on the energy management of a hybrid-electric aircraft
    (MDPI, 2022-03-08) Spinelli, Andrea; Balaghi Enalou, Hossein; Zaghari, Bahareh; Kipouros, Timoleon; Laskaridis, Panagiotis
    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, NOx 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 NOx 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 NOx 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 NOx emissions of up to 15%.
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    Battery pack technological considerations for hybrid-electric regional aircraft feasibility
    (Cambridge University Press (CUP), 2025-05) Spinelli, Andrea; Krupa, Gustavo P.; Kipouros, Timoleon; Laskaridis, Panos; Berseneff, Boris
    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 NOx 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.
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    An interactive framework to facilitate probabilistic set-based multidisciplinary design optimisation studies
    (IOP Publishing, 2023-06-28) Krupa, Gustavo P.; Spinelli, Andrea; Kipouros, Timoleon
    The upcoming stringent environmental aircraft regulations and the environmental, social and governance (ESG) framework targeting net zero emissions has forced aircraft manufacturers to seek innovative feasible technical solutions. Often, there is the need to consider entirely radical solutions, such as hybrid-electric aircraft, which are not fully understood when compared with conventional kerosene driven aircraft. The recent development of a probabilistic set-based multidisciplinary optimisation methodology has demonstrated the ability to explore trade-offs when the requirements are uncertain. In this way, the weaknesses and potential to enable the feasibility of hybrid-electric aircraft can be studied from a systems perspective but maintaining the connection with more detailed trade-off studies of components of the sub-systems. We have developed an interactive interface, where the user is guided through the steps of the design methodology and the produced data is visualised to aid an informative decision-making process. In our illustrative case study, decision-makers are enabled to interactively explore the hybrid-electric propulsion design space while considering the impact to the figures of merit from expected improvements in the coming years of key enabling technologies, such as energy storage.
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    Investigation of the operational flexibility of a regional hybrid-electric aircraft
    (IOP Publishing, 2023-06-28) Spinelli, Andrea; Krupa, Gustavo P.; Kipouros, Timoleon; Bersenef, Boris; Fiette, Sebastien
    The complexity of hybrid-electric aircraft propulsion systems is also characterized by the greater number of degrees of freedom of the energy management system, whose objective is to split the required power to fly the aircraft to the different available powertrains (i.e., gas turbines, electric motors, fuel cells, etc.). Typically, a single design mission is considered for assessing the performance of a hybrid-electric propulsion system, often with a simple constant split power between the batteries and gas turbine. A probabilistic set-based design space exploration methodology is used and allows us to study the effects of lifecycle analysis of the battery pack of a hybrid-electric 50-seater turboprop, while different mission scenarios are considered. Using this approach, it is possible to flexibly find multiple families of energy management strategies that can satisfy battery capacity requirements and the reduction of emissions simultaneously. Furthermore, the generated data can help the designers to understand the hierarchy of the requirements that drive the design of the propulsion system for a range of operating scenarios, with emphasis on the energy storage system. Hence, the airliners are offered enhanced operational flexibility of the aircraft for different and desirable mission profiles.
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    Multi-fidelity multi-disciplinary optimisation of propeller design by visual analytics
    (The Eurographics Association, 2024-09-09) Shubham, Shubham; Spinelli, Andrea; Kipouros, Timoleon
    This paper introduces a comprehensive framework for multi-fidelity, multi-disciplinary optimization of propeller design using visual analytics. The proposed methodology integrates advanced data visualization techniques, surrogate modelling and optimisation methodologies to handle high-dimensional data across various disciplines, including aerodynamics, aeroacoustics, and structures. By leveraging multi-fidelity simulations, the framework balances accuracy with computational efficiency, enabling detailed exploration and optimization of propeller designs. Interactive visualization tools in the framework facilitate the identification of optimal design parameters and trade-offs, highlighting its potential to improve decision-making in engineering design processes in terms of confidence and knowledge creation.
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    PDOPT: A Python library for Probabilistic Design space exploration and OPTimisation
    (The Open Journal, 2024-03-05) Spinelli, Andrea; Kipouros, Timoleon
    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. The Python package PDOPT aims to provide this capability without the high computational cost associated with the factorial-based design of experiments methods. Additionally, PDOPT performs the requirement mapping without explicit rule definition. Instead, it utilizes a probabilistic machine learning model to identify the areas of the design space most promising for user-provided requirements. This yields a plethora of feasible design points, assisting designers in understanding the system behaviour and selecting the desired configurations for further development.
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    Requirements uncertainty propagation in conceptual design using bayesian networks
    (International Council of the Aeronautical Sciences (ICAS), 2024-09-09) Spinelli, Andrea; Sharma, Ankit; Kipouros, Timoleon
    This paper presents the application of a Bayesian Network as a tool for propagating the uncertainty between the aircraft-level design and the component-level design. The framework is applied to an example case in UAV design for payload transport. By querying the model, we demonstrate its ability to capture the casual relationships of the design problem and propagating the effects of design decisions on other parameters and requirements.
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    Set-based design space exploration to investigate the effect of energy storage durability on the energy management strategy of a hybrid-electric aircraft
    (AIAA, 2023-01-19) Spinelli, Andrea; Krupa, Gustavo P.; Kipouros, Timoleon
    To investigate the key enabling technologies for hybrid-electric regional aircraft, several assumptions about the maturity and required level of technology are necessary. Within the EU-funded project FutPrint50, a decision-making framework based on Set-Based Design principles is being developed to address these uncertainties arising from operational requirements and technological feasibility levels. The methodology has been applied to study the effects of the energy storage durability and technology level on the energy management strategies of a regional hybrid-electric aircraft. Results highlight the key role of battery energy density on the durability of the battery pack and the viability of the hybrid-electric aircraft concept. Additionally, the trade-off between zero-day environmental compatibility and battery lifetime is identified alongside its causing mechanism. Optimal energy management strategies are suggested in light of this new information. Finally, statistical data of cell energy density is used to estimate the most probable year of feasibility of hybrid-electric propulsion for regional aircraft.
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    Use of Bayesian Networks to understand sustainability requirements
    (MDPI, 2025-03-13) Spinelli, Andrea; Kipouros, Timoleon
    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.