A modelling and simulation framework for the integrated design of aircraft systems

Cimmino, Nicola; Ponnusamy, SS; Garriga, Ana Garcia; Mainini, Laura

doi:10.1177/0954410019835726

Cited by 5 publications

(3 citation statements)

References 46 publications

(72 reference statements)

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“…As a fast, economical, safe, and efficient research approach, simulation modelling has been used in the aviation industry for various purposes, including aircraft design [44], optimization [45], manufacturing [46], air traffic management [47], pilot training [48], accident investigation [49], and MRO (Maintenance, Repair and Overhaul) [50]. For the fuel system, simulation can assist the system's design, verification, and optimization processes.…”

Section: Fuel System Simulationmentioning

confidence: 99%

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

King

Jennions

2023

Machines

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The fuel system, which aims to provide sufficient fuel to the engine to maintain thrust and power, is one of the most critical systems in the aircraft. However, possible degradation modes, such as leakage and blockage, can lead to component failure, affect performance, and even cause serious accidents. As an advanced maintenance strategy, Condition Based Maintenance (CBM) can provide effective coverage, by combining state-of-the-art sensors with data acquisition and analysis techniques to guide maintenance before the asset’s degradation becomes serious. Artificial Intelligence (AI), particularly machine learning (ML), has proved effective in supporting CBM, for analyzing data and generating predictions regarding the asset’s health condition, thus influencing maintenance plans. However, from an engineering perspective, the output of ML algorithms, usually in the form of data-driven neural networks, has come into question in practice, as it can be non-intuitive and lacks the ability to provide unambiguous engineering signals to maintainers, making it difficult to trust. Engineers are interested in a deterministic decision-making process and how it is being revealed; algorithms should be able to certify and convince engineers to approve recommended actions. Explainable AI (XAI) has emerged as a potential solution, providing some of the logic on how the output is derived from the input given, which may help users understand the diagnostic result of the algorithm. In order to inspire and advise data scientists and engineers who are about to develop and use AI approaches in fuel systems, this paper explores the literature of experiment, simulation, and AI-based diagnostics for the fuel system to make an informed statement as to the progress that has been made in intelligent fault diagnostics for fuel systems, emphasizing the necessity of giving unambiguous engineering signals to maintainers, as well as highlighting potential areas for future research.

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Section: Fuel System Simulationmentioning

confidence: 99%

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

King

Jennions

2023

Machines

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“…This performance assessment step requires models of the systems involved in aircraft operations in order to evaluate the architectures. In the context of the MISSION project, detailed dynamic models of the systems are being developed to study the system-level performance for design optimization, control design, and system integration [51]; most of the dynamic models are built in Modelica language, with the controls being designed in Matlab Simulink. The dynamic models of the actuation systems are integrated in the overall MISSION platform.…”

Section: Architecture Evaluationmentioning

confidence: 99%

“…More recently, the field of aerospace design has been enhanced by a broader use of machine learning applications for a multitude of specific design problems and applications [46][47][48][49][50]. Previous work in the context of the MISSION project focuses on the integration of the system-level dynamics with the aircraft-level [51] and the design of controls for multiple aircraft systems [52]. This paper leverages the modeling framework proposed in Garcia Garriga et al [53] to enable trade-off studies among multiple power architectures in the evaluation of aircraft system architectures and proposes a principled approach to reduce the architecture design space and speed up the identification of the optimal architecture.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Enabled Multi-Fidelity Platform for the Integrated Design of Aircraft Systems

Garriga

Mainini

Ponnusamy

2019

Journal of Mechanical Design

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The push toward reducing the aircraft development cycle time motivates the development of collaborative frameworks that enable the more integrated design of aircraft and their systems. The ModellIng and Simulation tools for Systems IntegratiON on Aircraft (MISSION) project aims to develop an integrated modelling and simulation framework. This paper focuses on some recent advancements in the MISSION project and presents a design framework that combines a filtering process to down-select feasible architectures, a modeling platform that simulates the power system of the aircraft, and a machine learning-based clustering and optimization module. This framework enables the designer to prioritize different designs and offers traceability on the optimal choices. In addition, it enables the integration of models at multiple levels of fidelity depending on the size of the design space and the accuracy required. It is demonstrated for the electrification of the Primary Flight Control System (PFCS) and the landing gear braking system using different electric actuation technologies. The performance of different architectures is analyzed with respect to key performance indicators (fuel burn, weight, power). The optimization process benefits from a data-driven localization step to identify sets of similar architectures. The framework demonstrates the capability of optimizing across multiple, different system architectures in an efficient way that is scalable for larger design spaces and larger dimensionality problems.

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A multi-fidelity platform using machine learning for integrated design of aircraft systems

Kalyan

2024

2024 International Conference on Emerging Systems and Intelligent Computing (ESIC)

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A modelling and simulation framework for the integrated design of aircraft systems

Cited by 5 publications

References 46 publications

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

A Machine Learning Enabled Multi-Fidelity Platform for the Integrated Design of Aircraft Systems

A multi-fidelity platform using machine learning for integrated design of aircraft systems

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