Optimization Problems of Crash-Resistant Fuel Systems Design Calculations for Helicopters Using Mathematical Modeling

Grachev, S. V.; Smagin, D.I.; Savelev, R. S.; Napreenko, K. S.

doi:10.1109/icmae50897.2020.9178903

Cited by 1 publication

(4 citation statements)

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“…Ref. [54] developed a dynamic mathematical model in their research. The model was developed in one dimension, as they believed this was sufficient to simulate the system operation of pumping the fuel from the fuel tank to the engine, using the control software SimInTech.…”

Section: Fuel System Simulationmentioning

confidence: 99%

“…Within this process, simulation experiments will be used in combination iteratively for further assessment and ver tion. If focus is placed on the design phase in the CADMID cycle (the phases before ufacturing), the impact of simulation modelling may be categorised into the follo four aspects: Preliminary design and optimization Fuel system simulations are primarily used for preliminary design and system misation ( [54,56,59,60]). They can represent the system at a conceptual level and pro a platform for the improvement on the system's construction or algorithms.…”

Section: Fuel System Simulationmentioning

confidence: 99%

“…To reduce cost, they developed a numerical simulation model in the Diesel-RK gram before the bench test to assess how the fuel system's parameters impacted Int Combustion Engine (ICE) performance. In this way, they found out the best configur Preliminary design and optimization Fuel system simulations are primarily used for preliminary design and system optimisation ( [54,56,59,60]). They can represent the system at a conceptual level and provide a platform for the improvement on the system's construction or algorithms.…”

Section: Fuel System Simulationmentioning

confidence: 99%

“…Firstly, a simple model for the fuel system, such as a model without spatial dependency or a 1-D model (with the spatial dependency on one axis), could run faster (with higher efficiency), but it may be impossible to describe the parameters' distribution in all directions of a complex shape (e.g., fuel tank). For instance, to make the model's runtime shorter, the steady-state model (for the Airbus A380's fuel system) in [55] was developed in Simulink (a high-level programming tool) and did not consider any transient effects from the fuel flow, while [54] simulated the fuel flow condition outside the fuel tank with 1-D modelling. On the other hand, some complex models or modelling the fuel system in higher dimensions can output more information with higher accuracy, such as the system's transient behaviour or parameters in every direction within the fuel tank or pump, but need much longer runtime [51], which could limit their application in some scenarios such as real-time control [63] and real-time simulation [55].…”

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confidence: 99%

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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%