Fast engine model for FMU-less small turbojet engines

Ekinci, Sinan; Yavrucuk, Ilkay

doi:10.1177/0954410019867013

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…According to the results, small deviations in fuel flow and specific fuel consumption values were obtained while using GasTurb ® and GSP-11 commercial software in the process of verifying performance indicators in various environmental conditions. Ekinci and Yavrucuk (2020) introduced an aero-thermal turbojet engine model, which they verified with experimental data found in the literature. They estimated the parameters of the created engine model by using regression analysis and applied it to real engine test data to validate the proposed approach.…”

Section: Introductionmentioning

confidence: 55%

Performance of a microjet using component map scaling

Coban¹,

Ekici

Çolpan

et al. 2021

AEAT

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Purpose This paper aims to investigate the cycle performance of a small size turbojet engine used in unmanned aerial vehicles at 0–5,000 m altitude and 0–0.8 Mach flight speeds with real component maps. Design/methodology/approach The engine performance calculations were performed for both on-design and off-design conditions through an in-house code generated for simulating the performance of turbojet engines at different flight regimes. These calculations rely on input parameters in which fuel composition are obtained through laboratory elemental analysis. Findings Exemplarily, according to comparative results between in-house developed performance code and commercially available software, there is 0.25% of the difference in thrust value at on-design conditions. Practical implications Once the on-design performance parameters and fluid properties were determined, the off-design operation calculations were performed based on the compressor and turbine maps and scaling methodology. This method enables predicting component maps and fitting them to real conditions. Originality/value A method to be used easily by researchers on turbojet engine performance calculations which best fits to real conditions.

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Section: Introductionmentioning

confidence: 55%

Performance of a microjet using component map scaling

Coban¹,

Ekici

Çolpan

et al. 2021

AEAT

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“…The Fuel Metering System (FMS) is a crucial component in a conventional aircraft fuel system; it is responsible for receiving signals from the Engine Control System to supply the required fuel flow for engine start-up, acceleration/deceleration, and stabilisation ( [22,23]). Typically, an FMS consists of the fuel pump, metering valve and differential pressure valve, which aims to pressurise the fuel, control the fuel flow rate by changing the orifice, and maintain a constant pressure difference across the FMS.…”

mentioning

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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Fast engine model for FMU-less small turbojet engines

Cited by 2 publications

References 20 publications

Performance of a microjet using component map scaling

Performance of a microjet using component map scaling

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

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