Gas turbine aero-engines real time on-board modelling: A review, research challenges, and exploring the future

Wei, Zhiyuan; Zhang, Shuguang; Jafari, Soheil; Nikolaidis, Theoklis

doi:10.1016/j.paerosci.2020.100693

Cited by 61 publications

(27 citation statements)

References 87 publications

(120 reference statements)

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“…The design and effectiveness of the model largely depend on prior knowledge from engineering experience and experiments. In systems with complex working conditions where engineers have very limited ability to describe system dynamics, such as aero-engines, the establishment of PBMs will be difficult [87]. There are some discrete methods that can mitigate this difficulty and help the PBMs as close to the real physical situation as possible [88], such as Finite Element Methods (FEM) [89]- [92], the boundary integral method and finite differences.…”

Section: ) Physics-based Modelmentioning

confidence: 99%

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Digital Twin in Aerospace Industry: A Gentle Introduction

et al. 2022

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Digital twin (DT), primarily a virtual replica of any conceivable physical entity, is a highly transformative technology with profound implications. Whether it be a product development, design optimisation, performance improvement, or predictive maintenance, digital twins are changing the ways work is undertaken in various industries with multifarious business applications. Aerospace industry, including its manufacturing base, is one such keen adopter of digital twins with an unprecedented interest in their bespoke design, development, and implementation across wider operations and critical functions. This, however, comes with some misconceptions about the digital twin technology and lack of understanding with respect to its optimal implementation. For instance, equating digital twin to an intelligent model while ignoring the essential components of data acquisition and visualisation, misleads the creators into building digital shadow or digital models, instead of the actual digital twin. This paper unfolds such intricacies of digital twin technology for the aerospace community in particular and others in general so as to remove the fallacies that affect their effective realisation for safety-critical systems. It comprises a comprehensive survey of digital twins and their constituent elements. Elaborating their characteristic stateof-the-art composition along with corresponding limitations, three dimensions of the future digital twins for aerospace sector, termed as aero-Digital Twins (aero-DTs), are proposed as an outcome of this survey. These include the interactive, standardisation, and cognitive dimensions of digital twins, which if leveraged diligently could help the aero-DT research and development community quadruple the efficiency of existing and future aerospace systems as well as their associated processes.

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Section: ) Physics-based Modelmentioning

confidence: 99%

“…DDMs give the opportunity for the aerospace industry in dealing with high-dimensional, nonconvex, and constrained, multi-objective problems, such as aero-engine diagnosis [87] and composite fabrication [67]. This is because DDMs can mine the potential connections between variables from the data without any prior physics knowledge.…”

Section: ) Data-driven Modelmentioning

confidence: 99%

Digital Twin in Aerospace Industry: A Gentle Introduction

et al. 2022

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“…Therefore, a hybrid scheme that combines physics-based and data-driven methods would be the best choice. In the area of modeling, where the target is that this is performed real time onboard for aero-engines, the main challenges identified are the need for dependable and reliable on-board models that cover safety-critical parameters, especially during transient operation (Wei et al, 2020).…”

Section: Gas Turbine Monitoring For Maintenance and Diagnosticsmentioning

confidence: 99%

Micro Gas Turbines in the Future Smart Energy System: Fleet Monitoring, Diagnostics, and System Level Requirements

et al. 2021

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The energy generation landscape is changing, pushed by stricter regulations for emissions control and green energy generation. The limitations of renewable energy sources, however, require flexible energy production sources to supplement them. Micro gas turbine based combined heat and power plants, which are used for domestic applications, can fill this gap if they become more reliable. This can be achieved with the use of an engine monitoring and diagnostics system: real-time engine condition monitoring and fault diagnostics results in reduced operating and maintenance costs and increased component and engine life. In order to allow the step change in the connection of small engines to the grid, a fleet monitoring system for micro gas turbines is required. A proposed framework combines a physics-based model and a data-driven model with machine learning capabilities for predicting system behavior, and includes a purpose-developed diagnostic tool for anomaly detection and classification for a multitude of engines. The framework has been implemented on a fleet of micro gas turbines and some of the lessons learned from the demonstration of the concept as well as key takeaways from the general literature are presented in this paper. The extension of fleet monitoring to optimal operation and production planning in relation to the needs of the grid will allow the micro gas turbines to fit in the future green energy system, connect to the grid, and trade in the energy market. The requirements on the system level for the widespread use of micro gas turbines in the energy system are addressed in the paper. A review of the current solutions in fleet monitoring and diagnostics, generally developed for larger engines, is included, with an outlook into a sustainable future.

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“…There are different modeling approaches available for this (Wei, Zhang, Jafari, & Nikolaidis, 2020). Exponential trajectory propagation is one among them.…”

Section: Short-term Fault Diagnostics Using Aannmentioning

confidence: 99%

Discrimination of Rapid and Gradual Deterioration for an Enhanced Gas Turbine Life-cycle Monitoring and Diagnostics

Fentaye

Zaccaria

Kyprianidis

2021

IJPHM

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Advanced engine health monitoring and diagnostic systems greatly benefit users helping them avoid potentially expensive and time-consuming repairs by proactively identifying shifts in engine performance trends and proposing optimal maintenance decisions. Engine health deterioration can manifest itself in terms of rapid and gradual performance deviations. The former is due to a fault event that results in a short-term performance shift and is usually concentrated in a single component. Whereas the latter implies a gradual performance loss that develops slowly and simultaneously in all engine components over their lifetime due to wear and tear. An effective engine life-cycle monitoring and diagnostic system is therefore required to be capable of discriminating these two deterioration mechanisms followed by isolating and identifying the rapid fault accurately. In the proposed solution, this diagnostic problem is addressed through a combination of adaptive gas path analysis and artificial neural networks. The gas path analysis is applied to predict performance trends in the form of isentropic efficiency and flow capacity residuals that provide preliminary information about the deterioration type. Sets of neural network modules are trained to filter out noise in the measurements, discriminate rapid and gradual faults, and identify the nature of the root cause, in an integrated manner with the gas path analysis. The performance of the proposed integrated method has been demonstrated and validated based on performance data obtained from a three-shaft turbofan engine. The improvement achieved by the combined approach over the gas path analysis technique alone would strengthen the relevance and long-term impact of our proposed method in the gas turbine industry.

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Gas turbine aero-engines real time on-board modelling: A review, research challenges, and exploring the future

Cited by 61 publications

References 87 publications

Digital Twin in Aerospace Industry: A Gentle Introduction

Digital Twin in Aerospace Industry: A Gentle Introduction

Micro Gas Turbines in the Future Smart Energy System: Fleet Monitoring, Diagnostics, and System Level Requirements

Discrimination of Rapid and Gradual Deterioration for an Enhanced Gas Turbine Life-cycle Monitoring and Diagnostics

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