| 31. |
-
Developments in Combustion Technology
- 2016
-
Samlingsverk (redaktörskap) (övrigt vetenskapligt/konstnärligt)abstract
- Over the past few decades, exciting developments have taken place in the field of combustion technology. The present edited volume intends to cover recent developments and provide a broad perspective of the key challenges that characterize the field. The target audience for this book includes engineers involved in combustion system design, operational planning and maintenance. Manufacturers and combustion technology researchers will also benefit from the timely and accurate information provided in this work. The volume is organized into five main sections comprising 15 chapters overall: - Coal and Biofuel Combustion - Waste Combustion - Combustion and Biofuels in Reciprocating Engines - Chemical Looping and Catalysis - Fundamental and Emerging Topics in Combustion Technology
|
|
| 32. |
-
Developments in Near-Infrared Spectroscopy
- 2017
-
Samlingsverk (redaktörskap) (övrigt vetenskapligt/konstnärligt)abstract
- Over the past few decades, exciting developments have taken place in the field of near-infrared spectroscopy (NIRS). This has been enabled by the advent of robust Fourier transform interferometers and diode array solutions, coupled with complex chemometric methods that can easily be executed using modern microprocessors. The present edited volume intends to cover recent developments in NIRS and provide a broad perspective of some of the challenges that characterize the field. The volume comprises six chapters overall and covers several sectors. The target audience for this book includes engineers, practitioners, and researchers involved in NIRS system design and utilization in different applications. We believe that they will greatly benefit from the timely and accurate information provided in this work.
|
|
| 33. |
- Diamantidou, Eirini, et al.
(författare)
-
A Robust Initialization Approach of Multi-Point Synthesis Schemes For Aero-Engine Conceptual Design
- 2021
-
Ingår i: AIAA Propulsion and Energy Forum, 2021. - Reston, Virginia : American Institute of Aeronautics and Astronautics Inc, AIAA. - 9781624106118
-
Konferensbidrag (refereegranskat)abstract
- During the last years, the aviation industry has shifted its focus towards increasing the aircraft efficiency. The constant drive to search for more efficient systems has led to the introduction of novel concepts. These concepts expand the design space but, at the same time, bring several challenges to the design process. One of the challenges is to develop a conceptual engine design model that can work effectively and provide consistently accurate solutions, even when there are dramatic changes in design constraints. In this work, a multipoint synthesis approach is developed which considers multiple points during the design phase. By incorporating multiple operating points into the design analysis phase, it is ensured that all performance requirements and design constraints are satisfied. A comparison between the traditional engine design approach and the proposed approach is presented to showcase the advantages of the proposed method. A parametric analysis of a geared turbofan configuration is conducted for both design approaches. Then, the multi-point synthesis approach is employed for the design space exploration of a conventional geared turbofan engine and a parallel-hybrid (or boosted) turbofan engine. To enable these studies, surrogate models are developed which utilize machine learning methods in a database of converged engine designs and can ensure the effective and fast operation of the engine model. It is concluded that this surrogate adapted algorithm improves computational efficiency and can be used to evaluate alternative designs.
|
|
| 34. |
- Diamantidou, Eirini, et al.
(författare)
-
Recent Advances in Boundary Layer Ingestion Technology of Evolving Powertrain Systems
- 2022
-
Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 14:3
-
Tidskriftsartikel (refereegranskat)abstract
- The increasing environmental concern during the last years is driving the research community towards reducing aviation’s environmental impact. Several strict goals set by various aviation organizations shifted the research focus towards more efficient and environmentally friendly aircraft concepts. Boundary Layer Ingestion (BLI) is currently investigated as a potential technology to achieve different design goals such as energy efficiency improvement and noise emission reductions in the next generation of commercial aircraft. The technology principle is to place the propulsive unit within the boundary layer generated by the airframe body. Although several studies showed its theoretical benefits, a multidisciplinary nature is introduced in the design phase. This imposes new challenges on the current design tools. An increasing number of publications are focusing on assessing this technology while taking into account interlinks between different disciplines. The goal of this work is to review the current state-of-the-art of BLI evaluation studies. Particular focus is given to the underlying assumptions of each work, the methodology employed, and the level of fidelity of the tools used. By organizing the available work in a comprehensive manner, the up-to-date results are interpreted. The current trends and trade-offs emerging from studies are presented. Through reviewing the ongoing published work, the next steps for further development of the methods that will assess this technology are derived.
|
|
| 35. |
- Efstathiadis, T., et al.
(författare)
-
Geometry Optimization of Power Production Turbine For A Low Enthalpy (<= 100 degrees C) ORC System
- 2015
-
Ingår i: Energy Procedia. - : Elsevier BV. - 1876-6102. ; 75, s. 1624-1630
-
Tidskriftsartikel (refereegranskat)abstract
- The present paper is examining the geometry optimization of a power production turbine, in the range of 100kW(el), for a low enthalpy Organic Rankine cycle system (<= 100 degrees C). In the last years, accelerated consumption of fossil fuels has caused many serious environmental problems such as global warming, ozone layer destruction and atmospheric pollution. It is this reason that a growing trend towards exploiting low-enthalpy content energy sources has commenced and led to a renewed interest in small-scale turbines for Organic Rankine Cycle applications. The design concept for such turbines can be quite different from either standard gas or steam turbine designs. The limited enthalpic content of many energy sources imposes the use of organic working media, with unusual properties for the turbine. A versatile cycle design and optimization requires the parameterization of the main turbine design. There are many potential applications of this power-generating turbine, including geothermal and concentrate solar thermal fields or waste heat of steam turbine exhausts. An integrated model of equations has been developed, thus creating a model to assess the performance of an organic cycle for various working fluids such as R134a and isobutane-isopentane mixture. The most appropriate working fluid has been chosen, taking its influence on both cycle efficiency and the specific volume ratio into consideration. This choice is of particular importance at turbine extreme operating conditions, which are strongly related to the turbine size. In order to assess the influence of various design parameters, a turbine design tool has been developed and applied to define the geometry of blades in a preliminary stage. Finally, as far as the working fluid is concerned, the mixture of 85% isopentane-15% isobutane has been chosen as the most suitable fluid for the low enthalpy ORC system, since its output net power is 10% higher compared to the output net power of R134a.
|
|
| 36. |
- Fentaye, Amare Desalegn, et al.
(författare)
-
A Review on Gas Turbine Gas-Path Diagnostics : State-of-the-Art Methods, Challenges and Opportunities
- 2019
-
Ingår i: Aerospace. - Zurich, Switzerland : MDPI. - 2226-4310. ; 6:7
-
Forskningsöversikt (refereegranskat)abstract
- Gas-path diagnostics is an essential part of gas turbine (GT) condition-based maintenance (CBM). There exists extensive literature on GT gas-path diagnostics and a variety of methods have been introduced. The fundamental limitations of the conventional methods such as the inability to deal with the nonlinear engine behavior, measurement uncertainty, simultaneous faults, and the limited number of sensors available remain the driving force for exploring more advanced techniques. This review aims to provide a critical survey of the existing literature produced in the area over the past few decades. In the first section, the issue of GT degradation is addressed, aiming to identify the type of physical faults that degrade a gas turbine performance, which gas-path faults contribute more significantly to the overall performance loss, and which specific components often encounter these faults. A brief overview is then given about the inconsistencies in the literature on gas-path diagnostics followed by a discussion of the various challenges against successful gas-path diagnostics and the major desirable characteristics that an advanced fault diagnostic technique should ideally possess. At this point, the available fault diagnostic methods are thoroughly reviewed, and their strengths and weaknesses summarized. Artificial intelligence (AI) based and hybrid diagnostic methods have received a great deal of attention due to their promising potentials to address the above-mentioned limitations along with providing accurate diagnostic results. Moreover, the available validation techniques that system developers used in the past to evaluate the performance of their proposed diagnostic algorithms are discussed. Finally, concluding remarks and recommendations for further investigations are provided.
|
|
| 37. |
- Fentaye, Amare Desalegn, et al.
(författare)
-
Aircraft engine performance monitoring and diagnostics based on deep convolutional neural networks
- 2021
-
Ingår i: Machines. - : MDPI. - 2075-1702. ; 9:12
-
Tidskriftsartikel (refereegranskat)abstract
- The rapid advancement of machine-learning techniques has played a significant role in the evolution of engine health management technology. In the last decade, deep-learning methods have received a great deal of attention in many application domains, including object recognition and computer vision. Recently, there has been a rapid rise in the use of convolutional neural networks for rotating machinery diagnostics inspired by their powerful feature learning and classification capability. However, the application in the field of gas turbine diagnostics is still limited. This paper presents a gas turbine fault detection and isolation method using modular convolutional neural networks preceded by a physics-driven performance-trend-monitoring system. The trend-monitoring system was employed to capture performance changes due to degradation, establish a new baseline when it is needed, and generatefault signatures. The fault detection and isolation system was trained to step-by-step detect and classify gas path faults to the component level using fault signatures obtained from the physics part. The performance of the method proposed was evaluated based on different fault scenarios for a three-shaft turbofan engine, under significant measurement noise to ensure model robustness. Two comparative assessments were also carried out: with a single convolutional-neural-network-architecture-based fault classification method and with a deep long short-term memory-assisted fault detection and isolation method. The results obtained revealed the performance of the proposed method to detect and isolate multiple gas path faults with over 96% accuracy. Moreover, sharing diagnostic tasks with modular architectures is seen as relevant to significantly enhance diagnostic accuracy.
|
|
| 38. |
- Fentaye, Amare Desalegn, et al.
(författare)
-
An intelligent data filtering and fault detectionmethod for gas turbine engines
- 2020
-
Ingår i: MATEC Web of Conferences 314. - : EDP Sciences. - 2261-236X.
-
Konferensbidrag (refereegranskat)abstract
- In a gas turbine fault diagnostics, the removal of measurementnoise and data outliers prior to the fault analysis is very essential. Theconventional filtering methods, particularly the linear ones, are notsufficiently accurate, which might possibly lead to the loss of criticallyimportant features in the fault analysis process. Conversely, the recordedaccuracies obtained from the non-linear filters are promising. Recently, thefocus has been shifted to the artificial neural network (ANN) based nonlinearfilters due to their capability of providing a robust identity map between theinput and output data, which can be efficiently exploited in the process offault diagnosis. This paper aims to present combined auto-associative neuralnetwork (AANN) and K-nearest neighbor (KNN) based noise reduction andfault detection method for a gas turbine engine application. The performanceof the developed method has been evaluated using data obtained from amodel simulation. The test results revealed that the developed hybrid methodis more effective and reliable than the conventional methods for the faultdetection of the gas turbine engine with negligible false alarms and misseddetections.
|
|
| 39. |
- Fentaye, Amare Desalegn, et al.
(författare)
-
Discrimination of rapid and gradual deterioration for an enhanced gas turbine life-cycle monitoring and diagnostics
- 2021
-
Ingår i: International Journal of Prognostics and Health Management. - : Prognostics and Health Management Society. - 2153-2648. ; 12:3, s. 1-16
-
Tidskriftsartikel (refereegranskat)abstract
- 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 lifecycle 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. © 2021, Prognostics and Health Management Society. All rights reserved.
|
|
| 40. |
- Fentaye, Amare Desalegn, et al.
(författare)
-
Gas turbine prognostics via Temporal Fusion Transformer
- 2024
-
Ingår i: Aeronautical Journal. - : CAMBRIDGE UNIV PRESS. - 0001-9240.
-
Tidskriftsartikel (refereegranskat)abstract
- Gas turbines play a vital role in various industries. Timely and accurately predicting their degradation is essential for efficient operation and optimal maintenance planning. Diagnostic and prognostic outcomes aid in determining the optimal compressor washing intervals. Diagnostics detects compressor fouling and estimates the trend up to the current time. If the forecast indicates fast progress in the fouling trend, scheduling offline washing during the next inspection event or earlier may be crucial to address the fouling deposit comprehensively. This approach ensures that compressor cleaning is performed based on its actual health status, leading to improved operation and maintenance costs. This paper presents a novel prognostic method for gas turbine degradation forecasting through a time-series analysis. The proposed approach uses the Temporal Fusion Transformer model capable of capturing time-series relationships at different scales. It combines encoder and decoder layers to capture temporal dependencies and temporal-attention layers to capture long-range dependencies across the encoded degradation trends. Temporal attention is a self-attention mechanism that enables the model to consider the importance of each time step degradation in the context of the entire degradation profile of the given health parameter. Performance data from multiple two-spool turbofan engines is employed to train and test the method. The test results show promising forecasting ability of the proposed method multiple flight cycles into the future. By leveraging the insights provided by the method, maintenance events and activities can be scheduled in a proactive manner. Future work is to extend the method to estimate remaining useful life.
|
|
